Thèses : « Energy management strategies for hybrid electric vehicles »

1

Serrao, Lorenzo. « A comparative analysis of energy management strategies for hybrid electric vehicles ». Columbus, Ohio : Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1243934217.

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Hegde, Bharatkumar. « Look-Ahead Energy Management Strategies for Hybrid Vehicles ». The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu153199304661774.

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Hägglund, Andreas, et Moa Källgren. « Impact of Engine Dynamics on Optimal Energy Management Strategies for Hybrid Electric Vehicles ». Thesis, Linköpings universitet, Fordonssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148890.

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In recent years, rules and regulations regarding fuel consumption of vehicles and the amount of emissions produced by them are becoming stricter. This has led the automotive industry to develop more advanced solutions to propel vehicles to meet the legal requirements. The Hybrid Electric Vehicle is one of the solutions that is becoming more popular in the automotive industry. It consists of an electrical driveline combined with a conventional powertrain, propelled by either a diesel or petrol engine. Two power sources create the possibility to choose when and how to use the power sources to propel the vehicle. The strategy that decides how this is done is referred to as an energy management strategy. Today most energy management strategies only try to reduce fuel consumption using models that describe the steady state behaviour of the engine. In other words, no reduction of emissions is achieved and all transient behaviour is considered negligible. In this thesis, an energy management strategy incorporating engine dynamics to reduce fuel consumption and nitrogen oxide emissions have been designed. First, the models that describe how fuel consumption and nitrogen oxide emissions behave during transient engine operation are developed. Then, an energy management strategy is developed consisting of a model predictive controller that combines the equivalent consumption minimization strategy and convex optimization. Results indicate that by considering engine dynamics in the energy management strategy, both fuel consumption and nitrogen oxide emissions can be reduced. Furthermore, it is also shown that the major reduction in fuel consumption and nitrogen oxide emissions is achieved for short prediction horizons.

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Capancioni, Alessandro <1992&gt. « Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity ». Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10044/1/PhD_Thesis_Capancioni.pdf.

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Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

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Sampathnarayanan, Balaji. « Analysis and Design of Stable and Optimal Energy Management Strategies for Hybrid Electric Vehicles ». The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357079732.

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Pahkasalo, Carolina, et André Sollander. « Adaptive Energy Management Strategies for Series Hybrid Electric Wheel Loaders ». Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166284.

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An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predicts the operating cycle, which can be done with Neural Network or Rule-Based methods. The prediction is then used in ECMS to compute the optimal power distribution of fuel and battery power. For a robust system it is important with stability implementations in ECMS to protect the machine, which can be done by adjusting the cost function that is minimized. The result from these implementations in a quasistatic simulation environment is an improvement in fuel consumption by 7.59 % compared to not utilizing the battery at all.

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Lohse-Busch, Henning. « Development and Applications of the Modular Automotive Technology Testbed (MATT) to Evaluate Hybrid Electric Powertrain Components and Energy Management Strategies ». Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29094.

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This work describes the design, development and research applications of a Modular Automotive Technology Testbed (MATT). MATT is built to evaluate technology components in a hybrid vehicle system environment. MATT can also be utilized to evaluate energy management and torque split control strategies and to produce physical measured component losses and emissions to monitor emissions behavior. In the automotive world, new technology components are first developed on a test bench and then they are integrated into a prototype vehicle for transient evaluation from the vehicle system perspective. This process is expensive and the prototype vehicles are typically inflexible in hardware and software configuration. MATT provides flexibility in component testing through its component module approach. The flexible combination of modules provides a vehicle environment to test and evaluate new technology components. MATT also has an open control system where any energy management and torque split strategy can be implemented. Therefore, the controlâ s impact on energy consumption and emissions can be measured. MATT can also emulate different types and sizes of vehicles. MATT is a novel, unique, flexible and powerful automotive research tool that provides hardware-based data for specific research topics. Currently, several powertrain modules are available for use on MATT: a gasoline engine module, a hydrogen engine module, a virtual scalable energy storage and virtual scalable motor module, a manual transmission module and an automatic transmission module. The virtual battery and motor module uses some component Hardware-In-the-Loop (HIL) principles by utilizing a physical motor powered from the electric grid in conjunction with a real time simulation of a battery and a motor model. This module enables MATT to emulate a wide variety of vehicles, ranging from a conventional vehicle to a full performance electric vehicle with a battery pack that has virtually unlimited capacity. A select set of PHEV research studies are described in this dissertation. One of these studies had an outcome that influenced the PHEV standard test protocol development by SAE. Another study investigated the impact of the control strategy on emissions of PHEVs. Emissions mitigation routines were integrated in the control strategies, reducing the measured emissions to SULEV limits on a full charge test. A special component evaluation study featured in this dissertation is the transient performance characterization of a supercharged hydrogen internal combustion engine on MATT. Four constant air-fuel ratio combustions are evaluated in a conventional vehicle operation on standard drive cycles. Then, a variable air fuel ratio combustion strategy is developed and the test results show a significant fuel economy gain compared to other combustion strategies, while NOx emissions levels are kept low.
Ph. D.

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Valenti, Giammarco. « Cooperative ADAS and driving, bio-inspired and optimal solutions ». Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/336890.

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Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.

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Wong, Yuk-sum. « System design and energy management strategy for hybrid electric vehicles ». Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/HKUTO/record/B3955885X.

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黃毓琛 et Yuk-sum Wong. « System design and energy management strategy for hybrid electric vehicles ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B3955885X.

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Bader, Benjamin. « An energy management strategy for plug-in hybrid electric vehicles ». Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134358.

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This dissertation formulates a proposal for a real time implementable energy management strategy (EMS) for plug-in hybrid electric vehicles. The EMS is developed to minimize vehicle fuel consumption through the utilisation of stored electric energy and high-efficiency operation of powertrain components. This objective is achieved through the development of a predictive EMS, which, in addition to fuel efficiency, is optimized in terms of computational cost and drivability. The requirement for an EMS in hybrid powertrain vehicles stems from the integration of two energy stores and converters in the powertrain; in the case of hybrid electric vehicles (HEVs) usually a combustion engine and one or more electric machines powered by a battery. During operation of the vehicle the EMS controls power distribution between engine and electric traction motor. Power distribution is optimized according to the operating point dependent efficiencies of the components, energy level of the battery and trip foreknowledge. Drivability considerations, e.g. frequency of engine starts, can also be considered. Due to high oil prices and legislative requirements caused by the environmental impact of greenhouse emissions, fuel economy has gained importance in recent years. In addition to increased fuel economy, powertrain hybridization permits the substituton of fuel for electrical energy by implementing an external recharging option for the battery. This vehicle class, incorporating a battery rechargeable via the electrical grid, is known as a plug-in HEV (PHEV). PHEV share characteristics of both HEVs and all-electric vehicles combining several advantages of both technologies. The rechargeable battery feature of the PHEVs makes their EMS development espe-cially challenging. For minimal fuel consumption, the battery is discharged optimally over the whole trip length, prioritising electrical energy when driving conditions are such that its use maximises the fuel saving that can be achieved. Therefore, an EMS for a PHEV depends heavily on the availability of a priori knowledge about the trip, i.e. the knowledge about future vehicle speed and road grade. This requires the driver to indi-cate the route before trip start. The route knowledge in combination with GPS or Galileo based next generation navigation systems using information from a geographic in-formation system (GIS) about terrain height profile, road type (e.g. motorway or country road), and legal speed limits can be evaluated by a speed prediction algorithm including information about the driver's behaviour for a detailed prediction of the trip. These navigation systems and algorithms in combination with expected future advances and the deployment of technologies such as intelligent transport systems (ITS) and vehicle-to-vehicle communication (V2V), will make more exact traffic information available to further improve prediction. Despite expected advances in prediction quality, inaccuracy of prediction data has to be considered and is therefore regarded in this work. The EMS proposed in this dissertation combines different approaches which are exe-cuted step by step. A first approximation of the energy distribution during the trip is based on a mixed integer linear program (MILP), which gives the optimal energy state of the battery during the trip. This is especially important for trips with long uphill, downhill or urban phases, i.e. sections with a particularly high or lower power requirement. The results from MILP are then used by a dynamic programming (DP) algorithm to calculate optimal torque and gear using a receding prediction horizon. Using a receding prediction horizon, an important reduction of computational cost is achieved. Lastly, from the DP results a rule-based strategy is extracted using a support vector machine (SVM). This last step is necessary to ensure the drivability of the vehicle also for inaccurate prediction data.

