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

• Published in: Journal of power sources 2013-05, Vol.229, p.159-169
• Main Authors: Xiong, Rui, He, Hongwen, Sun, Fengchun, Liu, Xinlei, Liu, Zhentong
• Format: Article
• Language: English
• Subjects: Adaptive extended Kalman filter; Applied sciences; Battery; Charge; Direct energy conversion and energy accumulation; Dynamics; Electric batteries; Electric charge; Electric power generation; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy; Energy. Thermal use of fuels; Estimates; Exact sciences and technology; Ground, air and sea transportation, marine construction; Hybrid vehicles; Joint estimation; Lithium-ion batteries; Peak power capability; Plug-in hybrid electric vehicles; Rational use of energy: conservation and recovery of energy; Road transportation and traffic; State of charge
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2012.12.003

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. ► A dynamic electrochemical polarization battery model is used for state estimation. ► An improved parameter identification test is used to get accurate model's parameters. ► The continuous peak power capability estimation approach is proposed. ► An AEKF-based SoC and peak power capability joint estimation approach is proposed. ► The robustness and reliability of the six steps joint estimator are evaluated.