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The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature].

With ongoing concerns about the elevated levels of ambient air pollution in urban areas and the contribution from heavy-duty diesel vehicles, hybrid electric vehicles are considered as a potential solution as they are perceived to be more fuel efficient and less polluting than their conventional engine counterparts. However, recent studies have shown that real-world emissions may be substantially higher than those measured in the laboratory, mainly due to operating conditions that are not fully accounted for in dynamometer test cycles. At the U.S. EPA National Fuel and Vehicle Emissions Laboratory (NVFEL) the in-use criteria emissions and energy efficiency of heavy-duty class 8 vehicles (up to 36280 kg) can be evaluated under controlled conditions in the heavy-duty chassis dynamometer test.

The Energy Finite Element Analysis (EFEA) has been validated in the past through comparison with test data for computing the structural vibration and the radiated noise for Naval systems in the mid to high frequency range. A main benefit of the method is that it enables fast computations for full scale models. This capability is exploited by using the EFEA for a submarine pressure hull design optimization study. A generic but representative pressure hull is considered. Design variables associated with the dimensions of the king frames, the thickness of the pressure hull in the vicinity of the excitation (the latter is considered to be applied on the king frames of the machinery room), the dimensions of the frames, and the damping applied on the hull are adjusted during the optimization process in order to minimize the radiated noise in the frequency range from 1,000Hz to 16,000Hz.

This work investigates the potential improvements in vehicle fuel economy possible by optimizing gear shift strategies to leverage a novel boosting device, an electrically assisted variable speed supercharger (EAVS), also referred to as a power split supercharger (PSS). Realistic gear shift strategies, resembling those commercially available, have been implemented to control upshift and downshift points based on torque request and engine speed. Using a baseline strategy from a turbocharged application of a MY2015 Ford Escape, a vehicle gas mileage of 34.4 mpg was achieved for the FTP75 drive cycle before considering the best efficiency regions of the supercharged engine.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Wet clutches are complex hydrodynamic devices used in both conventional and electrified drivetrain systems. They couple or de-couple powertrain components for applications such as automatic shifting, engine disconnect and torque vectoring. Clutch engagement behaviors vary greatly, depending on design parameters and operating conditions. Because of their direct impact on vehicle drivability and fuel economy, a predictive CAE model is desired for enabling analytical design verification processes. During engagement, a wet clutch transmits torque through viscous shear and asperity contact. A conventional Coulomb’s model, which is routinely utilized in shift simulations, is inadequate to capture non-linear hydrodynamic effects for higher fidelity analysis. Extensive research has been conducted over the years to derive hydrodynamic torque transfer models based on 1D squeeze film or 3D CFD. They are typically coupled with an elastic asperity contact model for mechanical torque transfer.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

Low voltage hybridization (<60 V) supports engine start/stop, regenerative braking, and constrained torque assist/regeneration at a low cost. This work studies the potential benefits of a novel hybrid system, called a power split supercharger (PSS). A 9 kW motor is shared between boosting the engine or providing hybrid functionalities, allowing it to couple with a small engine and still support good acceleration. However, the PSS operation is limited to only one of the parallel hybrid or boosting modes at each time instance. In this work an equivalent consumption minimization strategy (ECMS) is developed to select the PSS mode and the motor torque during hybrid mode. The PSS operation is simulated over standard EPA drive cycles with an engine mean value model that captures detailed air path and PSS dynamics.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

Flow control devices can enable vehicle drag reduction through the mitigation of separation and by modifying local and global flow features. Passive vortex generators (VG) are an example of a flow control device that can be designed to re-energize weakly-attached boundary layers to prevent or minimize separation regions that can increase drag. Accurate numerical simulation of such devices and their impact on the vehicle aerodynamics is an important step towards enabling automated drag reduction and shape optimization for a wide range of vehicle concepts. This work demonstrates the use of an open-source computational-fluid dynamics (CFD) framework to enable an accurate and robust evaluation of passive vortex generators in support of vehicle drag reduction. Specifically, the backlight separation of the Ahmed body with a 25° slant is used to evaluate different turbulence models including variants of the RANS, DES, and LES formulations.

Hybridization of vehicles is considered as the most promising technology for automakers and researchers, facing the challenge of optimizing both the fuel economy and emission of the road transport. Extensive studies have been performed on power-split hybrid electric vehicles (PS-HEVs). Despite of the fact that their excellent fuel economy performance in city driving conditions has been witnessed, a bottle neck for further improving the fuel economy of PS-HEVs has been encountered due to the inherent engine-generator-motor power circulation of the power-split system under medium-low speed cruising scenarios. Due to the special mechanical constraints of the power-split device (PSD), the conventional periodic cruising strategy like Pulse and Glide cannot be applied to PS-HEVs directly.

Previous work done at the University of Michigan shows the capability of the vacuum-insulated catalytic converter (VICC) to retain heat during soak and the resulting benefits in reducing cold start emissions. This paper provides an improved version of the design which overcomes some of the shortcomings of the previous model and further improves the applicability and benefits of VICC. Also, newer materials have been evaluated and their effects on heat retention and emissions have studied using the 1-D after treatment model. Cold start emissions constitute around 60% to 80% of all the hydrocarbon and CO emissions in present day vehicles. The time taken to achieve the catalyst light-off temperature in a three-way catalytic converter significantly affects the emissions and fuel efficiency. The current work aims at developing a method to retain heat in catalytic converter, thus avoiding the need for light-off and reducing cold start emissions effectively.

