Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network
In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/5959443 |
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| author | Mohamad Reda A. Refaai Jyothilal Nayak Bharothu T. V. V. Pavan Kumar Chodagam Srinivas M. Sudhakar Anirudh Bhowmick |
| author_facet | Mohamad Reda A. Refaai Jyothilal Nayak Bharothu T. V. V. Pavan Kumar Chodagam Srinivas M. Sudhakar Anirudh Bhowmick |
| author_sort | Mohamad Reda A. Refaai |
| collection | DOAJ |
| description | In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach. |
| format | Article |
| id | doaj-art-97739160cca54f6f9d40671c8a8f51ad |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-97739160cca54f6f9d40671c8a8f51ad2025-02-03T05:53:49ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/5959443Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural NetworkMohamad Reda A. Refaai0Jyothilal Nayak Bharothu1T. V. V. Pavan Kumar2Chodagam Srinivas3M. Sudhakar4Anirudh Bhowmick5Department of Mechanical EngineeringDepartment of Electrical & Electronics EngineeringElectrical and Electronics EngineeringDepartment of EEEDepartment of Mechanical EngineeringFaculty of Meteorology and HydrologyIn the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach.http://dx.doi.org/10.1155/2022/5959443 |
| spellingShingle | Mohamad Reda A. Refaai Jyothilal Nayak Bharothu T. V. V. Pavan Kumar Chodagam Srinivas M. Sudhakar Anirudh Bhowmick Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network International Journal of Photoenergy |
| title | Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network |
| title_full | Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network |
| title_fullStr | Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network |
| title_full_unstemmed | Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network |
| title_short | Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network |
| title_sort | battery energy forecasting in electric vehicle using deep residual neural network |
| url | http://dx.doi.org/10.1155/2022/5959443 |
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