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Suntharalingam, P. « Kinetic energy recovery and power management for hybrid electric vehicles ». Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6154.

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The major contribution of the work presented in this thesis is a thorough investigation of the constraints on regenerative braking and kinetic energy recovery enhancement for electric/hybrid electric vehicles during braking. Regenerative braking systems provide an opportunity to recycle the braking energy, which is otherwise dissipated as heat in the brake pads. However, braking energy harnessing is a relatively new concept in the automotive sector which still requires further research and development. Due to the operating constraints of the drivetrain architecture and the varying nature of the braking conditions, it is unlikely that all the stored kinetic energy of the vehicle can be recovered during braking.The research work in this thesis addresses the effect of braking conditions on kinetic energy recovery enhancement of the vehicle. The challenge in kinetic energy recovery enhancement lies in braking conditions, power/torque handling ability of the electric propulsion system, managing the dual braking systems, employed energy conversion techniques, and energy storage capacity. In this work a novel braking strategy is introduced to increase the involvement of the regenerative braking system, so as to increase the kinetic energy recovery while achieving the braking performance requirements. Initially mathematical modelling and simulation based analysis are presented to demonstrate the effects of braking power variation with respect to braking requirements. A novel braking strategy is proposed to increase the kinetic energy recovery during heavy braking events. The effectiveness of this braking strategy is analyzed using a simulation model developed in matlab- simulink environment. Anexperimental rig is developed to test various braking scenarios and their effects on kinetic energy recovery. A variety of braking scenarios are tested and results are presented with the analysis. At the end, suggestions are made to further continue this research in the future.

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Berntsson, Simon, et Mattias Andreasson. « Efficient Route-based Optimal Energy Management for Hybrid Electric Vehicles ». Thesis, Linköpings universitet, Fordonssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148565.

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The requirements on fuel consumption and emissions for passenger cars are getting stricter every year. This has forced the vehicle industry to look for ways to improve the performance of the driveline. With the increasing focus on electrification, a common method is to combine an electrical driveline with a conventional driveline that uses a petrol or diesel engine, thus creating a hybrid electric vehicle. To fully be able to utilise the potential of the driveline in such a vehicle, an efficient energy management strategy is needed. This thesis describes the development of an efficient route-based energy management strategy. Three different optimisation strategies are combined, deterministic dynamic programming, equivalent consumption minimisation strategy and convex optimisation, together with segmentation of the input data. The developed strategy shows a decrease in computational time with up to more than one hundred times compared to a benchmark algorithm. When implemented in Volvo's simulation tool, VSim, substantial fuel savings of up to ten percent is shown compared to a charge-depleting charge-sustain strategy.

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Tagner, Nikita. « Optimal Energy Management for Parallel Hybrid Electric Vehicles using Dynamic Programming ». Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209776.

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In this thesis, two optimal control problems for the control of hybrid electric vehicles are formulated. The first is general formulation where both velocity and state of charge can vary. The second is a formulation where the velocity is prespecified and therefore only the state of charge can vary. The first formulation takes significantly more time to solve with dynamic programming than the second formulation. For the most hilly drive cycle that was evaluated, 4:45 % fuel savings were obtained by using the general formulation over the formulation with prespecified velocity. For the least hilly cycle, this number dropped to 1:75%. When the lowest admissible velocity was lowered from 75 to 70 km/h, fuel savings of 0:52 % were obtained. From 80 to 70 km/h, the number increased to 1:92 %. In conclusion, if dynamic programming is to be implemented in real time on a hybrid electric vehicle the fuel savings for hilly roads where a low minimal velocity is allowed are potentially much greater than when using prespecifued velocity. However, for less hilly roads and where the velocity is not allowed to vary as much, it might be more beneficial in terms of fuel consumption to use the formulation with prespecified velocity and include abilities such as gear shifting or switching the engine on or off.
I denna avhandling formuleras två optimala styrningsproblem för reglering av hybridelektriska fordon. I den första, mer generella, formuleringen kan både hastighet och batteriladdning variera. I den andra formuleringen är hastigheten specifierad i förväg and därmed kan endast batteriladdningen variera fritt. Den första formuleringen tar betydligt längre tid att lösa med dynamisk programmering än den andra formuleringen. Av dem utvärderade körcyklerna gav den som var mest kuperade bränslebesparingar på 4:45 % om den löstes med den generella formuleringen istället för den där hastigheten är specifierad i förväg. När den lägsta tillåtna hastigheten sänktes från 75 till 70 km/h sparades 0:52 % bränsle. Däremot, om den lägsta tillåtna hastigheten sänktes från 80 till 70 km/h ökade besparingen till 1:92 %. Sammanfattningsvis, om dynamisk programmering ska implementras i realtid på ett hybridelektriskt fordon så är dem potentiella bränslebesparingarna betydligt högre om vägen är väldigt kuperad och en låg lägsta hastighet tillåts för den generella formuleringen än om formuleringen med hastighet specifierad på förhand väljs. Därmed, för vägar som inte är lika kuperade och där hastigheten inte tillåts att variera mycket kan, potentiellt, högre bränslebesparingar uppnås om formuleringen med förspecifierad hastighet väljs och förmågan att växla alternativt stänga av eller på motorn inkluderas.

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Caramia, Gabriele <1991&gt. « Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles ». Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9248/1/GabrieleCaramia_PhDThesis.pdf.

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In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

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Banvait, Harpreetsingh. « OPTIMAL ENERGY MANAGEMENT SYSTEM OF PLUG-IN HYBRID ELECTRIC VEHICLE ». ProQuest, 2009. http://hdl.handle.net/1805/2095.

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Indiana University-Purdue University Indianapolis (IUPUI)
Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

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Rajan, Brahmadevan V. P. « Plug in hybrid electric vehicle energy management system for real world driving ». Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/67891/.

