BATTERY HEALTH AND LIFESPAN PREDICTION DIAGNOSTIC AND MONITORING SYSTEM

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dc.contributor.authorVESHAL A/L K SANMUGAM
dc.date.accessioned2026-05-04T08:48:13Z
dc.date.issued2025
dc.descriptionBattery health degradation is a significant challenge that impacts the efficiency, safety, and longevity of battery-powered devices. Traditional monitoring methods often rely on reactive maintenance, leading to unexpected failures, reduced performance, and increased operational costs. This Final Year Project introduces an innovative solution for real-time battery health diagnostics and lifespan prediction through a comprehensive monitoring system. Leveraging advanced data analysis and predictive modeling techniques, the system processes key battery metrics such as voltage and temperature to evaluate performance and estimate the remaining lifespan. The proposed solution addresses the limitations of conventional battery monitoring by providing automated, real-time insights into battery status, helping users take proactive maintenance actions before failures occur. Core functionalities include data collection from sensors, processing of battery metrics, and predictive modeling to assess battery health. Notifications are generated based on analysis results to inform users of necessary interventions. By enhancing battery management practices, this system minimizes unexpected downtimes, optimizes performance, and contributes to more sustainable energy solutions. Through its innovative design, this project not only improves existing battery monitoring practices but also lays the groundwork for advancements in predictive maintenance and energy management technologies.
dc.identifier.urihttps://scholarhub.unimas.my/handle/123456789/613
dc.language.isoEnglish
dc.publisherUniversiti Malaysia Sarawak
dc.relation.ispartofseriesFaculty of Computer Science and Information Technology
dc.titleBATTERY HEALTH AND LIFESPAN PREDICTION DIAGNOSTIC AND MONITORING SYSTEM
dc.typeFinal Year Project

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