A battery state prediction system and its implementation method based on extreme learning model
A technology of battery status and extreme learning, which is applied in the direction of measuring electricity, measuring devices, and measuring electrical variables, etc., can solve the problems of large amount of calculation for parameter identification, low prediction efficiency, and high dependence on battery model parameters, so as to reduce the amount of calculation, The effect of improving accuracy and improving forecasting efficiency
Active Publication Date: 2021-03-30
GUANGZHOU HKUST FOK YING TUNG RES INST
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[0002] At present, the method of predicting the state of the battery is mainly realized through a sophisticated battery model, but this method is highly dependent on the parameters of the battery model. When the parameters of the battery change with the aging of the battery, the state of the battery The prediction accuracy will be greatly reduced, so this method can only be applied to batteries with a low degree of aging
In addition, the existing battery state prediction method based on the precise battery model requires a large amount of calculation for parameter identification, resulting in low prediction efficiency
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[0053] The present invention will be further explained and described below in conjunction with the accompanying drawings and specific embodiments of the description. For the step numbers in the embodiment of the present invention, it is only set for the convenience of explanation and description, and there is no limitation on the order of the steps. The execution order of each step in the embodiment can be carried out according to the understanding of those skilled in the art Adaptive adjustment.
[0054] A battery state prediction system based on an extreme learning model, including:
[0055] A battery submodel parameter generator for defining model parameters for the battery submodel and defining the initial state of the battery;
[0056] Multiple battery sub-models for outputting data describing the state of the battery based on the input model parameters and the initial state of the battery;
[0057]The weight calculator is used to calculate the weight corresponding to e...
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The invention discloses a battery state prediction system based on an extreme learning model and a realization method thereof. The system includes a plurality of battery sub-models, a weight calculator and a battery sub-model parameter generator. The method includes the steps of obtaining model parameters of the plurality of battery sub-models through the battery sub-model parameter generator; according to the obtained model parameters, calculating battery state data using the plurality of battery sub-models; performing weighted accumulation on the calculated battery state data of the plurality of battery sub-models; and according to results of the weighted accumulation, predicting the actual state of a battery. According to the invention, data describing the state of the battery is obtained through the plurality of battery sub-models, the prediction efficiency is increased and the amount of calculation is reduced; moreover, the output results of the battery sub-models are subjected toweighted accumulation through the weight calculator, and the accuracy of the prediction is increased; and in addition, the system and method of the invention can be applied to batteries with different degrees of aging, and are more practical. The system and method of the invention can be widely applied to the field of battery condition monitoring.
Description
technical field [0001] The invention relates to the field of battery state monitoring, in particular to a battery state prediction system based on an extreme learning model and an implementation method thereof. Background technique [0002] At present, the method of predicting the state of the battery is mainly realized through a sophisticated battery model, but this method is highly dependent on the parameters of the battery model. When the parameters of the battery change with the aging of the battery, the state of the battery The prediction accuracy will be greatly reduced, so this method can only be applied to batteries with a low degree of aging. In addition, the existing battery state prediction method based on a precise battery model requires a large amount of calculation for parameter identification, resulting in low prediction efficiency. Contents of the invention [0003] In order to solve the above technical problems, the object of the present invention is to p...
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IPC IPC(8): G01R31/367
CPCG01R31/367
Inventor 唐晓鹏姚科夏永晓贺振伟胡文贵高福荣
Owner GUANGZHOU HKUST FOK YING TUNG RES INST