Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (4): 133-144.doi: 10.11985/2022.04.014

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Development of V2G Optimal Frequency Regulation Strategy for Actively Suppressing Battery Aging

LUO Guoqing(), ZHANG Yongzhi(), JIA Yuanwei()   

  1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044
  • Received:2022-09-16 Revised:2022-10-19 Online:2022-12-25 Published:2023-02-03
  • Contact: ZHANG Yongzhi, E-mail:yzzhangbit@gmail.com

Abstract:

The continuous expansion of the penetration scale of renewable energy into the power grid has brought great challenges to the frequency stability of the power system. The participation of electric vehicles in V2G(vehicle-to-grid) frequency regulation as a mobile power source can effectively solve this problem. However, the implementation of V2G frequency regulation will aggravate battery aging, which greatly discourages EVs(electric vehicles) owners from participating in V2G services. Therefore, in order to suppress the negative impact of V2G frequency regulation on the battery aging of EVs, an optimization model aiming at suppressing battery aging by introducing a mechanism-based battery aging model is established, and then a new model is developed based on the model predictive control theory. The optimized controller realizes the real-time and efficient control of charging and discharging power of EVs. The effect of different look-ahead time lengths on the performance of the optimized controller is studied, and on this basis, the degree of influence of the developed controller on the aging of the battery under different state of health conditions is discussed. The simulation results show that deterioration of battery performance will exacerbate battery degradation during V2G frequency regulation. In addition, while ensuring a good tracking effect on the frequency regulation power signal, compared with the reference optimization control strategy, the proposed V2G frequency regulation strategy can reduce battery aging by up to 22.34%.

Key words: Electric vehicles, V2G, model predictive control, frequency regulation, battery degradation

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