Embedded Fiber-Optic Sensing for Accurate Internal Monitoring of Cell State in Advanced Battery Management Systems Part 2: Internal Cell Signals and Utility for State Estimation

Details

Event Journal of Power Sources

Authors

Anurag Ganguli
Bhaskar Saha
Andreas Schuh
Hegyi, Alex
Lars Sommer
Alexander Lochbaum
Saroj Sahu
Technical Publications
December 19th 2016
A key challenge hindering the mass adoption of Lithium-ion and other next-gen chemistries in advanced battery applications such as hybrid/electric vehicles (xEVs) has been management of their functional performance for more effective battery utilization and control over their life. Contemporary battery management systems (BMS) reliant on monitoring external parameters such as voltage and current to ensure safe battery operation with the required performance usually result in overdesign and inefficient use of capacity. More informative embedded sensors are desirable for internal cell state monitoring, which could provide accurate state-of-charge (SOC) and state-of-health (SOH) estimates and early failure indicators. Here we present a promising new embedded sensing option developed by our team for cell monitoring, fiber-optic sensors. High-performance large-format pouch cells with embedded fiber-optic sensors were fabricated. This second part of the paper focuses on the internal signals obtained from these FO sensors. The details of the method to isolate intercalation strain and temperature signals are discussed. Data collected under various xEV operational conditions are presented. An algorithm employing dynamic time warping and Kalman filtering was used to estimate state-of-charge with high accuracy from these internal FO signals. Their utility for high-accuracy, predictive state-of-health estimation is also explored.

Citation

Ganguli, A.; Saha, B.; Raghavan, A.; Kiesel, P.; Arakaki, K.; Schuh, A.; Schwartz, J.; Hegyi, A.; Sommer, L.; Lochbaum, A.; Sahu, S.; Alamgir, M. Embedded Fiber-Optic Sensing for Accurate Internal Monitoring of Cell State in Advanced Battery Management Systems Part 2: Internal Cell Signals and Utility for State Estimation. Energy and Environmental Science.

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