Method for estimating state of charge (SOC) of lithium battery by extended Kalman filter algorithm

Abstract

The invention discloses a method for estimating the state of charge (SOC) of a lithium battery by an extended Kalman filter algorithm. The method comprises the following steps that S1, an equivalent circuit model of the lithium battery is established; S2, a current source E is replaced with the voltage of circuit (VOC) of the lithium battery, and a first state equation and a first measurement equation of a system are established according to the equivalent circuit model of the lithium battery; S3, the first state equation and the first measurement equation are analogous to an EKF algorithm toobtain a second state equation and a second measurement equation correspondingly; S4, the SOC of the lithium battery is estimated by the EKF algorithm. The method has the advantages that the EKF algorithm does not depend on the setting of an SOC initial value, the filter process can be convergent in a short time even through the set initial value has large difference with a true value, and accurate SOC estimation is achieved.

Description

technical field [0001] The invention relates to the field of lithium batteries, in particular to a method for estimating the SOC of a lithium battery by using an extended Kalman filter algorithm. Background technique [0002] Due to the increasing shortage of non-renewable energy sources and people's increasing emphasis on environmental protection issues, electric vehicles have gradually become the mainstream choice of users due to their high energy-saving and zero-pollution characteristics. As one of the core components of electric vehicles, power batteries have always been the focus of electric vehicle research and development. To maintain and manage the battery of electric vehicles, ensure the safe and efficient operation of the battery, and optimize the driving range and driving experience of electric vehicles, it is first necessary to make an effective estimate of the operating state of the battery. The operating state of the battery includes State of Charge (SOC), Sta...

AI Technical Summary

Problems solved by technology

Among them, the formula of the ampere-hour integration method is simple, but it depends on the initial value. When there is an error in the initial SOC, the estimated SOC will always have an error. In addition, the ampere-hour integration method has higher requirements on sensor accuracy and sampling frequency

The open-circuit voltage method is simple to implement. When the battery is fully rested, the SOC of the battery can be obtained by looking up the table, but the resting time is often too long, and the application scenarios are limited.

The Kalman filter method uses the minimum mean square error criterion to estimate the state of the dynamic linear system, and can estimate the battery SOC in the working process, but because the battery is a complex nonlinear system, the application effect is not ideal

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