Millions of people around the world lack access to electricity.
Millions of people around the world lack access to electricity. Decentralised solar-battery systems are key for addressing this whilst avoiding carbon emissions and air pollution, but are hindered by relatively high costs and rural locations that inhibit timely preventative maintenance. When batteries in such systems fail, it can be difficult to replace them and can leave people stuck without access to power.
Knowing when the batteries are likely to fail is therefore crucial in planning repair logistics and minimising power supply downtime. Now a unique approach to calculating battery failure, affiliated to the Faraday Institution’s Multiscale Modelling project, has been shown to make predictions that are 15-20% more accurate than current approaches used on the same dataset. The paper, from the University of Oxford and the Faraday Institution, has been published today in Joule.
In order to test their approach, the authors partnered Bboxx, a next generation utility providing clean energy in developing countries, which provided real-world operating data. This avoided the limitation of past studies on battery health modelling, which have mainly used small datasets collected under laboratory conditions.
Over a period of up to 2 years, raw measured voltage, current and temperature data from more than 1000 operational batteries in Africa were collected via Bboxx. No additional sensors or requirements are required for this method, enabling the energy systems to stay continuously online.
Read more at University of Oxford
Photo Credit: cotrim via Pixabay
