The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective.

The traditional ampere-hour (Ah) integration method ignores the influence of battery health (SOH) and considers that the battery capacity will not change over time. To solve the above problem, we proposed a joint SOH-SOC estimation model based on the GWO-BP neural network to optimize the Ah integration method. The method completed SOH estimation through the GWO-BP neural network and introduced SOH into the Ah integration method to correct battery capacity and improve the accuracy of state of charge (SOC) estimation. In addition, the method also predicted the SOH of the battery, so the driver could have a clearer understanding of the battery aging level. In this paper, the stability of the joint SOH-SOC estimation model was verified by using different battery data from different sources. Comparative experimental results showed that the estimation error of the joint SOH-SOC estimation model could be stabilized within 5%, which was smaller compared with the traditional ampere integration method.

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