Battery SOC estimation method based on HCKF

Abstract

The invention discloses a battery SOC estimation method based on HCKF. On the basis of a battery electrochemical model, parameters are identified through a least square method; the CKF is used as a determined sampling type filtering algorithm; when the nonlinear equation is processed, a point set is generated according to the mean value and the covariance of the prior probability density distribution of the system state and a certain sampling strategy, then each sampling point in the point set is directly subjected to nonlinear propagation, and finally the mean value and the covariance of theposterior probability density distribution of the system state are calculated through weighted summation. According to the method, the nonlinear equation does not need to be linearized, linearizationerrors are eliminated, a Jacobian matrix in the EKF does not need to be calculated in the iterative process of the filtering algorithm, and the method is easier to use in practice; an HCKF algorithm combining a CKF and an H _ infinity filter is proposed to be used for estimating the SOC; the situation that SOC estimation is not accurate enough when battery model errors, unknown measurement noise characteristics and other problems exist is effectively avoided; and the robustness is greatly improved.

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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