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1
Bou Nader, Wissam S., Charbel J. Mansour, Maroun G. Nemer, and Olivier M. Guezet. "Exergo-technological explicit methodology for gas-turbine system optimization of series hybrid electric vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 10 (October 6, 2017): 1323–38. http://dx.doi.org/10.1177/0954407017728849.
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Significant research efforts have been invested in the automotive industry on hybrid electrified powertrains in order to reduce the dependence of passenger cars on oil. Electrification of powertrains resulted in a wide range of hybrid vehicle architectures. The fuel consumption of these powertrains strongly relies on the energy converter performance, as well as on the energy management strategy deployed on board. This paper investigates the potential of fuel consumption savings of a series hybrid electric vehicle using a gas turbine as an energy converter instead of the conventional internal-combustion engine. An exergo-technological explicit analysis is conducted to identify the best configuration of the gas-turbine system. An intercooled regenerative reheat cycle is prioritized, offering higher efficiency and higher power density than those of other investigated gas-turbine systems. A series hybrid electric vehicle model is developed and powertrain components are sized by considering the vehicle performance criteria. Energy consumption simulations are performed over the Worldwide Harmonized Light Vehicles Test Procedure driving cycle using dynamic programming as the global optimal energy management strategy. A sensitivity analysis is also carried out in order to evaluate the impact of the battery size on the fuel consumption, for self-sustaining and plug-in series hybrid electric vehicle configurations. The results show an improvement in the fuel consumption of 22–25% with the gas turbine as the auxiliary power unit in comparison with that of the internal-combustion engine. Consequently, the studied auxiliary power unit for the gas turbine presents a potential for implementation on series hybrid electric vehicles.
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Ou, Shiqi, Wan Li, Jie Li, Zhenhong Lin, Xin He, Jessey Bouchard, and Steven Przesmitzki. "Relationships between Vehicle Pricing and Features: Data Driven Analysis of the Chinese Vehicle Market." Energies 13, no. 12 (June 15, 2020): 3088. http://dx.doi.org/10.3390/en13123088.
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A full-scale understanding of the dynamics of the Chinese vehicle market can benefit stakeholders with respect to rational decision-making and effective long-term investment. This study attempts to discover the common vehicle pricing patterns in the Chinese market by quantifying statistical correlations among critical vehicle features from intrinsic powertrain systems to extrinsic market positioning. The data samples involve almost all passenger vehicle models sold in 2013 to 2019. After comparing multiple statistical methodologies, a log-transformation variant of the multinomial linear regression model was found to be the best one, and the goodness of fit shows that this model can offer stable estimates, which were validated using 2019 market data. The insights achieved are: (1) The price and major performance features of SUVs/crossovers are similar to those of sedans; (2) If all other explicit features remain the same, the price of a Japanese midsize sedan is 62% higher than that of a Chinese midsize sedan, and European midsize vehicles have the highest prices overall. (3) The incremental price of fuel consumption varies by vehicle class and fuel economy. For example, from 30 to 50 MPG, the vehicle price increases by $119 for a Chinese brand sedan vehicle, by $69 for a Chinese brand SUV.
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Petr, Tomáš. "EVALUATING ELECTRICITY CONSUMPTION OF SPECIALISED BATTERY ELECTRIC VEHICLES USING SIMULATION MODEL." ACC Journal 29, no. 1 (2023): 34–43. http://dx.doi.org/10.15240/tul/004/2023-1-003.
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Battery Electric Vehicles (BEVs) are widely seen as one of the available options to combat increasing greenhouse gas emissions. However, these vehicles’ use is less widespread than conventional combustion engine vehicles. One reason for this is their still relatively short range and long charging times. For this reason, it is becoming increasingly crucial in BEV development to use the most accurate simulation models that allow the impact on electricity consumption to be analyzed based on changes made to individual powertrain components. To this end, the author’s dissertation deals with developing a simulation model for estimating the power consumption of a BEV powertrain, describing the definition of the efficiency parameters of the individual powertrain components. The results from the simulation model were then compared with measurements performed in a test facility. The maximum deviation of approximately 8% was measured depending on the driving cycle and parameters.
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Maddumage, W. U., K. Y. Abeyasighe, M. S. M. Perera, R. A. Attalage, and P. Kelly. "Comparing Fuel Consumption and Emission Levels of Hybrid Powertrain Configurations and a Conventional Powertrain in Varied Drive Cycles and Degree of Hybridization." Science & Technique 19, no. 1 (February 5, 2020): 20–33. http://dx.doi.org/10.21122/2227-1031-2020-19-1-20-33.
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Hybrid electric powertrains in automotive applications aim to improve emissions and fuel economy with respect to conventional internal combustion engine vehicles. Variety of design scenarios need to be addressed in designing a hybrid electric vehicle to achieve desired design objectives such as fuel consumption and exhaust gas emissions. The work in this paper presents an analysis of the design objectives for an automobile powertrain with respect to different design scenarios, i. e. target drive cycle and degree of hybridization. Toward these ends, four powertrain configuration models (i. e. internal combustion engine, series, parallel and complex hybrid powertrain configurations) of a small vehicle (motorized three wheeler) are developed using Model Advisor software and simulated with varied drive cycles and degrees of hybridization. Firstly, the impact of vehicle power control strategy and operational characteristics of the different powertrain configurations are investigated with respect to exhaust gas emissions and fuel consumption. Secondly, the drive cycles are scaled according to kinetic intensity and the relationship between fuel consumption and drive cycles is assessed. Thirdly, three fuel consumption models are developed so that fuel consumption values for a real-world drive cycle may be predicted in regard to each powertrain configuration. The results show that when compared with a conventional powertrain fuel consumption is lower in hybrid vehicles. This work led to the surprisingly result showing higher CO emission levels with hybrid vehicles. Furthermore, fuel consumption of all four powertrains showed a strong correlation with kinetic intensity values of selected drive cycles. It was found that with varied drive cycles the average fuel advantage for each was: series 23 %, parallel 21 %, and complex hybrids 33 %, compared to an IC engine powertrain. The study reveals that performance of hybrid configurations vary significantly with drive cycle and degree of hybridization. The paper also suggests future areas of study.
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Angerer, C., B. Mößner, M. Lüst, S. Büchner, F. Sträußl, and M. Lienkamp. "Parameter-adaption for a vehicle dynamics model for the evaluation of powertrain concept designs." MATEC Web of Conferences 272 (2019): 01022. http://dx.doi.org/10.1051/matecconf/201927201022.
