Embedded Fiber Optic Sensing for Accurate State Estimation in Advanced Battery Management Systems
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Embedded Fiber Optic Sensing for Accurate State Estimation in Advanced Battery Management Systems
Lars Wilko Sommer1, Ajay Raghavan1, Peter Kiesel1, Bhaskar Saha1, Tobias Staudt1, Alexander Lochbaum1, Anurag Ganguli1, Chang-Jun Bae1, Mohamed Alamgir2 1 Palo Alto Research Center (PARC, a Xerox Company), Palo Alto, CA 94304, U.S.A. 2 LG Chem Power, Troy, MI 48083, U.S.A.
ABSTRACT The problems of using performance parameters such as voltage, current and temperature measured with electrical sensors in today’s battery management systems (BMS) are well known. These parameters can be weakly informative about cell state, particularly as cells age, and contribute to over-conservative utilization and oversizing of a battery pack. Fiber optic (FO) sensors can offer an interesting alternative to conventional electrical sensors, with several advantages such as high selective sensitivity to various parameters, light weight, robustness to EMI, and multiplexing capabilities. In this study, a particular type of FO sensors, fiber Bragg grating (FBG) sensors were externally attached to lithium ion pouch cells for monitoring additional informative cell parameter such as strain and temperature. Multiple charge and discharge cycle were performed to examine the qualification of these signals for cell state estimation in BMS. In comparison to corresponding measurements using conventional electrical sensors, the FBG signals showed very promising results for utilization in effective BMS. INTRODUCTION In recent years, the fast-growing market for alternative drive technologies such as electric vehicles (EVs) and hybrid electric vehicles (HEVs) has become an important part of the global automotive industry. However, fundamental issues have to be solved to realize the widespread adaption of these green mobility options. Today’s electric vehicles are limited by factors such as high costs, battery lifetime, driving range and the susceptibility for unexpected failure. Effective control and management of cell charge and discharge by battery management systems (BMS) is essential for good performance [1]-[3]. Prevalent BMS relies on monitoring external performance parameters such as voltage, current and temperature to estimate state of charge (SOC) and state of health (SOH) and to protect the batteries. SOC estimation is in general limited by the accuracy of these measurements. Conventional methods ensuring safe battery operation with the required range and available power involve expensive overdesign of the battery and tend to result in inefficient use of available battery capacity. Beyond SOC, SOH presents even greater challenges to measurement accuracy since the BMS must be able to ascertain and report battery capacity based on very slowly changing (and often ambiguous) parameters. In order to ensure adequate rated capacity over the life of the vehicle within this uncertainty, excess capacity is again added to the pack. The excess capacity translates to added mass, volume and cost. One approach to overcome those challenges is the development of
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