SoE online estimation method for variable-working-condition vehicle power battery

Abstract

The invention discloses a SoE online estimation method for a variable-working-condition vehicle power battery, and the method comprises the following steps: S1, building a compensation coefficient function of actual available energy, and building an OCV-SoE curve; S2, obtaining a state equation and an observation equation; S3, constructing a difference equation suitable for a recursive least square algorithm with a forgetting factor, identifying system parameters in real time, and obtaining the system parameters; S4, estimating the SoE state value of the vehicle power battery by using the system parameters obtained in the step S3 and combining an H infinity algorithm. According to the method, the recursive least square method with the forgetting factor is combined with the H infinity algorithm to identify and estimate the SoE, the forgetting factor can effectively reduce the influence of error accumulation, and the H infinity algorithm has no fixed hypothesis requirement on noise, so that the estimation is more in line with the requirements of actual working conditions, the estimation precision is higher, and the algorithm complexity is not high.

Description

technical field [0001] The invention relates to the technical field of vehicle power battery management, in particular to an online SoE estimation method for vehicle power batteries under variable working conditions. Background technique [0002] Battery SoE (State of Energy) is defined as the ratio of the current remaining energy of the battery to the rated energy. The SoE of the battery is a virtual state quantity similar to SoC (State of charge). For assisting in predicting the remaining mileage of electric vehicles, SoE is more instructive than SoC. Compared with other parameters of the battery, SoE can more comprehensively and accurately reflect the real energy state of the battery. SoE is closely related to the cruising range provided by the power battery, and accurate SoE can predict the remaining cruising range of electric vehicles. On the other hand, SoE, as an important parameter of the energy state, can be used as the basis for vehicle energy optimization, ratio...

AI Technical Summary

Problems solved by technology

The main ones are: power integration method, open circuit voltage method, neural network method, and model-based methods. The estimation accuracy of power integration and open circuit voltage is limited by many sensors and experimental conditions, and the estimation accuracy is not high; the degree of dependence of neural network on data Compared with high-cut, the calculation amount is relatively large; the current mainstream model-based method is considered to be based on the equivalent circuit model. The method is relatively mature, with high precision, and it is not difficult to realize it.

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