Lithium ion battery remaining useful life prediction method

Abstract

The invention discloses a lithium ion battery remaining useful life prediction method. The method comprises the following steps that state parameter change data of a battery model is obtained by applying a particle filtering algorithm, the data are imported into an exponential smoothing prediction model (ES) so as to obtain a state parameter prediction value, then the state parameter prediction value is brought into an observation equation to obtain a capacity observation prediction value, and finally, the observation prediction value is fed back to the particle filter to predict the remaininguseful life (RUL) of a battery. According to the method, the ES-PF prediction model can solve the problem that state parameters cannot be updated in the prediction state by adopting the particle filtering algorithm, so that the prediction error is increased along with the change of the prediction period, and the prediction precision of the particle filtering algorithm is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion batteries, and more specifically relates to a method for predicting the remaining life of lithium-ion batteries. Background technique [0002] Due to the advantages of high energy density, low self-discharge rate, no memory effect, and long cycle life, lithium-ion batteries are widely used in consumer electronics, electric vehicles and even aerospace as one of the most promising power sources. With the continuous charge and discharge cycle, the performance degradation of the battery, that is, aging, is inevitable. The deterioration of batteries can lead to performance degradation, financial loss, and even accidents. Therefore, it is very necessary to find an accurate and reliable battery remaining life prediction method to monitor battery decay and evaluate its reliability. [0003] The remaining battery life (RUL) prediction is to predict how many cycles / times will elapse from the current c...

AI Technical Summary

Problems solved by technology

However, the particle filter has a serious defect in the prediction function, that is, when there is no measured value in the prediction stage, the state of the particle filter cannot be updated, that is to say, the state value stays at the last known point, and the state value of the last known point is used. Point estimation is used to predict the remaining life of the battery, resulting in the longer the prediction period, the lower the prediction accuracy

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