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The powertrain design of multi-motor electric vehicles directly affects not only costs, consumption and acceleration, but also the handling of a vehicle. Therefore, a holistic powertrain design optimization needs to include a vehicle dynamics model in its objective function. While the parameters for the powertrain model result from the design variables that describe the powertrain, the parameters for the vehicle dynamics model must be adapted in a feasible way to ensure comparable results. Therefore, the authors present a method on how to adaptively parametrize a double-track vehicle dynamics model for the use in powertrain design optimization. Automated design calculations for all main chassis and suspension parts are used to determine the parameters for the model. A parameter variation proves the plausibility of the approach. The results show that an adaption of the suspension and chassis parameters due to changes in the powertrain make results more comparable but do not compensate for the effects on the vehicle handling. In particular, the steady state longitudinal load distribution still has major influences on the vehicle handling.
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Geng, Stefan, Thomas Schulte, and Jürgen Maas. "Model-Based Analysis of Different Equivalent Consumption Minimization Strategies for a Plug-In Hybrid Electric Vehicle." Applied Sciences 12, no. 6 (March 11, 2022): 2905. http://dx.doi.org/10.3390/app12062905.
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Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.
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König, Adrian, Sebastian Mayer, Lorenzo Nicoletti, Stephan Tumphart, and Markus Lienkamp. "The Impact of HVAC on the Development of Autonomous and Electric Vehicle Concepts." Energies 15, no. 2 (January 9, 2022): 441. http://dx.doi.org/10.3390/en15020441.
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Automation and electrification are changing vehicles and mobility. Whereas electrification is mainly changing the powertrain, automation enables the rethinking of the vehicle and its applications. The actual driving range is an important requirement for the design of automated and electric vehicles, especially if they are part of a fleet. To size the battery accordingly, not only the consumption of the powertrain has to be estimated, but also that of the auxiliary users. Heating Ventilation and Air Conditioning (HVAC) is one of the biggest auxiliary consumers. Thus, a variable HVAC model for vehicles with electric powertrain was developed to estimate the consumption depending on vehicle size and weather scenario. After integrating the model into a tool for autonomous and electric vehicle concept development, various vehicle concepts were simulated in different weather scenarios and driving cycles with the HVAC consumption considered for battery sizing. The results indicate that the battery must be resized significantly depending on the weather scenario to achieve the same driving ranges. Furthermore, the percentage of HVAC consumption is in some cases higher than that of the powertrain for urban driving cycles, due to lower average speeds. Thus, the HVAC and its energy demand should especially be considered in the development of autonomous and electric vehicles that are primarily used in cities.
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Geng, Stefan, Andreas Meier, and Thomas Schulte. "Model-Based Optimization of a Plug-In Hybrid Electric Powertrain with Multimode Transmission." World Electric Vehicle Journal 9, no. 1 (June 13, 2018): 12. http://dx.doi.org/10.3390/wevj9010012.
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Plug-in hybrid electric vehicles are developed in order to reduce the fuel consumption and the emission of carbon dioxide. Besides the series, parallel and power split configurations are commonly used for conventional hybrid electric vehicles, and multimode transmissions are used for plug-in hybrid electric vehicles, which are able to switch between different modes like parallel or series operation of the combustion engine and electric motor. Several concepts have already been discussed and presented. These concepts comprise novel structures and multi-speed operation for the combustion engine and the electric motor, respectively. For improving the fuel and energy consumption, model-based optimizations of multimode transmissions are performed. In the first step of the optimization, the optimal number of gears and transmission ratios, as well as the corresponding fuel and energy savings, are estimated. Based on these results, a new multimode transmission concept with two-speed transmissions for the combustion engine and the electric motor has been developed. The knowledge of the concrete concept enables the further optimizations of the transmission ratios and the transmission control. In order to prove the benefit of the new and optimized transmission concept, powertrain simulations have been carried out. The new powertrain concept is compared to a powertrain concept with single-speed transmissions for the internal combustion engine (ICE) and electric motor operation. The new transmission concept enables a significant improvement of the fuel consumption.
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Shen, Ye, Andreas Viehmann, and Stephan Rinderknecht. "Investigation of the power losses of the hybrid transmission DE-REX based on modeling and measurement." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 14 (February 18, 2019): 3646–57. http://dx.doi.org/10.1177/0954407019829655.
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Electric and hybrid powertrains are developed to reduce the energy and fuel consumption of vehicles. Recently, multi-speed transmission systems were discussed for further reduction of the energy consumption of electric vehicles. Therefore, analyzing the power losses of such transmissions is of interest. In this paper, the novel powertrain concept DE-REX (Two-Drive-Transmission with Range-Extender) and the experimental investigation of its overall power losses is first introduced. A method is then developed to model and analyze the power losses of this hybrid transmission based on experimental data. After the validation of the method, the overall power loss model is eventually applied to estimate the power losses of the transmission at other driving modes, which were not measured on the test rig. The method is used to understand the characteristics of power losses inside the transmission in a hybrid powertrain and to optimize powertrain power losses in future.
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Sigle, Sebastian, and Robert Hahn. "Energy Assessment of Different Powertrain Options for Heavy-Duty Vehicles and Energy Implications of Autonomous Driving." Energies 16, no. 18 (September 9, 2023): 6512. http://dx.doi.org/10.3390/en16186512.
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Heavy-duty vehicles (HDVs) are responsible for a significant amount of CO2 emissions in the transport sector. The share of these vehicles is still increasing in the European Union (EU); nevertheless, rigorous CO2 emission reduction schemes will apply in the near future. Different measures to decrease CO2 emissions are being already discussed, e.g., the electrification of the powertrain. Additionally, the impact of autonomous driving on energy consumption is being investigated. The most common types are fuel cell vehicles (FCEVs) and battery-only vehicles (BEVs). It is still unclear which type of powertrain will prevail in the future. Therefore, we developed a method to compare different powertrain options based on different scenarios in terms of primary energy consumption, CO2 emissions, and fuel costs. We compared the results with the internal combustion engine vehicle (ICEV). The model includes a model for the climatization of the driver’s cabin, which we used to investigate the impact of autonomous driving on energy consumption. It became clear that certain powertrains offer advantages for certain applications and that sensitivities exist with regard to primary energy and CO2 emissions. Overall, it became clear that electrified powertrains could reduce the CO2 emissions and the primary energy consumption of HDVs. Moreover, autonomous vehicles can save energy in most cases.
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Hashim, Mohd Syahmi, Muhamad Mansor, Yong Jia Ying, Vigna Kumaran Ramachandaramurthy, and Nazaruddin Abd Rahman. "Electric vehicle energy consumption modelling and analysis: a Malaysia case study." IOP Conference Series: Earth and Environmental Science 1281, no. 1 (December 1, 2023): 012072. http://dx.doi.org/10.1088/1755-1315/1281/1/012072.
