A Genetic Algorithm and RNN-LSTM Model for Remaining Battery Capacity PredictionSource: Journal of Computing and Information Science in Engineering:;2022:;volume( 022 ):;issue: 004::page 41009-1DOI: 10.1115/1.4053326Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Li-ion batteries have diversified applications in everyday life. The temperature change, overcharging and over-discharging, is playing critical roles in affecting battery life in a significant manner. In this paper, the deep learning-based method is applied for the prognostics of a single Li-ion battery. The proposed design uses a recurrent neural network variant, long–short-term memory. The model’s parameters are optimized through a genetic algorithm-based parameter selector. The method applies to a sequence of data values comprising of the voltage, the charge capacity, the current, and the temperature. The estimation of battery capacity is not only based on the current or defined state of battery, instead, it is generated on the complete data profile. The robustness of the model is tested by comparing it with techniques such as support vector regressor, Kalman filter, and neural networks on normal and noisy test sets. The paper also proposes a feature selection and engineering scheme for battery capacity prediction. The proposed model outperforms the techniques available in the literature with high generalization to noise and other perturbations. The model is independent of the section of the charging curve used for the prediction of battery capacity. Various experimentation has been conducted on the model and the results have been validated.
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contributor author | Singh, Mukul | |
contributor author | Bansal, Shrey | |
contributor author | Vandana | |
contributor author | Panigrahi, B. K. | |
contributor author | Garg, Akhil | |
date accessioned | 2022-05-08T09:31:16Z | |
date available | 2022-05-08T09:31:16Z | |
date copyright | 2/15/2022 12:00:00 AM | |
date issued | 2022 | |
identifier issn | 1530-9827 | |
identifier other | jcise_22_4_041009.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4285234 | |
description abstract | Li-ion batteries have diversified applications in everyday life. The temperature change, overcharging and over-discharging, is playing critical roles in affecting battery life in a significant manner. In this paper, the deep learning-based method is applied for the prognostics of a single Li-ion battery. The proposed design uses a recurrent neural network variant, long–short-term memory. The model’s parameters are optimized through a genetic algorithm-based parameter selector. The method applies to a sequence of data values comprising of the voltage, the charge capacity, the current, and the temperature. The estimation of battery capacity is not only based on the current or defined state of battery, instead, it is generated on the complete data profile. The robustness of the model is tested by comparing it with techniques such as support vector regressor, Kalman filter, and neural networks on normal and noisy test sets. The paper also proposes a feature selection and engineering scheme for battery capacity prediction. The proposed model outperforms the techniques available in the literature with high generalization to noise and other perturbations. The model is independent of the section of the charging curve used for the prediction of battery capacity. Various experimentation has been conducted on the model and the results have been validated. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Genetic Algorithm and RNN-LSTM Model for Remaining Battery Capacity Prediction | |
type | Journal Paper | |
journal volume | 22 | |
journal issue | 4 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4053326 | |
journal fristpage | 41009-1 | |
journal lastpage | 41009-17 | |
page | 17 | |
tree | Journal of Computing and Information Science in Engineering:;2022:;volume( 022 ):;issue: 004 | |
contenttype | Fulltext |