Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine


An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed primarily based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A completely unique approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with restricted initial training samples. The effectiveness of the moving window modeling technique is validated by each simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance throughout the estimation process. Additionally, the developed LSSVM model is continuously updated online with new samples throughout the battery operation, so as to reduce the influence of the changes in battery internal characteristics on modeling accuracy and estimation results when a amount of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a restricted number of initial training samples.

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