Nachimuthu Selvarani, Alsaif Faisal, Devarajan Gunapriya, Vairavasundaram Indragandhi
Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, India.
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
Sci Rep. 2025 Jan 11;15(1):1714. doi: 10.1038/s41598-025-85700-0.
In the recent era, Lithium ion batteries plays a significant role in EV industry due to their high specific energy density, power density, low self-discharge rate, and prolonged lifespan. Modeling the battery precisely and estimating its State of Charge with great precision is essential to improve the performance of the lithium-ion batteries. Though numerous methods has been proposed for estimating the SOC, accurate estimation approach is not proposed yet since all these approaches consider the discrete-time dynamics of the battery. Hence in this proposed approach, the implementation of Thevenin 2RC battery model in conjunction with the Unscented Kalman Bucy Filter (UKBF) for SOC estimation is suggested. Thevenin 2RC battery model is used to captures the nonlinear relationship between the battery's voltage, current, and SOC. The UKBF is then used to estimate the SOC by fusing the battery model with noisy measurements of the battery's voltage and current. The UKBF is able to handle the nonlinearity of the battery model and the noise in the measurements, resulting in a more accurate estimate of the SOC by capturing the continuous-time dynamics of the battery. The model is simulated in Matlab Simulink. With similar covariance noise and measurement noise taken into consideration, the battery's SOC is estimated using the EKF, UKF, and UKBF. The performance comparison indicate that the UKBF approach provides an accurate estimation of the SOC, with a significantly lower RMSE of 0.003276.
在当今时代，锂离子电池因其高比能量密度、功率密度、低自放电率和长寿命，在电动汽车行业中发挥着重要作用。精确建模电池并高精度估计其荷电状态对于提高锂离子电池的性能至关重要。尽管已经提出了许多估计荷电状态的方法，但由于所有这些方法都考虑电池的离散时间动态，尚未提出准确的估计方法。因此，在本提出的方法中，建议将戴维南2RC电池模型与无迹卡尔曼布西滤波器（UKBF）结合用于荷电状态估计。戴维南2RC电池模型用于捕捉电池电压、电流和荷电状态之间的非线性关系。然后，UKBF通过将电池模型与电池电压和电流的噪声测量值融合来估计荷电状态。UKBF能够处理电池模型的非线性和测量中的噪声，通过捕捉电池的连续时间动态，从而更准确地估计荷电状态。该模型在Matlab Simulink中进行了仿真。考虑到类似的协方差噪声和测量噪声，使用扩展卡尔曼滤波器（EKF）、无迹卡尔曼滤波器（UKF）和无迹卡尔曼布西滤波器（UKBF）估计电池的荷电状态。性能比较表明，UKBF方法提供了对荷电状态的准确估计，均方根误差（RMSE）显著降低至0.003276。
