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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Machine Learning-Based Estimation of Battery State of Charge and State of Health |
| Authors Name: | Skand Jeet , sonam khampa , rupali mandawat |
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IJRTI_212749
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| Published Paper Id: | IJRTI2605125 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | Precise battery State of Charge(SOC) and State of Health(SOH) estimation are critical to ensure aircraft safe, economic and dependable operation when embedded electronic devices, energy storage systems and electric vehicles. [1] Tradi-tional voltage- and current-based SOC/ SOH monitoring methods are often plagued by error build-up, model mismatch, and higher sensitivity to different operating environments. [2] In this paper, an integrated machine learning pipeline for joint SOC and SOH estimation using two separate numerical data set and multiple regressors algorithms is proposed. It covers EDA, normalization(StandardScaler), DataTrain/DataTest split(80/20), and four regression metrics; coefficient of determination(R2), root mean square error, and mean absolute error and mean absolute percentage error. The best stand-alone estimator for SOC prediction is Random Forest Regression with an average test R2 of 0.9137, RMSE of 6.16, MAE of 2.55, and MAPE of 6.67%.For or SOH prediction the Extra Trees Regression reaches near-perfect (R2) of 0.9955, RMSE 1.24, MAE 0.96, and MAPE of 1.75%. Its feature contribution analysis shows that route distance, longitude and altimetry features dominate SOC prediction, whereas internal resistance and capacity dominate SOH prediction, both in line with physical knowledge of battery performance. [6] The testing models are used in a web app based on Streamlit to provide real-time, user-friendly battery management without domain expertise. |
| Keywords: | Battery management systems, State of Charge, State of Health, Machine learning, Random Forest, Extra Trees. |
| Cite Article: | "Machine Learning-Based Estimation of Battery State of Charge and State of Health", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b173-b178, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605125.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605125
Registration ID:212749
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: b173-b178 Country: rohini, delhi, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605125 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605125 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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