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For an automotive application of a fuel cell power system, it is important to maximize the fuel conversion efficiency, while also providing the required peak power levels for vehicle performance. This paper first compares the fuel conversion efficiency and power density of a state-of-the-art direct-methanol fuel cell (DMFC) with the equivalent parameters of a state-of-the-art direct-hydrogen fuel cell (DHFC). The cell level comparison is then extended to the system level for a potential ZEV automotive application. It is concluded that a DMFC-powered vehicle can become directly competitive with a DHFC-powered ZEV (Zero Emission Vehicle) in any localities or market niches where ZEVs are “a condition of doing business” as a vehicle manufacturer. Following a brief outline of the experimental conditions used to generate the DMFC data reported and analyzed in this paper, a technique for optimizing the conversion efficiency of a DMFC is briefly reviewed.

Wind tunnel testing of reduced-scale models is a valuable tool for aerodynamic development during the early stages of a new vehicle program, when basic design themes are being evaluated. Both full-and reduced-scale testing have been conducted for many years at the General Motors Aerodynamics Laboratory (GMAL), but with increased emphasis on aerodynamic drag reduction, it was necessary to identify additional facilities to provide increased test capacity. With vehicle development distributed among engineering teams around the world, it was also necessary to identify facilities local to those teams, to support their work. This paper describes a cooperative effort to determine the correlation among five wind tunnels: GMAL, the Glenn L.

A GT-Power Fast Run Model simplified from detail model for HIL is verified with a bench test using the dSPACE Simulator. Firstly, the conversion process from a detailed model to FRM model is briefly described. Then, the spark timing, fuel pulse with control for FAR, and torque level control are developed for proof of concept. Moreover a series of FRM/Simulink co-simulation and HIL tests are conducted. In the summary, the test results are presented and compared with GT detailed model simulations. The test results show that the FRM/dSPACE HIL stays consistent in most variables of interest under 0.7-0.9 real-time factor condition between 1000 - 5000 RPM. The same steady-state can be reached by RCP controllers or with GT-Power internal controllers. The transient states are close using different control algorithm. The main purpose of HIL application is achieved, despite inconsistencies in performance data like fuel consumption.

To provide a feasible transitional solution from all-by-human driving style to fully autonomous driving style, this paper proposed concept and its control algorithm of a robust lane-keeping ‘co-pilot’ system. In this a semi-autonomous system, Learning based Model Predictive Control (LBMPC) theory is employed to improve system's performance in target state tracking accuracy and controller's robustness. Firstly, an approximate LTI model which describes driver-vehicle-road closed-loop system is set up and real system's deviations from the LTI system resulted by uncertainties in the model are regarded as bounded disturbance. The LTI model and bounded disturbances make up a nominal model. Secondly, a time-varying model which is composed of LTI model and an ‘oracle’ component is designed to observe the possible disturbances numerically and it is online updated using Extended Kalman Filter (EKF).

Optimizing/maximizing regen braking in a hybrid electric vehicle (HEV) is one of the key features for increasing fuel economy. However, it is known [1] that maximizing regen braking by braking the rear axle on a low friction surface results in compromising vehicle stability even in a vehicle which is equipped with an ESP (Enhanced Stability Program). In this paper, we develop a strategy to maximize regen braking without compromising vehicle stability. A yaw rate stability control system is designed for a hybrid electric vehicle with electric rear axle drive (ERAD) and a “hang on” center coupling device which can couple the front and rear axles for AWD capabilities. Nonlinear models of the ERAD drivetrain and vehicle are presented using bond graphs while a high fidelity model of the center coupling device is used for simulation.

A rough road ride assessment provides an insightful evaluation of vehicle responses beyond the frequency range of suspension or steering modes. This is when body structure influence on the vehicle performance can be detected by vehicle occupants. In this paper, a rough road is used to evaluate vehicle ride performance and multi-body simulation (MBS) models are developed along with finite-element (FE) representations of the vehicle body and structure. To produce high fidelity simulation results in the frequency range of interest, various vehicle subsystem modeling contents are examined. A case study of a vehicle model with two different structures is provided. Time histories and frequency based analyses are used to obtain insights into the effects of body structure on vehicle responses. Finally, two metrics (‘Isolation’ and ‘Shake’) are used to distinguish the vehicle ride performance.