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The energy management system (EMS) of hybrid electric vehicle controls the operation of two power plants; electric machine/battery and typically engine. Hence the fuel economy and emissions of hybrid vehicles strongly depend on the EMS. It is known that considering the future trip demand in devising an EMS control strategy enhance the vehicle and component performances. However existing such acausal EMS cannot be used in real time and would require prior knowledge of the trip vehicle speed profile (trip demand). Therefore rule based EMS which considers instantaneous trip demand in devising a control strategy are used. Such causal EMS are real time capable and simple in design. However rule based EMS are tuned for a set of driving cycles and hence their performance is vulnerable in real world driving. The research question is “How to design a real time capable acausal EMS for a plug in hybrid electric vehicle (PHEV) that can adapt to the uncertainties of real world driving”. In the research, the design and evaluation of a proposed EMS to deal and demonstrate in scenarios expected in real world driving respectively were considered. The proposed rule based acausal EMS is formulated over the estimated vehicle trip energy and driving information. Vehicle trip energy is the electric (battery) energy required to meet the trip demand estimated using known driving information. Driving information that can be considered are driver style, route distance and road types like urban and extra urban, with traffic as a sub function. Unlike vehicle speed, vehicle trip energy is shown to be relatively less dynamic in real world driving. For the proposed EMS evaluation, a commonly used parallel PHEV model was simulated. For driving information EMS was not integrated to a navigation system but manually defined. Evaluation studies were done for a driver, and traffic was not considered for simplicity. In the thesis, vehicle performance and credentials for real world applicability (real time capability and adaptability) of the proposed acausal EMS are demonstrated for various scenarios in real world driving; varied initial SOC, sequence of road types, trip distance and trip energy estimation. Over the New European Driving Cycle (NEDC) the proposed EMS vehicle performance is compared to a conventional rule based EMS. The proposed EMS fuel economy improvement is up to 11% with 5 times fewer number of engine stop-starts. Similarly in the validation study, with no prior knowledge of trip vehicle speed profile, the fuel economy improvement is up to 29% with 7 times fewer number of engine stop-starts. The simulation duration of the proposed EMS is as good as conventional rule based EMS. Hence the proposed EMS is potentially real time capable. The proposed EMS can adapt to a wide variation in trip energy (±15%) estimation and still perform better than the conventional rule based EMS. The proposed EMS can tolerate variation in trip demand estimation and no prior knowledge of trip vehicle speed profile is required, unlike other acausal EMS studies in the literature. A new PHEV EMS has been formulated. Through simulation it has been seen to deliver benefit in vehicle performance and real world applicability for varied scenarios as expected in real world driving. The key new step was to use vehicle trip energy in the formulation, which enabled rule based EMS to be acausal and potentially real time capable.

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Tang, Li. « Optimal energy management strategy for hybrid electric vehicles with consideration of battery life ». The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492508261913757.

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Cross, Patrick Wilson. « System Modeling and Energy Management Strategy Development for Series Hybrid Vehicles ». Thesis, Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24785.

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A series hybrid electric vehicle is a vehicle that is powered by both an engine and a battery pack. An electric motor provides all of the mechanical motive power to the transmission. Engine power is decoupled from the transmission by converting engine power into electricity which powers the electric motor. The mechanical decoupling of the engine from the transmission allows the engine to be run at any operating point (including off) during vehicle operation while the battery back supplies or consumes the remaining power. Therefore, the engine can be operated at its most efficient operating point or in a high-efficiency operating region. The first objective of this research is to develop a dynamic model of a series hybrid diesel-electric powertrain for implementation in Simulink. The vehicle of interest is a John Deere M-Gator utility vehicle. This model serves primarily to test energy management strategies, but it can also be used for component sizing given known load profiles for a vehicle. The second objective of this research is to develop and implement multiple energy management strategies of varying complexity from simple thermostat control to an optimal control law derived using dynamic programming. These energy management strategies are then tested and compared over the criteria of overall fuel efficiency, power availability, battery life, and complexity of implementation. Complexity of implementation is a critical metric for control designers and project managers. The results show that simple point-based control logic can improve upon thermostat control if engine efficiency maps are known. All control method results depend on the load profile being used for a specific application.

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Le, rhun Arthur. « Stochastic optimal control for the energy management of hybrid electric vehicles under traffic constraints ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX084/document.

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Cette thèse aborde la conception d'un Système de Gestion Énergétique (EMS), prenant en compte les contraintes de trafic, pour un véhicule hybride électrique. Actuellement, les EMS sont habituellement classé en deux catégories ceux proposant une architecture en temps réel cherchant un optimum local, et ceux qui recherchent un optimum global, plus coûteux en temps de calcul et donc plus approprié à un usage hors ligne. Cette thèse repose sur le fait que la consommation énergétique peut être modélisée précisément à l'aide de distributions de probabilité sur la vitesse et l'accélération. Dans le but de réduire la taille des données, une classification est proposé, basé sur la distance de Wasserstein, les barycentres des classes pouvant être calculés grâce aux itérations de Sinkhorn ou la méthode du Gradient Stochastique Alterné. Cette modélisation trafic a permis à une optimisation hors ligne de déterminer le contrôle optimal (le couple du moteur électrique) qui minimise la consommation de carburant du véhicule hybride sur un segment routier. Dans la continuité, un algorithme bi-niveau tirant avantage de cette information afin d'optimiser la consommation sur l'ensemble du trajet. Le niveau supérieur d'optimisation, étant déterministe, est suffisamment rapide pour une implémentation en temps réel. La pertinence du modèle de trafic et de la méthode bi-niveau est illustré à l'aide de données trafic générées par un simulateur, mais aussi grâce à des données réelles collectées prés de Lyon (France). Enfin, une extension de la méthode bi-niveau au problème d'éco-routage est envisagé, utilisant un graphe augmenté pour déterminer l'état de charge lors du chemin optimal
The focus of this PhD thesis is to design an optimal Energy Management System (EMS) for a Hybrid Electric Vehicle (HEV) following traffic constraints.In the current state of the art, EMS are typically divided between real-time designs relying on local optimization methods, and global optimization that is only suitable for off-line use due to computational constraints.The starting point of the thesis is that in terms of energy consumption, the stochastic aspect of the traffic conditions can be accurately modelled thanks to (speed,acceleration) probability distributions.In order to reduce the data size of the model, we use clustering techniques based on the Wasserstein distance, the corresponding barycenters being computed by either a Sinkhorn or Stochastic Alternate Gradient method.Thanks to this stochastic traffic model, an off-line optimization can be performed to determine the optimal control (electric motor torque) that minimizes the fuel consumption of the HEV over a certain road segment.Then, a bi-level algorithm takes advantage of this information to optimize the consumption over a whole travel, the upper level optimization being deterministic and therefore fast enough for real-time implementation.We illustrate the relevance of the traffic model and the bi-level optimization, using both traffic data generated by a simulator, as well as some actual traffic data recorded near Lyon (France).Finally, we investigate the extension of the bi-level algorithm to the eco-routing problem, using an augmented graph to track the state of charge information over the road network

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Fletcher, Thomas P. « Optimal energy management strategy for a fuel cell hybrid electric vehicle ». Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/25567.

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The Energy Management Strategy (EMS) has a huge effect on the performance of any hybrid vehicle because it determines the operating point of almost every component associated with the powertrain. This means that its optimisation is an incredibly complex task which must consider a number of objectives including the fuel consumption, drive-ability, component degradation and straight-line performance. The EMS is of particular importance for Fuel Cell Hybrid Electric Vehicles (FCHEVs), not only to minimise the fuel consumption, but also to reduce the electrical stress on the fuel cell and maximise its useful lifetime. This is because the durability and cost of the fuel cell stack is one of the major obstacles preventing FCHEVs from being competitive with conventional vehicles. In this work, a novel EMS is developed, specifcally for Fuel Cell Hybrid Electric Vehicles (FCHEVs), which considers not only the fuel consumption, but also the degradation of the fuel cell in order to optimise the overall running cost of the vehicle. This work is believed to be the first of its kind to quantify effect of decisions made by the EMS on the fuel cell degradation, inclusive of multiple causes of voltage degradation. The performance of this new strategy is compared in simulation to a recent strategy from the literature designed solely to optimise the fuel consumption. It is found that the inclusion of the degradation metrics results in a 20% increase in fuel cell lifetime for only a 3.7% increase in the fuel consumption, meaning that the overall running cost is reduced by 9%. In addition to direct implementation on board a vehicle, this technique for optimising the degradation alongside the fuel consumption also allows alternative vehicle designs to be compared in an unbiased way. In order to demonstrate this, the novel optimisation technique is subsequently used to compare alternative system designs in order to identify the optimal economic sizing of the fuel cell and battery pack. It is found that the overall running cost can be minimised by using the smallest possible fuel cell stack that will satisfy the average power requirement of the duty cycle, and by using an oversized battery pack to maximise the fuel cell effciency and minimise the transient loading on the stack. This research was undertaken at Loughborough University as part of the Doctoral Training Centre (DTC) in Hydrogen, Fuel Cells and Their Applications in collaboration with the University of Birmingham and Nottingham University and with sponsorship from HORIBA-MIRA (Nuneaton, UK). A Microcab H4 test vehicle has been made available for use in testing for this research which was previously used for approximately 2 years at the University of Birmingham. The Microcab H4 is a small campus based vehicle designed for passenger transport and mail delivery at low speeds as seen on a university campus. It has a top speed of approximately 30mph, and is fitted with a 1.2kW fuel cell and a 2kWh battery pack.