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Abstract Previously, the Electric vehicle (EV) range anxiety greatly hindered EV adoption. The fear of being stranded due to running out of battery becomes the main concern among EV drivers. This critical issue can be solved by deploying a bigger battery in EVs. However, deploying bigger size battery contributes to the higher price of the EV itself. Hence, the optimal design of the EV powertrain is a critical factor in ensuring EV meets the users’ needs and, simultaneously, can be owned by the potential owners at an affordable price. Accurate assessment of EV energy consumption is crucial in designing an optimal EV powertrain, particularly for developing energy-efficient driving techniques in both manual and autonomous driving. One method is to perform a real-life experiment to determine the EV energy consumption, which is expensive and time-consuming. An alternative to real-world testing is creating an EV energy consumption model with the existing driving cycles. This paper studies the parameters contributing to EV energy consumption based on EV driving cycles available in MATLAB Simulink. The findings of this study will provide insight into the factors that influence EV energy consumption and serve as a guideline in EV powertrain design.
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Lombardi, Simone, Manfredi Villani, Daniele Chiappini, and Laura Tribioli. "Cooling System Energy Consumption Reduction through a Novel All-Electric Powertrain Traction Module and Control Optimization." Energies 14, no. 1 (December 23, 2020): 33. http://dx.doi.org/10.3390/en14010033.
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In this work, the problem of reducing the energy consumption of the cooling circuit for the propulsion system of an all-electric vehicle is approached with two different concepts: improvement of the powertrain efficiency and optimization of the control strategy. Improvement of the powertrain efficiency is obtained through a modular design, which consists of replacing the electric powertrain with several smaller traction modules whose powers sum up to the total power of the original powertrain. In this paper, it is shown how modularity, among other benefits, also allows reducing the energy consumption of the cooling system up to 54%. The energy consumption of the cooling system is associated with two components: the pump and the fan. They produce a so-called auxiliary load on the battery, reducing the maximum range of the vehicle. In conventional cooling systems, the pump and the fan are controlled with a thermostat, without taking into account the energy consumption. Conversely, in this work a control strategy to reduce the auxiliary loads is developed and compared with the conventional approach, showing that the energy consumption of the cooling system can be reduced up to 27%. To test the control strategy, numerical simulations have been carried out with a 1-D model of the cooling system. On the other hand, all the thermal loads of the components have been calculated with a vehicle simulator, which is able to predict the vehicle’s behavior under different driving cycles.
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Pusztai, Zoltán, Péter Kőrös, Ferenc Szauter, and Ferenc Friedler. "Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle." Energies 15, no. 10 (May 16, 2022): 3631. http://dx.doi.org/10.3390/en15103631.
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In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.
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Liang, Peng, Huatuo He, Huafang Cui, and Minglang Zhang. "Research on Establishment of Vehicle Energy Distribution Model and Energy Consumption Optimization Based on Electric Hybrid System." World Electric Vehicle Journal 12, no. 4 (November 1, 2021): 213. http://dx.doi.org/10.3390/wevj12040213.
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In order to improve the adaptability and accuracy of the system average efficiency model in energy consumption analysis of working conditions, this paper presents a vehicle energy distribution model based on the layout and powertrain operation features of the electric hybrid system, and presents a vehicle energy consumption optimization method for control strategy and hardware quality optimization based on the guidance of the energy distribution model.
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Kaloun, Adham, Stéphane Brisset, Maxime Ogier, Mariam Ahmed, and Robin Vincent. "Comparison of Cycle Reduction and Model Reduction Strategies for the Design Optimization of Hybrid Powertrains on Driving Cycles." Energies 14, no. 4 (February 11, 2021): 948. http://dx.doi.org/10.3390/en14040948.
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Decision-making is a crucial and difficult step in the design process of complex systems such as the hybrid powertrain. Finding an optimal solution requires the system feedback. This can be, depending on the granularity of the models at the component level, highly time-consuming. This is even more true when the system’s performance is determined by its control. In fact, various possibilities can be selected to deliver the required torque to the wheels during a driving cycle. In this work, two different design strategies are proposed to minimize the fuel consumption and the cost of the hybrid powertrain. Both strategies adopt the iterative framework which allows for the separation of the powertrain design problem and its control while leading to system optimality. The first approach is based on model reduction, while the second approach relies on improved cycle reduction techniques. They are then applied to a parallel hybrid vehicle case study, leading to important cost reduction in reasonable delays and are compared using different metrics.
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Xu, Hao, Ran Tu, Tiezhu Li, and Haibo Chen. "Interpretable bus energy consumption model with minimal input variables considering powertrain types." Transportation Research Part D: Transport and Environment 119 (June 2023): 103742. http://dx.doi.org/10.1016/j.trd.2023.103742.
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Kivekäs, Klaus, and Antti Lajunen. "Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots." Energies 17, no. 4 (February 19, 2024): 966. http://dx.doi.org/10.3390/en17040966.
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Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.
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Kazemi, Reza, Mohsen Raf’at, and Amir Reza noruzi. "Nonlinear Optimal Control of Continuously Variable Transmission Powertrain." ISRN Automotive Engineering 2014 (January 1, 2014): 1–11. http://dx.doi.org/10.1155/2014/479590.
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Optimization of gear ratio with the objectives of fuel consumption reduction and vehicle longitudinal performance improvement has been the subject of many studies for years. Finding a strategy for changing gears with specific control objectives, especially in the design of vehicles equipped with Continuously Variable Transition system (CVT), which has advantage of arbitrary selection of gear ratio, has been the aim of some recent researches. Optimal control theory has rarely been used in the previous control approaches applied to such systems due to the limitations in the use of fast computational systems. The aim of this study is to design the aforementioned gear ratio change strategy and related control rules on the basis of optimal control. A driver model is also designed for the simulation of driving cycle using MATLAB Simulink Toolbar. Results of implementing optimal control rules in vehicle longitudinal movement simulation with the aim of fuel consumption reduction are finally represented. The presented method has the remarkable advantage of considerable fuel consumption reduction in comparison to other proposed approaches for gear ratio change strategies.
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Tran, Manh-Kien, Steven Sherman, Ehsan Samadani, Reid Vrolyk, Derek Wong, Mitchell Lowery, and Michael Fowler. "Environmental and Economic Benefits of a Battery Electric Vehicle Powertrain with a Zinc–Air Range Extender in the Transition to Electric Vehicles." Vehicles 2, no. 3 (June 27, 2020): 398–412. http://dx.doi.org/10.3390/vehicles2030021.
