A Battery State Estimation Method Based on Particle Swarm Optimization

Abstract

The invention belongs to the field of battery state estimation and relates to a battery state estimation method based on particle swarm optimization. First initialize the particle swarm and initialize the state estimator; then for each particle in the particle swarm, obtain the state posterior estimation, and compare the accuracy of the state posterior estimation to obtain the state estimation, and then obtain the state estimation of the next sampling point through the state posterior estimation State prior estimation; Finally, according to the accuracy of state posterior estimation, the particle swarm is evolved to obtain a new particle swarm. The present invention obtains battery parameter estimates through particle swarm optimization when estimating the state of the battery, and when the battery model has multiple local optimum points, it is easier to obtain the global optimal estimate of the battery model than the local optimization method, Therefore, the battery state estimation obtained by state filtering is more accurate.

Description

technical field [0001] The invention belongs to the field of battery state estimation and relates to a battery state estimation method based on particle swarm optimization. Background technique [0002] In the application of the secondary battery, it is necessary to estimate the state of the battery such as SOC. Existing technologies include ampere-hour integral method, voltage method, and state filter method. Among these methods, the state filtering method has the best long-term accuracy. One of the more commonly used methods in the state filtering method is the Kalman filter. [0003] When battery state estimation is performed by Kalman filtering, an accurate battery model needs to be obtained. However, current technologies often use fixed battery model parameters, which are difficult to adapt to changes in battery model parameters when the battery is in use. There is also a method to simultaneously estimate the battery state and battery parameters through Kalman filter...

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Problems solved by technology

[0003] When estimating the battery state through Kalman filtering, it is necessary to obtain an accurate battery model. However, the current technology often uses fixed battery model parameters, which is difficult to adapt to changes in battery model parameters when the battery is in use.

There is also a method to simultaneously estimate the battery state and battery parameters through Kalman filtering. The disadvantage of this method is that the theoretical basis of Kalman filtering is a linear model, and the battery system presents strong nonlinear characteristics. Decreased accuracy in battery system estimation

At the same time, if there are multiple local optimum points in the battery model, the Kalman filter is likely to fall into the local optimum point, so the search ability for the global optimum point is not strong

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