A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles. (15th October 2019) - Zheng, Yuejiu; Wang, Jingjing; Qin, Chao; Lu, Languang; Han, Xuebing; Ouyang, Minggao
Abstract: Real-time battery capacity estimation is very important for the battery management but usually has a low accuracy in electric vehicles due to the complicated real working conditions and the changing parameters during the battery lifespan. Traditional estimation methods, e.g. methods based on the empirical models such as the Arrhenius capacity aging model, or methods based on the state of charge, always suffer from the parameters mismatch during the long battery lifespan. In this paper, we put forward a method based on charging curve sections which can be easily achieved for electric vehicles. The proposed method uses the complete charging curves and the corresponding capacities in experiments as the training data for a certain battery type. The optimal fixed voltage window is then determined by the particle swarm optimization with a designed objective function focused on minimizing the error of linear capacity loss assumption. The capacity is finally estimated by calculating the charging capacities during the optimal fixed voltage window online. The proposed method is verified using the designed experimental data, and the error is proved to be small. Highlights: A method based on charging curve sections is proposed to estimate battery capacity. Optimal fixed voltage window for linear capacity loss estimation is found. Long-time and various aging experiments are carried out for verification. Proposed method has a good accuracy during the battery lifespan