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Добірка наукової літератури з теми "Energy management strategies for hybrid electric vehicles"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Energy management strategies for hybrid electric vehicles"

1

PIELECHA, Ireneusz, Wojciech CIEŚLIK, and Kinga FLUDER. "Analysis of energy management strategies for hybrid electric vehicles in urban driving conditions." Combustion Engines 173, no. 2 (April 1, 2018): 14–18. http://dx.doi.org/10.19206/ce-2018-203.

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The pursuit of fuel consumption reduction by vehicles leads to a sudden increase in the share of hybrid and electric drives in the vehicle market. Replacing hybrid vehicles with electric vehicles requires long-term technological solutions, both for the infrastructure and the vehicles themselves. Therefore, one of the leading types of passenger car drives is currently the hybrid drive. The generated work share of electric drives used to power hybrid vehicles is a determinant of the viability of using electric drives. The article estimates the operating time share of electric and hybrid modes operation in real driving conditions (RDC) based on the latest Toyota hybrid model. The research object was a vehicle from the crossover group equipped with a fourth generation hybrid drive. Analysis of the drives operation allowed to determine the conditions of energy flow and determine the work share of the electric drive in the total driving time.

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Panday, Aishwarya, and Hari Om Bansal. "A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle." International Journal of Vehicular Technology 2014 (November 18, 2014): 1–19. http://dx.doi.org/10.1155/2014/160510.

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Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

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Franceschi, Alessandro, Nicolò Cavina, Riccardo Parenti, Maurizio Reggiani, and Enrico Corti. "Energy Management Optimization of a Dual Motor Lithium Ion Capacitors-Based Hybrid Super Sport Car." Applied Sciences 11, no. 2 (January 19, 2021): 885. http://dx.doi.org/10.3390/app11020885.

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Nowadays, hybrid electric vehicles represent one of the main solutions for the reduction of greenhouse gases in the automotive sector. Alongside the reduction of CO2, hybrid electric vehicles serve as a strong alternative on drivability and performance to conventional internal combustion engine-based vehicles. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid electric vehicles can differ in the powertrain configuration and the choice of the energy storage system. Manufacturers have recently started to work on Lithium-Ion Capacitors (LiC) -based hybrid vehicles. This paper discusses the usage of a control-oriented vehicle and powertrain model to analyze the performance of a dual motor LiC-based hybrid V12 vehicle by Automobili Lamborghini. P3–P4 and P2–P4 parallel hybrid configurations have been selected and compared since they allow to fully exploit the potential of the LiC storage system characterized by high power. The validated model has been used to develop control strategies aimed at fuel economy and CO2 reduction, and in particular, both Rule Based Strategies (RBS) and Equivalent Consumption Minimization Strategies (ECMS) are presented in the paper. A critical comparison between the various powertrain configurations is carried out, keeping into account the peculiarities of the LiC technology and evaluating the performance of the different control approaches.

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Song, Ziyou, Heath Hofmann, Jianqiu Li, Jun Hou, Xuebing Han, and Minggao Ouyang. "Energy management strategies comparison for electric vehicles with hybrid energy storage system." Applied Energy 134 (December 2014): 321–31. http://dx.doi.org/10.1016/j.apenergy.2014.08.035.

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Shen, Caiying, Peng Shan, and Tao Gao. "A Comprehensive Overview of Hybrid Electric Vehicles." International Journal of Vehicular Technology 2011 (November 24, 2011): 1–7. http://dx.doi.org/10.1155/2011/571683.

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Анотація:

As the environmental pollution and energy crises are getting more and more remarkable, hybrid electric vehicles (HEVs) have taken on an accelerated pace in the world. A comprehensive overview of HEVs is presented in this paper, with the emphasis on configurations, main issues, and energy management strategies. Conclusions are discussed finally.

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Obu Showers, Samson, and Atanda Kamoru Raji. "State-of-the-art review of fuel cell hybrid electric vehicle energy management systems." AIMS Energy 10, no. 3 (2022): 458–85. http://dx.doi.org/10.3934/energy.2022023.

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<abstract> <p>The primary purpose of fuel cell hybrid electric vehicles (FCHEVs) is to tackle the challenge of environmental pollution associated with road transport. However, to benefit from the enormous advantages presented by FCHEVs, an appropriate energy management system (EMS) is necessary for effective power distribution between the fuel cell and the energy storage systems (ESSs). The past decade has brought a significant increase in the number of FCHEVs, with different EMSs having been implemented due to technology advancement and government policies. These methods are broadly categorised into rule-based EMS methods, machine learning methods and optimisation-based control methods. Therefore, this paper presents a systematic literature review on the different EMSs and strategies used in FCHEVs, with special focus on fuel cell/lithium-ion battery hybrid electric vehicles. The contribution of this study is that it presents a quantitative evaluation of the different EMSs selected by comparing and categorising them according to principles, technology maturity, advantages and disadvantages. In addition, considering the drawbacks of some EMSs, gaps were highlighted for future research to create the pathway for comprehensive emerging solutions. Therefore, the results of this paper will be beneficial to researchers and electric vehicle designers saddled with the responsibility of implementing an efficient EMS for vehicular applications.</p> </abstract>

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Dwivedi, Shailendra, та Yash Dave. "Energy Management And Control Strategies For Parallel Hybrid Eleсtriс Vehiсle". Journal of Futuristic Sciences and Applications 5, № 1 (2022): 65–70. http://dx.doi.org/10.51976/jfsa.512209.

