Estimation method of battery state of charge

Abstract

The present invention relates to an estimation method of the state of charge of a battery. The method includes the following steps that: at least two estimation models under different states of heath (SOH) of the battery are built based on extended Kalman filtering; calculation is performed on the estimation models under the different states of heath of the battery through an extended Kalman filtering algorithm, so that the estimated values of the state of charge of the battery which are corresponding to the estimation models under the different states of heath (SOH) of the battery can be obtained; and the weight values of the estimation models under the different states of heath (SOH) of the battery at one time point can be obtained based on the estimated values of the state of charge of the battery, so that the optimal estimation of the state of charge of the battery at this time point can be obtained. According to the estimation method of the state of charge of the battery of the invention, the problem of accuracy drift caused by a condition that the same algorithm is adopted to estimate the states of charge of different batteries in one battery pack under different states of heath of the batteries can be solved, and the stability and reliability of the battery state of charge algorithm can be improved.

Description

[0001] 【Technical field】 [0002] The invention relates to the field of battery charge state estimation methods, in particular to a battery charge state estimation method. [0003] 【Background technique】 [0004] Electric vehicles use electric energy as energy to drive electric motors to replace traditional vehicles that use gasoline as an energy source. [0005] BMS (Battery Management System, battery management system), as the core of electric vehicles, the "brain" of electric vehicles, is an important guarantee for the safe and stable operation of electric vehicles. [0006] As the battery SOH (State Of Health, state of health) decreases, the model parameters of the battery will change accordingly, and the internal DC resistance will become larger. However, in the same battery pack, there are differences in the state of health (SOH) of each single battery, so the internal impedance varies greatly. The battery model established by the battery parameters measured in a certain...

AI Technical Summary

Problems solved by technology

However, in the same battery pack, there are differences in the state of health (SOH) of each single battery, so the internal impedance varies greatly. The battery model established by the battery parameters measured in a certain SOH state of the battery can only It is more accurate to predict the state of charge value corresponding to the current SOH state of the battery, but it is impossible to accurately predict the state of charge value corresponding to the next SOH state. If the same battery model parameters are used for the Kalman filter algorithm, the algorithm will increase error, it is impossible to obtain more accurate calculation results

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