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Emissions and pollution from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been negatively affecting the global climate and public health. Electric vehicles (EVs) are a cleaner solution to reduce the emission and pollution caused by transportation. Lithium-ion (Li-ion) batteries are the main type of energy storage system used in EVs. The Li-ion battery pack must be considerably large to satisfy the requirement for the vehicle’s range, which also increases the cost of the vehicle. However, considering that most people use their vehicles for short-distance travel during daily commutes, the large pack is expensive, inefficient and unnecessary. In a previous paper, we proposed a novel EV powertrain design that incorporated the use of a zinc–air (Zn–air) battery pack as a range-extender, so that a smaller Li-ion pack could be used to save costs. The design and performance aspects of the powertrain were analyzed. In this study, the environmental and economic benefits of the proposed dual-battery powertrain are investigated. The results from the new powertrain were compared with values from a standard EV powertrain with one large Li-ion pack and a conventional internal combustion engine vehicle (ICEV) powertrain. In addition, an air pollution model is developed to determine the total amount of pollution released by the transportation sector on Highway 401 in Ontario, Canada. The model was then used to determine the effects of mass passenger EV rollout on pollution reduction.
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Liao, Peng, Donghong Ning, Tao Wang, and Haiping Du. "A Driving-Adapt Strategy for the Electric Vehicle with Magneto-Rheological Fluid Transmission Considering the Powertrain Characteristics." Sensors 22, no. 24 (December 8, 2022): 9619. http://dx.doi.org/10.3390/s22249619.
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The additional energy consumption caused by the incompatibility between existing electric vehicle (EV) powertrain characteristics and driving conditions inevitably curbs the promotion and development of EVs. Hence, there is an urgent demand for the driving-adapt strategy, which aims to minimize EV energy consumption due to both powertrain characteristics and driving conditions. In order to fully explore the EV driving-adapt potential, this paper equips the EV with a magneto-rheological fluid transmission (MRFT). First, an EV dynamics analysis of the driving conditions, the powertrain model considering the energy transmission process, and the driving-adapt transmission model considering magneto-rheological fluid (MRF) is conducted to clarify the quantitative relation between the driving conditions and the powertrain. Second, a driving-adapt optimization strategy in the specific driving condition is proposed. Finally, the results and discussions are executed to study (i) the determination of the MRFT fixed speed ratio and variable speed ratio range, (ii) the application potential analysis of the proposed strategy, and (iii) the feasibility analysis of the proposed strategy. The results indicate that (i) the urban driving condition has higher requirements for the MRFT, (ii) EVs equipped with MRFT achieve the expected driving performance at the most states of charge (SOCs) and environmental temperatures, except for the SOC lower than 10%, and (iii) the driving time with efficiency greater than 80% can be increased by the MRFT from 10.1% to 58.7% and from 66.8% to 88.8% in the urban and suburban driving conditions, respectively. Thus, the proposed driving-adapt strategy for the EV equipped with the MRFT has the potential to alleviate or eliminate the traffic problems caused by the incompatibility of the EV powertrain characteristics and the driving conditions.
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Sieklucki, Grzegorz, and Dawid Kara. "Design and Modelling of Energy Conversion with the Two-Region Torque Control of a PMSM in an EV Powertrain." Energies 15, no. 13 (July 3, 2022): 4887. http://dx.doi.org/10.3390/en15134887.
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This paper investigates the properties and design of energy conversion in an electric vehicle (EV) powertrain. Here, we combined the dynamics of vehicle motion with controlled electric propulsion, which is an EV powertrain. The control of two types of permanent magnet synchronous motors (PMSMs) was considered. An algorithm was developed for the determination of the static characteristics of two-region motor torque control. A constant torque and a constant power region were used in the powertrain of the EV. The design of the control system for the PMSM was considered in the d,q reference frame. A precise mechanical model of the EV and the determination of road loads is shown. The main results of this study were the selection of the PI controller parameters (in analytical form), which was carried out for the simplified motor model and then extended for the d,q model, and energy consumption during the WLTP standard driving cycle. The presented simulation results of the proposed control system with synchronous motors in the EV (Fisker Karma as an example) confirmed the approach taken for the selection of the controller. The presentation of the EV’s acceleration for an optimized powertrain, and hence its performance, is a novelty not found in other articles.
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Vijayagopal, Ram, and Aymeric Rousseau. "Benefits of Electrified Powertrains in Medium- and Heavy-Duty Vehicles." World Electric Vehicle Journal 11, no. 1 (January 18, 2020): 12. http://dx.doi.org/10.3390/wevj11010012.
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The benefits of electrified powertrains for light-duty vehicles are well understood, however sufficient published information is not available on the benefits of advanced powertrains on the various types of medium and heavy duty vehicles. Quantifying the benefits of powertrain electrification will help fleet operators understand the advantages or limitations in adopting electrified powertrains in their truck fleets. Trucks vary in size and shape, as they are designed for specific applications. It is necessary to model each kind of truck separately to understand what kind of powertrain architecture will be feasible for their daily operations. This paper examines 11 types of vehicles and 5 powertrain technology choices to quantify the fuel saving potential of each design choice. This study uses the regulatory cycles proposed by the US Environmental Protection Agency (EPA) for measuring fuel consumption.
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Parkar, Omkar, Benjamin Snyder, Adibuzzaman Rahi, and Sohel Anwar. "Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle." Energies 16, no. 13 (June 30, 2023): 5082. http://dx.doi.org/10.3390/en16135082.
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The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.
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Wegener, Marius, Thorsten Plum, Markus Eisenbarth, and Jakob Andert. "Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 4 (August 8, 2019): 992–1005. http://dx.doi.org/10.1177/0954407019867172.
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In this article, we analyze the interaction between powertrain technology, predictive driving functionalities, and inner-city traffic conditions. A model predictive velocity control algorithm is developed that utilizes dynamic traffic data as well as static route information to optimize the future trajectory of the considered ego-vehicle. This controller is then integrated into a state-of-the-art simulation environment for automated driving functionalities to calculate energy saving potentials for vehicles with a conventional gasoline engine powertrain and a P3-hybrid powertrain configuration as well as for a battery electric vehicle based on real driving measurements. The comparison of these powertrains under various traffic conditions shows that all three technologies profit from predictive driving functionalities. The determined reduction in energy demand ranges from 15% to more than 40%, but it is highly dependent on the boundary conditions and the selected powertrain technology. Specifically, it is shown that electrified powertrains can profit the most when the time-gap to the preceding vehicle is maintained at a high level. For a conventional powertrain, this effect is less pronounced and can be attributed to the efficiency characteristics of gasoline engines. It can be concluded that the development of advanced predictive driving functionalities requires microscopic simulation of inner-city traffic to achieve optimum results with regard to energy consumption.
