Battery SOC estimation method applying multiple models parallelly

Abstract

Te invention discloses a battery SOC estimation method applying multiple models parallelly. The method includes following steps: 1), acquiring battery state parameters; 2), normalizing the battery state parameters; 3), respectively substituting the battery state parameters after being normalized into a genetic algorithm optimized BP neural network estimation model, a self-adaptive neural-fuzzy reasoning system model and an OS-ELM neural network model to acquire three SOC estimation results; 4), according to a SOC-OCV relation, acquiring initial estimation voltage values respectively corresponding to the three SOC estimation results acquired in the step 3); 5), respectively calculating difference values between the initial estimation voltage values and voltage, and normalizing the difference values to acquire weighting coefficient of the initial estimation voltage values; 6), calculating a final estimated value of SOC according to the weighting coefficient. By utilizing three importantparameters of a battery, using the three models parallelly, using multiple linear system estimation results to describe a nonlinear system and then weighting, summing and estimating the battery SOC value, estimation accuracy is improved effectively.

Description

technical field [0001] The invention relates to the technical field of SOC estimation, in particular to a battery SOC estimation method using multiple models. Background technique [0002] In the environment of energy saving and environmental protection, electric vehicles will become the first choice for the long-term development of the automobile industry. At present, electric vehicles are developing rapidly, but the development is restricted by the service life of power batteries. As the power source of electric vehicles, the battery is the bottleneck restricting the development of electric vehicles, and the accurate estimation of the SOC of the battery is the premise to ensure the reliable operation of the vehicle. [0003] Common SOC estimation methods mainly include: ampere-hour integral method, open circuit voltage method, internal resistance method, Kalman filter method, neural network method, etc. The ampere-hour integration method is to obtain the power consumed b...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a battery SOC estimation method using multiple models, which can solve the problem of low accuracy of the existing battery SOC estimation method

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