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MUSIO, CLAUDIA. « Management strategies of electric vehicles and Concentrating Photovoltaic systems for microgrids ». Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266748.

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The present PhD dissertation is focused on the development of management strate- gies of electric vehicles and concentrating photovoltaic systems in microgrids (MGs). Firstly the MG concept and then the state-of-the-art analysis of the most important components (that are photovoltaic and energy storage systems and electric vehicles) are presented. Then, the first part of the thesis is focused on the concentrating photovoltaic (CPV) systems, the most promising new technology for improving the efficiency of PV systems. In particular, two prototypes characterization and the role of CPV systems in MGs are introduced. In fact, the knowledge of the CPV issues highlighted during the characterization process allows the development of a suitable EMS, in order to guarantee the quality, the reliability and the controllability of the MG and consequently of the main electrical power system, especially in presence of a large number of renewable energy sources (RESs). The second part of the dis- sertation deals with the analysis of two battery electric vehicles (BEVs) models. Nowadays, the exploitation of BEVs has to be placed in a future contest in which the vehicle batteries will perform different tasks in addition to driving purpose, such as the vehicle to grid (V2G) paradigm. Thus, an accurate model that reproduces the battery behavior under real dynamic driving conditions is mandatory, as well as its validation. Moreover, the EV modelling allows to make the EV feedback in- formation reliable for managing correctly and profitably an EV eet inside a MG. Consequently, in the last part, two management strategies (MSs) are presented. The former operates in a MG composed by office and laboratory loads, a CPV plant and a traditional at-plate PV one and a BEV eet. The MS proposed aims to maxi- mize the energy self-consumption by respecting both the driver needs and the MG requirements. The second MS manages the same MG by employing a stationary storage system instead of a BEV eet. In this case, the MS purpose is to guarantee a at-programmable power production profile at the DC node of the MG, even in case of severe weather conditions.

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Kang, Xueying. « Vehicle-infrastructure integration (VII) enabled plug-in hybrid electric vehicles (PHEVS) for traffic and energy management ». Connect to this title online, 2009.

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Uebel, Stephan, et Bernard Bäker. « Optimal Velocity and Power Split Control of Hybrid Electric Vehicles ». Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-219665.

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An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space.

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GHAZARIAN, DANIEL. « Estimating the energy consumption of steering assist systems in commercial hybrid electric vehicles : A basis for utilization strategies ». Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299354.

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The commercial vehicle manufacturer Scania CV AB has introduced a new steering functionality called Redundant Brake Steering (RBS) in vehicles with batteries as energy storage, i.e. hybrid-electric and electric vehicles. Although, the primary steering system in Scania’s electrified vehicles consists of an electrohydraulic steering system (EHS) and an electrical active steering system (EAS). The purpose of introducing RBS is to facilitate the implementation of the EAS as a secondary steering system according to current laws. Consequently, a major benefit is that the conventional back-up hydraulic steering system can be removed. By replacing the back-up system with the active steering system and RBS an entire auxiliary system in the vehicle is eliminated. One of the main ambitions of this study has been to investigate whether the new steering functionality could lead to reduced energy consumption related to steering systems. The approach in this thesis included a literature study of steering systems, brake-steering concepts and available vehicle data to establish methods to quantify the energy consumption of the involved systems. The methods were used to develop a numerical model in the software Matlab which could use collected vehicle data to estimate the energy consumption of the steering systems. To facilitate the data collection a case study was performed using a plug-in hybrid electric vehicle (PHEV) at Scania’s test track in Södertälje, Sweden. The results suggested that the combination of EAS and RBS has potential to assist as primary steering as the energy consumption related to steering could be decreased by up to 90.6%. Also, the results indicated that the load on the vehicle’s electrical circuit could be reduced significantly, while the main drawback is increased steering effort by the driver.
Lastbils -och busstillverkaren Scania CV AB har nyligen introducerat ett system vid namnet Redundant Brake Steering (RBS) för elektrifierade fordon. Det primära styrsystemet i dessa fordon består av ett elektro-hydraliskt system (EHS) samt ett elektriskt aktivt styrsystem (EAS). Bakgrunden till att introducera RBS har varit grundat i att kunna implementera EAS som sekundärt styrsystem för att tillfredställa nuvarande lagkrav. Utfallet har blivit att det sekundära hydrauliska styrsystemet har kunnat ersättas med bromsstyrningssystem (RBS) samt EAS. Den grundläggande ambitionen för denna studie har varit at utforska ifall det nya bromsstyrningssystemet kan bidra till minskad energiförbrukning av fordonets styrsystem. Denna studie har bland annat innefattat en litteraturstudie av styrsystem, koncept som berör bromsstyrning samt tillgänglig fordonsdata för att kunna utveckla beräkningsmetoder av energiförbrukning av respektive system. Metoderna användes för att utveckla en beräkningsmodell i mjukvaran Matlab som med hjälp av fordonsdata kunde uppskatta energiförbrukningen av styrsystemen. Fordonstester utfördes i en fallstudie för att kunna samla in mätningar av fordonsdata. Detta gjordes med hjälp av ett plug-in hybrid fordon (PHEV) på Scania’s provbana i Södertälje, Sverige. Resultaten indikerade att en kombination av EAS och RBS har potential att bistå som primärt styrsystem sett till energiförbrukningen vilken kunde reduceras med upp till 90.6%. Vidare visade resultaten att påfrestningen på fordonets elektriska system kunde minskas, dock med nackdelen att förarens ansträngning ökar.

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Almgren, Johan, et Gustav Elingsbo. « Route Based Optimal Control Strategy for Plug-In Hybrid Electric Vehicles ». Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138713.

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More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.

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Multani, Sahib Singh. « Pseudospectral Collocation Method Based Energy Management Scheme for a Parallel P2 Hybrid Electric Vehicle ». The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587653689067271.

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Oukkacha, Ismail. « Approche systémique de la gestion d'énergie électrique par stockage électrochimiques dédiés aux applications de transport “Electric Vehicles Energy Management using Lithium-batteries and Ultracapacitors” “Onboard energy management for electric vehicles applications — Using fuel cell and ultracapacitors” “Electric vehicles energy management using direct torque control -space vector pulse width modulation combined to polynomial controllers” “Energy management in Electric Vehicle based on frequency sharing approach, using Fuel cells, Lithium batteries and Supercapacitors” ». Thesis, Normandie, 2019. http://www.theses.fr/2019NORMLH27.

