Method for estimating state of charge (SOC) of lithium ion battery

Abstract

The invention discloses a Method for estimating state of charge (SOC) of lithium ion battery, which comprises the following steps: S1, establishing an equivalent circuit model of a lithium ion battery, collecting open-circuit voltage data and charge state data, and obtaining battery model parameters in different states based on the equivalent circuit model; S2, based on the Kirchhoff's law, obtaining a model state space equation through the equivalent circuit model and the battery model parameters; and S3, based on the model state space equation, estimating the state of charge of the lithium ion battery through a robust adaptive finite difference Kalman filtering method. According to the method, the calculation of a Jacobian matrix can be avoided, the process noise covariance is adjusted in real time, the observation value is reconstructed, and the accuracy and the convergence speed of the algorithm for estimating the state of charge and the robustness of non-Gaussian distribution measurement noise are improved.

Description

technical field [0001] The invention belongs to the technical field of battery management systems, and in particular relates to a method for estimating the state of charge SOC of a lithium-ion battery. Background technique [0002] Lithium-ion batteries have the advantages of high energy density, long cycle life, and no memory, and are widely used in electric vehicles and energy storage. High-performance battery management system (Battery Management System, BMS) is of great significance to improve battery life, reliability and performance. The battery state of charge SOC (State of Charge, SOC) is the ratio of the battery's current remaining capacity to the battery's nominal capacity. Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for the charge-discharge control, balance management and safety management of lithium-ion batteries. However, battery SOC cannot be measured directly, and can only be obtained indirectly through o...

AI Technical Summary

Problems solved by technology

In practical applications, the noise is usually changing, and the sensor will have certain errors or occasional outliers, so the observation noise is prone to non-Gaussian distribution

Ignoring the change of noise or the observation noise that does not meet the Gaussian assumption of the extended Kalman filter will lead to the degradation of the algorithm filtering performance or even divergence

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