Projects
Projects: Projects for Investigator |
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| Reference Number | EP/R035822/1 | |
| Title | Catalysing energy access in Africa through smarter energy storage management | |
| Status | Completed | |
| Energy Categories | Other Power and Storage Technologies(Energy storage) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Chemistry) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 50%; Sociological economical and environmental impact of energy (Other sociological economical and environmental impact of energy) 50%; |
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| Principal Investigator |
Dr D Howey Engineering Science University of Oxford |
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| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 April 2018 | |
| End Date | 31 March 2019 | |
| Duration | 12 months | |
| Total Grant Value | £98,450 | |
| Industrial Sectors | Energy | |
| Region | South East | |
| Programme | Energy : Energy | |
| Investigators | Principal Investigator | Dr D Howey , Engineering Science, University of Oxford |
| Web Site | ||
| Objectives | ||
| Abstract | This project enables the remote, automated management of distributed off-grid batteries powering solar home systems throughout sub-Saharan Africa. The research has two objectives: to examine solar home system (SHS) usage data to design smarter appliances for off-grid customers; and to optimise lithium ion battery (LIB) lifetime. M-KOPA collects daily usage data from >500,000 households in sub-Saharan Africa (SSA), however, there is a gap in effective use of the data for product management and design excellence. Using data science tools and machine learning algorithms, M-KOPA will develop approaches to maximise product battery life, and design smarter appliances. These tools will decrease the premature LIB failure rate (one of M-KOPA's greatest challenges). Oxford University will design battery failure prediction algorithms to increase the longevity, effectiveness, and reliability of future LIBs for off-grid customers. Thus, this project targets all three aspects of the energy trilemma: reducing emissions through increased life of LIBs in SSA, cost savings through optimal product design, and security of energy supply from more reliable SHS. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 14/09/18 | |
