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Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach

Author

Listed:
  • Song, Ziyou
  • Hofmann, Heath
  • Li, Jianqiu
  • Han, Xuebing
  • Ouyang, Minggao

Abstract

This paper utilizes the dynamic programming (DP) approach to deal with the integrated optimization problem for deriving the best configuration and energy split strategies of a hybrid energy storage system (HESS), including a battery and a supercapacitor (SC), for an electric city bus. Within the optimization process, a preset cost function is employed to evaluate the HESS life cycle cost based on a dynamic degradation model of the LiFePO4 battery, which is initially proposed by us. For system hybridization, the battery size is optimized according to the requested minimal mileage, while the optimal configuration of the SC pack (i.e., the numbers of the SC modules in series and parallel) is determined using the DP approach. It is shown that the life cycle cost of the HESS initially decreases rapidly with the addition of SCs, though the rate of this reduction decreases as the amount of SC increases. The HESS candidates occurring in the transition area can therefore be regarded as the best solutions. For the energy split strategy, several control rules can be extracted from the DP results, and a near-optimal rule-based strategy is proposed in this paper. When compared to the battery-only configuration, the HESS controlled by the rule-based strategy can reduce 47% and 60% of the ESS life cycle cost along the typical China Bus Driving Cycle and the Urban Dynamometer Driving Schedule, respectively. This paper also proves that a well-tuned rule-based strategy, which can be easily implemented in a vehicle, presents rather good performance when compared to the DP approach. In addition, the proposed strategy performance can be further improved by increasing the SC usage.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:139:y:2015:i:c:p:151-162
    DOI: 10.1016/j.apenergy.2014.11.020
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    References listed on IDEAS

    as
    1. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    2. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
    3. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2012. "An integrated optimization approach for a hybrid energy system in electric vehicles," Applied Energy, Elsevier, vol. 98(C), pages 479-490.
    4. 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.
    5. Xu, Liangfei & Ouyang, Minggao & Li, Jianqiu & Yang, Fuyuan & Lu, Languang & Hua, Jianfeng, 2013. "Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost," Applied Energy, Elsevier, vol. 103(C), pages 477-487.
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