Battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering

Abstract

The invention discloses a battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering (NPEKF). The method has the advantages of being simple in operation and high in precision. The method includes the steps that (1) time needed for the whole method is averagely divided into N time periods, each time period stands for one step, in another word, the kth time period stands for the kth step, k is less than or equal to N, and both k and N are positive integers; (2) a model of a battery system is established according to the Kirchhoff voltage and current theorem, and an error matrix d (k) of the model of the battery system and a model error allocation matrix G(k) of the battery system are obtained; (3) compensation is carried out on a prior state estimating equation obtained in the step (2); (4) a posterior state estimation equation of the battery system in the (k+1)th step is solved to obtain an SOC value; (5) according to the posterior state estimation result, obtained in the step (4), of the battery system in the (k+1)th step, the SOC value is compared with a true SOC value of a battery, effectiveness of an NPEKF algorithm is verified, k=n+1, and the step (3) is repeated until the Nth step is executed.

Description

technical field [0001] The invention relates to a method for estimating the state of charge SOC of a battery, in particular to a method for estimating the SOC of a battery based on nonlinear prediction extended Kalman filtering. Background technique [0002] As a key component of electric vehicles, vehicle-mounted power batteries are crucial to the power, economy and safety of the vehicle, and are a key factor restricting the scale development of electric vehicles. In order to maximize the performance of the power battery and prolong the service life of the battery, it is very important to manage the battery effectively, and accurately obtaining the state of charge (SOC) of the battery is the core technology of battery management. Battery SOC estimation is an important basis for judging whether the battery is overcharged or overdischarged, and whether it needs to be balanced or replace a single battery. Therefore, improving the accuracy of battery SOC estimation is of great...

AI Technical Summary

Problems solved by technology

However, this assumption often lacks a theoretical basis and cannot correctly reflect the real characteristics of the battery model error, resulting in a decrease in the accuracy of SOC estimation and even causing the filter to diverge.

Images