Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order-Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles

The accuracy of lithium-ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second-order fractional-order equivalent circuit model and uses an adaptive genetic algorithm for model parameter identification. Then, an improved adaptive fractional-order backward smoothing square root cubature Kalman filtering algorithm (AFOBS-SRCKF) is proposed by integrating Sage Husa adaptive filtering and backward smoothing processes to optimize the square root cubature Kalman filter for improving the accuracy and adaptability of real-time estimation of SOC in a complex environment. Finally, the algorithm is compared with the integer-order SRCKF, fractional-order SRCKF through simulation, and fractional-order backward smoothing SRCKF through simulation. Under complex operating conditions, the error sum of SOC estimation of the AFOBS-SRCKF algorithm is controlled within 1.0% and the convergence speed is improved by at least 30%. The results show that the AFOBS-SRCKF algorithm effectively improves the accuracy, stability, and convergence of SOC estimation. Herein, second-order fractional order to model lithium-ion battery equivalently is studied and full parameter identification of the model based on adaptive genetic algorithm is achieved. Then, improved adaptive fractional-order backward smoothing square root cubature Kalman filtering algorithm is proposed to improve the accuracy and adaptability of lithium-ion batteries for real-time estimation of state of charge in complex environments.image & COPY; 2023 WILEY-VCH GmbH