TY - GEN
T1 - Research on SOH Prediction Method of New Energy Vehicle Power Battery
AU - Yu, Zeqi
AU - Chen, Hanming
AU - Wang, Chongwen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The battery state of health (SOH) prediction is an important part of the new energy vehicle battery management system (BMS). Accurately predicting the SOH of the lithium-ion battery is of great significance for evaluating the health of the new energy vehicle power system and the remaining service life. The existing models for estimating the SOH of lithium-ion batteries have much room for improvement in terms of prediction accuracy and applicability. This article addresses the general accuracy and generalization problems of the existing lithium battery SOH prediction models. This paper proposes a lithium battery SOH prediction model based on the Temporal Convolutional Network, and uses particle swarm algorithm to optimize the model's hyper parameters. The model has high prediction accuracy on a variety of battery datasets. Subsequently, the transfer learning method is used to transfer the Temporal Convolutional Network model to the actual working condition data set, and the training set size is effectively reduced under the condition that the model prediction accuracy remains unchanged. Combined with the wavelet decomposition method, the Temporal Convolutional Network model is improved to achieve a fast and accurate estimation of the SOH of lithium batteries with fewer cycles.
AB - The battery state of health (SOH) prediction is an important part of the new energy vehicle battery management system (BMS). Accurately predicting the SOH of the lithium-ion battery is of great significance for evaluating the health of the new energy vehicle power system and the remaining service life. The existing models for estimating the SOH of lithium-ion batteries have much room for improvement in terms of prediction accuracy and applicability. This article addresses the general accuracy and generalization problems of the existing lithium battery SOH prediction models. This paper proposes a lithium battery SOH prediction model based on the Temporal Convolutional Network, and uses particle swarm algorithm to optimize the model's hyper parameters. The model has high prediction accuracy on a variety of battery datasets. Subsequently, the transfer learning method is used to transfer the Temporal Convolutional Network model to the actual working condition data set, and the training set size is effectively reduced under the condition that the model prediction accuracy remains unchanged. Combined with the wavelet decomposition method, the Temporal Convolutional Network model is improved to achieve a fast and accurate estimation of the SOH of lithium batteries with fewer cycles.
KW - Battery state of health prediction
KW - Lithium Battery RUL Prediction
KW - Particle Swarm Optimization
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85133963297&partnerID=8YFLogxK
U2 - 10.1109/ICTIS54573.2021.9798578
DO - 10.1109/ICTIS54573.2021.9798578
M3 - Conference contribution
AN - SCOPUS:85133963297
T3 - 6th International Conference on Transportation Information and Safety: New Infrastructure Construction for Better Transportation, ICTIS 2021
SP - 1348
EP - 1356
BT - 6th International Conference on Transportation Information and Safety
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Transportation Information and Safety, ICTIS 2021
Y2 - 22 October 2021 through 24 October 2021
ER -