A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters

Precise capacity and state-of-charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery. To lower the influence of cross interference between these two estimated states and possible divergence existing in two-way transmitted co-estimation framework, a novel double adaptive extended Kalman filters (AEKFs) based one-way transmitted co-estimation framework for capacity and SOC is proposed in this paper. Firstly, the model parameters of the first-order RC model and open-circuit-voltage (OCV) are online obtained by forgetting factor recursive least squares. With the first derivative of OCV versus SOC, the SOC inferred through OCV-SOC table and identified parameters are inputted into AEKF i to online estimate capacity. Subsequently, estimated capacity is further transmitted into AEKF 2 to predict SOC. By simulation driving cycles, the proposed co-estimation framework is compared with AEKF based SOC algorithm without capacity calibration, whose results indicate that the presented algorithm can lower the impact of inaccurate initial capacity value on SOC estimation to more effectively track SOC. Moreover, through robustness analysis results, it is clearly found that initial erroneous SOC values will not influence capacity estimation results due to the one-way transmitted characteristic of the proposed co-estimation framework and SOC can still be estimated accurately and robustly.

Automated summary

This paper proposes a novel co-estimation framework for battery capacity and SOC based on double adaptive extended Kalman filters, which achieves accurate estimation of capacity and SOC through online acquisition of model parameters and first-order derivatives. Simulation results demonstrate that the proposed algorithm can effectively reduce the impact of inaccurate initial capacity values on SOC estimation and exhibits robustness.