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Pielecha, Ireneusz, Filip Szwajca, and Kinga Skobiej. "Load Capacity of Nickel–Metal Hydride Battery and Proton-Exchange-Membrane Fuel Cells in the Fuel-Cell-Hybrid-Electric-Vehicle Powertrain." Energies 16, no. 22 (November 19, 2023): 7657. http://dx.doi.org/10.3390/en16227657.
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This article investigates the impact of loading on the hybrid powertrain of the FCAT-30 model, equipped with a proton-exchange-membrane fuel cell (PEMFC) and a nickel–metal hydride (NiMH) battery. This study involves analyzing structural component performance based on voltage and current measurements of the fuel cell, battery, and powertrain. Tests conducted under different load conditions reveal significant differences in battery current and fuel-cell voltage, highlighting the crucial role of the battery in the powertrain. External loading induces cyclic operation of the fuel cell, generating peak power. The energy balance analysis demonstrates that, under no-load conditions, the vehicle consumes 37.3% of its energy from the fuel cell, with a total energy consumption of 3597 J. Under load, the energy from the battery is significantly utilized, resulting in a constant fuel-cell share of approximately 19%, regardless of the vehicle’s load. This study concludes that the battery predominantly drives the powertrain, with the fuel cell acting as a secondary energy source. These findings provide valuable insights into the power distribution and energy balance in the hybrid powertrain. Using a load driving profile reduced the fuel-cell-stack energy contribution by 6.85% relative to driving without an external load.
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Liu, Huanlong, Dafa Li, Guanpeng Chen, Chixin Xie, Jiawei Wang, and Lei Feng. "Research on power coupling characteristics and acceleration strategy of electro-hydrostatic hydraulic hybrid power system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 235, no. 8 (January 25, 2021): 1445–59. http://dx.doi.org/10.1177/0959651820987729.
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Aiming at the adverse effect of the peak power of the electric motor of the battery-powered rail vehicles on the battery life and the driving range when starting or accelerating, a new type of electro-hydrostatic hydraulic hybrid powertrain is designed. This article proposes a novel power form that assists the vehicle to start or accelerate through two power coupling methods: torque coupling circuit and flow rate coupling circuit which have good power performance and energy-saving performance. A mathematical model for power coupling of hybrid power system is constructed, and the effects of key parameters of the system and different power coupling ratios on electric power consumption and power coupling characteristics are studied. Based on the simulation and test platform, the power coupling characteristics of the electro-hydrostatic hydraulic hybrid powertrain are simulated and experimentally researched. The results show that compared with the traditional electro-hydrostatic series system, the novel electro-hydrostatic hydraulic hybrid powertrain can effectively avoid the impact of electric motor power and reduce the power consumption. Based on the characteristics of power coupling, the acceleration strategy of minimum peak power is studied to control the key components of the power coupling process. Simulation and experimental results show that under the control of the new acceleration strategy, the electro-hydrostatic hydraulic hybrid powertrain has good electro-hydraulic power coupling characteristics. The electric power of the power system is greatly reduced during acceleration, which has better energy-saving characteristics and value for engineering applications.
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Vora, Ashish P., Xing Jin, Vaidehi Hoshing, Gregory Shaver, Subbarao Varigonda, and Wallace E. Tyner. "Integrating battery degradation in a cost of ownership framework for hybrid electric vehicle design optimization." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 6 (October 21, 2018): 1507–23. http://dx.doi.org/10.1177/0954407018802663.
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Prior design optimization efforts do not capture the impact of battery degradation and replacement on the total cost of ownership, even though the battery is the most expensive and least robust powertrain component. A novel, comprehensive framework is presented for model-based parametric optimization of hybrid electric vehicle powertrains, while accounting for the degradation of the electric battery and its impact on fuel consumption and battery replacement. This is achieved by integrating a powertrain simulation model, an electrochemical battery model capable of predicting degradation, and a lifecycle economic analysis (including net present value, payback period, and internal rate of return). An example design study is presented here to optimize the sizing of the electric motor and battery pack for the North American transit bus application. The results show that the optimal design parameters depend on the metric of interest (i.e. net present value, payback period, etc.). Finally, it is also observed that the fuel consumption increases by up to 10% from “day 1” to the end of battery life. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.
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Cao, Feng Ping, Li Fa Zhou, and Yong Di Wang. "Study on Optimization Matching Algorithm for Automotive Powertrain." Applied Mechanics and Materials 635-637 (September 2014): 1890–94. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1890.
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In order to reduce fuel consumption and ensure dynamic performance of the car, an automotive powertrain optimization algorithm was presented in the paper. Firstly, the evaluation index of automobile dynamic performance and fuel economy were introduced. Then, the objective function was built, and the transmission and main reducer transmission ratios were designed as variables, and parameters of the vehicle transmission system were optimized by using the genetic algorithm. Finally, a vehicle simulation model by SimulationX software was established, and the power and economy performance before and after optimization were compared and analyzed.
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Pitanuwat, Siriorn, Hirofumi Aoki, Satoru IIzuka, and Takayuki Morikawa. "Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling." World Electric Vehicle Journal 10, no. 2 (May 9, 2019): 22. http://dx.doi.org/10.3390/wevj10020022.
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In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.
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Braband, Matthias, Matthias Scherer, and Holger Voos. "Global Sensitivity Analysis of Economic Model Predictive Longitudinal Motion Control of a Battery Electric Vehicle." Electronics 11, no. 10 (May 14, 2022): 1574. http://dx.doi.org/10.3390/electronics11101574.
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Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential of model predictive control (MPC) methods to reduce energy consumption. However, this results in a complex control system with many parameters that affect the energy efficiency. Thus, an important question remains: how do these partially uncertain (system or controller) parameters influence the energy efficiency? In this article, a global variance-based sensitivity analysis method is used to answer this question. Therefore, a detailed powertrain model controlled by a longitudinal nonlinear MPC (NMPC) is developed and parameterized. Afterwards, a qualitative Morris screening is performed on this model, in order to reduce the parameter set. Subsequently, the remaining parameters are quantified using Generalized Sobol Indices, in order to take the time dependence of physical processes into account. This analysis reveals that the variations in vehicle mass, battery temperature, rolling resistance and auxiliary consumers have the greatest influence on the energy consumption. In contrast, the parameters of the NMPC only account for a maximum of 5% of the output variance.
