Physically-based Li-ion electrochemical cell models have been shown capable of predicting cell performance and degradation, but are computationally expensive for optimization-oriented design applications. Faster empirical models have been developed from experimental data, but are not generalizable to operating conditions outside of the range established by the calibration data. In this paper, a reduced-order capacity-loss model for graphite anodes is derived based upon the salient physical loss mechanisms to improve computational efficiency without sacrificing model fidelity. This model captures the two primary degradation mechanisms that occur in the graphite anode of a typical lithium ion cell: a) capacity loss due to Solid Electrolyte Interface (SEI) layer growth, and b) capacity loss due to isolation of active material. The model is calibrated and validated for a commercial 2.3-Ah cell with a Lithium Iron Phosphate (LFP) cathode and graphite anode. One data set is used for calibration, another four data sets are used for validation. The model matches experimental capacity degradation results within a 10% error. Moreover, the reported model is 2400×faster than currently existing more complex physically-based electrochemical models that are only slightly more accurate (less than 9% error).
Prior design optimization efforts do not capture the impact of battery degradation and replacement on the total cost of ownership, even though the battery is the most expensive and least robust powertrain component. A novel, comprehensive framework is presented for model-based parametric optimization of hybrid electric vehicle powertrains, while accounting for the degradation of the electric battery and its impact on fuel consumption and battery replacement. This is achieved by integrating a powertrain simulation model, an electrochemical battery model capable of predicting degradation, and a lifecycle economic analysis (including net present value, payback period, and internal rate of return). An example design study is presented here to optimize the sizing of the electric motor and battery pack for the North American transit bus application. The results show that the optimal design parameters depend on the metric of interest (i.e. net present value, payback period, etc.). Finally, it is also observed that the fuel consumption increases by up to 10% from “day 1” to the end of battery life. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.
This article performs a novel comparison of the life-cycle costs of the series and parallel architectures for plug-in hybrid electric vehicles. Economic viability is defined as having a payback period less than 2 years and number of battery replacements less than or equal to three over a vehicle life of 12 years along-with drivability and gradability constraints. Economic viability is compared for two plug-in hybrid electric vehicle applications (Medium-duty Truck and Transit Bus) using series and parallel architectures over multiple drivecycles, for three economic scenarios (viz. 2020, 2025 and 2030 where the fuel price, battery price and motor price are varied such that latter scenarios are more favorable for hybridization). One battery overnight recharge is assumed. The results demonstrate that by 2020 the plug-in hybrid electric vehicle transit buses are viable for the duty cycles Manhattan, Orange County, and China (Normal and Aggressive). By 2025, plug-in hybrid electric vehicle Class 6 trucks are viable for all duty cycles considered (Pick-up and delivery, Refuse and New York Composite). The parallel architectures generally require less than 50% of the initial cost of the series architecture, due to smaller motor sizes, driving earlier viability for parallel architectures. The transit bus scenarios generally achieve payback sooner than the medium-duty truck due to higher fuel cost savings, driving earlier viability for transit bus applications.
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