Abstract:

Aiming at the problems of low internal parameter identification accuracy and large charge state estimation error caused by complex working environment and aging of lithium-ion power batteries, this paper proposed a combined algorithm of multi-innovation least square 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 multi-temperature conditions during the full lifetime. Firstly, in order to solve the problem of low utilization rate of historical data by 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 multi-innovation least squares method. After that, the SOC was estimated by the square root cubature kalman filter. Finally, the proposed algorithm is verified by the experimental data of multi-temperature battery, and compared with the extended kalman filter and cubature kalman filter algorithms, the effectiveness of the proposed algorithm is proved. The experimental results show that the proposed algorithm can accurately reflect the internal parameters of the power battery and accurately estimate the SOC of the battery under the condition of multi-temperature lifetime. The average absolute voltage error is less than 40mV, and the SOC estimation error is controlled within 2%.

Key words: lithium-ion battery, multi-innovation least square algorithm, square root cubature Kalman filter, multi-temperature, state of charge