TY - GEN
T1 - Attention Based Mechanism for State-of-Health Estimation of Li-Ion Batteries
AU - Oza, Manthan
AU - Lamba, Ravita
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - To extend the lifespan of Lithium-ion (Li-ion) batteries and optimize their performance, an efficient State-of-Health (SoH) estimation technique is required. Deep learning techniques are capable of handling large datasets and are helpful in long-term predictions. This research proposes a hybridized deep learning architecture that consists of a Convolutional Neural Network (CNN), an attention mechanism, and a Long Short-Term Memory (LSTM) network for precise and efficient SoH prediction. The Pearson correlation coefficient (PCC) is adopted in data pre-processing to choose features that exhibit a strong correlation with each other. The selected parameters are then normalized within the range of 0 to 1 to make the training process fast. Following pre-processing, the CNN-Attention-LSTM model receives all these features as input. Performance indices elucidate a value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) as 0.0039, 0.0059, and 0.0042 respectively and indicate that the proposed model is capable of estimating effective and reliable SoH for Li-ion batteries.
AB - To extend the lifespan of Lithium-ion (Li-ion) batteries and optimize their performance, an efficient State-of-Health (SoH) estimation technique is required. Deep learning techniques are capable of handling large datasets and are helpful in long-term predictions. This research proposes a hybridized deep learning architecture that consists of a Convolutional Neural Network (CNN), an attention mechanism, and a Long Short-Term Memory (LSTM) network for precise and efficient SoH prediction. The Pearson correlation coefficient (PCC) is adopted in data pre-processing to choose features that exhibit a strong correlation with each other. The selected parameters are then normalized within the range of 0 to 1 to make the training process fast. Following pre-processing, the CNN-Attention-LSTM model receives all these features as input. Performance indices elucidate a value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) as 0.0039, 0.0059, and 0.0042 respectively and indicate that the proposed model is capable of estimating effective and reliable SoH for Li-ion batteries.
KW - Attention Mechanism
KW - Convolutional Neural Network (CNN)
KW - Long Short-Term Memory (LSTM)
KW - State-of-Health Estimation
UR - http://www.scopus.com/inward/record.url?scp=85208913714&partnerID=8YFLogxK
U2 - 10.1109/SEFET61574.2024.10718141
DO - 10.1109/SEFET61574.2024.10718141
M3 - Conference contribution
AN - SCOPUS:85208913714
T3 - 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
BT - 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
Y2 - 31 July 2024 through 3 August 2024
ER -