COBRA – Cloud/On-board Battery Remaining useful life Algorithm

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With the rapid adoption of Electric Vehicles (EVs) across the globe, the demand for Battery Management Systems (BMS) will see exponential growth over the next decade as the global market size increases from 1 Billion USD in 2020 to 6 Billion USD in 2030\. Lithium-ion battery technology has been and will continue to be, widely used in these new battery packs.The COBRA ("Cloud/On-board Battery Remaining useful life Algorithm") project will deliver brand new, practical, algorithms for predicting the Remaining Useful Life (RUL) of batteries; capable of running directly on the BMS and in the cloud. Such algorithms are not available in any existing BMS but will be a core feature of Eatron's new product line, BMSTAR(r). Accurate RUL prediction will increase the value and sustainability of battery packs and there is significant interest in this feature in the industry.Determining and understanding the internal state of a Lithium Ion battery is a difficult task but crucial for modelling battery ageing and hence the battery RUL. To reliably forecast remaining battery-life, this project will apply novel simplified battery ageing models, leveraging physics and Artificial Intelligence (AI) based approaches. This enables our RUL algorithms to account for both predictable key ageing mechanisms and any uncertainties that occur within the long term operational use of the battery.Accurate modelling of the performance and ageing of batteries is one of the key technical targets for the battery industry (outlined in the Faraday Battery Challenge). The algorithms developed for this project will lead to numerous benefits both economic and environmental: 1) extending first life of batteries by giving an accurate indication of the remaining life; 2) improving second-life applications by reducing the need for expensive testing; 3) increasing effective battery power/energy density by allowing safe utilisation of a wider operating window.The consortium members for the COBRA project (University of Warwick and Eatron Technologies) have the necessary knowledge and resources to deliver this project successfully. These include production grade automotive battery management technology, development labs, knowledge of complex physics and data-driven modelling, a pre-existing battery ageing dataset, embedded software development, system integration, and testing of complex systems.