Surplus electric quantity estimation method for power battery

Abstract

The invention discloses a surplus electric quantity estimation method for a power battery. A current detector and a digital-analog converter enable collected voltage and current data to pass through acollection card; the data are transmitted to a surplus electric quantity estimation module through a low pass filter; a fractional order model is built, so as to improve the accuracy of estimating terminal voltage; a fractional order model parameter and a nonlinear function on a surplus electric quantity of open circuit voltage are identified by using a current and voltage response of a battery pack; an adaptive extended kalman filter-based surplus electric quantity estimation strategy is provided for a fractional order battery model, so as to solve the problem that a noise variance of a system is unknown and improve the surplus electric quantity estimation accuracy. The model built by the method disclosed by the invention can effectively describe charging and discharging physical characteristics of the power battery and estimate the surplus electric quantity in real time, and is high in convergence, high in estimation accuracy and applicable to the surplus electric quantity estimation of the power battery of an electric automobile.

Description

technical field [0001] The invention relates to the technical field of new energy vehicle battery management, in particular to a method for estimating the remaining power of a power battery. Background technique [0002] In response to environmental degradation and energy crisis, the electric vehicle industry is developing rapidly. Power lithium batteries are the main energy source of pure electric vehicles, and the remaining power of lithium batteries is similar to the oil volume of traditional vehicles. It is used for the power distribution strategy of electric vehicles and to protect batteries from overcharging, overdischarging, rapid aging, and fire and explosion hazards, play a vital role. Further, accurate remaining charge estimation can both improve the driving range per charge and prolong the useful life of the battery. However, the remaining power cannot be directly measured by on-board sensors. Moreover, lithium battery is a typical nonlinear system. Therefore,...

AI Technical Summary

Problems solved by technology

In the extended Kalman filter, the capacitance in the equivalent circuit is considered to be an integer order, but in fact the capacitance has a fractional order characteristic, which causes a large modeling error and affects the accuracy of the remaining power estimation

In addition, the accuracy of the extended Kalman filter algorithm strongly depends on the accuracy of the noise variance, which further affects the accuracy of the remaining power estimation

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