Battery SOC (state of charge) estimation method by utilizing vehicle-mounted charging machine identification battery parameter

Abstract

The invention relates to a battery SOC (state of charge) estimation method by utilizing a vehicle-mounted charging machine identification battery parameter. A battery model parameter is identifies through a pulse experiment which is performed by an electric automobile vehicle-mounted bidirectional charging machine on a battery, the battery capacity is recalibrated by utilizing the bidirectional charging machine to perform deep charging and discharging on the battery, extended Kalman filtering is performed then according to an updated battery parameter to estimate a state of charge (SOC) of the battery, and then an influence of a battery parameter changed, which is caused by aging, on battery SOC estimation precision is overcome. A changing battery parameter can be effectively tracked, and a selection of initial data is not depended on; the battery parameter is identified through charging and discharging which are performed by the vehicle-mounted bidirectional charging machine on the battery, and an advantage that an electric automobile is provided with a power electronic conversion device is fully used; in the whole parameter identification process, an electric energy released by the battery is grid-connected in a feedback manner, and energy loss is small; and the method is simple and practicable, and tedious operation that a traditional off-line identification battery parameter method needs to detach a battery box is prevented.

Description

technical field [0001] The invention relates to the technical field of electric vehicle power battery management systems, in particular to a method for estimating the state of charge of a battery using a vehicle-mounted bidirectional charger to identify battery parameters. Background technique [0002] The power battery is a key component that affects the service life, safety and economy of electric vehicles. Efficient and reliable management through the battery management system (Battery Management System, BMS) is an important guarantee for the safe driving of vehicles. The accurate estimation of the battery state of charge (State of Charge, SOC) is the core technology of the battery management system, which has an important impact on the safe and efficient use of power batteries. [0003] At present, the SOC estimation methods mainly include the classic open circuit voltage method and the ampere-hour integration method, intelligent algorithm and extended Kalman filter met...

AI Technical Summary

Problems solved by technology

However, with the long-term use of electric vehicles, battery aging will cause changes in battery model parameters and nonlinear attenuation of battery capacity. If EKF still uses the initial battery parameters in the calculation process, it will bring serious errors to the estimation.

The offline parameter identification method needs to remove the battery box from the vehicle and use external equipment to perform charging and discharging experiments on the battery to recalibrate the battery parameters. The disassembly process is quite cumbersome and difficult to operate.

[0005] To sum up, the power battery is a nonlinear and time-varying system. If the extended Kalman filter algorithm always uses the fixed battery model parameters as the state variables to estimate the SOC, the estimated error will increase as the battery ages. If the battery model parameters and battery capacity are recalibrated through off-line charging and discharging experiments, the battery box of the whole vehicle needs to be disassembled, which is cumbersome and difficult to perform

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