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Ce sujet s’inscrit dans la continuité des activités de recherche du laboratoire GREAH sur les problématiques de la gestion optimale d’énergie électrique embarqué à bord des véhicules électriques hybrides. En effet, le couplage de plusieurs sources de natures différentes entraîne des problématiques de dimensionnement, de qualité d’énergie et de la durée de vie des éléments interconnectés. Pour les applications de transport par exemple, les principaux facteurs de ces problématiques reposent sur : - les fluctuations de la puissance demandée par la chaîne de propulsion/ traction, la durée de vie limitée des éléments de stockage d’énergie électrique, l’absence de profil de mission standard réaliste et la nécessité d’optimisation de la consommation énergétique du bord. La méthode adéquate pour l’étude des systèmes multi-sources passe par une approche systémique. Cette approche est nécessaire pour établir des modèles comportementaux des sources et des convertisseurs en vue de l'élaboration des stratégies de gestion optimale des flux énergétiques entre les organes. Le premier objectif de la thèse repose sur le développement des modèles de comportement des batteries et supercondensateurs soumis aux contraintes thermiques et électriques spécifiques aux applications de transport. Le second vise le développement de la stratégie de la gestion d’énergie prenant en compte de l’impact de la température et les fluctuations de puissance demandée par la chaîne de propulsion/ traction (charge)
The research work presented in this document is a continuation of the GREAH laboratory research activities on the issues of optimal energy management on board of electric and hybrid electric vehicles. Indeed, the coupling of several electrical energy sources with different characteristics causes several issues like energy sources sizing, energy exchange quality and the lifetime of the interconnected elements. In the case of transport applications, the main factors of these problems are based on the high fluctuations in the power required by the propulsion/traction chain; the limited life expectancy of the electrical energy storage elements; the lack of realistic standard mission profile and the need to optimize the electric vehicles energy consumption. The appropriate method for studying the multi-source systems is by using systemic approach. This approach is necessary to establish behavioral models of energies sources and power converters for the development of optimal energy management strategies. The contribution of this thesis is focused on the investigation and the development of energy management strategies considering the electrical energy sources performances and their state of functioning according to the power fluctuations from the propulsion/traction chain, which presents the load in a touristic vehicle

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Kok, Theodorus Antonius Hendrik. « Development of a strategy for the management and control of multiple energy sources within series hybrid electric vehicles ». Thesis, University of Sunderland, 2015. http://sure.sunderland.ac.uk/6580/.

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The battery in an EV is designed according to a power to energy ratio and is a trade-off in the design of the pack. It also suffers from effects such as rate capacity effect, ripple effects and inefficiency under charging. These effects result in losses through which the capacity and life span of the batteries are compromised affecting range and drivability. In this thesis a novel development path resulting in a novel Power and Energy Management Strategy (PEMS) is presented. The effects of (dis)charging a battery are researched and converted to an energy optimisation formula and result in reduced power demand for the converter which reduces weight. The resulting Power Management Strategy (PMS) aims to recover energy more efficiently into UC while responding fast to a change in demand. The effects of converters on the battery current ripple are researched and discussed, resulting in an optimal topology layout, improved battery life and reduced losses. Through the use of Markov Chain analysis and a newly derived Bias function a predictive Energy Management Strategy (EMS) is developed which is practical to use in EVs. This resulted in a PEMS which because of the fast PMS results in a fast response time. The use of Markov Chain results in predictive EMS and improves the efficiency of the energy sources and allows the design to be reduced in size. Through the design methodology used the parallel topology (the battery converter parallel to the UC Module) was rated preferred choice over battery only and battery with UC Module. The rating was based on capacity, ripple control, weight, 10 year cost, potential for motor controller efficiency improvement, range and efficiency. v The combination of method and PEMS resulted in an improved life expectancy of the pack to over 10 year (up from 7) while increasing range and without sacrificing drivability.

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Jankord, Gregory J. « Control of Criteria Emissions and Energy Management in Hybrid Electric Vehicles with Consideration of Three-Way Catalyst Dynamics ». The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1590685712358423.

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Koprubasi, Kerem. « Modeling and control of a hybrid-electric vehicle for drivability and fuel economy improvements ». The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1220543044.

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Arasu, Mukilan T. « Energy Optimal Routing of Vehicle Fleet with Heterogeneous Powertrains ». The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1566150970771138.

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Joševski, Martina Verfasser], Dirk [Akademischer Betreuer] [Abel et Jakob Lukas [Akademischer Betreuer] Andert. « Predictive energy management of hybrid electric vehicles with uncertain torque demand forecast for on-road operation / Martina Joševski ; Dirk Abel, Jakob Lukas Andert ». Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1189332698/34.

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Caramia, Gabriele. « Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11755/.

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Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

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Wang, Dian. « Microgrid based on photovoltaic energy for charging electric vehicle stations : charging and discharging management strategies in communication with the smart grid ». Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2584.

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Le développement rapide des véhicules électriques (EVs) augmente la demande de puissance, ce qui provoque une charge supplémentaire sur le réseau public et augmente les fluctuations de la charge. Par conséquent, la forte pénétration des EVs est freinée. Un algorithme simulé en temps réel et basé sur des règles est élaboré pour les bornes de recharge des EVs alimentées par un micro-réseau DC afin de faire face aux incertitudes du comportement des utilisateurs des EVs. L'algorithme prend en considération les choix arbitraires et aléatoires proposés via l'interface homme-machine. Les résultats de simulation sont obtenus sous MATLAB / Simulink et vérifient la faisabilité de la stratégie de gestion proposée. Cette stratégie présente de bonnes performances en garantissant un contrôle précis. Par ailleurs, les algorithmes d'optimisation de délestage et de la restauration des EVs (SROA) pour la recharge de la puissance de la batterie peuvent être utilisés pour répondre aux besoins des utilisateurs. Aussi les algorithmes SROA maintiennent l'équilibre de la puissance de la station de recharge des EVs. Les algorithmes SROA prennent en compte l'intermittence de la source photovoltaïque (PV), la limitation de capacité du stockage et la limitation de puissance du réseau public. En comparant les résultats de la simulation aux algorithmes basés sur les règles, les algorithmes SROA proposés respectent le choix de l'utilisateur, réduisent le temps de charge total, augmentent le plein débit et maximisent l'utilisation de la puissance disponible. Les résultats de la simulation montrent la faisabilité et l'efficacité des algorithmes SROA. En outre, une station de charge basée sur le PV pour les EVs peut participer à la résolution de certains problèmes liés au pic de puissance. D'autre part, la technologie de véhicule à réseau (V2G) est conçue et appliquée pour fournir des services auxiliaires au réseau pendant les périodes de pointe, et V2G considère la dualité de la batterie des EVs « charge et source ». Ainsi, un algorithme de recherche dynamique des pics et de vallées est proposé pour une station de recharge des EVs afin d'atténuer l'impact sur le réseau public. Cet algorithme réduit ainsi le coût énergétique du réseau public. Les résultats de la simulation démontrent bien l'efficacité de l'algorithme de recherche des pics et des vallées. L'algorithme peut garantir l'équilibre du réseau public, satisfaire la demande de charge des utilisateurs des EVs et, surtout, réduire le coût énergétique du réseau public
The rapid development of electric vehicles (EVs) increases the power demand, which causes an extra burden on the public grid increasing the load fluctuations, therefore, hindering the high penetration of EVs. A real-time rule-based algorithm for electric vehicle (EV) charging stations empowered by a DC microgrid is proposed to deal with the uncertainties of EV users’ behaviour considering its arbitrary and random choices through the human-machine interface, meanwhile considering most of the users’ choices. The simulation results obtained under MATLAB/Simulink verify the feasibility of the proposed management strategy that presents a good performance in terms of precise control. In addition, EV shedding and restoration optimization algorithms (SROA) for battery charging power can be used to meet user needs while maintaining EV charging station power balance, taking into consideration the intermittency of the photovoltaic (PV) source, the capacity limitation of the storage, and the power limitation of the public grid. The simulation results show that compared with rule-based algorithm, the proposed SROA respect the user's choice while reducing total charging time, increasing the full rate, and maximizing the available power utilization, which shows the feasibility and effectiveness of SROA. Furthermore, a PV based charging station for EVs can participate to solve some peak power problems. On the other hand, vehicle to grid (V2G) technology is designed and applied to provide ancillary services grid during the peak periods, considering the duality of EV battery “load-source”. So, a dynamic searching peak and valley algorithm, based on energy management, is proposed for an EV charging station to mitigate the impact on the public grid, while reducing the energy cost of the public grid. Simulation results demonstrate the proposed searching peak and valley algorithm effectiveness, which can guarantee the balance of the public grid, meanwhile satisfy the charging demand of EV users, and most importantly, reduce the public grid energy cost

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Vallur, Rajendran Avinash. « A Methodology for Development of Look Ahead Based Energy Management System Using Traffic In Loop Simulation ». The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514828055131881.

