State-of-charge estimation of lithium-ion batteries in rapid temperature-varying environments based on improved battery model and optimized adaptive cubature Kalman filter
In pursuit of high-precision and robust state monitoring of lithium-ion batteries under an environment with rapid temperature fluctuations,we propose a state-of-charge(SOC)estimation method based on an improved battery model and an optimized adaptive cubature Kalman filter(CKF).First,the discrepancies in SOC definition between a pseudo-two-dimensional electrochemical model and an equivalent circuit model are discussed.Introducing the improved battery model,the SOC results from the equivalent circuit model,calculated by ampere-hour integration,are rectified using intermediate variables.Subsequently,model parameters influenced by environmental temperature are identified from open-circuit voltage and dynamic stress test data under various constant-temperature environments.Moreover,the traditional CKF is optimized based on principles of matrix diagonalization and adaptive covariance matrix,bolstering overall stability and the ability of the proposed SOC estimation method to handle random sampling noise.Finally,experimental validation under six diverse battery operating conditions in rapidly temperature-varying environments demonstrates the accuracy of the established improved battery model and the effectiveness of the proposed SOC estimation method,even under random sampling noise.The results demonstrate the versatility of the proposed SOC estimation method across various battery operating conditions in rapidly temperature-varying environments,with an estimated root mean square error of approximately 1.3%under random sampling noise.
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