Machine Learning for Sustainable Battery Optimization: A Data Driven Approach
Open accessAbstract
The escalating demand for batteries in portable devices and electric vehicles has underscored the necessity for effective and sustainable battery management.This research delves into applying Machine Learning (ML) techniques across various facets of battery research, encompassing performance prediction, materials discovery, and remaining useful life (RUL) estimation.Specifically, regression analysis, clustering algorithms, and materials informatics are employed to address these pivotal areas.The findings substantiate the efficacy of ML in accurately forecasting battery metrics such as state of charge (SOC) and state of health (SOH).The research introduces a novel clustering-based approach for RUL prediction, enhancing the precision of lifespan estimates for batteries.Furthermore, ML-driven materials discovery expedites identifying promising materials with superior properties, thereby contributing to the advancement of next-generation batteries.Integrating ML into battery research can substantially improve battery performance, reliability, and sustainability.The results underscore the potential of ML as a transformative tool for addressing the challenges associated with battery technology and paving the way for a more sustainable energy future.
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Compared to traditional physics-based models, data-driven methods capture the complex nonlinear degradation patterns of batteries and achieve high-precision SOH estimation without intricate electrochemical modeling.
“Compared to traditional physics-based models, data-driven methods leverage deep analysis of historical operational data to effectively capture the complex nonlinear degradation patterns of batteries, thereby achieving highprecision SOH estimation without relying on intricate electrochemical modeling [113-116].”
From The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges (2025) · cites this paper as [116]
The discussion on p. 3 says neural-network models capture nonlinear battery behavior and predict SOH more precisely; the Proposed methodology section says models analyze real-world battery data to predict degradation. The copy does not compare these methods with traditional physics-based models or establish that they achieve SOH estimates without intricate electrochemical modeling. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCompared to traditional machine learning methods, neural networks more effectively capture the complex nonlinear characteristics of batteries, thereby enhancing estimation accuracy.
“Compared to traditional machine learning methods, neural networks more effectively capture the complex nonlinear characteristics of batteries, thereby enhancing estimation accuracy [114-116].”
From The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges (2025) · cites this paper as [116]
The paper states that artificial neural networks can capture nonlinear battery behavior and enable more precise monitoring and control (discussion of ML applications; page not specified). It does not establish that neural networks outperform traditional machine-learning methods in estimation accuracy. Its Results and discussion section instead compares MR, KNN, DT, and RF, finding RF most effective among those models. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cited by
Sorted by citations
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A Review of Battery Energy Storage Optimization in the Built Environment
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Capacity Degradation Estimation of Lithium-Ion Batteries Using Explainable Machine Learning
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“Moreover, KNN, Decision Trees and Random Forest were applied to predict SoH, SoC, and RUL simultaneously, with Random Forest model achieved strong predictive accuracy [13].”
Influential
- Authors
- Department of Computer Engineering, V. I. O. I. T., Patil, A., Mahalle, P., & Research & Development, V. I. O. T.
- Affiliations
- Vishwakarma Institute of Technology (IN)
- ORCID
- Mahalle, Parikshit
- Venue
- Engineered Science
- Publisher
- Engineered Science Publisher
- Published
- 2024
- ISSN
- 2576-988X (ISSN-L)
- 2576-9898
- OpenAlex
- W4404482707
- Cited by
- 4
- References
- 0
- Citations by year
- 2025: 4
- FWCI
- 0.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Engineered Science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- battery management, battery performance prediction, materials discovery, remaining useful life prediction, state of charge estimation, state of health estimation, materials informatics, sustainable batteries, electric vehicles
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