CAREER: Enabling High Performance Battery Charging Systems: Adaptive and Optimal Charging Algorithms Based on Dynamic Battery Characteristics

职业：实现高性能电池充电系统：基于动态电池特性的自适应最优充电算法

• 批准号: 1454578
• 负责人: Sung Yeul Park
• 金额: $ 50万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454578&HistoricalAwards=false
• 关键词: CAREER; Enabling; Performance; Battery; Charging

Lithium-ion batteries are excellent energy storage devices due to their high energy density. Lithium-ion batteries are rather costly, and it is desirable to extend their cycle life as much as possible. However, conventional battery charging algorithms do not consider the time-varying electrochemical properties of batteries because the theory behind electrochemical behavior is complex and computationally challenging to incorporate within charging algorithms. The primary target of this project is to provide a new, innovative approach enabling high charging efficiency and long cycle life in electric vehicles, renewable energy storage, and building back-up power applications. Adaptive charging algorithms based on electrochemical parameters will enhance charging efficiency and reduce thermal stress by adjusting the charging current with respect to the internal state of the batteries. As a result, this project will not only provide a method for improved charging efficiency, which will enable increased cycle life, energy savings, and reduced maintenance costs; but also power electronics technologies will now be able to embed control capability with respect to time-varying electrochemical behavior in their charging algorithms without extra cost. It is our expectation that we will gain a profound understanding of the complex behavior occurring within high power battery stacks as a result of this research, and from this we may significantly improve the performance and affordability of such systems. Thus, the outcomes of this project are not only expected to fundamentally advance the field of battery charging algorithms, but also to have broad and highly positive societal impact. More specifically, the objective of this project is to investigate a novel framework that integrates dynamic battery impedance measurements into battery charging algorithms in order to enhance the charging efficiency, reliability, and cycle life of batteries. The variations in impedance are correlated with changes in the electrochemical state, and, particularly, the degree of conductivity within the battery. The research thrusts will be: 1) effective and efficient on-line measurement of the time-varying electrochemical impedance, 2) development of an adaptive sinusoidal charging algorithm, and 3) construction of a real-time hardware-in-the-loop test-bed for battery life cycle testing and validation of the concomitant adaptive charging algorithms.A fundamental understanding of battery charging and advanced energy interfaces will provide a significant leap in the development of charging algorithms supporting energy efficient, reliable, and long cycle life batteries. Moreover, results of this research will not only improve the capabilities of today's state-of-the-art energy storage technologies, but will be applicable to all battery chemistries because the internal phenomena of other electrochemical devices exhibit similarities. This ensures that this research will benefit energy storage system designers, regardless of their choice of materials and design. This also has the secondary benefit of improving the efficiency and lifespan of integrated renewable energy sources featuring electrochemical storage as a constituent component. The results of this research will be integrated into existing graduate and undergraduate courses related to power electronics and power systems. Beyond graduate/undergraduate student education, project based learning programs will be developed and disseminated via technical school presentations, K-12 student demonstrations, and summer school teacher workshops.

锂离子电池由于其高能量密度而成为优良的能量存储装置。锂离子电池相当昂贵，并且期望尽可能地延长它们的循环寿命。然而，传统的电池充电算法不考虑电池的时变电化学性质，因为电化学行为背后的理论是复杂的，并且在计算上具有挑战性，难以并入充电算法。该项目的主要目标是提供一种新的创新方法，实现电动汽车、可再生能源存储和建筑备用电源应用的高充电效率和长循环寿命。基于电化学参数的自适应充电算法将通过相对于电池的内部状态调节充电电流来提高充电效率并减小热应力。因此，该项目不仅将提供一种提高充电效率的方法，从而提高循环寿命，节省能源并降低维护成本;而且电力电子技术现在将能够在其充电算法中嵌入时变电化学行为的控制能力，而无需额外成本。我们期望通过这项研究，我们将深刻了解高功率电池组中发生的复杂行为，并由此显着提高此类系统的性能和可负担性。因此，该项目的成果不仅有望从根本上推动电池充电算法领域的发展，而且还将产生广泛和高度积极的社会影响。更具体地说，该项目的目标是研究一种新的框架，将动态电池阻抗测量集成到电池充电算法中，以提高电池的充电效率，可靠性和循环寿命。阻抗的变化与电化学状态的变化相关，特别是与电池内的导电性程度相关。研究重点将是：1）时变电化学阻抗的有效且高效的在线测量，2）自适应正弦充电算法的开发，以及3）实时硬件在环测试的构建-用于电池寿命周期测试和验证伴随的自适应充电算法的试验台。对电池充电和先进能量接口的基本理解将为充电技术的发展提供重大飞跃支持节能、可靠和长循环寿命电池的算法。此外，这项研究的结果不仅将提高当今最先进的储能技术的能力，而且将适用于所有电池化学，因为其他电化学设备的内部现象具有相似性。这确保了这项研究将有利于储能系统设计人员，无论他们选择的材料和设计。这还具有提高集成可再生能源的效率和寿命的第二个好处，其特征在于电化学存储作为组成部件。这项研究的结果将被整合到现有的研究生和本科生课程相关的电力电子和电力系统。除了研究生/本科生教育，基于项目的学习计划将通过技术学校演示，K-12学生演示和暑期学校教师研讨会开发和传播。