Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

For the battery management system, accurate estimation of the state of charge and state of health is of great significance. Herein, the ternary Li-ion battery is taken as the research object; the second-order resistor-capacitor (RC) equivalent circuit is taken advantage of to characterize the battery performance. A method for calculating the state of health of Li-ion batteries based on capacity fading was established. A novel forgetting factor dual particle filter algorithm is proposed for co-estimation of the state of charge and state of health by combining the forgetting factor and the particle filter algorithm. The state of charge and state of health of Li-ion batteries under Beijing Bus Dynamic Stress Test conditions are evaluated. In the state of charge estimation, the maximum error, mean absolute error, and root mean square error is 1.1395%, 0.4916%, and 0.5145% in Beijing Bus Dynamic Stress Test condition, 1.8125%, 0.6329%, and 0.7955% in Dynamic Stress Test condition, compared with the extended Kalman filter, unscented Kalman filter, and particle filter algorithms, all reduced obviously. In the state of health estimation, compared with the Random Forest and adaptive dual extended Kalman filter-based fuzzy inference system, the mean execution time and convergence time are 11.14 seconds and 0.44 second in Dynamic Stress Test condition and 15.17 seconds and 0.63 second in Beijing Bus Dynamic Stress Test condition; the results show lower computation complexity and faster convergence speed, which play an important role in promoting the further application of lithium-ion batteries. Novelty Statement A new forgetting factor dual particle filter algorithm is proposed to realize the co-estimation the battery state of charge and state of health. The capacity and the state of health were estimated for Dynamic Stress Test and Beijing Bus Dynamic Stress Test conditions, respectively, and the estimation results of the two common conditions were analyzed. SOC estimator is impressive for its high accuracy and quick convergence. The estimated SOH information has better convergence performance and shorter running time.