温度和老化影响下的锂离子电池荷电状态和健康状态联合估计方法研究

Lithium-ion batteries are widely used in electric vehicles, energy storage power stations and electronic products. Accurate state of charge(SOC) and state of health(SOH) are the basis for its safe and efficient application. However, the nonlinear dynamics of batteries due to temperature and aging state changes severely affect the accuracy of state estimation. Taking the nickel manganese cobalt lithium-ion battery as an example to carry out research: ① The open circuit voltage behavior characteristics of the battery at different aging stages, temperature and SOC ranges are analyzed, and an open circuit voltage model considering aging, temperature and SOC is proposed; ② The equivalent circuit model is established, and a joint estimation method of SOC and SOH of the battery based on multi-scale extended Kalman filter algorithm(MEKF) and fading memory approximate weighted total least squares algorithm(FMAWTLS) is proposed. Among them, MEKF is used to estimate model parameters at the macro scale, SOC is estimated at the micro scale, and FMAWTLS is used to estimated SOH; ③ The algorithm is verified by using battery data of different aging states and temperatures, and the results show that the maximum errors of SOC and SOH are both less than 3 %. The established open circuit voltage model and joint estimation method provide new ideas for SOC and SOH estimation under the influence of temperature and aging.