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

随着电动汽车(EVS)在全球的迅速普及，电池管理系统(BMS)的需求将在未来十年呈指数级增长，全球市场规模将从2020年的10亿美元增加到2030年的60亿美元。锂离子电池技术已经并将继续在这些新的电池组中广泛使用。COBRA(云/车载电池剩余使用寿命算法)项目将提供全新的、实用的电池剩余使用寿命(RUL)预测算法；能够直接在BMS上和云中运行。这种算法在任何现有的BMS中都不可用，但将成为Eatron的新产品线BMSTAR(R)的核心功能。准确的RUL预测将增加电池组的价值和可持续性，这一特性在业界引起了极大的兴趣。确定和了解锂离子电池的内部状态是一项困难的任务，但对于模拟电池老化和电池RUL至关重要。为了可靠地预测剩余电池寿命，该项目将应用新颖的简化电池老化模型，利用基于物理和人工智能(AI)的方法。这使得我们的RUL算法能够考虑到可预测的关键老化机制和电池长期运行使用中出现的任何不确定性。对电池的性能和老化进行准确的建模是电池行业的关键技术目标之一(在Faraday电池挑战赛中概述)。为该项目开发的算法将带来许多经济和环境效益：1)通过准确指示剩余寿命来延长电池的第一寿命；2)通过减少昂贵测试的需要来改进第二生命应用；3)通过允许安全地利用更广泛的运行窗口来提高电池的有效功率/能量密度。眼镜蛇项目(华威大学和伊特隆技术公司)的联盟成员拥有成功交付该项目所需的知识和资源。这些包括生产级汽车电池管理技术、开发实验室、复杂物理和数据驱动建模知识、现有电池老化数据集、嵌入式软件开发、系统集成和复杂系统测试。

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.