<p>The short life of electric vehicle (EV) batteries is an important factor limiting the popularization of EVs. A hybrid energy storage system (HESS) for EVs combines Li-ion batteries with supercapacitors, so that the supercapacitor shares the peak power during the starting and braking, effectively solving the problem of irreversible capacity degradation of Li-ion batteries. Herein, an energy management strategy for HESS was designed based on battery degradation to extend the service life of the EV battery. First, to obtain accurate battery degradation characteristics, a cycling charge–discharge test was designed. Using this test, the effects of discharge rate and state of charge on battery degradation rate were determined, and the battery degradation model was constructed. Based on the working map of the motor obtained through the bench test, a data-based model of the power system was constructed to accurately characterize the energy consumption of the EV under different operating conditions. Second, rule-based and optimization-based energy management strategies that consider battery degradation were designed using the fuzzy algorithm and dynamic programming (DP) algorithm. The effectiveness of the devised control strategies was assessed using the model-in-the-loop and hardware-in-the-loop test platforms. The fuzzy control strategy had a simple structure and could be rapidly calculated but showed worse performance than the DP-based strategy in terms of battery degradation. The DP-based energy management strategy resulted in 16.68% lower capacity degradation than the fuzzy strategy.</p>

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Energy management strategy that optimizes battery degradation for electric vehicles with hybrid energy storage system

  • Jian Wang,
  • Chaofeng Pan,
  • Zhongxing Li

摘要

The short life of electric vehicle (EV) batteries is an important factor limiting the popularization of EVs. A hybrid energy storage system (HESS) for EVs combines Li-ion batteries with supercapacitors, so that the supercapacitor shares the peak power during the starting and braking, effectively solving the problem of irreversible capacity degradation of Li-ion batteries. Herein, an energy management strategy for HESS was designed based on battery degradation to extend the service life of the EV battery. First, to obtain accurate battery degradation characteristics, a cycling charge–discharge test was designed. Using this test, the effects of discharge rate and state of charge on battery degradation rate were determined, and the battery degradation model was constructed. Based on the working map of the motor obtained through the bench test, a data-based model of the power system was constructed to accurately characterize the energy consumption of the EV under different operating conditions. Second, rule-based and optimization-based energy management strategies that consider battery degradation were designed using the fuzzy algorithm and dynamic programming (DP) algorithm. The effectiveness of the devised control strategies was assessed using the model-in-the-loop and hardware-in-the-loop test platforms. The fuzzy control strategy had a simple structure and could be rapidly calculated but showed worse performance than the DP-based strategy in terms of battery degradation. The DP-based energy management strategy resulted in 16.68% lower capacity degradation than the fuzzy strategy.