A new online SOC estimation method using broad learning system and adaptive unscented Kalman filter algorithm

The accurate estimation of lithium batteries’ state of charge (SOC) is important for extending battery life and preventing accidents. To improve the battery model’s adaptability to variations in actual operating conditions, this paper proposes a new hybrid SOC estimation method. The battery model is first built based on the broad learning system (BLS) to simulate the battery’s voltage characteristics. Subsequently, the adaptive unscented Kalman filter algorithm is applied for SOC estimation. We introduce the Bernstein inequality (BI) to guide the BLS model’s online update process. With the BI method, the redundant incremental data is not used for battery model updates, which improves the model’s online learning efficiency. Finally, dynamic test operation data is collected from different temperatures to validate the proposed SOC estimation algorithm. Experimental results manifest that the SOC estimation error can be limited to 0.51 %. In addition, the proposed method has satisfactory training and online learning time consumption. •A new hybrid SOC estimation method based on a combination of BLS and adaptive UKF algorithm is proposed.•A method to consider new sample data based on BI method to guide the incremental learning of the battery model is proposed.•The method exhibits excellent online learning and generalization ability.•The method can improve accuracy and reduce the online learning time of redundant data.