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

SBIR Phase II: Plug-and-play intelligent charging hardware and software that increases safety, performance and life of lithium ion and lithium metal batteries
SBIR 第二阶段:即插即用智能充电硬件和软件,可提高锂离子和锂金属电池的安全性、性能和寿命
批准号:
1951242
负责人:
Daniel Konopka
金额:
$69.83万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-04-15 至 2023-03-31
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中文摘要
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英文摘要
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.
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