Power battery SOC (state of charge) estimation method based on expansion Kalman particle filter algorithm

Abstract

The invention discloses a power battery SOC (state of charge) estimation method based on an expansion Kalman particle filter algorithm. A conventional method has the problems of unsatisfied on-line estimation needs, large accumulation errors, diverging filtering and the like. An expansion Kalman filtering method and a particle filter method are combined and important density functions are generated through expansion Kalman filtering, so that a particle degeneration phenomenon is avoided and the estimation precision is enhanced. By using the provided method, the SOC of a battery can be effectively estimated and the precision is quite high, thereby being suitable for the SOC estimation of various batteries.

Description

technical field [0001] The invention discloses a power battery SOC estimation method based on an extended Kalman particle filter algorithm, and belongs to the technical field of lithium batteries. Background technique [0002] As a key technical component of an electric vehicle, the performance of the power battery directly affects the performance of the vehicle. The battery state of charge (State of Charge, SOC) is used to describe the amount of remaining battery power, which in turn reflects the driving range of electric vehicles. Power battery state of charge estimation is an important function of the battery management system, and it is also a technical difficulty that needs to be solved urgently in its development. Real-time and accurate SOC estimation is of great significance to battery performance, service life and the development of electric vehicles. The high nonlinearity of the power battery makes it difficult for many filtering methods to obtain accurate estimati...

AI Technical Summary

Problems solved by technology

The ampere-hour measurement method is simple and easy to implement, but the cumulative error is large, and the accuracy of the measuring equipment is high; the open circuit voltage method is only suitable for estimation after the battery has been left for a long enough time, and cannot be estimated in real time; the neural network can be estimated online, but the disadvantage is that it requires A large amount of training data; the Kalman filter method linearizes the nonlinear system, but for systems with high nonlinear intensity, it is easy to cause the filtering effect to decline or even diverge. The particle filter algorithm has problems such as particle degradation and algorithm jitter

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