Version 1
: Received: 17 November 2023 / Approved: 20 November 2023 / Online: 20 November 2023 (14:02:30 CET)
Version 2
: Received: 2 January 2024 / Approved: 3 January 2024 / Online: 3 January 2024 (09:43:49 CET)
Al-Meer, M.H. A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization. World Electr. Veh. J.2024, 15, 38.
Al-Meer, M.H. A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization. World Electr. Veh. J. 2024, 15, 38.
Al-Meer, M.H. A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization. World Electr. Veh. J.2024, 15, 38.
Al-Meer, M.H. A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization. World Electr. Veh. J. 2024, 15, 38.
Abstract
The precise determination of the State-of-Health (SOH) of lithium-ion batteries is critical in the domain of battery management systems. The proposed model in this research paper emulates any deep learning or machine learning model by utilizing a Look Up Table (LUT) memory to store all activation inputs and their corresponding outputs. The operation that follows the completion of training is referred to as the LUT memory preparation procedure. The lookup operation performed on this method simply substitutes for the inference process. This is achieved by discretizing the input data and features before binarizing them. The term for the aforementioned operation is the LUT inference method. The procedure was evaluated in this study using two distinct neural network architectures: a bidirectional long short-term memory (LSTM) architecture and a standard fully connected neural network (FCNN). It is anticipated that considerably greater efficiency and velocity will be achieved during the inference procedure when the pre-trained deep neural network architecture is inferred directly. The principal aim of this research is to construct a lookup table that effectively establishes correlations between the SOH of lithium-ion batteries and ensures a degree of imprecision that is tolerable. According to the results obtained from the NASA PCoE lithium-ion battery dataset, the proposed methodology exhibits performance that is largely comparable to that of the initial machine learning models. Utilising the error assessment metrics RMSE, MAE, and (MAPE), the accuracy of SOH prediction has been quantitatively evaluated. The indicators mentioned above demonstrate a significant degree of accuracy when predicting SOH.
Keywords
Lithium-Ion Batteries; SoH; SoC; RUL; Batteries; Deep Learning; LUT
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
3 January 2024
Commenter:
Mohamed Almeer
Commenter's Conflict of Interests:
Author
Comment:
1. The abstract is a bit changed. 2: reorganised the subsections and moved a bit. 3: Some additions to the results and discussions. 4: Some background was removed. 5: Some paragraphs have been joined.
Commenter: Mohamed Almeer
Commenter's Conflict of Interests: Author
2: reorganised the subsections and moved a bit.
3: Some additions to the results and discussions.
4: Some background was removed.
5: Some paragraphs have been joined.