Enhancing Battery Management System Optimization: A Secure AI Modeling Approach
Rupali S. Joshi, Assistant Professor, PVG College of Science and Commerce, Pune-India.
Abstract:
The expansion of electronic devices has resulted in an increase in e-waste as the globe struggles with pollution and global warming. Batteries pose a serious environmental risk because they discharge heavy metals into landfills. The goal of this research is to create safe Machine Learning-based Battery Management Systems (BMS) that maximize battery lifecycles while providing strong defence against potential threats.
The study explores the design of battery materials, emphasizing advances in material science that lengthen battery life and lessen environmental effect. Cutting-edge technologies with promise include solid-state electrolytes and special cathode/anode compositions. Regression models are used to predict battery degradation, while clustering techniques are used to group batteries based on common characteristics. Machine learning approaches support data-driven decision-making in the battery sector. Temperature, charge/discharge rates, and depth of discharge are a few examples of critical performance parameters that have an impact on environmental sustainability and battery health.
However, there may be security hazards associated with the use of AI/ML models in battery optimization. The need of creating strong and secure AI/ML techniques is emphasized by this study in order to guard against vulnerabilities and guarantee dependable and secure deployment in a range of applications. The project intends to build a future where the benefits of electronic devices are maximized while minimizing battery waste and environmental impact through the implementation of strong security mechanisms.
This paper concludes by offering a thorough overview of secure machine learning-based battery optimization techniques and highlighting the critical role that these techniques play in establishing environmental balance and guaranteeing the security of AI/ML models. It will take cooperation between scientists, decision-makers, and the general public to fulfill this sustainable future vision and safeguard the health of the earth and future generations.
Keywords: Lithium-ion batteries, artificial intelligence, vulnerability and attack mitigation, battery optimization, secure AI/ML models, battery management systems (BMS), battery material design, machine learning
