State of charge estimation considering lithium battery temperature and aging
To solve the low internal parameter identification accuracy and large charge state estimation error caused by complex working environments and the aging of lithium-ion power batteries,this study proposed a combined algorithm of a multi-innovation least squares method and square root cubature Kalman filter to estimate the charge state of lithium-ion batteries,and realized the state estimation of power batteries under multitemperature conditions during the its lifetime.First,to solve the low utilization rate of historical data by the traditional least squares method,the multi-innovation theory was incorporated into the least squares method,a first-order RC equivalent circuit was used to establish the battery model,and the internal parameters of the battery were identified by the multi-innovation least squares method.Subsequently,the SOC was estimated by the square root cubature Kalman filter.Finally,the effectiveness of the proposed algorithm was verified by comparing the experimental data of the multitemperature battery with that obtained using the extended Kalman filter and cubature Kalman filter algorithms.The experimental results showed that the proposed algorithm could accurately reflect the internal parameters of the power battery and estimate the SOC of the battery under the condition of the multitemperature lifetime.The average absolute voltage error was less than 40 mV,and the SOC estimation error was controlled within 2%.
lithium-ion batterymulti-innovation least square algorithmsquare root cubature Kalman filtermulti-temperaturestate of charge
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维普
