A battery SOC estimation method and system based on dynamic impedance correction

Abstract

The invention discloses a battery SOC estimation method and system based on dynamic impedance correction. The method comprises the following steps: a mathematic model of open-circuit voltages and SOC of a battery at different temperatures are obtained; off-line identification is carried out to obtain parameters of a battery equivalent circuit model; according to the obtained mathematic model and the parameters of the battery equivalent circuit model, a complete battery model and parameters of the battery model are obtained; through adoption of a battery SOC estimation algorithm based on the model, the residual electric quantity of the battery can be estimated; and through a dynamic impedance correction method, the parameters of the complete battery model are calculated, and the parameters of the complete battery model and the residual electric quantity of the battery are updated in real time. According to the invention, through the dynamic impedance correction method, the parameters of the battery model are calculated; the dynamic impedance in a battery usage process is used as a correction basis for battery model parameters; based on the dynamic battery model updated in real time and with higher precision, the SOC is corrected; the precision is high; and the complexity of the algorithm is relatively low. The battery SOC estimation method and system based on dynamic impedance correction is widely applicable to the field of batteries.

Description

technical field [0001] The invention relates to the battery field, in particular to a battery SOC estimation method and system based on dynamic impedance correction. Background technique [0002] Electric vehicles, as a representative of energy conservation and environmental protection in the automotive industry, are developing rapidly, while lithium batteries have become the main power source of electric vehicles due to their excellent characteristics such as high energy density, long cycle life, high safety and low self-discharge rate. However, a lithium battery is a strongly coupled, highly nonlinear system involving complex physical and electrochemical changes. Moreover, the driving conditions of electric vehicles are harsh and the interference is serious. How to realize the estimation of the state of charge (SOC) of the power battery under complex working conditions is very important for prolonging the life of the battery, accurately predicting the remaining mileage of ...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method is simple and easy, but the initial error cannot be determined, and it will accumulate errors over time; the open circuit voltage method requires the battery to stand for a long time, which is only suitable for laboratory use; the neural network method requires a lot of training data, and the algorithm results are closely related to the training method; the fuzzy algorithm has strong subjective factors and also requires a large amount of data; the synovium observer is often accompanied by the problem of output tremor; the Kalman filter method has a moderate amount of calculation but depends on the model Serious; the particle filter method has high precision but the algorithm is very complicated

[0004] Except for the ampere-hour integration method, all the above methods need to correct the estimated SOC through model feedback (including implicit model feedback), while the battery model feedback method based on fixed parameters estimates the battery state. Under working conditions, the accuracy requirements are often not met.

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