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A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle

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  • Liu, Hanwu
  • Lei, Yulong
  • Fu, Yao
  • Li, Xingzhong

Abstract

To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range-extended electric vehicle, a novel hybrid-point-line energy management strategy (H–P-LEMS) has been proposed from a multi-scale view. First, a multi-objective optimization (MOO) model is established which takes into account energy consumption, emissions and battery life. The barebones multi-objective particle swarm optimization is applied for solving the MOO problem. And a dynamic programming-optimized algorithm is applied to obtain the optimal curve/area of APU to establish objective function of MOO. Then, an adaptive approach uses a fuzzy logic controller with the battery consideration to adjust parameters in real time. Simulation results show that there is a clear conflict that three optimization objectives cannot be optimal at the same time and the final optimization solution with optimal comprehensive evaluation index is selected to evaluate the performance of the proposed methodology. Finally, the simulation and experimental results thoroughly indicate that the proposed H–P-LEMS has better balance than conventional rule-based energy management strategy (EMS). As expected, economy improvement, emission reduction and prolonging the battery service life are kept in balance effectively. And this result can be used to develop EMS to improve comprehensive performance levels.

Suggested Citation

  • Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222002602
    DOI: 10.1016/j.energy.2022.123357
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    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    2. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    3. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    4. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    5. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    6. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    7. Li, Junqiu & Jin, Xin & Xiong, Rui, 2017. "Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus," Applied Energy, Elsevier, vol. 194(C), pages 798-807.
    8. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    9. Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    10. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    11. Tarroja, Brian & Shaffer, Brendan & Samuelsen, Scott, 2015. "The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies," Energy, Elsevier, vol. 87(C), pages 504-519.
    12. Ximing Wang & Hongwen He & Fengchun Sun & Xiaokun Sun & Henglu Tang, 2013. "Comparative Study on Different Energy Management Strategies for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 6(11), pages 1-20, October.
    13. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    14. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    15. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    16. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    17. Hanwu Liu & Yulong Lei & Yao Fu & Xingzhong Li, 2020. "An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle," Energies, MDPI, vol. 13(6), pages 1-21, March.
    18. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    19. Jiao, Bin & Lian, Zhigang & Gu, Xingsheng, 2008. "A dynamic inertia weight particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(3), pages 698-705.
    20. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    21. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
    22. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    23. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    24. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei, 2017. "Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 123(C), pages 89-107.
    25. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
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