基于粒子和容积卡尔曼滤波的锂离子电池SOC估计OA

Accurate estimation of State of Charge(SOC)for lithium-ion batteries is crucial for implementing battery balancing during charging and discharging,thus extending battery lifespan.Given the complex chemical characteristics of lithium-ion batteries and the non-time-varying nature of SOC estimation using the Kalman filter algorithm,a novel method for SOC estimation for lithium-ion batteries is proposed.Firstly,combining the precision and practicality of lithium-ion battery models,a second-order equivalent model for lithium-ion batteries is presented.Subsequently,utilizing the Adaptive Forgetting Factor Recursive Least Squares(AFFRLS)algorithm with adaptive forgetting factors,the impedance-capacitance parameters of the second-order model are identified online,establishing a filtering relationship for impedance-capacitance parameters.The proposed method integrates Particle Filter(PF)and Cubature Kalman Filter(CKF)algorithms,termed Particle Cubature Kalman Filter(PF-CKF),to estimate SOC for lithium-ion batteries.Finally,Hybrid Pulse Power Characterization(HPPC)experiments are designed to validate the accuracy and stability of this method.