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For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources—batteries, Fuel cell and a supercapacitor system, —this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines , fuel cell systems, supercapacitor systems or flywheel systems.

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Sorlei, Ioan-Sorin, Nicu Bizon, Phatiphat Thounthong, Mihai Varlam, Elena Carcadea, Mihai Culcer, Mariana Iliescu, and Mircea Raceanu. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies." Energies 14, no. 1 (January 5, 2021): 252. http://dx.doi.org/10.3390/en14010252.

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Анотація:

With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.

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Ma, Yunfei, Di Shi, Jieni Song, and Sishi Zuo. "Review on Energy Management Strategies of PHEV." Highlights in Science, Engineering and Technology 3 (July 8, 2022): 144–57. http://dx.doi.org/10.54097/hset.v3i.703.

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Анотація:

The problems of air pollution and the shortage of energy sources are increasingly significant, and the traditional automobile industry plays a vital role in it. Consequently, Energy management strategy can adjust the energy allocation between the engine and the motor, achieving the purpose of improving fuel economy. Meanwhile, it can optimize the battery balance so that the comprehensive cost of the hybrid vehicle can be reduced, apart from the limited emissions and conserved energy. This paper first introduces the machinery configuration of the parallel hybrid electric vehicles, and then classifies their working patterns under different working conditions. After analysing the existing energy management strategies, we introduce and analyse the rule-based ones by two frequently used methods: logic threshold method and fuzzy logic control. Additionally, strategies based on optimization, including instantaneous and global optimization strategy, are also introduced and analysed, with each advantages, limitations and potential innovations. Finally, the contradiction between practicability and control effect is identified, which is one of the crucial problems to be resolved.

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Millo, Federico, Luciano Rolando, Rocco Fuso, Erik Bergshoeff, and Farshid Shafiabady. "Analysis of Different Energy Management Strategies for Complex Hybrid Electric Vehicles." Computer-Aided Design and Applications 11, sup1 (May 30, 2014): S1—S10. http://dx.doi.org/10.1080/16864360.2014.914399.

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Дисертації з теми "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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3

Hägglund, Andreas, and 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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Анотація:

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, and 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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Анотація:

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 and 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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Книги з теми "Energy management strategies for hybrid electric vehicles"

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Williamson, Sheldon S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2.

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Engineers, Society of Automotive, and Future Transportation Technology Conference and Exposition (1997 : San Diego, Calif.), eds. Electric/hybrid vehicles: Alternative powerplants, energy management, and battery technology. Warrendale, PA: Society of Automotive Engineers, 1997.

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Li, Yuecheng, and Hongwen He. Deep Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-79206-9.

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4

Rizzoni, Giorgio, Simona Onori, and Lorenzo Serrao. Hybrid Electric Vehicles: Energy Management Strategies. Springer London, Limited, 2015.

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Rizzoni, Giorgio, Simona Onori, and Lorenzo Serrao. Hybrid Electric Vehicles: Energy Management Strategies. Springer, 2015.

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Williamson, Sheldon S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. Springer, 2013.

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7

Williamson, Sheldon S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. Springer, 2013.

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8

Williamson, Sheldon S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. SPRINGER, 2020.

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9

Williamson, Sheldon S. Energy Management Strategies for Electric and Plug-In Hybrid Electric Vehicles. Springer London, Limited, 2013.

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10

Williamson, Sheldon S. S. Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles. Springer, 2016.

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Частини книг з теми "Energy management strategies for hybrid electric vehicles"

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Williamson, Sheldon S. "Hybrid Electric and Fuel Cell Hybrid Electric Vehicles." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 31–63. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_4.

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Williamson, Sheldon S. "Electric and Plug-in Hybrid Electric Vehicle Drive Train Topologies." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 7–14. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_2.

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Williamson, Sheldon S. "EV and PHEV Energy Storage Systems." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 15–29. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_3.

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Williamson, Sheldon S. "On-Board Power Electronic Battery Management." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 91–150. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_6.

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Onori, Simona. "Model-Based Optimal Energy Management Strategies for Hybrid Electric Vehicles." In Optimization and Optimal Control in Automotive Systems, 199–218. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05371-4_12.

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Williamson, Sheldon S. "EV and PHEV Battery Charging: Grid and Renewable Energy Interface." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 151–85. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_7.

