Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study
Abstract
:1. Introduction
2. An Overview of the Transformer Model
3. Datasets
4. State of Charge Estimation
Methodology | Dataset | RMSE | MAE |
---|---|---|---|
Transformer with SSL [35] | LG 18650HG2 [33] | 1.9%: 20% of training data 0.9%: constant temperature 1.19%: varying temperature | 0.44%: constant temperature 1.7%: varying temperature |
2-Encoder Transformer + I&I Observer [36] | LiFeO4 chemistry-based battery [37] | <1% | —— |
Convolutional Transformer network (CTN) + SPKF [38] | Panasonic 18650PF [39] | 0.93% | 0.81% |
CNN-Informer [40] | Panasonic 18650PF [39] and INR [42] | 0.86% | 0.77% |
BERTtery [43] | 5 large-scale NMC cells and a battery pack serviced for 8 months in an EV | 0.5% | 2% |
Transformer with SPMe [44] | CALCE [45] | <2% | —— |
TTSNet + Kalman filter (post-processing) [46] | —— | 0.69% | 0.5% |
Comparative Study [47]: Transformer, LSTM, Bi-LSTM, and SVR | NASA [34], BMW i3 [48], Stanford University [49], and Musoshi electric vehicles. | 0.99%: Transformer | —— |
5. State of Health Estimation
Methodology | Dataset | RMSE | MAE |
---|---|---|---|
DAE -> Transformer (DeTramsformer) [50] | NASA [34] and CALCE [45] | 8% 7% | 7% 6% |
[PCA, PCC, and feature scaling] + CNN–Transformer [51] | NASA [34] | ≈0.55% | ≈0.55% |
CNN-MVIP-Trans [52] | NASA [34] and Oxford [53] | 0.5% 0.3% | —— |
PCC + encoder-only Transformer [54] | NASA [34] | 2.9% | 2.6% |
Vision Transformer (ViT) [55] | NASA [34] and CALCE [45] | 0.46% 0.47% | 0.36% 0.37% |
Transformer with EIS analysis [56] | CLUC [57] | 0.64% | 0.51% |
Transformer–GRU [58] | NASA [34] | 1.19% | 0.62% |
1D-CNN + T-LSTM [59] | Aging experiment on 7 Prospower ICR18650P batteries, and CLUC [57] | 0.66% | 0.53% |
ITFT (Bi-LSTM) [60] | MIT [61] | SOH: 0.13% RUL: 0.67% | —— |
MCC + Informer [62] | MIT [61] CALCE [45] | 0.2% 1.8% | 0.2% 1.0% |
SGEformer [63] | NASA [34] and CALCE [45] | 0.96% | 0.01% |
Encoder-only Transformer [66] | The study used a dataset that was generated specifically for this work based on measurements from 3 real EVs in 3 years. | 1.31% | —— |
PCA -> Stacked DAE -> Transformer for RUL [67] | NASA [34] | 0.2% | 0.17% |
DAE -> KF-Transformer [68] | NASA [34] and CALCE [45] | 3.45%2.52% | —— |
6. Conclusions
- For SOC estimation, it is obvious that in most of the studies, there is a need to utilize post-processing methods to attenuate the severe fluctuations in the output predictions of the Transformer-based model; this is carried out using filters and state observers.
- For the SOH, there is always a need for preprocessing feature extraction to extract the features from the raw aging datasets to be passed to the Transformer model to infer the SOH; CNNs, DAEs, and correlation analysis methods may be used for this.
- It is evident from the reviewed manuscripts that Transformer-based models can achieve impressive estimation accuracy, with an RMSE below 1% for both states.
- Nevertheless, with more than a million learnable parameters, the computational complexity of these models results in daunting processing.
- Addressing the implementation of Transformer-based models in real time on microcontrollers is crucial in future work, to demonstrate the feasibility of deploying this approach in the BMS of an EV.
Funding
Conflicts of Interest
Abbreviations
BEV | Battery electric vehicle |
LIB | Lithium-ion battery |
SOC | State of charge |
SOH | State of health |
FNN | Feedforward neural network |
RNN | Recurrent neural network |
LSTM | Long–short-term memory |
NLP | Natural language processing |
UDDS | Urban Dynamometer Driving Schedule |
I&I | Immersion and invariance |
CTN | Convolutional Transformer network |
CNN | Convolutional neural network |
BERT | Bidirectional encoder representation from Transformers |
SPMe | Single-particle model with electrolyte dynamics |
TTSNet | Temporal Transformer-based sequence network |
SVR | Support vector regression |
RUL | Remaining useful life |
DAE | Denoising Auto-Encoder |
PCA | Principal component analysis |
PCC | Pearson correlation coefficient |
MVIP | Multiview information perception framework |
ViT | Vision Transformer |
CLUC | Cavendish Laboratory of the University of Cambridge |
EIS | Electrochemical impedance spectroscopy |
GRU | Gated recurrent unit |
VMD | Variational mode decomposition |
PSO | Particle swarm optimization |
ITFT | Improved temporal fusion Transformer |
TPE | Tree-structure Parzen estimator |
MCC | Multiple correlation coefficient |
SGE | Seasonal and growth embedding |
KF | Kalman filter |
MLP | Multilayer perceptron |
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Guirguis, J.; Ahmed, R. Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study. Energies 2024, 17, 3502. https://doi.org/10.3390/en17143502
Guirguis J, Ahmed R. Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study. Energies. 2024; 17(14):3502. https://doi.org/10.3390/en17143502
Chicago/Turabian StyleGuirguis, John, and Ryan Ahmed. 2024. "Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study" Energies 17, no. 14: 3502. https://doi.org/10.3390/en17143502
APA StyleGuirguis, J., & Ahmed, R. (2024). Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study. Energies, 17(14), 3502. https://doi.org/10.3390/en17143502