As part of its midterm evaluation of the 2022-2025 light-duty greenhouse gas (GHG) standards, the Environmental Protection Agency (EPA) has been acquiring fuel efficiency data from testing of recent engines and vehicles. The benchmarking data are used as inputs to EPA’s Advanced Light Duty Powertrain and Hybrid Analysis (ALPHA) vehicle simulation model created to estimate GHG emissions from light-duty vehicles. For complete powertrain modeling, ALPHA needs both detailed engine fuel consumption maps and transmission efficiency maps. EPA’s National Vehicle and Fuels Emissions Laboratory has previously relied on contractors to provide full characterization of transmission efficiency maps. To add to its benchmarking resources, EPA developed a streamlined more cost-effective in-house method of transmission testing, capable of gathering a dataset sufficient to broadly characterize transmissions within ALPHA.

Wet clutch packs are widely used in today’s automatic transmission systems for gear-ratio shifting. The frictional interfaces between the clutch plates are continuously lubricated with transmission fluid for both thermal and friction management. The open clutch packs shear transmission fluid across the rotating plates, contributing to measurable energy losses. A typical multi-speed transmission includes as many as 5 clutch packs. Of those, two to three clutches are open at any time during a typical drive cycle, presenting an opportunity for fuel economy gain. However, reducing open clutch drag is very challenging, while meeting cooling requirements and shift quality targets. In practice, clutch design adjustment is performed through trial-and-error evaluation of hardware on a test bench. The use of analytical methodologies is limited for optimizing clutch design features due to the complexity of fluid-structure interactions under rotating conditions.

This paper describes a novel design and verification process for analytical methods used in the development of advanced combustion strategies in internal combustion engines (ICE). The objective was to improve brake thermal efficiency (BTE) as part of the US Department of Energy SuperTruck program. The tools and methods herein discussed consider spray formation and injection schedule along with piston bowl design to optimize combustion efficiency, air utilization, heat transfer, emission, and BTE. The methodology uses a suite of tools to optimize engine performance, including 1D engine simulation, high-fidelity CFD, and lab-scale fluid mechanic experiments. First, a wide range of engine operating conditions are analyzed using 1-D engine simulations in GT Power to thoroughly define a baseline for the chosen advanced engine concept; secondly, an optimization and down-select step is completed where further improvements in engine geometries and spray configurations are considered.

Low-Pressure Exhaust Gas Recirculation (LP-EGR) has been shown to be an effective means of improving fuel economy and suppressing knock in downsized, boosted, spark ignition engines. LP-EGR is particularly beneficial at low-speed, high-load conditions, but can lead to combustion instability at lower loads. The transport delays inherent in LP-EGR systems slow the reduction of intake manifold EGR concentrations during tip-out events, which may lead to excessive EGR concentrations at low load. This paper explores leveraging Variable Valve Timing (VVT) as a means of improving the rate of reduction of intake manifold EGR concentration prior to tip-out. At higher boost levels, high valve overlap may result in intake manifold gas passing directly to the exhaust manifold. This short-circuiting behaviour could potentially improve EGR evacuation rates.

Low Pressure (LP) Exhaust Gas Recirculation (EGR) promises fuel economy benefits at high loads in turbocharged SI engines as it allows better combustion phasing and reduces the need for fuel enrichment. Precise estimation and control of in-cylinder EGR concentration is crucial to avoiding misfire. Unfortunately, EGR flow rate estimation using an orifice model based on the EGR valve ΔP measurement can be challenging given pressure pulsations, flow reversal and the inherently low pressure differentials across the EGR valve. Using a GT-Power model of a 1.6 L GDI turbocharged engine with LP-EGR, this study investigates the effects of the ΔP sensor gauge-line lengths and measurement noise on LP-EGR estimation accuracy. Gauge-lines can be necessary to protect the ΔP sensor from high exhaust temperatures, but unfortunately can produce acoustic resonance and distort the ΔP signal measured by the sensor.

The efficiency of power transmission through a Van Doorne type Continuously Variable Transmission (CVT) can be improved by allowing a small amount of relative slip between the engine and driveline side pulleys. However, excessive slip must be avoided to prevent transmission wear and damage. To enable fuel economy improvements without compromising drivability, a CVT control system must ensure accurate tracking of the gear ratio set-point while satisfying pointwise-in-time constraints on the slip, enforcing limits on the pulley forces, and counteracting driveline side and engine side disturbances. In this paper, the CVT control problem is approached from the perspective of Model Predictive Control (MPC). To develop an MPC controller, a low order nonlinear model of the CVT is established. This model is linearized at a selected operating point, and the resulting linear model is extended with extra states to ensure zero steady-state error when tracking constant set-points.

The development of advanced model-based engine control strategies, such as economic model predictive control (eMPC) for diesel engine fuel economy and emission optimization, requires accurate and low-complexity models for controller design validation. This paper presents the NOx and smoke emissions modeling of a light duty diesel engine equipped with a variable geometry turbocharger (VGT) and a high pressure exhaust gas recirculation (EGR) system. Such emission models can be integrated with an existing air path model into a complete engine mean value model (MVM), which can predict engine behavior at different operating conditions for controller design and validation before physical engine tests. The NOx and smoke emission models adopt an artificial neural network (ANN) approach with Multi-Layer Perceptron (MLP) architectures. The networks are trained and validated using experimental data collected from engine bench tests.