Lithium iron phosphate battery rest electric quantity estimation method based on closed-loop hybrid algorithm

Abstract

The invention discloses a lithium iron phosphate battery rest electric quantity estimation method based on a closed-loop hybrid algorithm. The estimation method is a rest electric quantity estimation hybrid algorithm based on two kinds of closed algorithms in an electric automobile battery platform period. According to open circuit voltage change features of a lithium iron phosphate battery, a work interval of the battery is divided into a platform period and a non-platform period; when a battery initial value is in the non-platform period, the particle filtering is used for performing rest electric quantity estimation; when the initial value is in the platform period, firstly, the boundless Kalman filtering is used for tracking to a position near a truth value; then, the particle filtering is used for estimation. In the platform period, the particle filtering and the boundless Kalman filtering are combined; firstly, the boundless Kalman filtering is used for tracking a rest electric quantity initial value to obtain a value similar to the rest electric quantity true value, and then, the particle filtering is used for rest electric quantity estimation; an accurate and high-robustness result is obtained as much as possible.

Description

technical field [0001] The invention belongs to the technical field of methods for estimating the remaining power of an electric vehicle power supply, and in particular relates to a method for estimating the remaining power of a lithium iron phosphate battery based on a closed-loop hybrid algorithm. Background technique [0002] Lithium-ion battery is currently the battery with the highest specific energy. It also has the advantages of small thermal effect, no memory effect, high charging efficiency and long life. Lithium-ion battery has become the first choice for electric vehicle power batteries. [0003] The state of charge (SOC) is one of the most basic and important functions in the power battery management system, which directly affects the reliability, safety and service life of the battery. The remaining power cannot be directly measured, and the remaining power can be estimated by electrical performance parameters such as current, voltage, or battery internal resist...

AI Technical Summary

Problems solved by technology

Generally speaking, the particle filter is more accurate and robust than the infinite Kalman filter. However, when the initial value of the battery is in the plateau period, due to the algorithm defect of the particle filter itself, the error is huge, and the remaining power cannot be estimated.

Images