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Instantaneous Optimization Energy Management for Extended-Range Electric Vehicle Based on Minimum Loss Power Algorithm
Technical Paper
2013-24-0073
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Most of the existing energy management strategies for Extended-Range Electric Vehicles (E-REVs) are heuristic, which restricts coordination between the battery and the Range Extender. This paper presents an instantaneous optimization energy management strategy based on the Minimum Loss Power Algorithm (MLPA) for a fuel cell E-REV. An instantaneous loss power function of power train system is constructed by considering the charge and discharge efficiency of the battery, together with the working efficiency of the fuel cell Range Extender. The battery working mode and operating points of the fuel cell Range Extender are decided by an instantaneous optimization module (an artificial neural network) that aims to minimize the loss power function at each time step. In order to solve the local optimum problem, a Range Extender output power gain coefficient is introduced, which can automatically adjust the output power of the Range Extender according to the residual amount of on-board hydrogen. Thus battery energy and hydrogen may be extinguished at approximately the same time, allowing global optimal results. To validate the proposed strategy, the energy management strategy presented in this paper is realistically implemented onto a real fuel cell E-REV. Simulation and dynamometer test results prove that the proposed instantaneous optimization energy management strategy can dramatically improve fuel economy performance and adaptability under a broad range of driving conditions compared to rule-based strategies.
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Citation
Song, K. and Zhang, T., "Instantaneous Optimization Energy Management for Extended-Range Electric Vehicle Based on Minimum Loss Power Algorithm," SAE Technical Paper 2013-24-0073, 2013, https://doi.org/10.4271/2013-24-0073.Also In
References
- Dong , T. , Zhao , F. , Li , J. et al. Design method and control optimization of an Extended Range Electric Vehicle IEEE international Conference Vehicle Power and Propulsion (VPPC) 2001 1 6
- Hu , P. , Seibel , J. , Zhang , H. et al. Strategy of Range Extending Electric Vehicle Based on User's Approval The 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition ShenZhen, China Nov. 5 9 2010
- Ryu , J. , Park , Y. , Sunwoo , M. Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition Journal of Power Sources 2010 195 17 5735 5748
- Waldner , J. , Wise , J. , Crawford , C. , and Dong , Z. Development and Testing of an Advanced Extended Range Electric Vehicle SAE Technical Paper 2011-01-0913 2011 10.4271/2011-01-0913
- Paganelli , G. , Guezennec , Y. , and Rizzoni , G. Optimizing Control Strategy for Hybrid Fuel Cell Vehicle SAE Technical Paper 2002-01-0102 2002 10.4271/2002-01-0102
- Sun , D. , Lin , X. , Qin , D. et al. Power-balancing Instantaneous Optimization Energy Management for a Novel Series-parallel Hybrid Electric Bus Chinese journal of mechanical engineering 2012 25 6 1161 1170 10.3901/CJME.2012.06.1161
- SONG , K. Study of Development Processes and Methods for Power Train System of Mini-Electric Vehicle with Range Extender[D] Shanghai Tongji University 2012
- Zhang Xi , Mi Chris Vehicle Power Management: Modeling, Control and Optimization London Springer 2011