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

JOURNAL OF POWER SOURCES

卷:341

Article

Ganguli, Anurag¹  Saha, Bhaskar¹  Raghavan, Ajay¹  Kiesel, Peter¹  Arakaki, Kyle¹  Schuh, Andreas¹  Schwartz, Julian¹  Hegyi, Alex¹  Sommer, Lars Wilko¹  Lochbaum, Alexander¹  Sahu, Saroj¹  Alamgir, Mohamed² 

[1] Palo Alto Res Ctr, Palo Alto, CA 94304 USA

[2] LG Chem Power, Troy, MI 48083 USA

关键词: Fiber-optic sensors;    Battery management systems;    State-of-charge;    State-of-health;    Lithium-ion;    Electric vehicle;   

DOI  :  10.1016/j.jpowsour.2016.11.103

来源: Elsevier

【 摘 要 】

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 (FO) sensors. High-performance large-format pouch cells with embedded FO 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. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

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