Online estimation method for state of charge (SOC) of battery based on NARX model

Abstract

The invention discloses a method for estimating the state of charge (SOC) of a secondary battery. Modeling on dynamic characteristics of the different kinds of batteries is achieved by using the strong learning capacity of a non-linear auto-regressive exogenous neural network, and the state of charge of the battery is estimated on line based on the built model by using an extended Kalman filteringalgorithm. The process of modeling on the dynamic characteristics of the battery based on NARX is offline, and the SOC estimation is performed on line in real time. The instantaneity of SOC estimation is not affected by the training process of the model. According to the method provided by the invention, the accurate simulation of the dynamic characteristics of the battery can be achieved withouttoo much training set sample data, the accurate estimation of the battery SOC can be achieved; the method has the characteristics that the online estimation is small in calculated quantity, and the cheap digital processor can be used for operating, is applicable to the battery management system based on the low-cost microcontroller to predict the SOC of the battery, and has the advantages of realtime, high efficiency and low cost.

Description

technical field [0001] The invention relates to a new method for establishing equivalent models for secondary batteries (including lithium-ion batteries, nickel-metal hydride batteries, lead-acid batteries, etc., hereinafter referred to as batteries), and combining the extended Kalman filter algorithm for battery life prediction. Background technique [0002] In recent years, new energy technologies represented by electric vehicles and smart grids have developed rapidly. In these fields, the battery is the core energy storage component, and its life and reliability have a decisive impact on the performance of the entire system. The online estimation of the remaining capacity of the battery (SOC, State-of-Charge) is the core function of the battery management system. one. Traditional residual capacity estimation methods include coulomb counting method, open circuit voltage method, and electrical impedance method. The coulomb counting method is greatly affected by the initia...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the shortcomings of the existing methods based solely on data modeling or equivalent circuit modeling in the prediction of the remaining capacity of secondary batteries, a hybrid equivalent model based on data models and circuit models is now provided. The modeling method, based on the established hybrid equivalent model, combined with the extended Kalman filter algorithm to estimate the remaining capacity of the battery in real time and accurately, makes the SOC-based operation in the battery management system more reliable

Images