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Zou, Yuan, Dong-ge Li, and Xiao-song Hu. "Optimal Sizing and Control Strategy Design for Heavy Hybrid Electric Truck." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/404073.
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Due to the complexity of the hybrid powertrain, the control is highly involved to improve the collaborations of the different components. For the specific powertrain, the components' sizing just gives the possibility to propel the vehicle and the control will realize the function of the propulsion. Definitely the components' sizing also gives the constraints to the control design, which cause a close coupling between the sizing and control strategy design. This paper presents a parametric study focused on sizing of the powertrain components and optimization of the power split between the engine and electric motor for minimizing the fuel consumption. A framework is put forward to accomplish the optimal sizing and control design for a heavy parallel pre-AMT hybrid truck under the natural driving schedule. The iterative plant-controller combined optimization methodology is adopted to optimize the key parameters of the plant and control strategy simultaneously. A scalable powertrain model based on a bilevel optimization framework is built. Dynamic programming is applied to find the optimal control in the inner loop with a prescribed cycle. The parameters are optimized in the outer loop. The results are analysed and the optimal sizing and control strategy are achieved simultaneously.
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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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Kwon, Laeun, Dae-Seung Cho, and Changsun Ahn. "Degradation-Conscious Equivalent Consumption Minimization Strategy for a Fuel Cell Hybrid System." Energies 14, no. 13 (June 24, 2021): 3810. http://dx.doi.org/10.3390/en14133810.
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The design of an energy management strategy is critical to improving the fuel efficiency of a vehicle system with an alternative powertrain system, such as hybrid electric vehicles or fuel cell electric vehicles. In particular, in fuel cell electric vehicles, the energy management strategy should consider system degradation and fuel savings because the hardware cost of the fuel cell system is much higher than that of a conventional powertrain system. In this paper, an easily implantable near-optimal energy management controller is proposed. The proposed controller distributes power generation between the fuel cell and the battery to simultaneously minimize system degradation and fuel usage. The controller is designed to consider the degradation cost and fuel cost in the framework of the equivalent consumption minimization strategy concept. The proposed controller was validated with a fuel cell electric vehicle model in MATLAB/Simulink (MathWorks, Natick, MA, USA). The proposed control strategy showed significant overall cost reduction compared to a thermostat control strategy and a conventional Equivalent Consumption Minimization Strategy (ECMS) strategy.
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Wang, Zhengwu, Yang Cai, Yuping Zeng, and Jie Yu. "Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain." Applied Sciences 9, no. 19 (September 29, 2019): 4068. http://dx.doi.org/10.3390/app9194068.
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This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4WD hybrid electric vehicle (plug-in 4WD HEV)’s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization’s mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0–120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.
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Min, Kyunghan, Gyubin Sim, Seongju Ahn, Myoungho Sunwoo, and Kichun Jo. "Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles." Sensors 19, no. 19 (September 26, 2019): 4171. http://dx.doi.org/10.3390/s19194171.
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The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.
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Salamone, Sara, Basilio Lenzo, Giovanni Lutzemberger, Francesco Bucchi, and Luca Sani. "On the Investigation of Energy Efficient Torque Distribution Strategies through a Comprehensive Powertrain Model." Sustainability 13, no. 8 (April 20, 2021): 4549. http://dx.doi.org/10.3390/su13084549.
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In electric vehicles with multiple motors, the torque at each wheel can be controlled independently, offering significant opportunities for enhancing vehicle dynamics behaviour and system efficiency. This paper investigates energy efficient torque distribution strategies for improving the operational efficiency of electric vehicles with multiple motors. The proposed strategies are based on the minimisation of power losses, considering the powertrain efficiency characteristics, and are easily implementable in real-time. A longitudinal dynamics vehicle model is developed in Simulink/Simscape environment, including energy models for the electrical machines, the converter, and the energy storage system. The energy efficient torque distribution strategies are compared with simple distribution schemes under different standardised driving cycles. The effect of the different strategies on the powertrain elements, such as the electric machine and the energy storage system, are analysed. Simulation results show that the optimal torque distribution strategies provide a reduction in energy consumption of up to 5.5% for the case-study vehicle compared to simple distribution strategies, also benefiting the battery state of charge.
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Ezemobi, Ethelbert, Gulnora Yakhshilikova, Sanjarbek Ruzimov, Luis Miguel Castellanos, and Andrea Tonoli. "Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles." World Electric Vehicle Journal 13, no. 2 (February 1, 2022): 33. http://dx.doi.org/10.3390/wevj13020033.
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The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.
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Liu, Yanwei, Jiansheng Liang, Jiaqing Song, and Jie Ye. "Research on Energy Management Strategy of Fuel Cell Vehicle Based on Multi-Dimensional Dynamic Programming." Energies 15, no. 14 (July 18, 2022): 5190. http://dx.doi.org/10.3390/en15145190.
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The powertrain of a fuel cell vehicle typically consists of two energy sources: a proton electrolyte membrane fuel cell (PEMFC) stack and a battery package. In this paper, multi-dimensional dynamic programming (MDDP) is used to solve the energy management strategy (EMS) of fuel cell hybrid powertrain. This study built a fuel cell hybrid powertrain model, in which the battery model is built based on the Thevenin equivalent circuit. In order to improve the calculating efficiency and maintain the accuracy of the algorithm, the state variables in each stage are divided into primary and secondary. In the reverse solution process, the corresponding relationship between the multi state variables grid and the optimal cumulative function has been changed from three-dimensional to two-dimensional. The EMS based on MDDP is applied to component sizing of a commercial vehicle. Simulations were conducted using MATLAB under the C-WTVC working condition. By analyzing the fuel economy and system durability, the optimal component combination of comprehensive performance is obtained. Compared with the EMS based on dynamic programming (DP), the proposed method effectively improves the calculation accuracy: the hydrogen consumption can be reduced by 3.10%, and the durability of the fuel cell and battery can be improved by 1.08% and 0.13%, respectively.
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Laurén, Mika, Giota Goswami, Anna Tupitsina, Suraj Jaiswal, Tuomo Lindh, and Jussi Sopanen. "General-Purpose and Scalable Internal-Combustion Engine Model for Energy-Efficiency Studies." Machines 10, no. 1 (December 30, 2021): 26. http://dx.doi.org/10.3390/machines10010026.
