Abstract
An accurate prediction of batteries' future degradation is a key solution to relief the users' anxiety on battery lifespan and electric vehicles' driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this article, a feed-forward migration neural network (NN) is proposed to predict the batteries' aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging data set. This base model is then transformed by an input-output slope and bias correction (SBC) method structure to capture the degradation of target cell. To enhance the model's nonlinear transfer capability, the SBC model is further integrated into a four-layer NN and easily trained via the gradient correlation algorithm. The proposed migration NN is experimentally verified with four different commercial batteries. The predicted root-mean-square errors (RMSEs) are all lower than 2.5% when using only the first 30% of aging trajectories for NN training. In addition, the illustrative results demonstrate that a small-sized feed-forward NN (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.
Original language | English |
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Article number | 9028181 |
Pages (from-to) | 363-374 |
Number of pages | 12 |
Journal | IEEE Transactions on Transportation Electrification |
Volume | 6 |
Issue number | 2 |
Early online date | 9 Mar 2020 |
DOIs | |
Publication status | Published - Jun 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Aging trajectory prediction
- electric vehicle
- lithium-ion battery management
- model migration
- neural network (NN)