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.

From the design space explored for series architecture plug-in hybrid electric vehicle transit buses by the authors, one powertrain and control design is selected to provide maximum benefit to investment ratio. Sensitivity analysis is performed for this powertrain configuration. Vehicle parameters (including vehicle mass, coefficient of drag, coefficient of rolling resistance), usage parameters (drivecycle, annual vehicle miles traveled, number of recharges in a day, recharge current, and battery temperature), and economic parameters (fuel price, motor price, and battery price) are varied to understand their effect on the number of required battery replacements, net present value, payback period, and fuel consumption reduction. It is shown that battery temperature has the most significant impact, particularly on the number of battery replacements and net present value and, as such, must be well controlled in practice. It is shown that to maintain the battery at 20°C, for ambient temperatures between −5°C and 45°C, 0.8–1.8% excess fuel is required across all drivecycles for the considered plug-in hybrid electric vehicle transit bus powertrain configuration. In addition, the well-to-wheel emissions of criteria pollutants resulting from the usage of this plug-in hybrid electric vehicle transit bus in Indiana and California are calculated and compared with the conventional transit bus, using the GREET (Greenhouse Gases, Regulated Emissions and Energy Use in Transportation) Model. With a single over night charge, the plug-in hybrid electric vehicle transit bus operating in either Indiana or California produces 50% less CO 2 and other greenhouse gases as compared to a conventional transit bus.

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.

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 experimental data sets are used for validation. The model matches experimental capacity degradation results within a 20% error. Moreover, the reported model is 2400× faster than currently existing more complex physically-based electrochemical models that are only slightly more accurate (in some cases).

Within electrified vehicle powertrains, lithium-ion battery performance degrades with aging and usage, resulting in a loss in both energy and power capacity. As a result, models used for system design and control algorithm development would ideally capture the impact of those efforts on battery capacity degradation, be computationally efficient, and simple enough to be used for algorithm development. This paper provides an assessment of the state-of-the-art in lithium-ion battery degradation models, including accuracy, computational complexity, and amenability to control algorithm development. Various aging and degradation models have been studied in the literature, including physically-based electrochemical models, semi-empirical models, and empirical models. Some of these models have been validated with experimental data; however, comparisons of pre-existing degradation models across multiple experimental data sets have not been previously published. Three degradation models, a 1-d electrochemical model (AutoLion ST, or ALST), a semi-empirical model (from the National Renewable Energy Laboratory) and an empirical model (published in the literature), are compared against three published experimental data sets for a 2.3-Ah commercial graphite/LiFePO 4 cell. The results show that the physically-based model is best able to capture results across all three representative data sets with an error less than 10 %, but is 24× slower than the empirical model, and 4000× slower than the semi-empirical model, making it unsuitable for powertrain system design and model-based algorithm development. Despite being computationally efficient, the semi-empirical and empirical models, when used under conditions that lie outside the calibration data set, exhibit up to 60% error in capacity loss prediction. Such models require expensive experimental data collection to recalibrate for every new application. Thus, in the author's opinion, there exists a need for a physically-based model that generalizes well across operating conditions, are computationally efficient for model-based design, and simple enough for control algorithm development.

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 two data sets are used for validation. The model matches experimental capacity degradation results within 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 8% error).

The light-duty vehicle market has seen some adoption of hybrid electric vehicles that is not reflected in the heavy-duty market. The major challenges associated with the heavy-duty segment are: (i) greater emphasis on economic viability, (ii) reluctance to take on risk associated with new technologies, and (iii) numerous diverse applications that preclude a one-size-fits-all approach to hybrid-electric power train design. To overcome these challenges, a model-based framework is required that enables the exploration and optimal design of powertrain architectures for diverse applications while capturing the impact of hybridization on the economics of ownership under different economic scenarios. This paper demonstrates such a framework that incorporates powertrain simulation and battery degradation models to predict fuel consumption, electrical energy consumption, and battery replacements. These results are combined with economic assumptions to enable the exploration of a large design space (which spans powertrain design & control variables, noise variables, and economic scenarios) from a total cost-of ownership perspective to provide better insights to vehicle integrators, component manufacturers, and buyers of heavy-duty hybrid electric vehicles. The methodology is applied to series plug-in hybrid electric and extended-range electric powertrain architectures for medium duty truck applications. The results show that under the assumptions made, economically favorable solutions for series plug-in hybrid electric medium-duty trucks exist in the 2020 time-frame for the NY Composite Truck drive cycle, while for the HTUF Refuse Truck and HTUF Class 6 P&D Truck drive cycles, feasible solutions are not obtained until 2025 and 2030 time-frames respectively. (C) 2017 Elsevier Ltd. All rights reserved.

The optimal design of hybrid electric vehicle powertrains from a systems perspective is critical to realize the maximum benefits of hybridization for a given application, especially in the heavy-duty vehicle space due to the large number of unique applications. This paper proposes a novel framework that enables parametric design optimization of hybrid electric vehicles while accounting for the degradation of the electric battery and its impact over the lifecycle of the vehicle. This framework captures the impact of battery degradation on fuel consumption and battery replacement over the vehicle life by integrating a battery model capable of predicting degradation, and degraded performance, into the drivecycle simulation. These results are incorporated into a lifecycle economic analysis that enables the use of specific economic metrics (including net present value, payback period, and internal rate of return) as optimization objectives. To demonstrate the framework, the electric motor and battery sizes are optimized for a North American transit bus application. The results show that the optimal component sizes depend on the metric of interest, i.e. different optimum parameter sets are obtained when the objective is different. Further, these optimum parameter sets are different if the objective is simply the "day 1" fuel consumption. For example, while optimizing for fuel consumption leads to selection of the largest available battery pack and electric motor, optimizing for payback period leads to the selection of a smaller battery back. Lastly it was also observed that the fuel consumption increases by up to 10% from "day 1 " to End-of-Life of the battery. 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.

Hybrid powertrains with multiple sources of power have generated new control challenges in the automotive industry. Purdue University's participation in EcoCAR 2, an Advanced Vehicle Technology Competition managed by the Argonne National Laboratories and sponsored by GM and DOE, has provided an exciting opportunity to create a comprehensive test-bench for the development and validation of advanced hybrid powertrain control strategies. As one of 15 competing university teams, the Purdue EcoMakers are re-engineering a donated 2013 Chevrolet Malibu into a plug-in parallel- through-the-road hybrid-electric vehicle, to reduce its environmental impact without compromising performance, safety or consumer acceptability.This paper describes the Purdue team's control development process for the EcoCAR 2 competition. It describes the team's efforts towards developing a complete vehicle model of a Parallel-through-the road PHEV which can leverage SIL and HIL simulation platforms for control development. A HIL test-bench was developed for real-time controller testing. The use of parameterized models, a prototyping controller and a unique interfacing philosophy allows the team to transition quickly between the SIL, HIL and vehicle platforms, thus providinga comprehensive test environment for the design and validation of various hybrid supervisory control strategies. Some preliminary data from the team's SIL and HIL simulations has also been presented.