Peter Kiesel: Fiber sensor systems for automotive applications

An optically based smart monitoring system prototype for battery packs promises better range and greater efficiency for electric vehicles.

23 September 2015

Peter Kiesel is principal scientist in the Electronic Materials and Devices Laboratory at PARC, a Xerox company, in Palo Alto, CA. He conducts research in compact optical detection systems, ultrasensitive light detection, and nitride-based light emitters. His major interests focus on developing and fabricating novel optoelectronic devices and detection systems for applications in bio- and nanotechnology. Kiesel is author or coauthor of more than 240 scientific publications including more than 90 refereed journal articles, 71 issued US patents, and 4 book chapters. He has organized many international workshops and conferences and has been the principal investigator on more than 16 research and development projects covering a large variety of optoelectronic devices and sensing systems.

Under the ARPA-E Advanced Management and Protection of Energy-Storage Devices (AMPED) program for advanced battery management systems, PARC, a Xerox company, and LG Chem Power (LGCPI) are developing SENSOR (Smart Embedded Network of Sensors with an Optical Readout), an optically based smart monitoring system prototype for battery packs. The system will use fiber optic sensors embedded inside Lithium-ion battery cells to measure parameters indicative of cell state online, such as state-of-charge (SOC) and state-of-health (SOH). SENSOR will leverage PARC's low-cost, compact wavelength-shift detection technology and intelligent algorithms to enable effective real-time performance management, optimized battery design, and improved safety. While SENSOR is initially targeting batteries for hybrid and electric vehicles, it will be extendable to battery systems for other challenging domains such as aircrafts, satellites, grid storage, and military vehicles.

The team has already presented exciting results achieved over the project's first half focused on cell-level state features detectable, initial SOX algorithm development, low-cost optical readout development, and early validation test results at SPIE DSS 2015 and elsewhere.

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