Extended Kalman particle filtering based SOC (State Of Charge) estimation method and system

Abstract

The invention relates to an extended Kalman particle filtering based SOC (State Of Charge) estimation method and system. The method comprises the steps of performing a discharging-standing test on a battery through a single battery test system so as to acquire an SOC-OCV (Open Circuit Voltage) function relational expression; building an equivalent circuit model of the battery; generating a particle set according to initial probability distribution; performing online parameter identification on the model according to the current data; and performing extended Kalman particle filtering on the particles by using the updated model; and updating a particle weight, and judging whether resampling is required or not according to the number of effective particles. In allusion to problems of low estimation accuracy, existence of an accumulative error, high requirements for model precision and the like of an existing SOC estimation method, the invention adopts the method for online model parameter identification to improve the model precision and adopts the method of combining Kalman filtering and particle filtering to improve the SOC estimation accuracy. The method provided by the invention can effectively estimate the SOC and suppress noises, has the advantage of high precision, and can be applied to the field of a battery management system.

Description

technical field [0001] The invention relates to the field of electric vehicle power battery management systems, in particular to an SOC estimation method and system based on extended Kalman particle filter. Background technique [0002] When the oil crisis broke out, energy and environmental issues gradually attracted people's attention, and people urgently needed new energy vehicles to solve these problems, so electric vehicles gained attention. As the power source of electric vehicles, power batteries undertake all or part of the power output of electric vehicles. During the working process, the electric vehicle battery pack often produces overcharge and overdischarge phenomena due to long charging and discharging time, which not only affects the performance of the battery, but also shortens the service life of the battery and reduces the cost performance of the vehicle. The SOC (State Of Charge) of the power battery is the remaining power of the power battery, which is o...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method is theoretically simple and has a small amount of calculation, but it does not have the effect of feedback correction, and there will be cumulative errors; the neural network method is suitable for simulating highly nonlinear systems, but it needs to establish an accurate neural network model and requires a large number of Data training; the Kalman filter method can reduce the amount of data storage, but has high requirements for model accuracy

[0004] Except for the ampere-hour integration method above, other methods need to correct the SOC value according to the model feedback, which requires high model accuracy, and power batteries usually have highly nonlinear characteristics, and the model with fixed parameters obviously cannot meet the high precision requirements.

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