Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data
In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional f...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/14/2247 |
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| author | Anas Al-Rahamneh Irene Izco Adrian Serrano-Hernandez Javier Faulin |
| author_facet | Anas Al-Rahamneh Irene Izco Adrian Serrano-Hernandez Javier Faulin |
| author_sort | Anas Al-Rahamneh |
| collection | DOAJ |
| description | In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems. |
| format | Article |
| id | doaj-art-8e79d2b0a71e49e5924741b4f191b3f8 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8e79d2b0a71e49e5924741b4f191b3f82025-08-20T03:58:26ZengMDPI AGMathematics2227-73902025-07-011314224710.3390/math13142247Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation DataAnas Al-Rahamneh0Irene Izco1Adrian Serrano-Hernandez2Javier Faulin3Integrated Group of Logistics and Transportation-Operations Research (GILT-OR), Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, SpainIntegrated Group of Logistics and Transportation-Operations Research (GILT-OR), Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, SpainIntegrated Group of Logistics and Transportation-Operations Research (GILT-OR), Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, SpainIntegrated Group of Logistics and Transportation-Operations Research (GILT-OR), Institute of Smart Cities, Public University of Navarre, 31006 Pamplona, SpainIn pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems.https://www.mdpi.com/2227-7390/13/14/2247machine learningdeep learningsimulationagent-based modelingbattery management systemstate of health estimation |
| spellingShingle | Anas Al-Rahamneh Irene Izco Adrian Serrano-Hernandez Javier Faulin Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data Mathematics machine learning deep learning simulation agent-based modeling battery management system state of health estimation |
| title | Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data |
| title_full | Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data |
| title_fullStr | Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data |
| title_full_unstemmed | Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data |
| title_short | Machine Learning-Based State-of-Health Estimation of Battery Management Systems Using Experimental and Simulation Data |
| title_sort | machine learning based state of health estimation of battery management systems using experimental and simulation data |
| topic | machine learning deep learning simulation agent-based modeling battery management system state of health estimation |
| url | https://www.mdpi.com/2227-7390/13/14/2247 |
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