Estimation of battery capacity degeneration based on an improved neural fuzzy inference system under dynamic operating conditions

Abstract

In the long-term use process, the performance of battery continues to decline. The capacity degeneration prediction of battery is crucial, which can effectively avoid some security risks. At present, most of the methods using fuzzy systems to predict battery capacity degeneration are based on historical capacity data, and these methods do not consider the variable charging and discharging conditions. Therefore, an improved adaptive neural fuzzy inference system (ANFIS) applied to random operating conditions is proposed in this paper, which is more suitable for practical application. Features are extracted from raw data and the initial system structure is determined by the correlation coefficients of the features. The fuzzy cluster center is obtained and optimized by fuzzy c-means (FCM) and adaptive particle filter. What's more, the activation mechanism is applied to reduce the number of fuzzy rules. The randomized battery dataset of National Aeronautics and Space Administration (NASA), the temperature-varying dataset and the current-varying dataset of the Center for Advanced Life Cycle Engineering (CALCE) laboratory are used to demonstrate the effectiveness of the proposed system, with the RMSE of 3.73 %, 4.09 % and 3.48 % respectively. Compared with the existing methods, the proposed system has higher accuracy and interpretability relatively.