Battery charge state estimation method based on minimum error entropy extended Kalman filtering

Abstract

The invention provides a battery state-of-charge estimation method based on minimum error entropy extended Kalman filtering. The method comprises the following steps: establishing a battery equivalent model, identifying parameters, obtaining a relation curve of open circuit voltage OCV and SOC, establishing a system state equation and a measurement equation, and filtering by adopting minimum error entropy extended Kalman filtering. The technical problem that a traditional Kalman filter is poor in robustness under the non-Gaussian noise condition is solved, and the method has the advantages of being high in precision and robustness. Simulation and experiment results show that under the condition that noise is non-Gaussian, SOC estimation precision and robustness of the method are obviously superior to those of a traditional extended Kalman filtering algorithm, and safe use and efficient management of the battery are further guaranteed.

Description

technical field [0001] The present invention relates to the field of battery health management, in particular to a method for estimating battery state of charge. Background technique [0002] The state of charge (SOC) of the battery is a representation of the remaining capacity of the battery and an important indicator reflecting the operating state of the battery. In practice, the SOC cannot be obtained by direct measurement, but can only be obtained by other external characteristics of the battery. Parameters (current, voltage, battery internal resistance, temperature) are obtained by indirect calculation. [0003] At present, a lot of research has been done on battery state of charge estimation at home and abroad. The commonly used SOC estimation methods can be divided into four categories: [0004] (1) Open-circuit voltage method: By directly measuring the open-circuit voltage (OCV), the SOC is estimated according to the mapping relationship between OCV and SOC. This m...

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Problems solved by technology

This method requires the battery to be unloaded for a long time to obtain OCV, which is not suitable for online estimation

[0005] (2) Ampere-hour integration method: By directly measuring the current in the circuit, the initial SOC value is added to the cumulative integral of the current during charging and discharging. This method is simple in principle, but it depends on the initial SOC value and the measurement accuracy of the current will also be It has an impact on the SOC estimate, and the accuracy is not high

But in reality, the error of the sensor is unknown, and the measured value of current or voltage may be inaccurate or have singular values, which will make the traditional methods such as extended Kalman decrease in accuracy or even fail to converge. Therefore, it is necessary to develop a method with high accuracy and Robust SOC Estimation Algorithm

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