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This study investigates the effect of 50-kW (about 2C) direct current fast charging on a full-size battery electric vehicle's battery pack in comparison to a pack exclusively charged at 3.3 kW, which is the common alternating current Level 2 charging power level. Comparable scaled charging protocols are also independently applied to individual cells at three different temperatures, 20 °C, 30 °C, and 40 °C, to perform a comparative analysis with the packs. Dominant cell-level aging modes were identified through incremental capacity analysis and compared with full packs to gain a clear understanding of additional key factors that affect pack aging. While the cell-level study showed a minor impact on performance due to direct current fast charging, the packs showed a significantly higher rate of capacity fade under similar charging protocols. This indicates that pack-level aging cannot be directly extrapolated from cell evaluation. Delayed fast charging, completing shortly before discharge, was found to have less of an impact on battery degradation than conventional alternating current Level 2 charging.

Within the past decade, the average price for automotive lithium-ion battery (LIB) packs has fallen roughly by 80% [1]. This has played a substantial role in increasing the driving range of mass market electric drive vehicles (EDVs) and the demand for EDVs [2]. Additionally, the increasing availability of direct current fast charging (DCFC) stations is working synergistically in aiding EDV adoption and utility. For instance, a 25% annual increase in electric vehicle miles was documented in areas where 50 to 120-kW DCFC stations were available [3, 4]. Thus, continued DCFC network expansion, along with faster charging, could significantly increase the utility of battery electric vehicles and alleviate consumers’ range anxiety to a comfortable level. EDV charging speeds are not yet comparable to the fueling speed of conventional gasoline engines, which is typically less than 10 minutes [5]. Higher- power charging stations up to 400 kW are necessary to achieve a 10-minute recharge [5]. Additional challenges will be encountered in realizing this extreme charging speed, from battery cells to vehicle systems, and from charging infrastructure hardware to charging network economic feasibility. On the battery side, the increased charging rate associated with extreme fast charging could adversely affect battery performance and life (i.e., state of health [SOH]). Besides the cell-level aging, additional pack-level aging factors could come into play under fast charge conditions. Thus, it is paramount to understand the effects of fast charging on LIB’s SOH, from the pack to cell level and to identify the most critical factors affecting battery SOH. This understanding would benefit battery developers, automotive original equipment manufacturers, and electric vehicle supply equipment developers, allowing for sensible design and management of the LIB pack to satisfy target life requirements in a cost-effective way. This presentation will discuss some of the implications associated with different charging protocols, e.g., alternating current level 2 (AC L2), direct current fast charging (DCFC), and combined AC L2 and DCFC, on cells as well as full packs at different temperatures. The effect of delayed fast charging, in which charging completes shortly before the next discharge, on the battery SOH will also be shown. Finally, pack design considerations that require understanding of aspects extending beyond scaling the performance at the cell level will be discussed. References S. M. Knupfer, R. Hensley, P. Hertzke, P. Schaufuss, Electrifying insights: How automakers can drive electrified vehicle sales and profitability, McKinsey & Company (2017) B. Nykvist, M. Nilsson, Rapidly falling costs of battery packs for electric vehicles, Nat. Clim. Change 5, 329-332 (2015) N. Lutsey, S. Searle, S. Chambliss, A. Bandivadekar, Assessment of leading electric vehicle promotion activities in United States cities, Int. Counc. Clean Transp., July 2015. M. McCarthy. California ZEV policy update- SAE 2017 Government/Industry meeting presentation, Washington DC, Jan 2017. S. Ahmed, I. Bloom, A. N. Jansen, T. Tanim, E. Dufek, A. Pesaran, A. Burnham, R. B. Carlson, F. Dias, K. Hardy, M. Keyser, C. Kreuzer, A. Markel, A. Meintz, C. Michelbacher, M. Mohanpurkar, P. A. Nelson, D. C. Robertson, D. Scoffield, M. Shirk, T. Stephens, R. Vijayagopal, J. Zhang, Enabling fast charging – A battery technology gap assessment, J. Power Sources, 367, 250-262 (2017)

Laboratory testing of coin cells, batteries, battery packs and modules is essential in development of durable, reliable and safe battery systems. Battery testing hardware and software have improved significantly over the past decade, but the effectiveness of the laboratory test results from advanced battery testing equipment depends on its accuracy and precision. Battery testing hardware is capable of measuring only primary parameters such as current, voltage and time during testing. Hence any uncertainty or error in measuring these primary parameters will result in loss of accuracy and precision in subsequent analyses and erroneous prediction of battery performance [1, 2]. Analysis of battery systems goes beyond the scope of the primary parameters, however, and depends largely on the derived critical secondary performance parameters such as capacity, energy, power, and Coulombic efficiency. Moreover, for advanced failure mode analysis diagnostic and prognostic tools such as differential capacity and state of charge estimation are used. The battery degradation mechanisms and failure modes can be quantified from differential capacity analysis by evaluating loss of active material, loss of lithium inventory, and change in reaction kinetics [3]. State of charge (SOC) is another critical parameter that defines the performance state of the battery. Uncertainty in SOC estimation limits battery usage during operation [4]. Combined SOC and differential capacity analyses depend on the calculation of parameters such as capacity which in turn depend on accurate and precise primary parameter measurements. This work addresses an approach to estimate uncertainty in primary measured parameters. Equations to identify uncertainty in derived complex parameters will be discussed. A quantitative analysis of uncertainty in critical analysis and performance parameters will be presented. The results show how errors propagate from the primary measurements to the more advanced diagnostic and prognostic methods as well as the subsequent effects on performance analysis of battery systems. References: [1] John L. Morrison, Gary L. Hunt, Donna J. Marts, and Chester G. Motloch, “Uncertainty Study INEEL EST Laboratory Battery Testing Systems, Vol 2: Applications of Results to INEEL Testers,” INEL/EXT-01-00505, Idaho National Laboratory (2003) [2] Barry N. Taylor and Chris E. Kuyatt, “Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results,” NIST Technical Note, 1297 (1994) [3] Matthieu Dubarry, Arnaud Devie, and Bor Yann Liaw, “ The Value of Battery Diagnostics and Prognostics,”J. Energy Power Sources, 1, 242 (2014). Zhe Li, Jun Huang, Bor Yann Liaw and Jianbo Zhang, “On State-of-Charge Determination for Lithium-ion Batteries,” J Power Sources, 348, 281, (2017)