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Predicting fuel economy during early stages of concept development or feasibility study for a new type of powertrain configuration is an important key factor that might affect the powertrain configuration decision to meet CAFE standards. In this paper an efficient model has been built in order to evaluate the fuel economy for a new type of charge sustaining series hybrid vehicle that uses a Genset assembly (small 2 cylinders CNG fueled engine coupled with a generator). A first order mathematical model for a Li-Ion polymer battery is presented based on actual charging /discharging datasheet. Since the Genset performance data is not available, normalized engine variables method is used to create powertrain performance maps. An Equivalent Consumption Minimization Strategy (ECMS) has been implemented to determine how much power is supplied to the electric motor from the battery and the Genset.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

The automotive cooling system configuration has remained fixed for many decades with a large radiator plus fan, coolant pump, and bypass valve. To reduce cooling system power consumption, the introduction of multiple computer-controlled heat exchangers may offer some benefits. A paradigm shift from a single large radiator, sized for maximum load, to n-small radiators with individual flow control valves should allow fine tuning of the heat rejection needs to minimize power. In this project, a series of experimental scenarios featuring two identical parallel radiators have been studied for low thermal load engine cooling (e.g., idling) in ground transportation applications. For high thermal load scenarios using two radiators, the fans required between 1120 - 3600 W to maintain the system about the coolant reference temperature of 85oC.

Phase change materials (PCMs) are extensively used in many engineering areas for thermal management purposes. This paper investigated the application of PCMs for vehicular systems, especially for the thermal protection of vehicle lighting systems based on light emitting diodes (LEDs). Lighting systems based on LEDs offer many advantages, however, also pose a smaller margin of error for thermal management. This paper analyzed the combined use of PCMs with metal foam for cooling systems. The cooling performance was studied numerically under different porosity values of the metal foam, and different boundary conditions. The cooling performance was also compared to a solid metal sink system (SMS) and was found to offer several distinct cooling characteristics.

Fracture strain data provide essential information for material selection and serve as an important failure criterion in computer simulations of crash events. Traditionally, the fracture strain was measured by evaluating the thinning at fracture using tools such as a microscope or a point micrometer. In the recent decades, digital image correlation (DIC) has evolved as an advanced optical technique to record full-field strain history of materials during deformation. Using this technique, a complete set of the fracture strains (including major, minor, and thickness strains) can be approximated for the material. However, results directly obtained from the DIC can be dependent on the experiment setup and evaluation parameters, which potentially introduce errors to the reported values.

Heat rejection in ground vehicle propulsion systems remains a challenge given variations in powertrain configurations, driving cycles, and ambient conditions as well as space constraints and available power budgets. An optimization strategy is proposed for engine radiator geometry size scaling to minimize the cooling system power consumption while satisfying both the heat removal rate requirement and the radiator dimension size limitation. A finite difference method (FDM) based on a heat exchanger model is introduced and utilized in the optimization design. The optimization technique searches for the best radiator core dimension solution over the design space, subject to different constraints. To validate the proposed heat exchanger model and optimization algorithm, a heavy duty military truck engine cooling system is investigated.

This paper presents development of a multi-scale material model for a 980 MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning heat treatment (QP980), based on integrated computational materials engineering principles (ICME Model). The model combines micro-scale material properties defined by the crystal plasticity theory with the macro-scale mechanical properties, such as flow curves under different loading paths. For an initial microstructure the flow curves of each of the constituent phases (ferrite, austenite, martensite) are computed based on the crystal plasticity theory and the crystal orientation distribution function. Phase properties are then used as an input to a state variable model that computes macro-scale flow curves while accounting for hardening caused by austenite transformation into martensite under different straining paths.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

Experimental evaluations were made of single- and double-pass heat exchangers for automotive application. The study was concerned primarily with the effect of the working parameters, air and water mass flow rates and the inlet water temperature, on the average and local heat transfer coefficients. An automotive radiator having two water-side passes was fabricated and tested. The experimental results were compared with those for a single-pass unit. The study showed that the overall coefficient of heat transfer of the single-pass radiator was higher than that of the double-pass radiator.

