A combination state of charge estimation method for ternary polymer lithium battery considering temperature influence

Under the actual operating conditions of electric vehicle, the temperature of the battery system changes with the ambient temperature, its influence is rarely considered on state of charge (SOC) estimation. Aiming at this problem, this work explores the relationship between temperature and battery discharge capacity, capacity loss, coulomb efficiency and open circuit voltage, and establishes a temperature correction model. Then, a temperature-based coulomb counting (TBCC) method is proposed. To eliminate the cumulative errors of SOC estimation, a TBCC combined adaptive particle filter (APF) combination estimation method is proposed, which can update the temperature-related variables, and estimates the system state with the identified model parameters. The estimation method adapts to the temperature change of the power lithium battery while taking into account the superiority of the APF in non-linear and non-Gaussian systems, so as to improve the accuracy of the SOC estimation in the changing temperature range. Simulations under urban dynamometer driving schedule (UDDS) are conducted, and the results show that compared with the method without temperature correction, the proposed combination method can improve the accuracy and convergence ability of SOC estimation under the influence of temperature.

Automated summary

This work explores the relationship between temperature and battery performance in electric vehicles, proposing a temperature-based coulomb counting method to improve SOC estimation accuracy. By combining adaptive particle filter, the method enhances SOC estimation accuracy and convergence while considering temperature changes.