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Rohith, J., and G. T. Mahesha. "Review of Energy Management Strategies in Plug-in Hybrid-Electric Vehicles." In Lecture Notes in Electrical Engineering, 83–99. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0588-9_8.

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Maherchandani, Jai Kumar, R. R. Joshi, Ritesh Tirole, Raju Kumar Swami, and Bibhu Prasad Ganthia. "Performance Comparison Analysis of Energy Management Strategies for Hybrid Electric Vehicles." In Recent Advances in Power Electronics and Drives, 245–54. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9239-0_18.

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Williamson, Sheldon S. "Introduction." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 1–6. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_1.

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Williamson, Sheldon S. "EV and PHEV Well-to-Wheels Efficiency Analysis." In Energy Management Strategies for Electric and Plug-in Hybrid Electric Vehicles, 243–53. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7711-2_10.

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Тези доповідей конференцій з теми "Energy management strategies for hybrid electric vehicles"

1

Gonder, Jeffrey, and Tony Markel. "Energy Management Strategies for Plug-In Hybrid Electric Vehicles." In SAE World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-0290.

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2

Raboaca, Maria Simona, Nicu Bizon, and Oana Vasilica Grosu. "Energy management strategies for hybrid electric vehicles - vosviwer bibliometric analysis." In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2020. http://dx.doi.org/10.1109/ecai50035.2020.9223123.

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3

Tazelaar, Edwin, Bram Veenhuizen, Jose Jagerman, and Ton Faassen. "Energy Management Strategies for fuel cell hybrid vehicles; an overview." In 2013 World Electric Vehicle Symposium and Exhibition (EVS27). IEEE, 2013. http://dx.doi.org/10.1109/evs.2013.6915039.

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4

Xiaolai He, M. Parten, and T. Maxwell. "Energy Management Strategies for a Hybrid Electric Vehicle." In 2005 IEEE Vehicle Power and Propulsion Conference. IEEE, 2005. http://dx.doi.org/10.1109/vppc.2005.1554610.

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5

Pisu, Pierluigi, Giorgio Rizzoni, Cristian Musardo, and Benedetto Staccia. "A Comparative Study of Supervisory Control Strategies for Hybrid Electric Vehicles." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59996.

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Hybrid Electric Vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. Big obstacles to the control design are the model complexity and the necessity of “a priori” knowledge of torque and velocity profiles for optimal torque split. This paper presents and compares four different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle.

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6

"SS recent advances in energy management strategies for hybrid electric vehicles." In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8217243.

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7

Krishna P.S., Praveena, Jayalakshmi N.S., and Akash Kedlaya. "Energy Management Strategies for Hybrid Energy Storage System in Electric Vehicles: A Review." In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2020. http://dx.doi.org/10.1109/conecct50063.2020.9198655.

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8

Salazar, Mauro, Arian Houshmand, Christos G. Cassandras, and Marco Pavone. "Optimal Routing and Energy Management Strategies for Plug-in Hybrid Electric Vehicles." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917131.

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9

Liu, Teng, Xiaolin Tang, Jiaxin Chen, Hong Wang, Wenhao Tan, and Yalian Yang. "Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition." In 2020 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2020. http://dx.doi.org/10.1109/vppc49601.2020.9330856.

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10

Baert, J., S. Jemei, D. Chamagne, D. Hissel, S. Hibon, and D. Hegy. "Modeling and Energy Management Strategies of a Hybrid Electric Locomotive." In 2012 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2012. http://dx.doi.org/10.1109/vppc.2012.6422766.

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Звіти організацій з теми "Energy management strategies for hybrid electric vehicles"

1

Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.

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2

Development of an Adaptive Efficient Thermal/Electric Skipping Control Strategy Applied to a Parallel Plug-in Hybrid Electric Vehicle. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0737.

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In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy - ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications.

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3

Comparative Analysis on Fuel Consumption Between Two Online Strategies for P2 Hybrid Electric Vehicles: Adaptive-RuleBased (A-RB) vs Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0740.

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Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made. Second, the two sub-optimal strategies are described in detail and then implemented on the HEV model to derive the fuel-optimal control strategy managing the power split between the thermal and electric engine to satisfy the driver's power request, including the engine on/off operating mode and the best gear selection. Finally, the two proposed strategies are tested on different driving cycles and then compared to other commercial strategies available in literature, such as the Equivalent Consumption Minimization Strategy (ECMS) and a RuleBased (RB) strategy. The results show that the A-ECMS is more conservative in terms of state of charge (SoC) compared to the A-RB. In fact, in the A-ECMS the SoC is always within the admissible range with considerable margin from the upper and lower limits for tested cycles, while in the A-RB a deep discharge of the battery is allowed. This behavior leads to a better fuel consumption of the A-RB compared to the A-ECMS, both in the WLTC and in the FTP-75 cycle.

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