Comprehensive co-estimation of lithium-ion battery state of charge, state of energy, state of power, maximum available capacity, and maximum available energy

In developing an efficient battery management system (BMS), accurate battery state estimation is always required. However, the trade-off between computational efficiency and accuracy of state estimation is hard to maintain. This work proposes the comprehensive co-estimation method for battery states, maximum available capacity, and maximum available energy estimation. The existing correlation between different battery states is effectively utilized to achieve high accuracy and reduce the computational burden. A combined state of charge (SOC) and state of energy (SOE) estimation using the dual forgetting factor adaptive extended Kalman filter (DFFAEKF) algorithm and experimental quantitative relations between SOC and SOE are utilized to estimate the SOC and SOE. Due to low computational cost and simplicity, the multiple constraints model-based SOP estimation using the Rint model is employed. The maximum available capacity and maximum available energy estimation are performed using a new sliding window-approximate weighted total least square (SW-AWTLS) algorithm. The performance of the proposed co-estimation method is validated by two different chemistry battery cells under dynamic load profiles at different operating temperatures. Moreover, the comparison with other existing co-estimation methods is also conducted, whose results indicate the superior accuracy of the proposed comprehensive co-estimation method. • A comprehensive co-estimation method is developed for the battery states and parameters estimation. • For the battery states (SOC, SOE, SOP) estimation, robust and less computational burden methods are considered. • A new SW-AWTLS method is utilized for battery maximum available capacity/energy estimation. • High accuracy and strong robustness of the proposed co-estimation method are validated under dynamic operating conditions.