Battery Modeling Considering Hysteresis Effect and Temperature Adaptability. Issue 3 (5th January 2023) - Huang, Kai; Sun, Kai; Guo, Yongfang; Wang, Zipeng; Li, Senmao
Abstract : The accuracy of battery modeling plays a decisive role in the reliable operation and accurate state evaluation of the battery. In practical applications, most of the existing battery models do not consider the hysteresis effect which is an important factor affecting the accuracy of the battery model. To relieve the negative effects of the hysteresis effect, a new equivalent circuit model (ECM) is proposed. Specifically, an open‐circuit voltage submodel considering temperature and a hysteresis submodel considering temperature and state of charge are developed. When the two submodels are combined with the traditional ECM, the new model can simulate the behavior of the battery well under varying working conditions. Moreover, certain model parameters fluctuate greatly during parameter identification and it does not conform to the actual change law. To improve the accuracy of parameter identification, a constrained forgetting factor recursive least squares (C‐FFRLS) method is proposed herein. The performance of the proposed ECM and C‐FFRLS is verified using two typical operating conditions at different temperatures. The experimental results show that the proposed model has high modeling accuracy. In addition, compared with FFRLS, C‐FFRLS can further improve the accuracy of the proposed model. Abstract : To relieve the negative effects of the hysteresis effect, a new equivalent circuit model (ECM) is proposed. It can simulate the behavior of the battery well under varying working conditions. Moreover, to improve the accuracy of parameter identification, the conception of a constrained ratio is introduced and a new constrained forgetting factor recursive least squares (C‐FFRLS) method is proposed.