A single radiator cooling system architecture has been widely applied in ground vehicles for safe equipment (e.g., engine block, electronics, and motors) temperature control. The introduction of multiple smaller heat exchangers provides additional energy management features and alternate pathways for continued operation in case of critical subsystem failure. Although cooling performance is often designed for maximum thermal loads, systems typically operate at a fraction of the peak values for most of their life cycle. In this project, a two-radiator configuration with variable flow rates and valve positions has been mathematically modelled and experimentally validated to study its performance feasibility. A multi-node resistance-capacitance thermal model was derived using the ε−NTU approach with accompanying convective and conductive heat transfer pathways within the system.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

The strain-induced diffusionless shear transformation of retained austenite to martensite during straining of transformation induced plasticity (TRIP) assisted steels increases strain hardening and delays necking and fracture leading to exceptional ductility and strength, which are attractive for automotive applications. A novel technique that provides the retained austenite volume fraction variation with strain with improved precision is presented. Digital images of the gauge section of tensile specimens were first recorded up to selected plastic strains with a stereo digital image correlation (DIC) system. The austenite volume fraction was measured by synchrotron X-ray diffraction from small squares cut from the gage section. Strain fields in the squares were then computed by localizing the strain measurement to the corresponding region of a given square during DIC post-processing of the images recorded during tensile testing.

The integration of electric motors into ground vehicle propulsion systems requires the effective removal of heat from the motor shell. As the torque demand varies based on operating cycles, the generated heat from the motor windings and stator slots must be rejected to the surroundings to ensure electric machine reliability. In this paper, an electric motor cooling system design will be optimized for a light duty autonomous vehicle. The design variables include the motor cradle volume, the number of heat pipes, the coolant reservoir dimensions, and the heat exchanger size while the cost function represents the system weight, overall size, and performance. The imposed requirements include the required heat transfer per operating cycle (6, 9, 12kW) and vehicle size, component durability requirement, and material selection. The application of a nonlinear optimization package enabled the cooling system design to be optimized.

With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey.

Diesel engine exhaust poses an ongoing threat to human health as well as to the environment. Automotive exhaust treatment systems have been developed over the years to reduce the large amount of diesel particulate matter (DPM) released to the atmosphere. Current systems can be categorized as selective catalytic reduction, catalytic converters, and diesel particulate filters. This study presents an emission system that focuses on the removal of exhaust particles using Brownian diffusion of DPM toward fog drops followed by cyclonic separation of DPM rich fog drops. The experimental system consisted of a 13.2 kW diesel engine, heat exchanger to cool the exhaust to saturation temperature, ultrasonic fogger, cyclone separator, and recovery of waste particulate. Representative emission tests have been performed at five different diesel engine speeds and corresponding crankshaft loads.

Capacitance sensing is the technology that detects the presence of nearby objects by measuring the change in capacitance. A change in capacitance is triggered either by a change in dielectric constant, area of overlap or distance of separation between the electrodes of the capacitor. It is a technology that finds wide use in applications such as touch screens, proximity sensing etc. Drawing motivation from such applications, this paper investigates how capacitive sensing can be employed to detect the presence and posture of occupants inside vehicles. Compared to existing solutions, the proposed approach is low-cost, easy to deploy and highly efficient. The sensing system consists of a capacitance-sensing mat that is embedded with copper foils and an associated sensing circuitry. Inside the mat the foils are arranged in rows and columns to form several touch-nodes across the surface of the mat.

This paper focuses on redesigning the headlamp subsystem functional architecture. The design involves meeting three major functional requirements: Achieving the lumen requirements according to Economic Commission for Europe (ECE) 324 regulations, Meeting the illumination pattern, and Maintaining the Light Emitting Diode’s (LED) junction temperature at 90°C. White LEDs are considered in the design to satisfy the functional requirements due to their high lumen efficacy, compact size, and long life. These benefits, when compared to existing headlight systems benchmarked, present enough potential to warrant further conceptual virtual prototyping. The prototyping focused on solutions that allowed control of sizing and numbering of LEDs, illumination pattern limits, and temperature to achieve the multiple functions a dynamic headlight system. A primary challenge in this design is to maintain the LED’s junction temperature within a recommended operational range.