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

这个小型企业创新研究(SBIR)项目的更广泛的影响/商业潜力是使电动汽车得到更多的使用；此外，该技术将电池寿命延长一倍的潜力将减少废弃电池对环境的影响。该项目通过使用100%的电池续航里程并最大限度地提高可用能源容量，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年增长到680亿美元)和年度混合动力汽车和电动汽车市场(预计到2025年每年超过1000万辆)预期增长的关键推动因素。这个SBIR第二阶段项目建议优化电池快速充电和容量保持目标的技术。电池性能的提高通常受限于对电极、电解液或电池结构的化学和材料改进，限制了交易空间(即，需要功率与能量的权衡)。拟议的充电技术和相关软件将通过控制电极表面现象，如锂电镀和树枝晶形成，选择性地针对各种性能指标优化电池设计，否则会在正常使用期间导致永久性容量损失，并加速限制充电速度的内部物理过程。技术任务包括：1)演示商用锂离子电池和制造的锂金属电池的性能改进；2)将工艺从小型电池和模块调整到电动汽车电池组；3)使用预测学习(PL)和机器学习(ML)系统开发基于改进的传感和反馈控制算法；4)验证和验证现场可编程门阵列(FPGA)和片上系统(SoC)格式。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.