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 researchers at the University of Oxford has been published 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.
Professor David Howey, who leads Oxford’s Battery Intelligence Lab, says: “Our approach is unique in showing how physics-based machine learning can work in real-world battery applications at scale. We use advanced probabilistic machine learning techniques to infer battery internal resistance as a function of current, temperature, state of charge and time, enabling calibration to standard conditions.”
He adds, “The success of the approach is due to the combination of a population-wide health model and a battery-specific health indicator that becomes increasingly informative towards end of life.”
The techniques provide insight into the factors that drive battery aging, such as extremes of voltage and temperature, and the method is applicable to any battery that can be represented with a simple electrical circuit model.
Professor Howey explains, “These results are of interest to a wide audience of battery operators and customers and can be used to accelerate innovation in understanding battery performance, especially if organisations make operational data more widely available in the way Bboxx have pioneered here. We are delighted that this research paper is a first of its kind demonstration of a scalable approach for getting insights from field data”.
Bboxx has agreed to make the data – more than 600 million rows of operational measurements from real battery systems – openly available. Professor Howey says: “We hope this will prove to be a key resource for the community and kickstart efforts to analyse field data for new insights into battery performance.”
Read the article in Joule: ‘Predicting battery end of life from solar off-grid system field data using machine learning’.
Text taken from the University of Oxford Department of Engineering Science press release.
Check out the Faraday 2021 conference video from Antti Aitio for more about this machine-learning approach:
Read Professor David Howey’s blog to find out the story behind the paper.
Bboxx is a next generation utility, transforming lives and unlocking potential through access to energy. Bboxx manufactures, distributes and finances decentralised solar powered systems in developing countries. It is scaling through forging strategic partnerships and its innovative technology Bboxx Pulse®, a comprehensive management platform using IoT technology. Through affordable, reliable, and clean utility provision, Bboxx is bringing people into the digital economy, creating new markets, and enabling economic development in off-grid communities and those living without a reliable grid connection. The company is positively impacting the lives of more than 2 million people with its products and services in over 27 markets, directly contributing to 11 of the 17 United Nations Sustainable Development Goals. So far, Bboxx has deployed more than 500,000 solar home systems. Bboxx has over 1,000 staff across nine offices including in the Democratic Republic of Congo, Kenya, Rwanda, and Togo, with its head office in the UK and its manufacturing operations in China. In 2019, Bboxx was the winner of the Zayed Sustainability Prize in the Energy category – testament to the way the company is making a meaningful difference to people’s lives around the world.