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

COBRA – 云端/车载电池剩余使用寿命算法

• 批准号: 10007474
• 金额: $ 16.94万
• 依托单位: EATRON TECHNOLOGIES LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10007474
• 关键词: COBRA; Cloud; board; Battery; Remaining

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.

随着全球电动汽车（EV）的迅速采用，对电池管理系统（BMS）的需求将在未来十年内的指数增长，因为全球市场规模从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且将继续在这些新电池组中广泛使用。眼镜蛇（“云/机上电池剩余的使用寿命算法”）项目将提供全新的，实用的，实用的算法，以预测电池的剩余使用寿命（RUL）；能够直接在BMS和云中运行。此类算法在任何现有的BMS中都不可用，但将是Eatron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，并且对行业中的这一功能引起了重大兴趣。确定和了解锂离子电池的内部状态是一项艰巨的任务，但对于对电池老化建模以及电池RUL至关重要。为了可靠地预测剩余的电池寿命，该项目将采用新颖的简化电池老化模型，利用物理和人工智能（AI）方法。这使我们的RUL算法能够说明可预测的关键老化机制和电池长期运营使用中发生的任何不确定性。准确的建模电池的性能和衰老是电池行业的关键技术目标之一（在Faraday电池挑战中概述了）。为该项目开发的算法将带来经济和环境的许多好处：1）通过准确表明其余寿命来延长电池的第一寿命； 2）通过减少对昂贵测试的需求来改善第二人寿应用； 3）通过允许安全利用更广泛的操作窗口来增加有效的电池电量/能量密度。COBRA项目（Warwick和Eatron Technologies）的财团成员具有成功交付该项目的必要知识和资源。其中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的知识，预先存在的电池老化数据集，嵌入式软件开发，系统集成以及复杂系统的测试。