Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application
Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend...
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| author | Chisom Onyenagubo Yasser Ismail Radian Belu Fred Lacy |
| author_facet | Chisom Onyenagubo Yasser Ismail Radian Belu Fred Lacy |
| author_sort | Chisom Onyenagubo |
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| description | Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. |
| format | Article |
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| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-9b5b75230bf5436783abe64e33b4bf7f2025-08-20T03:26:24ZengMDPI AGAlgorithms1999-48932025-05-0118630310.3390/a18060303Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based ApplicationChisom Onyenagubo0Yasser Ismail1Radian Belu2Fred Lacy3Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USADepartment of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USADepartment of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USADepartment of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USAEspecially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts.https://www.mdpi.com/1999-4893/18/6/303lithium-ion (Li-ion) batteriesremaining useful life (RUL)machine learning (ML)artificial intelligence (AI)linear regressionneural networks |
| spellingShingle | Chisom Onyenagubo Yasser Ismail Radian Belu Fred Lacy Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application Algorithms lithium-ion (Li-ion) batteries remaining useful life (RUL) machine learning (ML) artificial intelligence (AI) linear regression neural networks |
| title | Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application |
| title_full | Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application |
| title_fullStr | Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application |
| title_full_unstemmed | Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application |
| title_short | Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application |
| title_sort | forecasting the remaining useful life of lithium ion batteries using machine learning models a web based application |
| topic | lithium-ion (Li-ion) batteries remaining useful life (RUL) machine learning (ML) artificial intelligence (AI) linear regression neural networks |
| url | https://www.mdpi.com/1999-4893/18/6/303 |
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