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

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是使更多的电动汽车使用；此外，该技术将电池寿命延长一倍的潜力将减少处置电池对环境的影响。该项目通过实现100%的电池续航里程和最大可用能量容量，将持续行驶里程提高50-100倍，加速了电动汽车的普及。该项目是全球锂离子电池市场（预计到2022年将增长到680亿美元）和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。该SBIR二期项目提出了优化电池快速充电和容量保持目标的技术。电池性能的提升通常受到电极、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权衡）。所提出的充电技术和相关软件将通过控制电极表面现象（如锂电镀和枝晶形成）来选择性地优化电池设计的各种性能指标，否则在正常使用过程中会导致永久容量损失，并加速内部物理过程，限制充电速率。技术任务包括：1)商用锂离子电池和制造锂金属电池的性能改进演示；2)从小电池和模块到电动汽车电池组的过程适应；3)利用预测学习（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）格式的验证和验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.