Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles

This paper uses an adaptive extended Kalman filter (AEKF)-based method to jointly estimate the State of Charge (SoC) and peak power capability of a lithium-ion battery in plug-in hybrid electric vehicles (PHEVs). First, to strengthen the links of the model's performance with battery's SoC, a dynamic electrochemical polarization battery model is employed for the state estimations. To get accurate parameters, we use four different charge-discharge current to improve the hybrid power pulse characteristic test. Second, the AEKF-based method is employed to achieve a robust SoC estimation. Third, due to the PHEVs require continuous peak power for acceleration, regenerative braking and gradient climbing, the continuous peak power capability estimation approach is proposed. And to improve its applicability, a general framework for six-step joint estimation approach for SoC and peak power capability is proposed. Lastly, a dynamic cycle test based on the urban dynamometer driving schedule is performed to evaluate the real-time performance and robustness of the joint estimation approach. The results show that the proposed approach can not only achieve an accurate SoC estimate and its estimation error is below 0.02 especially with big initial SoC error; but also gives reliable and robust peak power capability estimate.