Sensor-lean Estimation and Monitoring for Second Life EV Batteries

This NSF project aims to advance the national prosperity and welfare with enhanced sustainable electrification systems. The project will bring transformative change to the repurposing process of retired electric vehicle batteries with reduced instrumentation cost and shortened testing time. This will be achieved by the development of a novel estimation and monitoring framework that requires fewer sensors for each cell string.

The intellectual merits of the project include the development of a sensor-lean and computation-efficient estimation and monitoring framework for large distributed systems, with a specific focus on second life electric vehicle batteries to reduce the repurposing cost and to maximize their lifespan. The broader impacts of the project include environment improvement with less emissions, enhancements to undergraduate and graduate degree programs for STEM workforce development, and inclusion of undergraduate students in electric vehicle racing competition.

Existing approaches on battery cell parameter and state estimation generally require extensive testing of each individual cell, which may not scale up for the large number of retired electric vehicle batteries. To address this, this project will develop a transformative estimation and monitoring framework for second life electric vehicle batteries. Four closely integrated research objectives are planned: (1) Develop a novel dense extended Kalman filter to simultaneously estimate parameters for a large number of connected cells during the repurposing process, without requiring sensor measurements for each cell individually; (2) Develop an online sensor-lean behavior monitoring scheme based on temporal logic to extend the battery lifespan in second life applications; (3) Develop a stochastic hybrid filtering approach with novel model condensing to enable real-time cell level monitoring with limited sensor measurements; and (4) Evaluate and validate the proposed framework on a residential wind energy generation system.

Collectively, advances from these research endeavors are expected to make second life electric vehicle batteries and more affordable and more durable, and will create new computationally-efficiency estimation mechanism for large distributed systems with limited sensing capability.

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.