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Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

Hybrid Electric Vehicles (HEV) utilize a High Voltage (HV) battery pack to improve fuel economy by maximizing the capture of vehicle kinetic energy for reuse. Consequently, these HV battery packs experience frequent and rapid charge-discharge cycles. The heat generated during these cycles must be managed effectively to maintain battery cell performance and cell life. The HV battery pack cooling system must keep the HV battery pack temperature below a design target value and maintain a uniform temperature across all of the cells in the HV battery pack. Herein, the authors discuss some of the design points of the air cooled HV battery packs in Ford Motor Company’s current model C-Max and Fusion HEVs. In these vehicles, the flow of battery cooling air was required to not only provide effective cooling of the battery cells, but to simultaneously cool a direct current high voltage to low voltage (DC-DC) converter module.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

“Real world emissions” is an emerging area of focus in motor vehicle related air quality. These emissions are commonly recorded using portable emissions measurement systems (PEMS) designed for regulatory application, which are large, complex and costly. Miniature PEMS (mPEMS) is a developing technology that can significantly simplify on-board emissions measurement and potentially promote widespread use. Whereas full PEMS use analyzers to record NOx, CO, and HCs similar to those in emissions laboratories, mPEMS tend to use electrochemical sensors and compact optical detectors for their small size and low cost. The present work provides a comprehensive evaluation of this approach. It compares measurements of NOx, CO, CO2 and HC emissions from five commercial mPEMS to both laboratory and full regulatory PEMS analyzers. It further examines the use of vehicle on-board diagnostics data to calculate exhaust flow, as an alternative to on-vehicle exhaust flow measurement.

Selection of the DC-link capacitance value in an HEV/EV e-Drive power electronic system depends on numerous factors including required voltage/current ratings of the capacitor, power dissipation, thermal limitation, energy storage capacity and impact on system stability. A challenge arises from the capacitance value selection based on DC-link stability due to the influence of multiple hardware parameters, control parameters, operating conditions and cross-coupling effects among them. This paper discusses an impedance-based methodology to determine the minimum required DC-link capacitance value that can enable stable operation of the system in this multi-dimensional variable space. A broad landscape of the minimum capacitance values is also presented to provide insights on the sensitivity of system stability to operating conditions.

Automotive manufacturers continue to move further toward powertrain electrification. There are already many hybrid electric vehicles on the market that are based on a variety of system architectures. Ford Motor Company has investigated a new Dual Drive configuration that promises to overcome some of the attribute deficiencies associated with current architectures. The primary objective of this development project was to demonstrate the fuel economy potential of this system in a vehicle. To accomplish this objective, the team used an internally developed, formal Controls Development Process (CDP) for the control system design and validation. This paper describes the development of the vehicle control system in the context of this process.

Only a part of the energy released from the fuel during combustion is converted to useful work in an engine. The remaining energy is wasted and the exhaust stream is a dominant source of the overall wasted energy. There is renewed interest in the conversion of this energy to increase the fuel efficiency of vehicles. There are several ways this can be accomplished. This work involves the utilization thermoelectric (TE) materials which have the capability to convert heat directly into electricity. A model was developed to study the feasibility of the concept. A Design of Experiment was performed to improve the design on the basis of higher power generation and less TE mass, backpressure, and response time. Results suggest that it is possible to construct a realistic device that can convert part of the wasted exhaust energy into electricity thereby improving the fuel economy of a gas-electric hybrid vehicle.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

This paper presents a purge system model developed for hybrid electric vehicle (HEV) applications. Assessment of purge capability is critical to HEV vehicles due to frequent engine off operation which limits carbon canister purging. The purge model is comprised of subsystems representing purge control strategy, carbon canister and engine plant. The paper is focused on modeling of the engine purge control feature. The purge model validation and purge capability predictions for an example HEV vehicle are presented and discussed.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

During the course of emissions and fuel economy (FE) testing, vehicles that are calibrated to meet Tier 3 emissions requirements currently must demonstrate compliance on Tier 3 E10 fuel while maintaining emissions capability with Tier 2 E0 fuel used for FE label determination. Tier 3 emissions regulations prescribe lower sulfur E10 gasoline blends for the U.S. market. Tier 3 emissions test fuels specified by EPA are required to contain 9.54 volume % ethanol and 8-11 ppm sulfur content. EPA Tier 2 E0 test fuel has no ethanol and has nominal 30 ppm sulfur content. Under Tier 3 rules, Tier 2 E0 test fuel is still used to determine FE. Tier 3 calibrations can have difficulty meeting low Tier 3 emissions targets while testing with Tier 2 E0 fuel. Research has revealed that the primary cause of the high emissions is deactivation of the aftertreatment system due to sulfur accumulation on the catalysts.

