Abstract
As the worldwide environmental crisis worsens, electric vehicles (EVs) are establishing themselves as ecofriendly alternatives to conventional fossil fuel vehicles. Lithium-ion batteries (LIBs) are a typical source of energy for EVs, but it is important to predict their life in order to ensure safe and optimal operation. However, because LIBs degrade in a nonlinear fashion and their state of health varies depending on operating conditions, achieving fast and accurate cycle life prediction has been a challenge. More importantly, on-board estimation is necessary because even the identical battery cells manufactured by the same company vary in their cycle lifetimes and operational characteristics, which we cannot specify in advance. In this paper, we propose a set of novel features that enable on-board battery cycle life prediction while maintaining high memory efficiency and low calculation complexity. The features’ performances were evaluated using a variety of machine learning models, ranging from simple linear elastic nets to nonlinear neural networks.
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Abbreviations
- BMS:
-
battery management system
- CC:
-
constant current
- CNN:
-
convolutional neural network
- DNN (NN):
-
deep neural network
- Enet:
-
elastic net
- EV:
-
electric vehicle
- GB:
-
gradient boosting
- GPR:
-
gaussian process regression
- KRR:
-
kernel ridge regression
- LFP:
-
lithium iron phosphate
- LIB:
-
lithium-ion battery
- LSTM:
-
long short term memory
- MAPE:
-
mean absolute percentage error
- ML:
-
machine learning
- NB:
-
naïve bayes
- PCA:
-
principal component analysis
- PI:
-
permutation importance
- PSO:
-
particle swarm optimization
- Qd:
-
discharge capacity
- RFR:
-
random forest regression
- RMSE:
-
root mean square error
- RNN:
-
recurrent neural networks
- RUL:
-
remaining useful life
- SDAE:
-
stacked denoising autoencoder
- SEI:
-
solid electrolyte interphase
- SOH:
-
state of health
- SVR:
-
support vector regression
- TPWPME:
-
two phase wiener process with measurement errors
- XGB:
-
extreme gradient boosting
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Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (NRF-2016R1A5A1009592). The Institute of Engineering Research at Seoul National University provided research facilities for this work.
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Shin, J., Kim, Y. & Lee, J.M. Feature construction for on-board early prediction of electric vehicle battery cycle life. Korean J. Chem. Eng. 40, 1850–1862 (2023). https://doi.org/10.1007/s11814-023-1476-1
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DOI: https://doi.org/10.1007/s11814-023-1476-1