AUTOSAR(AUTomotive Open System ARchitecture) establishes an industry standard for OEMs and the supply chain to manage growing complexity to the automotive electronics domain. Increased focus on software based features will prove to be a key differentiator between vehicle platforms. AUTOSAR serves to standardize automotive serial data communication protocols, interaction with respect to hardware peripherals within an ECU and allow ECU implementer to focus on development of unique customer focused features that distinguish product offerings. Adoption strategy and impact assessment associated with leveraging AUTOSAR for an E/E Architecture and the potential challenges that need to be considered will be described in this publication. This publication will also illustrate development strategies that need to be considered w.r.t deploying AUTOSAR like data exchange, consistency to BSW software implementation, MCAL drivers etc.

In automotive noise control, the hood liner is an important acoustic part for mitigating engine noise. The random incidence absorption coefficient is used to quantify the component level acoustic performance. Generally, air gaps, type of substrate materials, density of the substrate materials and Air Flow Resistivity (AFR) of the cover scrim are the dominant control factors in the sound absorption performance. This paper describes a systematic experimental investigation of how these control factors affect flat sample performance. The first stage of this study is full factorial measurement based on current available solutions from sound absorber suppliers. The acoustic absorption of different hood liner constructions, with variations in materials, density, air gaps, and scrims was measured.

Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

This paper presents the most recent advancement in the vehicle development process using the one-step or auto Transfer Path Analysis (TPA) in conjunction with the superelement, component mode synthesis, and automated multi-level substructuring techniques. The goal is to identify the possible ways of energy transfer from the various sources of excitation through numerous interfaces to given target locations. The full vehicle model, consists of superelements, has been validated with the detailed system model for all loadcases. The forces/loads can be from rotating components, powertrain, transfer case, chain drives, pumps, prop-shaft, differential, tire-wheel unbalance, road input, etc., and the receiver can be at driver/passenger ears, steering column/wheel, seats, etc. The traditional TPA involves two solver runs, and can be fairly complex to setup in order to ensure that the results from the two runs are consistent with subcases properly labeled as input to the TPA utility.

An investigation into two new control strategies for the vehicle Anti-lock Braking System (ABS) are made for a possible replacement of current non-optimal slip control methods. This paper applies two techniques in order to maximize the braking force without any wheel locking. The first considers the power dissipated by the brake actuator. This power method does not use slip to construct its reference signal for control. A heuristic approach is taken with this algorithm where one searches for the maximum power dissipated. This can open up easier implementation of regenerative braking concurrently with ABS on an electro-hydraulic braking system. Parameter scheduling is explored in this algorithm. The second algorithm employs the use of perturbation based Extremum Seeking Control (ESC) to provide a reference slip and a Youla controller in a negative feedback loop.

The Oppenheim Correlation (OPC) is a new empirical algorithm, which allows a simple estimate of heat losses to the wall during the combustion in IC-engine. In present paper the results of different applications of OPC will be shown. Even if there are still several needs and ideas for further research it can be stated, that the OPC is a promising possibility of modeling the wall heat losses and due to its simplicity it has to be recommended to the engine community. The OPC can be used not only for didactics purposes, but also for quick simulation of wall heat losses and eventually for the on-line regulation of the cooling system.

Vehicle steering wheel pull is a condition experienced by customers where a constant torque at the steering wheel is required to maintain a straight path. Steering wheel pull may be accompanied by the secondary effects of steering wheel angle misalignment and vehicle thrust angle “dog-tracking.” EPS pull compensation is a feature that can automatically compensate vehicle steering wheel pull. This paper examines customer benefits, operating principles, effectiveness, and robustness of EPS pull compensation in vehicles. Vehicle road test data indicate EPS can correct a severe vehicle steering wheel pull. Using fundamental physics equations, an analysis tool is derived to support further investigation of steering wheel angle misalignment and vehicle thrust angle. The final section presents a designed experiment revealing parameters most influencing vehicle robustness to chassis and road characteristics.

