Likelihood-function-particle-filter-based power battery state-of-charge estimation method and system

Abstract

The invention relates to a likelihood-function-particle-filter-based power battery state-of-charge (SOC) estimation method and system. A state equation and a measurement equation are obtained by a battery Thevenin model; after parameter initialization, state prediction is carried out and a mean value and a covariance of a state prediction value are calculated; and sampling is carried out again and a sampling distribution function is reconstructed. A voltage prediction value of a battery end is calculated, a particle weight is calculated, weight normalization is carried out, and an effective particle number is calculated. The effective particle number Neff is compared with an effective particle number threshold value; when the Neff is less than Nthr, a Laplacian distribution is used as a likelihood function and a variance regulatory factor and a working condition adaptation factor are introduced, thereby modifying a variance of the likelihood function in an adaptive mode and thus realizing adaptation to different working conditions of the power battery. And then an updated SOC estimation value and covariance are obtained finally. According to the system, a microcontroller is connected with a voltage sensor and a current sensor; and all program execution modules are arranged in the microcontroller. According to the invention, the effective particle number is increased; overcorrection of the variance is effectively avoided; and the estimation precision is high.

Description

technical field [0001] The invention relates to the technical field of electric vehicle power battery charge state estimation, in particular to a method and system for power battery charge state estimation using a Laplace distribution likelihood function particle filter with a variance adjustment factor. Background technique [0002] In recent years, due to the excellent performance of lithium-ion batteries, it has been widely used in electric vehicles, portable electronic devices, aerospace and other fields. Power battery state of charge (State of Charge, SOC) is an important parameter for the battery management system (Battery Management System, BMS) to describe the state of the battery. Accurate estimation of the state of charge can prevent the battery from overcharging and over-discharging, and effectively prolong the service life of the battery. [0003] At present, researchers have given many methods for estimating the state of charge SOC in real time. [0004] The am...

AI Technical Summary

Problems solved by technology

[0008] Gaussian particle filtering to predict the probability density function N(x k-1|k-1 ,P k|k-1 ), as an importance probability density function, but without considering the latest measurement information, there is a large deviation between the particle distribution of importance sampling and the particles generated by the posterior probability distribution, and the normalized importance weights are only concentrated on some particles. As a result, the weight of other particles is almost close to 0, which increases the variance of the weight, so the number of effective particles decreases, and the filtering performance decreases.

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