A Novel SOC Estimation Method for Lithium-ion Power Batteries

Abstract

The invention discloses a novel method for estimating the SOC of a lithium-ion power battery. The equivalent circuit model of the battery is established, and the parameters of the established battery model are identified by using the least squares algorithm; the battery open-circuit voltage UOCV and the corresponding SOC relationship, using the combination of the Shepherd model and the Nernst model to obtain the corresponding function, which fits the relationship between UOCV and SOC; builds the state equation and observation equation for SOC estimation, and the STF algorithm has a strong robustness to model uncertainty. Rod, strong ability to track sudden changes, EKF and STF algorithm estimation of SOC was verified by constant current discharge experiment and UDDS working condition experiment, the results show that STF algorithm is more accurate than EKF algorithm in estimating SOC, and the convergence better.

Description

technical field [0001] The invention relates to a novel method for estimating the SOC of a lithium-ion power battery. Background technique [0002] The estimation of the battery state of charge has always been the focus and difficulty of the battery management system. Accurate estimation of the battery SOC is of great significance for improving battery usage efficiency, extending battery life, improving battery safety and reliability, and vehicle energy management, but SOC cannot Direct measurement can only be estimated by other battery parameters such as battery output voltage and current. [0003] At present, the commonly used SOC estimation algorithms at home and abroad are: the ampere-hour integral method, which cannot give the initial value of SOC, and the inaccurate current measurement will lead to the cumulative error of SOC; the open circuit voltage method, which uses the corresponding relationship between the open circuit voltage of the battery and SOC It is simple...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention discloses a novel method for estimating the SOC of a lithium-ion power battery, which uses a strong tracking filter to estimate the SOC to overcome the inaccurate shortcoming of the extended Kalman filter for estimating the SOC. The strong tracking filter consists of The extended Kalman filter is transformed, mainly for the inaccurate estimation and divergence of the filter caused by the uncertainty of the system model, and has the following advantages: (1) It has strong robustness to the model uncertainty; (2) The ability to track the sudden change state is extremely strong, even when the system reaches the equilibrium state, it still maintains the ability to track the slow change state and the sudden change state; (3) Moderate computational complexity

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