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MambaLithium: Selective state space model for remaining-useful-life, state-of-health, and state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries is crucial in electric vehicles and new energy industry. Remaining-useful-life (RUL), state-of-health (SOH) and state-of-charge (SOC) are three key states of lithium-ion batteries. As Mamba (Structured state space sequence models with selection mechanism and scan module, S6) has achieved remarkable success in sequence modeling tasks, this repository proposes a Mamba-based model to predict RUL, SOH and SOC.

Requirements

The code has been tested running under Python 3.7.4, with the following packages and their dependencies installed:

numpy==1.16.5
matplotlib==3.1.0
sklearn==0.21.3
pandas==0.25.1
pytorch==1.7.1

The RUL and SOH data used in this repository were downloaded from https://github.com/WenPengfei0823/PINN-Battery-Prognostics. The SOC data used in this repository was downloaded from https://github.com/GuoKent/Hybrid_time_series_forecasting_model. Some code of the Mamba model is from https://github.com/alxndrTL/mamba.py

RUL and SOH prediction

Following previous research (Kong et al., 2021; Wen et al., 2024), two datasets named CaseA and CaseB in the ./data folder are used for evaluation.

RUL prediction

python main.py --task RUL

SOH prediction

python main.py --task SOH

We adopt an argument parser by package argparse in Python, and the options for running code are defined as follow:

parser = argparse.ArgumentParser()
parser.add_argument('--use-cuda', default=False,
                    help='CUDA training.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100,
                    help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
                    help='Learning rate.')
parser.add_argument('--wd', type=float, default=1e-5,
                    help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=16,
                    help='Dimension of representations')
parser.add_argument('--layer', type=int, default=2,
                    help='Num of layers')
parser.add_argument('--task', type=str, default='SOH',
                    help='RUL or SOH')
parser.add_argument('--case', type=str, default='A',
                    help='A or B')                    

args = parser.parse_args()
args.cuda = args.use_cuda and torch.cuda.is_available()

SOC prediction

The ./data folder includes three datasets for SOC prediction: DST, FUDS, US06. Following previous research (Yang et al., 2019; Chen et al., 2024), either of these datasets can be testing set, and the corresponding other two datasets are used for training. Users can choose SOC measured under different temperature (Celsius degree: 0C, 10C, 25C, 30C, 40C, 50C) for prediction.

python soc.py

We adopt an argument parser by package argparse in Python, and the options for running code are defined as follow:

parser = argparse.ArgumentParser()
parser.add_argument('--use-cuda', default=False,
                    help='CUDA training.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100,
                    help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
                    help='Learning rate.')
parser.add_argument('--wd', type=float, default=1e-5,
                    help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=16,
                    help='Dimension of representations')
parser.add_argument('--layer', type=int, default=2,
                    help='Num of layers')
parser.add_argument('--test', type=str, default='FUDS',
                    help='Test set')
parser.add_argument('--temp', type=str, default='25',
                    help='Temperature')                    

args = parser.parse_args()
args.cuda = args.use_cuda and torch.cuda.is_available()

References

Chen et al., An LSTM-SA model for SOC estimation of lithium-ion batteries under various temperatures and aging levels, J Energy Storage, 2024

Kong et al., Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries, Energy, 2021

Wen et al., Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries, IEEE TIV, 2024

Yang et al., State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network, IEEE Access, 2019

Citation

@article{shi2024lithium,
  title={MambaLithium: Selective state space model for remaining-useful-life, state-of-health, and state-of-charge estimation of lithium-ion batteries},
  author={Zhuangwei Shi},
  journal={arXiv preprint arXiv:2403.05430},
  year={2024},
}