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A multi-objective optimization energy management strategy for power split HEV based on velocity prediction

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  • Wang, Weida
  • Guo, Xinghua
  • Yang, Chao
  • Zhang, Yuanbo
  • Zhao, Yulong
  • Huang, Denggao
  • Xiang, Changle

Abstract

Under the complicated driving conditions, the sharp acceleration and deceleration actions would cause the high-rate charge and discharge current of electric driving system in hybrid electric vehicle (HEV), which brings about a serious impact on the battery lifetime. The hybrid energy storage system (HESS) combined with battery and ultracapacitor (UC), would be a possible solution to this problem. For HEV with HESS, in addition to improving fuel economy, realizing the protection of battery is also an important objective. However, improving one aspect performance may sacrifice another aspect performance. The tradeoff between multiple optimization objectives remains a challenge for energy management design. Aiming at this problem, a multi-objective optimization energy management strategy based on velocity prediction for a dual-mode power split HEV with HESS is proposed in this paper. Firstly, to get the precise predictive input sequence, generalized regression neural network (GRNN) is used to predict future velocity. Secondly, the power distribution of dual-mode power spilt HEV with HESS is described as a rolling optimization problem in the prediction horizon of model predictive control (MPC). A new cost function considering the fuel consumption and the protection of the battery is brought forward, and the optimization problem is solved using Pontryagin's minimum principle (PMP). Moreover, the Powell-Modified algorithm is introduced to execute the solving process of PMP. Finally, the proposed strategy is verified by comparing it with four other strategies under four different driving cycles. Compared to the rule-based strategy, the proposed strategy reduces root mean square (RMS) of battery current and fuel consumption by up to 18.5 % and 18.9 %, respectively.

Suggested Citation

  • Wang, Weida & Guo, Xinghua & Yang, Chao & Zhang, Yuanbo & Zhao, Yulong & Huang, Denggao & Xiang, Changle, 2022. "A multi-objective optimization energy management strategy for power split HEV based on velocity prediction," Energy, Elsevier, vol. 238(PA).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019629
    DOI: 10.1016/j.energy.2021.121714
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    References listed on IDEAS

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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, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    3. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    4. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    5. Song, Ziyou & Hou, Jun & Hofmann, Heath & Li, Jianqiu & Ouyang, Minggao, 2017. "Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles," Energy, Elsevier, vol. 122(C), pages 601-612.
    6. Yang, Chao & Wang, Muyao & Wang, Weida & Pu, Zesong & Ma, Mingyue, 2021. "An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm," Energy, Elsevier, vol. 219(C).
    7. Yuying Wang & Xiaohong Jiao & Zitao Sun & Ping Li, 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 10(11), pages 1-21, November.
    8. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    9. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
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    Cited by:

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    4. Olha Prokopenko & Marina J rvis & Gunnar Prause & Inna Kara & Hanna Kyrychenko & Oleksandr Kochubei & Maryna Prokopenko, 2022. "Economic Features of the Use of Electric Vehicles in Delivery Services in Estonia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 340-349, November.
    5. Gao, Renjing & Zhou, Guangli & Wang, Qi, 2024. "Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC," Energy, Elsevier, vol. 295(C).
    6. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    7. Du, Yi & Cui, Naxin & Cui, Wei & Li, Tao & Ren, Fei & Zhang, Chenghui, 2023. "AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging," Energy, Elsevier, vol. 277(C).

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