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Hybrid powertrains that combine electric machines and internal-combustion engines offer substantial opportunities to increase the energy efficiency and minimize the exhaust emissions of vehicles and nonroad working machines. Due to the wide range of applications of such powertrains, simulation tools are used to evaluate and compare the energy efficiency of hybrid powertrains for application-specific working cycles in virtual environments. Therefore, the accurate modeling of the powertrain components of a hybrid system is important. This paper presents an agile calculation tool that can generate realistic fuel consumption data of a scalable diesel engine. This method utilizes a simple efficiency model of the combustion and crank train friction model to generate the fuel consumption map in the operating area of a typical diesel engine. The model parameters are calibrated to produce accurate fuel consumption data in the initial phase of system-level simulations. The proposed method is also validated by using three real engine datasets, and the comparison of results is presented.
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Hamza, Karim, John Willard, Kang-Ching Chu, and Kenneth P. Laberteaux. "Modeling the Effect of Power Consumption in Automated Driving Systems on Vehicle Energy Efficiency for Real-World Driving in California." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (March 19, 2019): 339–47. http://dx.doi.org/10.1177/0361198119835508.
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Vehicle automation has drawn much attention in recent years, as it is perceived to usher in new levels of safety, convenience, and energy efficiency in transportation. Much uncertainty and speculation still exist regarding how automated driving (AD) would affect the overall transportation energy, as a result of some factors that are difficult to predict, such as changes in driving patterns and induced travel demand. There is also much speculation about the optimum vehicle powertrain for which AD systems are to be mounted. This study focuses on a less discussed, less speculative issue that pertains to both transportation energy efficiency and powertrain suitability. The impact of the power consumption in an AD system (for sensors, data processing, and vehicle controls) is analyzed for various powertrains via a publicly open-source simulation code, for more than 59,000 real-world vehicle trips obtained from the California Household Travel Survey. Study of scenarios of power consumption in the AD system that range from present-day values (about 3 kW) to future targets (0.5 kW) reveal interesting trends in vehicle energy efficiency. At 0.5 kW power consumption, the AD system can be of minimal impact to vehicle efficiency; however, at present-day levels of AD power consumption, the electric driving range (for electric vehicles and plug-in hybrids) could be shortened by 27–47% and fuel consumption could increase by up to 37% compared with the same vehicle model with no AD system.
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Manaf, Muhammad Zaidan Abdul, Nik Abdullah Nik Mohamed, Mohamad Shukri Zakaria, Mohd Noor Asril Saadun, and Mohd Hafidzal Mohd Hanafi. "Modeling of Flywheel Hybrid Powertrain to Optimize Energy Consumption in Mechanical Hybrid Motorcycle." Applied Mechanics and Materials 393 (September 2013): 287–92. http://dx.doi.org/10.4028/www.scientific.net/amm.393.287.
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The creation of internal combustion engine is a significant milestone in power engineering world which simplified high mechanical energy demand jobs like moving vehicle and machinery. Even though the internal combustion engine gives lot of advantages, however, this type of engine is incapable to convert the heat energy from fuel combustion to the mechanical energy efficiently. Small capacity engine e.g. motorcycle engine having the power conversion efficiency between 25-30%. Therefore, alternative power source is required to support the internal combustion engine in order to increase the overall system efficiency. These phenomena give encouragement to implement the hybridization process. This is to increase the system efficiency in transferring power to the wheel. Hybridization processes e.g. flywheel as secondary power source can increase power transfer efficiency between 30%-80%. Hence, the purpose of this research is to develop the mathematical model of the power transfer efficiency of flywheel hybrid motorcycle by using back trace simulation method. This model will record the amount of energy use in acceleration phase of the driving cycle. Subsequently, the efficiency ratio of motorcycle power transfer is calculated and comparison of those ratios between the conventional motorcycle and the hybrid motorcycle is made. The outstanding results show that the hybrid motorcycle is capable to conserve the energy used up to 36% compare to the conventional motorcycle that wasted energy up to 200%. As a conclusion, flywheel as the secondary power source is capable to supply enough energy to propel the motorcycle forward.
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Kopczyński, Artur, Paweł Krawczyk, and Jakub Lasocki. "Parameters selection of extended-range electric vehicle supplied with alternative fuel." E3S Web of Conferences 44 (2018): 00073. http://dx.doi.org/10.1051/e3sconf/20184400073.
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In this paper modelling of extended-range electric vehicle powertrain. The model consists of sub-models of the investigated vehicle with its resistance forces, traction electric motor, range extender supplied with alternative fuel, and Li-Ion battery. Working point parameters of the range extender engine were defined to achieve low liquefied petroleum gas consumption. The model allowed to study possible parameters of vehicle range extender and battery size. The results show the higher influence of range extender power than battery energy capacity on the vehicle range. The defined range extender and battery parameters allow to significantly extend the vehicle range with low fuel consumption. This research provided ground for the further investigation of range extender control strategies.
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Xia, Chaoying, Jiaxiang Bi, and Jianning Shi. "Investigation of a Cup-Rotor Permanent-Magnet Doubly Fed Machine for Extended-Range Electric Vehicles." Energies 16, no. 5 (March 4, 2023): 2455. http://dx.doi.org/10.3390/en16052455.
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This paper investigates a cup-rotor permanent-magnet doubly fed machine (CRPM-DFM) for extended-range electric vehicles (EREVs). The topology and operating principle of the powertrain system based on CRPM-DFM are introduced. Then, the mathematical model of CRPM-DFM is established and the feedback linearization control of CRPM-DFM is given to realize the decoupling control of flux and torque. Moreover, the torque characteristic of CRPM-DFM is analyzed and the load torque boundaries with sinusoidal steady-state solution of CRPM-DFM is deduced. In addition, the MTPA control is derived to improve the efficiency of CRPM-DFM, and the efficiency of CRPM-DFM regarding various operating modes is investigated. Furthermore, the speed optimization strategy of ICE is proposed to reduce fuel consumption. Finally, the driving performance and fuel economy of the powertrain system are verified by simulation.
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Tan, De Rong, Chun Ying Dong, and Wang Jing. "Design and Simulation on the Powertrain of Plug-in Hybrid Electric Vehicle." Applied Mechanics and Materials 253-255 (December 2012): 2192–96. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.2192.
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In this paper, according to actual power demands of the bus, its powertrain was designed. In this process, the lowest oil consumption was the goal on the foundation of meeting basic power need. The co-simulation with Plug-in vehicle model was realized in Cruise, using the control strategy model which was established by the Stateflow in Simulink. Simulation results indicated that modified Plug-in hybrid electric bus not only can reach the expectant power indictors, but also improve the fuel economy. So, this research played a leading role in the design and research of hybrid buses’ parameters
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Zhu, Zhen, Yanpeng Yang, Dongqing Wang, Yingfeng Cai, and Longhui Lai. "Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System." Agriculture 12, no. 3 (March 21, 2022): 436. http://dx.doi.org/10.3390/agriculture12030436.
