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

Kangkang Xu, Tailong He, Pan Yang, Xianbing Meng, Chengjiu Zhu and Xi Jin

Energy, 2024, vol. 309, issue C

Abstract: 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.

Keywords: Lithium-ion battery; State of charge; Broad learning system; Bernstein inequality; Adaptive unscented Kalman filter (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:309:y:2024:i:c:s036054422402694x

DOI: 10.1016/j.energy.2024.132920

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