A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF

Zhenhua Cui, Le Kang, Liwei Li, Licheng Wang and Kai Wang

Energy, 2022, vol. 259, issue C

Abstract: Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 °C and −10 °C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries.

Keywords: Lithium-ion battery; State of charge; Neural network; Unscented Kalman filter; Hybrid method; Transfer learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:259:y:2022:i:c:s0360544222018345

DOI: 10.1016/j.energy.2022.124933

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