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Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive Cycles

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Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive Cycles Gratis copy for Kandler Smith Copyright 2012 SAE International NREL/CP-5400-53817. Posted with permission. E-mailing, copying and internet posting are prohibited Presented at the 2012 SAE World Congress and Downloaded Thursday, June 07, 2012 05:57:08 PM Exhibition, April 24-26, 2012, Detroit, Michigan Comparison of Plug-In Hybrid Electric Vehicle 2012-01-0666 Published Battery Life Across Geographies and Drive Cycles 04/16/2012 Kandler Smith, Matthew Earleywine, Eric Wood, Jeremy Neubauer and Ahmad Pesaran National Renewable Energy Laboratory doi:10.4271/2012-01-0666 A battery's aging behavior directly impacts the applications ABSTRACT and environments to which it is suited and to what degree the In a laboratory environment, it is cost prohibitive to run battery must be oversized to achieve the desired service life. automotive battery aging experiments across a wide range of Unlike batteries for consumer electronics, automotive possible ambient environment, drive cycle, and charging batteries face large variations in thermal environment and scenarios. Because worst-case scenarios drive the duty-cycle. Hybrid-electric vehicle (HEV) batteries presently conservative sizing of electric-drive vehicle batteries, it is achieve more than 10 years of life by using a small portion, useful to understand how and why those scenarios arise and generally less than 25%, of their total energy. Conservatism what design or control actions might be taken to mitigate in battery sizing directly impacts battery cost. Worst-case them. In an effort to explore this problem, this paper applies a aging conditions drive the need to oversize batteries, and it is semi-empirical life model of the graphite/nickel-cobalt- important to explore degradation impacts for a range of aluminum lithium-ion chemistry to investigate calendar possible duty cycles to identify and understand such worst degradation for various geographic environments and cases. Control strategies that extend battery life may also help simplified cycling scenarios. The life model is then applied to reduce the market cost of EDVs. analyze complex cycling conditions using battery charge/ discharge profiles generated from simulations of plug-in From the system perspective, significant stressors to a electric hybrid vehicles (PHEV10 and PHEV40) vehicles lithium-ion (Li-ion) battery include exposure to high across 782 single-day driving cycles taken from a Texas temperatures, exposure to high states of charge (SOCs) and travel survey. Drive cycle statistics impacting battery life are charge voltages, calendar age, depth of discharge (DOD), and compared to standard test cycles. the rate and frequency of charge/discharge cycles. Various models in the literature, ranging from physics-based [3,4] to INTRODUCTION semi-empirical [5,6] and empirical [7,8,9], describe the dependence of battery resistance and capacity fade on various Electric-drive vehicles (EDVs) offer the potential to reduce aging factors. Based on aging datasets for the graphite/nickel- reliance on fossil fuels; however, the fuel displacement of cobalt-aluminum (NCA) Li-ion chemistry, the authors [6,10] EDVs will be elusive until they achieve meaningful market developed a physically justified semi-empirical model penetration. Batteries are often the most expensive allowing interpolation/extrapolation from laboratory-tested component of the EDV. Further reductions in battery cost, conditions to arbitrary duty cycles likely to be encountered in weight, and volume are required to make the vehicles more real-world environments. The life degradation model is attractive in the marketplace. To compete with conventional suitable for battery system engineering and techno-economic vehicles, EDVs and their batteries must achieve a 10- to 15- analysis of Li-ion batteries. year life [1]. Cost analyses of light-duty EDVs generally show that periodic battery replacement (e.g., every 5 years) is This paper specifically considers aging scenarios for plug-in not warranted and the battery should be designed to last the hybrid electric vehicles (PHEVs) with 10 and 40 mile (16 and life of the vehicle [2]. 