A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles
Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a n...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10605904/ |
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| author | Praveen Abbaraju Subrata Kumar Kundu |
| author_facet | Praveen Abbaraju Subrata Kumar Kundu |
| author_sort | Praveen Abbaraju |
| collection | DOAJ |
| description | Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs. |
| format | Article |
| id | doaj-art-ebb7a354e81d4457b12058cd2edd4f01 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-ebb7a354e81d4457b12058cd2edd4f012025-01-24T00:02:38ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01544545310.1109/OJITS.2024.343084310605904A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric VehiclesPraveen Abbaraju0https://orcid.org/0000-0002-2530-2027Subrata Kumar Kundu1https://orcid.org/0009-0002-2249-5115Advanced Technology Development Department, Hitachi Astemo Americas, Inc., Farmington Hills, MI, USAAdvanced Technology Development Department, Hitachi Astemo Americas, Inc., Farmington Hills, MI, USAElectric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.https://ieeexplore.ieee.org/document/10605904/Data-centric AIfederated learningstate of health (SoH)connected vehicles |
| spellingShingle | Praveen Abbaraju Subrata Kumar Kundu A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles IEEE Open Journal of Intelligent Transportation Systems Data-centric AI federated learning state of health (SoH) connected vehicles |
| title | A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles |
| title_full | A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles |
| title_fullStr | A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles |
| title_full_unstemmed | A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles |
| title_short | A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles |
| title_sort | novel federated x0026 ensembled learning based battery state of health estimation for connected electric vehicles |
| topic | Data-centric AI federated learning state of health (SoH) connected vehicles |
| url | https://ieeexplore.ieee.org/document/10605904/ |
| work_keys_str_mv | AT praveenabbaraju anovelfederatedx0026ensembledlearningbasedbatterystateofhealthestimationforconnectedelectricvehicles AT subratakumarkundu anovelfederatedx0026ensembledlearningbasedbatterystateofhealthestimationforconnectedelectricvehicles AT praveenabbaraju novelfederatedx0026ensembledlearningbasedbatterystateofhealthestimationforconnectedelectricvehicles AT subratakumarkundu novelfederatedx0026ensembledlearningbasedbatterystateofhealthestimationforconnectedelectricvehicles |