Lithium battery remaining capacity estimation method based on integral Kalman filtering

Abstract

The invention discloses a lithium battery remaining capacity estimation method based on integral Kalman filtering. The method comprises the steps: 1, establishing an equivalent circuit model of a lithium battery; 2, establishing a state equation and an observation equation according to the equivalent circuit model; 3, performing parameter identification on parameters in the equivalent circuit model; 4, establishing a state equation of discrete time by taking SOC as a state variable; 5, determining the size of a Gauss-Hermite integral solving point and a weight corresponding to the integral solving point; 6, continuously estimating the residual electric quantity of the lithium battery in the discharging process by adopting an operation process of integral Kalman filtering. The method is simple in step and convenient to implement, estimates the residual electric quantity of the lithium battery through the integral Kalman filtering algorithm, is good in dynamic adaptability to the lithiumbattery and high in estimation precision, can be effectively applied to the fields of new energy vehicles and the like which have high requirements on the estimation instantaneity and the estimationprecision of the residual electric quantity of the lithium battery, and is remarkable in effect and convenient to popularize.

Description

technical field [0001] The invention belongs to the technical field of lithium batteries, in particular to a method for estimating the remaining power of lithium batteries based on integral Kalman filtering. Background technique [0002] Battery state of charge (SOC), also known as battery remaining capacity, is an important indicator of lithium-ion batteries, generally defined as the ratio of battery remaining capacity to nominal capacity. In view of the significance of SOC estimation of lithium-ion batteries, a lot of resources have been invested in algorithm research at home and abroad, and good estimation results have been achieved. However, there are still some problems in the existing SOC estimation methods, so the research on SOC estimation algorithms is still a current research hotspot. Commonly used lithium battery SOC estimation methods include ampere-hour integration method, open circuit voltage method, neural network method, and Kalman filter method. [0003] I...

AI Technical Summary

Problems solved by technology

In the existing technology, the estimation accuracy of the remaining power of the lithium battery under the dynamic UDDS working condition needs to be further improved

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