64 km, respectively) nominal electric ranges. The PHEVs, referred to as PHEV10 and PHEV40, respectively, have two modes of operation. In the charge depletion (CD) mode, Gratis copy for Kandler Smith Copyright 2012 SAE International E-mailing, copying and internet posting are prohibited Downloaded Thursday, June 07, 2012 05:57:08 PM vehicle motive power is primarily provided by the battery. When the CD energy is depleted, the vehicle switches to a (1) charge sustaining (CS) mode supported by both a gasoline- fueled internal combustion engine and battery-powered Cell capacity is assumed to be controlled by either loss of motor. Recharge of the battery is achieved by connection to cyclable Li or loss of active sites: the electrical grid. The paper is organized as follows. Section II summarizes the (2) battery life model and gives example results for the graphite/NCA chemistry. Section III analyzes battery aging where with time under variable temperature and SOC conditions representative of a PHEV10 and PHEV40 in 100 different geographic areas throughout the United States. Section IV (3) analyzes battery aging for a distribution of hypothetical PHEV cycling scenarios. The cycling scenarios are generated by simulating PHEV10 and PHEV40 vehicles using 782 (4) second-by-second speed-versus-time driving profiles recorded during single-day travel surveys of light-duty Equations (2, 3, 4) are simplifications of observations from vehicles in Texas. experimental data [11]. Note that electrode site capacity, Qsites, in (2) may be expanded to include separate terms for LIFE MODEL negative electrode sites and positive sites; however, it is MODEL DESCRIPTION typical for one electrode to limit active-site capacity and hence only one site-capacity term is included here. Battery aging is caused by multiple phenomena related to both cycling and time. Battery degradation is accelerated with Models (1), (3), and (4) are readily fit to a resistance or the DOD and frequency of cycling, elevated temperature, and capacity trajectory measured for one storage or cycling elevated voltage exposure, among other factors. At the condition. With multiple storage- and cycling-condition battery terminals, the observable effects of degradation are an datasets, functional dependence can be built for rate constants increase in resistance and a reduction in capacity. These two a1(T,Voc,ΔDOD), a2(T,Voc,ΔDOD), b1(T,Voc,ΔDOD), and effects can be correlated with power and energy losses that c (T,V ,ΔDOD). For a general rate constant θ, the present cause battery end-of-life in an application. Mechanisms for 1 oc model assumes Arrhenius dependence on temperature T: resistance growth include loss of electrical conduction paths in the electrodes, fracture and isolation of electrode sites, growth of film layers at the electrode surface, and degradation of electrolyte. Mechanisms for capacity loss include fracture, isolation, and chemical degradation of electrode material, as well as permanent loss of cyclable (5) lithium from the system as a byproduct of side reactions. Tafel dependence on open-circuit voltage Voc: Under storage conditions, the dominant fade mechanism is typically growth of a resistive film layer at the electrode surface. As the layer grows, cyclable lithium is also consumed from the system, reducing available capacity. In (6) the present model, resistance growth and lithium capacity loss are assumed to be proportional to the square-root of time, and Wöhler dependence [12] on individual swings in depth- t1/2, typical of diffusion-limited film-growth processes [3]. of-discharge ΔDOD: Under cycling-intense conditions, degradation is mainly caused by structural degradation of the electrode matrix and active sites. Cycling-driven degradation is assumed to be proportional to the number of cycles, N. (7) Cell resistance growth due to calendar- and cycling-driven mechanisms is assumed to be additive: Gratis copy for Kandler Smith Copyright 2012 SAE International E-mailing, copying and internet posting are prohibited Downloaded Thursday, June 07, 2012 05:57:08 PM The combination of individual stress factors is assumed to be Figure 1 shows resistance and capacity fade rates for storage multiplicative: at different constant temperatures and SOCs. Fade rates are relatively insensitive to temperature when the cell is stored at less than 40% SOC. At SOC greater than 80%, there is strong sensitivity to temperature. The life model captures SOC/ (8) temperature interaction using the exponential of Voc/T term in The life model was fit to laboratory aging datasets [13, 14, Eq. (6). In Figure 1, dotted lines provide reference examples 15, 16] for the Li-ion graphite/NCA chemistry. The NCA of maximum allowable degradation rates to achieve 5, 8, and chemistry has generally graceful aging characteristics and is 10 years of service life. These examples assume battery end- expected to achieve 8 or more years of life when sized of-life is controlled by 20% cyclable-lithium capacity loss appropriately for a vehicle application. The present model and 20% resistance growth (17% power loss). The value of does not capture possible accelerating fade mechanisms that 20% degradation is arbitrarily chosen for this example. Note could occur beyond 30% capacity fade. Other degradation that cycling will cause additional degradation to the storage mechanisms not captured by the model include fast charge- results shown
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