Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model
Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/2/125 |
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| author | Vinay Vakharia Milind Shah Pranav Nair Himanshu Borade Pankaj Sahlot Vishal Wankhede |
| author_facet | Vinay Vakharia Milind Shah Pranav Nair Himanshu Borade Pankaj Sahlot Vishal Wankhede |
| author_sort | Vinay Vakharia |
| collection | DOAJ |
| description | Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility. |
| first_indexed | 2024-03-11T09:09:24Z |
| format | Article |
| id | doaj.art-cddfe4c4b4de4b34841eedb746c971ea |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T09:09:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-cddfe4c4b4de4b34841eedb746c971ea2023-11-16T19:07:55ZengMDPI AGBatteries2313-01052023-02-019212510.3390/batteries9020125Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM ModelVinay Vakharia0Milind Shah1Pranav Nair2Himanshu Borade3Pankaj Sahlot4Vishal Wankhede5Department of Mechanical Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar 382426, Gujarat, IndiaDepartment of Mechanical Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar 382426, Gujarat, IndiaDepartment of Mechanical Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar 382426, Gujarat, IndiaMechanical Engineering Department, Medi-Caps University, Indore 453331, Madhya Pradesh, IndiaMechanical Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, IndiaDepartment of Mechanical Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar 382426, Gujarat, IndiaAccurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility.https://www.mdpi.com/2313-0105/9/2/125Li-ion batteryexplainable AIjellyfish optimizationstacked-LSTMGRU |
| spellingShingle | Vinay Vakharia Milind Shah Pranav Nair Himanshu Borade Pankaj Sahlot Vishal Wankhede Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model Batteries Li-ion battery explainable AI jellyfish optimization stacked-LSTM GRU |
| title | Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model |
| title_full | Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model |
| title_fullStr | Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model |
| title_full_unstemmed | Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model |
| title_short | Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model |
| title_sort | estimation of lithium ion battery discharge capacity by integrating optimized explainable ai and stacked lstm model |
| topic | Li-ion battery explainable AI jellyfish optimization stacked-LSTM GRU |
| url | https://www.mdpi.com/2313-0105/9/2/125 |
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