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Tulpule, Pinak J. « Control and optimization of energy flow in hybrid large scale systems - A microgrid for photovoltaic based PEV charging station ». The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1313522717.

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Varia, Adhyarth C. « In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles ». The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208.

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Stroe, Nicoleta. « Contributions à la commande prédictive pour la gestion d'énergie d'un véhicule hybride électrique ». Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2073.

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Pour un véhicule électrique hybride, un des challenges les plus compliqués d’une perspective orientée contrôle est la distribution de puissance entre le moteur thermique et les machines électriques, problème appelé gestion d’énergie. La question qui surgit est comment exploiter le degré de liberté supplémentaire - la voie électrique - afin que la performance véhicule, liée à la réduction de la consommation carburant, soit améliorée. Le besoin de robustesse des stratégies de contrôle encourage le choix de méthodes basées sur des modèles et dans le contexte actuel de possibilité d’acquisition des données télémétriques, la commande prédictive à base de modèle apparaît comme une option attractive, motivée aussi par sa capacité à gérer des contraintes. La plupart des stratégies courantes de gestion d’énergie sont orientées application et par conséquent, un modèle générique du GMP hybride offrirait plus de flexibilité. Cette thèse se construit sur deux axes principaux : la synthèse d’un modèle générique et d’une stratégie de contrôle basée sur la commande prédictive pour la gestion d’énergie, avec un mécanisme de calibration auto-adaptable. Si la distribution de couple est l’enjeu majeur du contrôle, d’autres fonctionnalités peuvent être introduites, comme l’arrêt/démarrage du moteur et le découplage du GMP des roues. Cette dernière est généralement traitée dans la littérature avec une stratégie basée sur cartographies, mais ici une approche analytique innovante a été proposée. Une validation sur un modèle haute-fidélité d’un véhicule hybride léger (mild) avec une transmission double-embrayage clôture les travaux et montre le potentiel de la stratégie proposée
For a hybrid electric vehicle, one of the most challenging aspects from a control perspective is the power split between the engine and the motors, problem referred to as energy management. The question that arises is how to exploit the additional degree of freedom - the electric path - such that the vehicle performance related to consumption is improved. The required robustness for the control strategies encourage the choice of model-based methods and in the present context of telemetry data acquisition, model predictive control emerges as an attractive option, motivated in addition by its ability to handle the constraints. Most of the current energy management strategies are application-oriented and therefore, a generic hybrid powertrain model would provide more flexibility. This thesis is developed on two main axes: the synthesis of a generic model and of a MPC-based control strategy for the energy management, with a self-tuning mechanism. If the torque split is the main control objective, other functionalities can be introduced, such as engine stop/start and coasting. The latter is usually handled in the literature with a map-based strategy, but here an analytical solution was proposed. A validation on a vehicle high-fidelity model for a mild hybrid with a dual-clutch transmission closes the work and shows the potential of the proposed strategy

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Kalina, Emil. « Aplikace ultrakapacitorů v dopravních systémech ». Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233433.

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The work deals with relatively new components allowing electric energy accumulation – ultracapacitors. It focuses on their application in traffic systems – in independent electric vehicles. Design and verification of a system with ultracapacitor and DC/DC adaptive converter was done. Control of the adaptive converter modifies very positively the time wafeform of the traction accumulator current during the drive cycle. The designed connection of ultracapacitor and DC/DC converter implemented in the drive structure of experimental electric vehicle with induction machine contributes to increment the action radius of the vehicle by 16% (determined by experimental verification). This result was achived particularly by limitation of traction accumulator current peaks, And by more effective storage of energy gained by recuperative braking of the vehicle as well. The core of the system is a control of the adaptive converter in order to provide an active filtration of the accumulator’s current to its long-period mean value, i.e. elimination of current (power) peaks. These are caused by acceleration from non-zero initial vehicle speed or by recuperative braking. This is done by a subsidiary current loop. The converter has a superior voltage regulation loop, which sets in long-time period the voltage of ultracapacitors to the proper value – indirectly dependent on the speed of the vehicle. This ensures the appropriate energy management of the ultracapacitor. In the following, properties of test set of ultracapacitors were verified. Finally, methods of suppression of capacity variability influence in series connection of these components were compiled and critically reviewed.

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Denis, Nicolas. « Système de gestion d'énergie d'un véhicule électrique hybride rechargeable à trois roues ». Thèse, Université de Sherbrooke, 2014. http://hdl.handle.net/11143/5856.

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Résumé : Depuis la fin du XXème siècle, l’augmentation du prix du pétrole brut et les problématiques environnementales poussent l’industrie automobile à développer des technologies plus économes en carburant et générant moins d’émissions de gaz à effet de serre. Parmi ces technologies, les véhicules électriques hybrides constituent une solution viable et performante. En alliant un moteur électrique et un moteur à combustion, ces véhicules possèdent un fort potentiel de réduction de la consommation de carburant sans sacrifier son autonomie. La présence de deux moteurs et de deux sources d’énergie requiert un contrôleur, appelé système de gestion d’énergie, responsable de la commande simultanée des deux moteurs. Les performances du véhicule en matière de consommation dépendent en partie de la conception de ce contrôleur. Les véhicules électriques hybrides rechargeables, plus récents que leur équivalent non rechargeable, se distinguent par l’ajout d’un chargeur interne permettant la recharge de la batterie pendant l’arrêt du véhicule et par conséquent la décharge de celle-ci au cours d’un trajet. Cette particularité ajoute un degré de complexité pour ce qui est de la conception du système de gestion d’énergie. Dans cette thèse, nous proposons un modèle complet du véhicule dédié à la conception du contrôleur. Nous étudions ensuite la dépendance de la commande optimale des deux moteurs par rapport au profil de vitesse suivi au cours d’un trajet ainsi qu’à la quantité d’énergie électrique disponible au début d’un trajet. Cela nous amène à proposer une technique d’auto-apprentissage visant l’amélioration de la stratégie de gestion d’énergie en exploitant un certain nombre de données enregistrées sur les trajets antérieurs. La technique proposée permet l’adaptation de la stratégie de contrôle vis-à-vis du trajet en cours en se basant sur une pseudo-prédiction de la totalité du profil de vitesse. Nous évaluerons les performances de la technique proposée en matière de consommation de carburant en la comparant avec une stratégie optimale bénéficiant de la connaissance exacte du profil de vitesse ainsi qu’avec une stratégie de base utilisée couramment dans l’industrie. // Abstract : Since the end of the XXth century, the increase in crude oil price and the environmental concerns lead the automotive industry to develop technologies that can improve fuel savings and decrease greenhouse gases emissions. Among these technologies, the hybrid electric vehicles stand as a reliable and efficient solution. By combining an electrical motor and an internal combustion engine, these vehicles can bring a noticeable improvement in terms of fuel consumption without sacrificing the vehicle autonomy. The two motors and the two energy storage systems require a control unit, called energy management system, which is responsible for the command decision of both motors. The vehicle performances in terms of fuel consumption greatly depend on this control unit. The plug-in hybrid electric vehicles are a more recent technology compared to their non plug-in counterparts. They have an extra internal battery charger that allows the battery to be charged during OFF state, implying a possible discharge during a trip. This particularity adds complexity when it comes to the design of the energy management system. In this thesis, a complete vehicle model is proposed and used for the design of the controller. A study is then carried out to show the dependence between the optimal control of the motors and the speed profile followed during a trip as well as the available electrical energy at the beginning of a trip. According to this study, a self-learning optimization technique that aims at improving the energy management strategy by exploiting some driving data recorded on previous trips is proposed. The technique allows the adaptation of the control strategy to the current trip based on a pseudo-prediction of the total speed profile. Fuel consumption performances for the proposed technique will be evaluated by comparing it with an optimal control strategy that benefits from the exact a priori knowledge of the speed profile as well as a basic strategy commonly used in industry.

