Lithium battery SOC estimation method for preventing from filter divergence

Abstract

The present invention discloses a lithium battery SOC estimation method for preventing from filter divergence, and relates to the technical field of electric car lithium battery state estimation. Themethod mainly comprises the steps of: I, obtaining a lithium battery SOC starting value through an open-circuit voltage method; II, considering influences of system noise and observation noise, and employing an improved Sage-Husa adaptive extended Kalman filtering algorithm to estimate a real-time SOC value; and III, employing filter divergence criterion for the previous step to perform filter divergence determination, when the filter divergence determination does not meet a criterion condition, constructing an index freezing factor at a Kalman gain matrix to avoid filter divergence. On the basis of the extended Kalman filtering algorithm, a noise estimation value device is added to ensure the estimation precision of the SOC, avoid the generation of filter divergence in a severe work condition and improve the stability of the system estimation.

Description

technical field [0001] The invention relates to the technical field of lithium battery state estimation for electric vehicles, in particular to a lithium battery SOC estimation method for preventing filter divergence. Background technique [0002] The state of charge (SOC) of the power battery is the link between the driver and the pure electric vehicle. The driver uses this parameter to know the remaining mileage of the electric vehicle he is driving and whether it needs to be charged. The SOC value is an important parameter in the battery management system of pure electric vehicles, which is mainly reflected in the following aspects: (1) as the source of the driver's intuitive judgment on the remaining energy of the vehicle. The driver usually intuitively judges the remaining mileage of the electric vehicle through the SOC value, and makes a comprehensive judgment on the driving control of the vehicle. (2) Make a reference for the vehicle control strategy. The vehicle co...

AI Technical Summary

Problems solved by technology

The neural network method has a strong self-learning ability. Because it adopts a parallel processing structure, it has a good ability to deal with highly nonlinear system problems; its disadvantage is that the neural network training needs a large number of screening experimental measurement data as initial data, training Long time and not portable with different battery types

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