CPS: Synergy: Adaptive Management of Large Energy Storage Systems for Vehicle Electrification

具有100 s/1000 s电池的大型电池系统正被用于为各种物理平台供电。 例如，汽车正在从传统的动力系统过渡到（插电式）混合动力和电动车辆（EV）。 为了实现电动汽车的预期效率，需要在电池管理的架构和算法方面进行重大改进。 该项目将开发一种新的综合电池管理架构，称为智能电池管理系统（SBMS）。该研究有望弥合网络物理系统（CPS）研究和电气化行业社区之间存在的巨大差距，提供环境友好型解决方案，提高CPS的认识，并开发熟练的人力资源。该项目将整合并增强电池管理系统（BMS），包括电池充电状态（SoC）和健康状态（SoH）算法以及电池组和电池级别的电源管理策略。 具体来说，它包括五个主要的研究任务：（i）设计一个动态可重构的能量存储系统，以承受恶劣的内部和外部应力;（ii）开发电池级热管理算法;（iii）开发高效，可靠的充电和放电调度算法在混合能量存储系统;（iv）开发一个可重构的能量存储系统，以适应恶劣的内部和外部应力。（iv）发展一套全面的诊断/预测（P/D）算法，并调整系统参数以作出最佳决定;以及（v）构建测试床并在该测试床上评估所提出的体系结构和算法。这项研究将推进大规模储能系统管理的最新技术，显著延长其使用寿命和运行时间，这是广泛的电池供电物理平台的关键。也就是说，SBMS将使电池能够承受过大的压力，并以低成本为物理平台提供更长时间的动力。SBMS还将作为CPS研究各个方面的基本框架，集成（网络）动态控制和P/D机制以及（物理）储能系统动态。

Large battery systems with 100s/1000s cells are being used to power various physical platforms. For example, automobiles are transitioning from conventional powertrains to (plug-in) hybrid and electric vehicles (EVs). To achieve the desired efficiency of EVs, significant improvements are needed in the architecture and algorithms of battery management. This project will develop a new comprehensive battery management architecture, called Smart Battery Management System (SBMS). The research is expected to bridge the wide gap existing between cyber-physical system (CPS) research and electrification industry communities, provide environment-friendly solutions, increase the awareness of CPS, and develop skilled human resources. This project will incorporate and enhance a battery management system (BMS) by including battery state-of-charge (SoC) and state-of-health (SoH) algorithms as well as power management strategies on both pack and cell levels. Specifically, it consists of five main research tasks: (i) design a dynamically reconfigurable energy storage system to tolerate harsh internal and external stresses; (ii) develop cell-level thermal management algorithms; (iii) develop efficient, dependable charge and discharge scheduling algorithms in hybrid energy storage systems; (iv) develop a comprehensive, diagnostic/prognostic (P/D) algorithm with system parameters adjusted for making optimal decisions; and (v) build a testbed and evaluate the proposed architecture and algorithms on the testbed. This research will advance the state-of-the-art in the management of large-scale energy storage systems, extending their life and operation-time significantly, which is key to a wide range of battery-powered physical platforms. That is, SBMS will enable batteries to withstand excessive stresses and power physical platforms for a much longer time, all at low costs. SBMS will also serve as a basic framework for various aspects of CPS research, integrating (cyber) dynamic control and P/D mechanisms, and (physical) energy storage system dynamics.