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Dai, Ping. « Réjection de perturbation sur un système multi-sources - Application à une propulsion hybride ». Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2251/document.

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Ce mémoire porte sur l'étude d'un système de gestion d'énergie électrique dans un système multi-sources soumis à des perturbations exogènes. L'application visée est l'alimentation d'une propulsion hybride diesel/électrique équipée d'un système d'absorption des pulsations de couple. Les perturbations exogènes considérées peuvent être transitoires ou persistantes. Une perturbation transitoire correspond à une variation rapide du couple de charge, due par exemple à une accélération ou une décélération du véhicule. Une perturbation persistante provient du système de compensation des pulsations de couple générées par le moteur thermique. Le premier objectif du contrôle est de maintenir constante la tension du bus continu. Le deuxième objectif est d'absorber dans un système de stockage rapide constitué de super condensateur ces perturbations qui peuvent à terme provoquer une usure prématurée de la batterie. Le troisième objectif est de compenser l'auto-décharge dans le super condensateur en maintenant constante sa tension nominale. Les deux sources (batterie et super condensateur) sont reliées au bus continu par l'intermédiaire de deux convertisseurs boost DC/DC. La commande consiste à piloter les rapports cycliques de chaque convertisseur. C'est un système non linéaire où la commande est multiplicative de l'état. L'approche classique consistant à résoudre les équations Francis-Byrnes-Isidori ne s'applique pas directement dans ce cas où la sortie et la matrice d'interconnection dépendent de la commande. De plus, si cette approche est bien adaptée au rejet de perturbations persistantes, elle montre ces limites pour le rejet de perturbations non persistantes combiné à des objectifs de régulation. Notre approche a consisté à écrire le système sous un formalisme Port-Controlled Hamiltonian et à s'affranchir de la contrainte de la dépendance de la matrice d'interconnection avec la commande en utilisant la théorie des perturbations singulières. La commande du système dégénéré peut ensuite être calculée par une approche passive. Les performances de cette commande ont été testées en simulation et à l'aide d'un banc d'essai expérimental. Les résultats montrent l'efficacité du système d'absorption des différents types de perturbation tout en respectant les deux objectifs de régulation
This thesis presents the research of energy management in a battery/ultracapacitor hybrid energy storage system with exogenous disturbance in hybrid electric vehicular application. Transient and harmonic persistent disturbances are the two kinds of disturbances considered in this thesis. The former is due to the transient load power demand during acceleration and deceleration, and the latter is introduced from the process of the internal combustion engine torque ripples compensation. Our control objective is to absorb the disturbances causing battery wear via the ultracapacitor, and meanwhile, to maintain a constant DC voltage and to compensate the self-discharge in the ultracapacitor to maintain it operating at the nominal state of charge. The object system is nonlinear due to the multiplicative relation between the input and the state. The traditional approach to solve Francis-Byrnes-Isidori equations cannot be directly applied in this case since the interconnect matrix depends on the control input. Besides, even if this approach is well suited to the rejection of persistent disturbances, it shows the limits for the case of non-persistent disturbances which is also our object. Our contributed control method is realized through a cascade control structure based on the singular perturbation theory. The ultracapacitor current with the fastest motion rate is controlled in the inner fast loop through which we impose the desired dynamic to the system. The reduced system controlled in the outer slow loop is a Hamiltonian system and the controller is designed via interconnection and damping assignment. Simulations and experiments have been carried out to evaluate the control performance. A contrast of the system responses with and without the control algorithm shows that, with the control algorithm, the ultracapacitor effectively absorbs the disturbances; and verifies the effectiveness of the control algorithm

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Bastida, Molina Paula. « Estudio de sistemas renovables avanzados para el desarrollo energético sostenible ». Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/172548.

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Tesis por compendio
[ES] La energía juega un papel fundamental en el desarrollo sostenible de las comunidades. Así, proporcionar recursos energéticos fiables, económicamente aceptables, medioambientalmente respetuosos y socialmente beneficiosos, resulta esencial para el desarrollo sostenible de las mismas. A pesar de la universalidad de dicha definición, el uso de la energía está muy vinculada al nivel de desarrollo de los países. De este modo, la problemática energética de los países desarrollados contrasta enormemente con la de los países en desarrollo. En esta tesis doctoral se ha identificado la principal problemática energética de ambas realidades: grave impacto medioambiental de los modelos de generación del transporte tradicionales en los países desarrollados y pobreza energética en los países en desarrollo. A partir del compendio de artículos científicos de esta tesis doctoral se ha caracterizado el uso de sistemas renovables avanzados que permite solucionar dicha problemática de forma sostenible. En concreto, el principal problema energético en países desarrollados ha sido tratado mediante la planificación energética y el diseño óptimo de sistemas híbridos de energías renovables (HRES por sus siglas en inglés) en electrolineras, necesarios para la introducción de vehículos eléctricos como alternativa de movilidad sostenible. Por otro lado, el estudio de metodologías de diseño óptimas de HRES off grid y de las estufas para cocinar mejoradas mediante gasificación de biomasa se ha focalizado en la inaccesibilidad eléctrica y a sistemas de cocina limpia que sufren las comunidades en desarrollo. Así, esta tesis aporta una serie de metodologías para optimizar y adecuar los sistemas renovables presentados para el desarrollo energético sostenible de las comunidades. Además, no sólo demuestra la idoneidad de estos sistemas para dicho fin, sino también su versatilidad de aplicación en función del nivel de crecimiento de las comunidades.
[CA] L'energia juga un paper fonamental en el desenvolupament sostenible de les comunitats. Així, proporcionar recursos energètics fiables, econòmicament acceptables, mediambientalment respectuosos i socialment beneficiosos, resulta essencial per al desenvolupament sostenibles de les mateixes. A pesar de la universalitat d'aquesta definició, l'ús de la energia està vinculada al nivell de desenvolupament dels països. D'aquesta manera, la problemàtica energètica dels països desenvolupats contrasta enormement amb la dels països en desenvolupament. A aquesta tesis doctoral s'ha identificat la principal problemàtica energètica d'ambdues realitats: greu impacte mediambiental dels models de generació del transport tradicional en els països desenvolupats i pobresa energètica en els països en desenvolupament. A partir del compendi d'articles científics d'aquesta tesis doctoral s'ha caracteritzat l'ús de sistemes renovables avançats que permet solucionar aquesta problemàtica de manera sostenible. En concret, el principal problema energètic en països desenvolupats s'ha tractat mitjançant la planificació energètica i el disseny òptim de sistemes híbrids d'energies renovables (HRES, per les seues segles en anglès) en electrolineres, necessaris per la introducció de vehicles elèctrics com alternativa de mobilitat sostenible. D'altra banda, l'estudi de metodologies de disseny òptimes de HRES off grid i d'estufes per a cuinar millorades mitjançant gasificació de biomassa s'ha focalitzat en la inaccessibilitat elèctrica i a sistemes de cuina neta que pateixen les comunitats en desenvolupament. Així, aquesta tesis aporta una sèrie de metodologies per optimitzar i adequar el sistemes renovables presentats per al desenvolupament energètic sostenible de les comunitats. A més, no tan sols demostra la idoneïtat d'aquests sistemes per a aqueix fi, sinó també la seua versatilitat d'aplicació en funció del nivell de creixement de les comunitats.
[EN] Energy plays a significant role for the sustainable development of communities. Hence, supplying reliable energy resources, which result economically acceptable, environmentally friendly and socially beneficial, arises as essential for their sustainable development. Despite the universality of such definition, the energy use is highly linked to the development degree of the countries. Thus, energy problems of developed countries sharply contrast with those of developing countries. This doctoral thesis identifies the main energy issues of both realities: severe environmental impact of energy generation models for traditional transport in developed countries and energy poverty in developing countries. The compendium of scientific papers of this doctoral dissertation characterizes the use of advanced renewable energy systems to solve such problems in a sustainable way. Namely, the main energy issue in developed countries has been addressed by means of energy planning and the optimal design of Hybrid Renewable Energy Systems (HRES) in electric vehicle charging stations, which ensure the introduction of electric vehicles as a sustainable mobility alternative. Moreover, the study of methodologies for the optimal design of off grid HRES and improved cooking stoves based on biomass gasification have approached the inaccessibility to electricity and to clean cooking systems that developing communities suffer. Therefore, this thesis provides a number of methodologies to optimize and adapt the presented renewable energy systems for the sustainable energy development of communities. Furthermore, it demonstrates not only the suitability of these systems for such aim, but also their versatility of application regarding the growing degree of the communities.
Bastida Molina, P. (2021). Estudio de sistemas renovables avanzados para el desarrollo energético sostenible [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172548
TESIS
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Pinto, Jonathan Hunder Dutra Gherard. « Conversor modular multinível aplicado a sistema híbrido de armazenamento de energia ». Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/6501.

