A predictive analytics-driven battery management system for sustainable e-mobility in East Africa

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dc.contributor.authorOchiel, Michael
dc.date.accessioned2025-03-03T06:29:58Z
dc.date.available2025-03-03T06:29:58Z
dc.date.issued2024-08
dc.description.abstractIn pursuit of the United Nations Sustainable Development Goals (UN SDGs) seven and thirteen, East African countries are swiftly transitioning to electric mobility solutions for clean transportation and climate action. However, this transition presents a challenge in repurposing and maintenance of used electric vehicle (EV) batteries due to limited specialized knowledge and equipment in the region. Despite the growing popularity of electric vehicles, a significant gap exists in understanding viable battery components for second life applications in East Africa. This study addresses this gap by designing a predictive analytics-driven battery management system tailored to the region's needs. The developed system integrates hardware and software, employing a data-driven approach to analyze sensor data for decision support and enable remote monitoring of repurposed batteries. Compared to existing works, this research emphasizes the use of predictive algorithms to monitor battery health in second life applications and provision for remote monitoring. This innovative approach significantly advances the understanding and implementation of battery repurposing in East Africa. By offering a sustainable solution for e-mobility, this study promotes a cleaner and greener future while reducing energy costs for organizations and domestic users.en_US
dc.identifier.urihttp://doi.org/10.58694/20.500.12479/2928
dc.language.isoenen_US
dc.publisherNM-AISTen_US
dc.titleA predictive analytics-driven battery management system for sustainable e-mobility in East Africaen_US
dc.typeThesisen_US

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