TY - GEN
T1 - A Multi-Channel Deep-Learning Prediction Algorithm for Battery State-of-Health Indicator
AU - Patrizi, Gabriele
AU - Catelani, Marcantonio
AU - Ciani, Lorenzo
AU - Song, Yuchen
AU - Liu, Datong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Lithium batteries are an essential part of many modern technologies. In most mission-critical applications it is essential to evaluate the current state-of-health (SOH) of the battery during its operating life using adequate condition monitoring tools. The acquired diagnostic data can then be used to forecast the future degradation trend of the battery, and thus to predict the remaining useful life of the device. In this work, a multi-channel deep-learning algorithm based on a Long Short-Term Memory Neural Network is presented in order to predict the future degradation trend of a set of battery's parameters. In order to test and validate the performances of the algorithm, a battery degradation dataset provided by the Toyota Research Institute has been used. The results illustrated the ability of the proposed method to predict the future degradation of several parameters, like charge and discharge capacity, internal resistance, required charging time and maximum overheating of the batteries. Using this approach, it is possible to correctly predict the degradation trend of battery SOH and the remaining useful life even after only few cycles of the battery life.
AB - Lithium batteries are an essential part of many modern technologies. In most mission-critical applications it is essential to evaluate the current state-of-health (SOH) of the battery during its operating life using adequate condition monitoring tools. The acquired diagnostic data can then be used to forecast the future degradation trend of the battery, and thus to predict the remaining useful life of the device. In this work, a multi-channel deep-learning algorithm based on a Long Short-Term Memory Neural Network is presented in order to predict the future degradation trend of a set of battery's parameters. In order to test and validate the performances of the algorithm, a battery degradation dataset provided by the Toyota Research Institute has been used. The results illustrated the ability of the proposed method to predict the future degradation of several parameters, like charge and discharge capacity, internal resistance, required charging time and maximum overheating of the batteries. Using this approach, it is possible to correctly predict the degradation trend of battery SOH and the remaining useful life even after only few cycles of the battery life.
KW - Lithium batteries
KW - Long short-term memory
KW - Neural networks
KW - Prognostics and health management
KW - Remaining life assessment
UR - https://www.scopus.com/pages/publications/85185763881
U2 - 10.1109/MetroXRAINE58569.2023.10405745
DO - 10.1109/MetroXRAINE58569.2023.10405745
M3 - 会议稿件
AN - SCOPUS:85185763881
T3 - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
SP - 816
EP - 821
BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023
Y2 - 25 October 2023 through 27 October 2023
ER -