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Tractors are usually applied in field operations, road transport, and other operations. Modern agriculture has higher design requirements for tractor powertrains due to the complicated working environments and various operations. To meet the driving requirements of the tractor under multiple operations, a mechanic-electronic-hydraulic powertrain system (MEH-PS) for tractors has been designed according to the characteristics of the hydro-mechanical composite transmission and electromechanical hybrid system. The principle of multiple driven and transmission modes of MEH-PS are introduced, the speed regulation characteristic curve of hydro-mechanical transmission (HMT) is given, and the related power element model, tractor model, and efficiency model are established. The HMT optimal economy transmission ratio control strategy and hybrid rule-based optimization energy management strategy were developed. Take three typical tractor operations for analysis: ploughing, harvesting, and transport. The results show that the engine operating points are mainly distributed in the higher load area, the tractor maintains high system efficiency, and the relative error between simulated and tested fuel consumption is within 5%, which further proves the reliability of the model. The solution also showed lower fuel consumption in all three operations compared to DLG’s announced PowerShift tractors and CVT tractors. Thus, the powertrain system can meet the tractor’s drive requirements under complex operating conditions and maintain high efficiency and is therefore suitable for tractors that need to operate frequently in the field and on the road.
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Broatch, Alberto, Pablo Olmeda, Benjamín Plá, and Amin Dreif. "Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains." Energies 16, no. 1 (December 22, 2022): 107. http://dx.doi.org/10.3390/en16010107.
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Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles.
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Anubhav S, Tony Sabu, Madhav Hari, and Joemon C.T. "Simulation of Graphene Battery and other Battery Technologies in an EV Powertrain." ARAI Journal of Mobility Technology 2, no. 4 (November 19, 2022): 411–17. http://dx.doi.org/10.37285/ajmt.2.4.9.
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The motivation for this work is to find a better and efficient energy storage solution for electric vehicle. It is done by comparing the performance of three different batteries, which are: Lead Acid battery, Li-ion battery and Graphene battery. In this paper, an electric vehicle model is created in Simulink using MATLAB software. The constructed model is based on the existing electric car TATA Nexon EV. Also, unlike the real car the model presented has a different battery pack and the battery parameters such as SOC, current, voltage, distance, velocity, and weight are changed to carry out the comparison between different battery technologies. The model will be simulated to obtain data regarding vehicle performance, energy consumption and range on the new FTP75 test cycle. The obtained know-how will help on later improvements of the electric model regarding methods to improve the vehicle performance and the simulation helps to choose the right powertrain for the vehicle without carrying out any real-life experiments.
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Budak, Kobie, Charlton Epperson, Will Forna, Thomas Liuzzo, Benjamin Sullivan, and Vikram Mittal. "Analyzing and Evaluating Alternatives for the Bradley Fighting Vehicle Powertrain." Industrial and Systems Engineering Review 10, no. 2 (December 25, 2022): 135–41. http://dx.doi.org/10.37266/iser.2022v10i2.pp135-141.
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The United States Army relies on its fleet of combat vehicles to allow for freedom to maneuver on the battlefield. In turn, these vehicles rely on their powertrains, particularly, the engine and transmission, to properly function. This study evaluates the benefits obtained from upgrading the powertrain for the family of medium-tracked vehicles, focusing on the M2A3 Bradley Fighting Vehicle. These benefits are quantified by changes in performance, reliability, and sustainability through the use of a value model. The value model provides an overall score that quantifies for the benefit of upgrading the engine. Vehicle performance is captured through a tractive-effort analysis which converts engine and transmission performance data to vehicle performance measures. Reliability is modeled through a bottom-up component analysis. The study presents a drive-cycle analysis for the M2A3 to estimate the amount of fuel consumption to provide a sustainability metric. The study found that while some improvement can be realized through changing the engine, a much larger benefit can be gained from modifying both the engine and transmission.
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Dumitru, Ilie, Matei Vînătoru, Dragos Tutunea, and Alexandru Dima. "Considerations Regarding Validation through Simulation of Some Board System Information’s for Powertrain Performance Optimization." Applied Mechanics and Materials 822 (January 2016): 346–53. http://dx.doi.org/10.4028/www.scientific.net/amm.822.346.
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The authors present in this paper the opportunities offered by the use of software platforms (MULTISIM) in the validation of informational systems for optimizing economical consumption. So after some research (based on identification methodology system type) a physical-mathematical model can be developed that offers the possibility to optimize fuel consumption in the economy pole depending on specific correlations of operating regimes. Resulted relationships allow designing of information systems by proposing solutions which acquire information on movement speed, engine RPM in load (by determining the actual torque through correlations of exhaust gas temperature, moment and torque).Having these relationships we can design, for example, system version with discrete components or with digital system for surveillance engine operation.
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González Palencia, Juan C., Van Tuan Nguyen, Mikiya Araki, and Seiichi Shiga. "The Role of Powertrain Electrification in Achieving Deep Decarbonization in Road Freight Transport." Energies 13, no. 10 (May 13, 2020): 2459. http://dx.doi.org/10.3390/en13102459.
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Decarbonizing road freight transport is difficult due to its reliance on fossil fuel internal combustion engine vehicles (ICEVs). The role of powertrain electrification in achieving deep decarbonization in road freight transport was studied using a vehicle stock turnover model, focusing on Japan. Twelve vehicle types were considered; combining four powertrains, ICEV, hybrid electric vehicle (HEV), battery electric vehicle (BEV) and fuel cell electric vehicle (FCEV); and three vehicle size classes, normal, compact and mini-sized vehicles. A scenario-based approach was used; considering a Base scenario, and three alternative scenarios targeting powertrain electrification. Between 2012 and 2050, tank to wheel CO2 emissions decrease 42.8% in the Base scenario, due to the reduction of vehicle stock, the improvement of vehicle fuel consumption and the adoption of HEVs. Diffusion of FCEVs in normal vehicles and BEVs in compact and mini-sized vehicles achieves the largest tank to wheel CO2 emissions reductions, up to 44.6% compared with the 2050 baseline value. The net cash flow is positive over the whole time horizon, peaking at 6.7 billion USD/year in 2049 and reaching 6.6 billion USD/year by 2050. Powertrain electrification is not enough to achieve any of the CO2 emissions reduction targets in road freight transport.