{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:22:46Z","timestamp":1778167366467,"version":"3.51.4"},"reference-count":0,"publisher":"Soft Computing Research Society","isbn-type":[{"value":"9788197567056","type":"print"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>Accurately predicting the State of Charge (SOC) in lithium-ion batteries is essential for efficient energy management in electric vehicles. Due to the nonlinear characteristics of battery behavior, which depend heavily on temperature and operating conditions, SOC estimation presents a significant engineering challenge. This study proposes a data-driven approach using Gaussian Process Regression (GPR) to predict SOC, leveraging a comprehensive dataset with voltage, current, temperature, and average historical measurements. The model was trained and optimized through Bayesian hyperparameter tuning, and itsperformance was evaluated across four temperature conditions (-10\u00b0C, 0\u00b0C, 10\u00b0C, and 25\u00b0C) to assess robustness. The results demonstrate high prediction accuracy, with an overall Root Mean Square Error (RMSE) below 0.02 and Maximum Absolute Error (MAE) below 0.1 across all temperature settings. These metrics confirm the model\u2019s reliability and adaptability to varied thermal environments, highlighting the potential of GPR for practical SOC estimation in automotive applications. This approach provides a practical alternative to traditional electrochemical models, supporting advancements in battery health monitoring and energy management.<\/jats:p>","DOI":"10.56155\/978-81-975670-5-6-10","type":"book-chapter","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T10:47:21Z","timestamp":1746010041000},"page":"121-130","source":"Crossref","is-referenced-by-count":3,"title":["Gaussian Process Regression for State of Charge Prediction: Overcoming Temperature Variability in Li-ion Battery Applications"],"prefix":"10.56155","author":[{"given":"Md Ismail","family":"Hossain","sequence":"first","affiliation":[]},{"given":"Khandoker Mainul","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Tarifuzzaman","family":"Riyad","sequence":"additional","affiliation":[]},{"given":"Md Rafsan","family":"Jany","sequence":"additional","affiliation":[]},{"given":"Al Shahriar","family":"Zishan","sequence":"additional","affiliation":[]},{"given":"Mohammad Nasir","family":"Uddin","sequence":"additional","affiliation":[]}],"member":"32276","published-online":{"date-parts":[[2024]]},"container-title":["Computational Intelligence and Machine Learning"],"original-title":[],"deposited":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T10:47:22Z","timestamp":1746010042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.publications.scrs.in\/chapter\/978-81-975670-5-6\/10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9788197567056"],"references-count":0,"URL":"https:\/\/doi.org\/10.56155\/978-81-975670-5-6-10","relation":{},"subject":[],"published":{"date-parts":[[2024]]}}}