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Este trabalho tem como contribuição o desenvolvimento de uma estratégia de equa-lização das tensões em um conversor multinível modular, como parte integrante de um sistema híbrido de armazenamento de energia. O conversor modular multinível realiza a conexão em série de módulos supercapacitores, o que possibilita aumentar a ten-são sem prejudicar a transferência rápida de energia. Em relação à outras topologias, este trabalho permite reduzir a quantidade, volume e massa do elemento magnético da estrutura do conversor. Um banco de baterias de íons de lítio também integra o sistema por intermédio de um conversor estático. Como é a fonte de maior densidade de energia, fornece a potência média requerida pelo carga. A associação com uma fonte de transferência rápida de energia permite aumentar o desempenho dinâmico, a eficiência energética e a vida útil da bateria. Com efeito, tem-se um sistema híbrido de armazenamento de energia que requer estratégias de gestão para múltiplas fontes de suprimento. Os resultados simulados considerando a estimativa da demanda de po-tência de um protótipo de veículo elétrico, são adequados e propiciam os fundamentos necessários para a construção de um protótipo.
This work is a contribution to develop a strategy equalization of tensions in a mo-dular multilevel converter as part of a hybrid system energy storage. The multilevel modular converter realizes the series connection of supercapacitor modules, which al-lows to increase the voltage without cause damages to the quick energy transfer. In relation to other topologies, it allows reduction of the quantity, volume and mass of the magnetic element of the converter structure. A lithium-ion battery bank also integrates the system via a voltage boost converter. This battery is the source of high energy density, which provides the average power required by the load. The association with a fast transfer power source allows for increased dynamic performance, energy efficiency and service life. In fact, there is a hybrid energy storage system that requires mana-gement strategies for multiple sources of supply. The simulated results were obtained considering the power demand estimation of an electric vehicle prototype.

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Mesbahi, Tedjani. « Influence des stratégies de gestion d’une source hybride de véhicule électrique sur son dimensionnement et sa durée de vie par intégration d’un modèle multi-physique ». Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0004/document.

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Ce mémoire contribue à l’amélioration des performances d’une source de stockage hybride embarquée alimentant un véhicule électrique. La solution investiguée est composée de l’association de batteries Li-ion et de super condensateurs, dans le but d’obtenir, par rapport aux solutions classiques, un gain en masse et en durée de vie pour une certaine plage d’autonomie du véhicule. Notre objectif est de mettre à profit l’utilisation de nouvelles méthodes de gestion de la source hybride et de quantifier le gain obtenu. Un modèle multi-physique incluant les aspects électrique, thermique et vieillissement a été développé et intégré dans l’algorithme de gestion d’énergie afin d’évaluer la dégradation progressive des performances des éléments de stockage au cours des cycles de conduite selon la stratégie de gestion implantée. De nouvelles stratégies de gestion ayant pour objectif d’agir sur la durée de vie ont été évaluées. Leur impact sur les performances de la source en termes de masse, coût et durée de vie a pu être quantifié et montre bien que par une meilleure gestion des puissances, il est possible de mieux utiliser le stockeur hybride, ouvrant ainsi la voie à de nouvelles approches de gestion d’énergie pour ces systèmes
This thesis contributes to the improvement of hybrid embedded source performances supplies an electric vehicle. The studied solution is composed of Li-ion batteries and supercapacitors hybridization, with an aim to achieve improved performances in terms of weight and lifetime over traditional solutions. Our main goal is to take the best advantage of new energy management strategies of the hybrid embedded source and quantify obtained improvements. A multi-physic model including electric, thermal and aging behaviors is developed and integrated into the algorithm of energy management in order to evaluate the gradual degradation of storage components performances during driving cycles and implemented control strategy. New energy management strategies intended to act on the lifetime of hybrid embedded source have been evaluated. Their impact on the performances of the source in terms of weight, cost and lifetime has been quantified and clearly shows that it is possible to make better use of hybrid embedded source thanks to a good power sharing, thus opening the way to new approaches of energy management for these systems

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(7480409), RISHIKESH MAHESH BAGWE. « MODELING AND ENERGY MANAGEMENT OF HYBRID ELECTRIC VEHICLES ». Thesis, 2019.

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This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine efficiency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

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Bagwe, Rishikesh Mahesh. « Modeling and Energy Management of Hybrid Electric Vehicles ». Thesis, 2019. http://hdl.handle.net/1805/21090.

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Indiana University-Purdue University Indianapolis (IUPUI)
This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

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Cheng, Wen-Chieh, et 鄭文傑. « Research of Energy Management Strategy Applied to Series Hybrid Electric Vehicles ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/12785631199130824946.

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碩士
國立臺灣大學
機械工程學研究所
101
In this research, the SOC of the battery system is applied to amend the power that the generator need to produce, helping keep the SOC of the battery system close to the ideal value and reduce the SOC swing; the difference between the needed power and the real power of the generator is used to dynamically limit the generated power change rate, keeping the power efficiency high at normal driving situation and avoiding the battery current from overloading when accelerating and decelerating rapidly. By using this method, the oil efficiency and the battery life can be improved. This research uses ADVISOR to simulate the performance of the SHEV system at NEDC cycle, compares with other control strategies, and simulates and analyzes the performance under rapid accelerating and decelerating, constant speed climbing, and driving on mountain road at high speed. Finally, this research discusses and simulates the influence of system aging on the performance of the control strategy.

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Pinto, Cláudio Filipe Azevedo. « Sizing and energy management of a distributed hybrid energy storage system for electric vehicles ». Doctoral thesis, 2018. https://hdl.handle.net/10216/111989.

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Pinto, Cláudio Filipe Azevedo. « Sizing and energy management of a distributed hybrid energy storage system for electric vehicles ». Tese, 2018. https://hdl.handle.net/10216/111989.

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