Battery SOC estimation method applying multiple models parallelly

A multi-model, battery technology, applied in the field of SOC estimation, can solve the problem of low accuracy of the battery SOC estimation method, and achieve the effect of improving the estimation accuracy

Inactive Publication Date: 2018-06-29
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a battery SOC estimation met...

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  • Battery SOC estimation method applying multiple models parallelly
  • Battery SOC estimation method applying multiple models parallelly
  • Battery SOC estimation method applying multiple models parallelly

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Embodiment Construction

[0024] Such as figure 1 A battery SOC estimation method using multiple models is shown, including a training phase and an estimation phase, wherein the training phase includes the following steps:

[0025] 1) The detection device (LECU) in the battery box is used to detect the battery state parameters. The LTC6803 chip is used to collect the battery voltage U, the Hall current sensor is used to detect the battery current I, and the NTC temperature sensing resistor is used to detect the temperature T; The battery status parameters of n time nodes in the time period, the battery status of the first time node is recorded as: [U 1 , I 1 ,T 1 , t 1 ], the battery status of the node at the second time is recorded as: [U 2 , I 2 ,T 2 , t 2 ]...The battery status of the nth time node is recorded as: [U n , I n ,T n , t n ].

[0026] 2) Using the normalization equation: The battery status of the i-th time node [U i , I i ,T i , t i ], i=1,2,...,n, normalized to: [U' ...

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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...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/388
Inventor 王业琴陈基础杨艳陈语嫣郭畅夏奥运桑英军武莎莎
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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