SBIR Phase II: Plug-and-play intelligent charging hardware and software that increases safety, performance and life of lithium ion and lithium metal batteries

该小型企业创新研究（SBIR）项目的更广泛影响/商业潜力是使更多的电动汽车使用;此外，该技术使电池寿命增加一倍的潜力将减少废弃电池对环境的影响。该项目通过实现100%的电池工作范围和最大化可用能量容量来加速电动汽车的采用，将持续行驶里程增加50- 100倍。该项目是全球锂离子电池市场（预计到2022年将增长至68 B美元）和年度混合动力和电动汽车市场（预计到2025年每年将超过1000万辆）预期增长的关键推动因素。SBIR第二阶段项目提议优化电池快充技术和容量保持目标。电池性能的进步通常受到电极、电解质或电池结构的化学和材料改进的限制，从而限制了交易空间（即，需要功率与能量的折衷）。 所提出的充电技术和相关软件将通过控制电极表面现象（例如锂电镀和枝晶形成）来选择性地优化电池设计以用于各种性能指标，否则电极表面现象在正常使用期间会导致永久性容量损失并加速限制充电速率的内部物理过程。技术任务包括：1）证明商用锂离子电池和制造的锂金属电池的性能改进; 2）从小型电池和模块到电动汽车电池组的过程的适应; 3）使用预测学习（PL）和机器学习（ML）系统开发精细的传感和基于反馈的控制算法; 4）现场可编程门阵列（FPGA）和片上系统（SoC）的验证和确认该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的评估支持影响审查标准。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable greater electric vehicle use; furthermore, the technology’s potential to double battery life will reduce the environmental impact of disposed batteries. This project accelerates electric car adoption by enabling use of 100% of battery operating ranges and maximize usable energy capacity, increasing ongoing driving ranges by 50-100x. This project is a key enabler for expected growth in the global lithium-ion battery market (expected to grow to $68 B by 2022) and the annual hybrid and electric car market (forecast to exceed 10 million vehicles annually by 2025).This SBIR Phase II project proposes to optimize the technology for battery fast charge and capacity retention targets. Battery performance advancements are most often limited by chemistry and materials improvements to electrodes, electrolytes, or cell structure limiting the trade space (i.e., requiring power vs. energy tradeoffs). The proposed charging technology and associated software will selectively optimize cell design for various performance metrics by controlling electrode surface phenomena, such as lithium plating and dendrite formation, that otherwise cause permanent capacity loss during normal use and accelerate internal physical processes limiting charge rate. Technical tasks include: 1) Demonstration of performance improvements to commercial Li-Ion and fabricated Li-metal battery cells; 2) Adaptation of the process from small cells and modules to electric vehicle battery packs; 3) Development of refined sensing and feedback-based control algorithms using Predictive Learning (PL) and Machine Learning (ML) systems; 4) Verification and validation for Field Programmable Gate Array (FPGA) and System on a Chip (SoC) formats.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.