Research Problem
Lack of specialized equipment, knowledge, and infrastructure for repurposing and maintaining used electric vehicle batteries in the East African E-mobility market, leading to challenges for companies adopting second hand EVs in terms of ensuring sustainability.
The absence of robust battery repurposing systems results in suboptimal battery lifecycle management, leading to incomplete utilization cycles. This deficiency hinders the efficient transition of batteries through their intended usage stages, from initial deployment to eventual recycling, thereby undermining overall resource sustainability and circular economy objectives
Existing research has not adequately addressed the viability of battery components for second-life applications in the East African Region and in addition there is scarcity of data and studies on battery performance in second life applications
Findings
- Most Vehicles imported have a battery SOH of 70%
- At 50% SOH Batteries are no longer viable for traction.
- Key features that affect battery SOH are; voltage, temperature and
- From a selection of 5 algorithms Random Forest performs best for SOH prediction with an RMSE of 0131087
- System reliability at 80% from user validation survey
- Response time of 30.86 seconds for 1000 users (potential scalability issue)
Impact
- An innovative predictive analytics-driven battery management system customized for the EV industry, enhancing efficiency and sustainable resource
- Cost reductions in energy storage, facilitated by the optimized repurposing of used EV batteries not only promoting economic sustainability but also enhancing competitiveness in the energy sector.
- Contributing to positive environmental and socioeconomic impacts, aligning with global sustainability goals and driving progress towards a cleaner, greener
- Platform to continuously obtain data on second life batteries and how they perform in the region
Research Credits
Funders:
- Cenit@EA financed my MSc studies and the project
Research Team:
- Michael Ochiel – Student
- Dr. Ramadhani Sinde – Academic Supervisor
- Prof. Kisangiri Michael – Academic supervisor
- Mr. Godfrey Naman – Academic supervisor
- Mr. Joshua Oduor – Industrial Supervisor
Industrial collaborators
- Drivelectric RnD team helped with system requirements gathering and system validation.
- Advance Mobility Center Training on electric vehicle teardown
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