Vehicle certification data are used to study the effectiveness of the major powertrain technologies used by car manufacturers to reduce fuel consumption. Methods for differentiating vehicles effectively were developed by leveraging theoretical models of engine and vehicle fuel consumption. One approach normalizes by displacement per unit distance, which puts both fuel used and vehicle work in mean effective pressure units, and is useful when comparing engine technologies. The other normalizes by engine rated power, a customer-relevant output metric. The normalized work/power is proportional to weight/power, the most fundamental performance metric. Certification data for 2016 and 2017 U.S. vehicles with different powertrain technologies are compared to baseline vehicles with port fuel injection (PFI) naturally aspirated engines and six-speed automatic transmissions.

Auto-manufacturers are under increasing pressure to develop powertrain systems for automotive vehicles, which are more efficient regarding fuel consumption, less polluting and still keep high performance levels. Hybrid electrical vehicles (HEV) are considered the most promising technology in sight, considering a time horizon of more ore less twenty years. HEVs combine benefits of electrical vehicles, such zero emission, low noise and high torques at low velocities and advantages of conventional vehicles, such as large autonomy, great reliability and high levels of performance. This paper is focused on the major elements of an HEV powertrain: electrical motors, internal combustion engine (ICE) and batteries, which are described. The paper also presents a comparison of two possible HEV configurations: series and parallel. The mathematical model of a small hybrid vehicle is developed using software ADVISOR.

This paper describes SHEV, a computer program created to simulate hybrid electric vehicles. SHEV employs the time-stepping technique in order to evaluate energy flow in series hybrids, and makes use of a unique method in order to speed up the fuel economy estimation. This estimation method is a refinement of the “state of charge matching” method and is explained in detail. The graphic user interfaces employed in SHEV make it easy to use and give it a look similar to regular Windows‚ applications. This paper also gives some examples of the screens created by the program, depicts its main flowchart, and describes a battery model optimized for this application.

Ford Motor Company has developed a full hybrid electric vehicle with a power-split hybrid powertrain. There are constraints imposed by the high voltage system in such an HEV, that do not exist in conventional vehicles. A significant controls problem that was addressed in the Ford Escape and Mercury Mariner Hybrids was the determination of the desired powertrain operating point such that the vehicle attributes of fuel economy, performance and drivability are met, while satisfying these new constraints. This paper describes the control system that addressed this problem and the tests that were designed to verify its operation.

Global concerns involving smog, ozone formation, carcinogens and greenhouse gases have produced increasingly stringent governmental emission regulations worldwide. In the United States, the Environmental Protection Agency (EPA) introduced Tier II emissions regulations and the California Air Resources Board (CARB) established Low Emission Vehicles (LEV II) and Partial Zero Emission Vehicles (PZEV) legislation. These initiatives have created the most stringent emissions regulations to date. Vehicle manufacturers have had to improve their evaporative emission control systems to comply with these standards. The evaporative emission control system is engineered to protect our environment from fuel vapor emissions. The carbon canister is the most important component of the evaporative emissions system due to its ability to capture fuel vapors continuously during the life of the vehicle. Ford Motor Company redesigned its carbon canisters after utilizing Experimental Design techniques.

This paper presents the development of an analytical model that complements laboratory based experiments to provide a tool for Selective Catalyst Reduction (SCR) applications. The model calibration is based on measured data from NOx reduction performance tests as well as ammonia (NH3) adsorption/desorption tests over select SCR catalyst formulations in a laboratory flow reactor. Only base metal/zeolite SCR samples were evaluated. Limited validations are presented that show the model agrees well with vehicle data from Environmental Protection Agency Federal Test Procedure (EPA FTP) emission assessments. The model includes energy and mass balances, several different NH3 reactions with NOx, NH3 adsorption and desorption algorithms, and NH3 oxidation.

This paper presents a study of urea SCR catalyst aging characteristics and implementation into an analytical model that complements laboratory based experiments for a dynamometer-aged SCR brick. The model calibration is based on measured data taken from a 120k-mile simulated dynamometer-aged base metal/zeolite SCR. Dynamometer aging led to non-uniform axial deterioration with more severe deactivation toward the front of the SCR brick compared to the rear. Data from a 120k-mile simulated hydrothermally oven-aged SCR (uniform axial aging) is used to establish baseline aged NOx performance and NH3 adsorption/desorption behavior. An axial deterioration factor is applied to the baseline model to account for differences between oven and dynamometer aging. The model is exercised using engine out vehicle data to examine how different aging processes (oven vs. dynamometer) affect overall NOx performance during the EPA FTP (Environmental Protection Agency Federal Test Procedure).