This investigation utilizes energy analysis and statistical methods to optimize step gear automatic transmissions gear selection for fuel consumption. A full factorial matrix of simulations using energy analysis was performed to determine the optimal number of gears and gear ratios that provide the best fuel consumption performance for a particular vehicle - engine application. The full factorial matrix setup as a design of experiment (DOE) was applied to five vehicle applications, each with two engines to examine the potential differences that variations in road load and engine characteristics might have on optimal transmission gearing selection. The transmission gearing options considered in the DOE were number of gears, launch gear ratio and top gear ratio. Final drive ratio was also included due to its global influence on vehicle performance and powertrain operating speeds and torque.

The car as a self-contained microcosm is undergoing radical changes due to the advances of electronic technology. We need to rethink what a "car'' really is and the role of electronics in it. Electronics is now essential to control the movements of a car, of the chemical and electrical processes taking place in it, to entertain the passengers, to establish connectivity with the rest of the world, to ensure safety. What will an automobile manufacturer's core competence become in the next few years? Will electronics be the essential element in car manufacturing and design? We will address some of these issues and we will present some important developments in the area of system design that can strongly impact the way in which a car is designed.

Evaluation of one year of in-use operating data from first generation Chevrolet Volt Extended-Range Electric Vehicle (E-REV) retail customers determined trip initial Internal Combustion Engine (ICE) starts were reduced by 70% relative to conventional vehicles under the same driving conditions. These Volt drivers were able to travel 74% of their total miles in EV without requiring the ICE's support. Using this first generation Volt data, performance of the second generation Volt is projected. The Southern California Association of Governments (SCAG) Regional Travel Survey (RTS) data set was also processed to make comparisons between realistic PHEV constraints and E-REV configurations. A Volt characteristic E-REV was found to provide up to 40 times more all-electric trips than a PHEV over the same data set.

The fact that, in our times, the execution of the exothermic process of combustion (‘heat release”) remains virtually uncontrolled is astonishing. Upon an attempt to rationalize this anomaly on historical grounds, technological means to rectify this astounding state of affairs are presented. They are based on the premise that, in the course of this process, the cylinder-piston enclosure is, in effect, a full-fledged chemical reactor. The salient feature of control is then active intervention into chemical reaction by turbulent jets. Principal elements of the control system are, as in any feedback mechanism, (1) sensors, (2) actuators and (3) a governor. The object of the first is to measure the profile of pressure - the useful output of the process. The second consists of a set of turbulent jet generators for injection of fuel and its mixing with air, as well as for ignition.

Quantifying Hybrid Electric Vehicle (HEV) emissions and fuel consumption is a difficult problem for a number of different reasons: 1) HEVs can be configured in significantly different ways (e.g., series or parallel); 2) the Auxiliary Power Unit (APU) can consist of a wide variety of engines, fuel types, and sizes; and 3) the APU can be operated very differently depending on the energy management system strategy and the type of driving that is performed (e.g., city vs. highway driving). With the future increase of HEV penetration in the vehicle fleet, there is an important need for government agencies and manufacturers to determine HEV emissions and fuel consumption. In this paper, several critical issues associated with HEV emissions and fuel consumption are identified and analyzed, using a sophisticated set of HEV and emission simulation modeling tools.

The automotive industry is one of the most competitive enterprises in the world. Customers face an ever-expanding number of entries in each market segment vying for their business. Sales price, brand image, marketing, etc. all play a role in purchase decisions, but the factor distinguishing products that consistently perform in the market place is the ability to satisfy the customer. Steering character plays a critical role in the customer driving experience and can be one of the most heavily debated topics during a new vehicle program. The proliferation of EPS steering systems now allows engineers to calibrate steering feel to almost any desired specification. This raises a key question: What subjective & objective characteristics satisfy customers in a particular market segment?

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.