Securing energy horizons: Cloud-driven based machine learning methods for battery management systems

Article type: Research Article

Authors: Zekrifa, Djabeur Mohamed Seifeddine^a | Saravanakumar, R.^{b; *} | Nair, Sruthi^c | Pachiappan, Krishnagandhi^d | Vetrithangam, D.^e | Kalavathi Devi, T.^f | Ganesan, T.^g | Rajendiran, M. ^h | Rukmani Devi, S.ⁱ

Affiliations: [a] Higher School of Food Science and Agri-Food Industry, Ahmed Hamidouche Av, Oued Smar, Algiers, Algeria | [b] Department of Wireless Communication, Institute of ECE, Saveetha School of Engineering, Savetha Institute of Medical and Technical Science, Chennai, Tamil Nadu, India | [c] Department of Computer Science and Engineering (Data Science), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India | [d] Department of Electrical and Electronics Engineering, Nandha Engineering College, Erode, Tamil Nadu, India | [e] Department of Computer Science and Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India | [f] Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Erode, Tamil Nadu, India | [g] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [h] Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India | [i] Department of Computer Science, Saveetha College of Liberal Arts and Sciences, SIMATS Deemed to be University, Chennai, Tamil Nadu, India

Correspondence: [*] Corresponding author. R. Saravanakumar, Department of Wireless Communication, Institute of ECE, Saveetha School of Engineering, Savetha Institute of Medical and Technical Science, Chennai, Tamil Nadu 602105, India. E-mail: saravanakumarr.sse@gmail.com.

Abstract: The increasing need for effective energy storage solutions has led to the prominence of lithium-ion batteries as a crucial technology across multiple industries. The proficient administration of these batteries is imperative in order to guarantee maximum efficiency, prolong their longevity, and uphold safety measures. This study presents a novel methodology for enhancing battery management systems (BMS) through the integration of cloud-based solutions, artificial intelligence (AI), and machine learning approaches. In this study, we present a conceptual framework that utilises cloud computing to augment the practical functionalities of battery management systems (BMS) specifically in the context of lithium-ion batteries. The incorporation of cloud computing facilitates the implementation of scalable data storage, remote monitoring, and processing resources, hence enabling the execution of real-time analysis and decision-making processes. By leveraging the capabilities of machine learning and artificial intelligence, our methodology focuses on addressing crucial battery metrics, including the state of charge (SoC) and state of health (SoH). Through the ongoing collection and analysis of data obtained from battery systems that are deployed in real-world settings, the framework iteratively improves its predictive models, hence facilitating precise assessment of battery states. Ensuring safety is a crucial element in the management of batteries. The solution we propose utilises anomaly detection algorithms driven by artificial intelligence to detect potential safety issues, facilitating prompt responses and mitigating dangerous circumstances. In order to showcase the efficacy of our methodology, we offer practical implementations in several industries, encompassing the integration of renewable energy, use of electric vehicles, and optimisation of industrial processes. Through the utilisation of cloud-based machine learning techniques, we are able to enhance the efficiency of energy storage and consumption, while simultaneously enhancing the dependability and security of battery systems. This study highlights the potential of the proposed framework to revolutionise battery management paradigms, thereby guaranteeing secure and efficient energy prospects for a sustainable future.

Keywords: Battery management system, state of health, state of charge, artificial intelligence, machine learning, cloud-based solutions

DOI: 10.3233/JIFS-236391

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 3029-3043, 2024