A battery SOC estimation method based on hckf

Abstract

The invention discloses a battery SOC estimation method based on HCKF. On the basis of the battery electrochemical model, the parameters are identified by the least square method, and CKF is used as a definite sampling filter algorithm. When dealing with nonlinear equations, the point set is generated according to a certain sampling strategy according to the mean and covariance of the prior probability density distribution of the system state. , and then each sampling point in the point set is directly propagated nonlinearly, and finally the mean value and covariance of the posterior probability density distribution of the system state are calculated by weighted summation. There is no need to linearize the nonlinear equation, the linearization error is eliminated, and the Jacobian matrix in the EKF does not need to be calculated during the iterative process of the filtering algorithm, which is easier to use in practice. The HCKF algorithm combining CKF and H_∞ filter is proposed to estimate SOC, which effectively avoids the inaccurate SOC estimation when there are problems such as battery model error and unknown measurement noise characteristics, and greatly improves the robustness.

Description

technical field [0001] The invention belongs to the field of lithium batteries, in particular to a battery SOC estimation method based on HCKF (H-Infinity Cubature Kalman Filter, H-Infinity Cubature Kalman Filter). Background technique [0002] As a backup power source, batteries have been widely used in communications, power systems, military equipment, electric vehicles and other fields. In the process of using the battery, the most important thing is to know the SOC (state of charge) of the battery. Different from the terminal voltage and current of the battery, the SOC of the battery cannot be directly measured by the sensor. It must be estimated by measuring other parameters such as the terminal voltage and current and using certain mathematical models and algorithms. Accurate SOC estimation remains a challenging task due to complex battery dynamics as well as different operating conditions, such as ambient temperature, self-discharge rate, hysteresis, regeneration, an...

AI Technical Summary

Problems solved by technology

Although some people use CKF for SOC estimation at this stage, they have not completely solved the problem that the battery model parameters are not accurate enough in the process of battery SOC estimation, the statistical characteristics of system noise and observation noise are unknown, and the problems do not conform to Gaussian distribution, which may lead to the entire The system converges slowly or even fails to converge, and will always bring a certain error to the estimated result of SOC

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