BATTERY RETIREMENT TRAJECTORY PREDICTION OF ELECTRIC VEHICLE BASED ON REAL VEHICLE DATA

Zhou Yafu; Shi Hongyu

doi:10.19912/j.0254-0096.tynxb.2022-0506

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (5) : 510-517. DOI: 10.19912/j.0254-0096.tynxb.2022-0506

BATTERY RETIREMENT TRAJECTORY PREDICTION OF ELECTRIC VEHICLE BASED ON REAL VEHICLE DATA

Zhou Yafu^1,2, Shi Hongyu^1,2

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Abstract

Aiming at the problem that battery capacity is difficult to predict and battery retirement time is difficult to determine in the vehicle environment, using the daily charging segment data of the vehicle to estimate the current maximum available power of the battery. Constructing the sequence relationship between the current maximum available capacity of the battery and the driving mileage to describe the degradation characteristics of the battery, which provides a reliable basis for the prediction of remaining life. Firstly, under the charging condition of the battery, estimating the current available maximum capacity of the battery by ampere hour integral method, and correcting the obtained capacity value by Kalman filter. Then, predicting the decline trajectory of battery capacity under vehicle mileage by long short-term memory （LSTM） neural network model. The results show that this method can accurately predict the battery capacity and provide a reliable basis for the determination of vehicle retirement time, the root mean square error of different training sets is lower than that of polynomial regression model and Gaussian regression model, and the prediction accuracy is improved by at least 12.7%, which has strong applicability and practical significance.

Key words

electric vehicles / life prediction / power battery / echelon utilization / battery capacity / Kalman filtering / LSTM network model

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Zhou Yafu, Shi Hongyu. BATTERY RETIREMENT TRAJECTORY PREDICTION OF ELECTRIC VEHICLE BASED ON REAL VEHICLE DATA[J]. Acta Energiae Solaris Sinica. 2022, 43(5): 510-517 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0506

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