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

带有100s/1000s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总成向(插电式)混合动力和电动汽车(EVS)过渡。为了实现电动汽车所期望的效率，需要在电池管理的体系结构和算法方面进行重大改进。该项目将开发一种新的全面的电池管理架构，称为智能电池管理系统(SBMS)。这项研究有望弥合网络物理系统(CPS)研究与电气化行业之间存在的巨大差距，提供环境友好型解决方案，提高CPS的意识，并开发熟练的人力资源。该项目将整合和增强电池管理系统(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储能系统，以承受严酷的内外压力；(Ii)开发单元级的热管理算法；(Iii)开发高效、可靠的混合储能系统的充放电调度算法；(Iv)开发一个综合的、诊断/预测(P/D)算法，通过调整系统参数来做出最优决策；以及(V)建立一个试验台，并在试验台上对所提出的体系结构和算法进行评估。这项研究将推进大规模储能系统管理的最先进水平，显著延长其寿命和运行时间，这是广泛的电池供电物理平台的关键。也就是说，SBM将使电池能够承受过大的压力，并以更低的成本为物理平台提供更长时间的电力。SBMS还将作为CPS研究各个方面的基本框架，整合(网络)动态控制和P/D机制，以及(物理)能量储存系统动力学。

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.