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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.

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.

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.

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.

This paper focuses on the cranktrain design for Ford's HEV DI Diesel Engine called the DIATA. The design started with the piston pin. The minimum piston pin diameter for the lowest reciprocation weight was achieved by tapering the small end of the connecting rod. Geometry constraints sized the connecting rod's big end diameter, oil film analyses determined the width, and an FEA verified the design. Next, the crankshaft mains were sized to reach an acceptable factor of safety, bending and torsional stiffness, and oil films. Finally, the flywheel was sized to be the minimum weight to reduce transmission gear rattle to an acceptable level.

Due to the complex powertrain layout in hybrid vehicles, different configurations concerning internal combustion engine, electric motor and transmission can be combined - as is demonstrated by currently produced hybrid vehicles ([1], [2]). At the Institute for Combustion Engines (VKA) at RWTH Aachen University a combination of simulation, Design of Experiments (DoE) and numerical optimization methods was used to optimize the combustion engine, the powertrain configuration and the operation strategy in hybrid powertrains. A parametric description allows a variation of the main hybrid parameters. Parallel as well as power-split hybrid powertrain configurations were optimized with regard to minimum fuel consumption in the New European Driving Cycle (NEDC). Besides the definition of the optimum configuration for engine, powertrain and operation strategy this approach offers the possibility to predict the fuel consumption for any modifications of the hybrid powertrains.

Automotive companies have invested a fortune over the last three decades developing real-time embedded control strategies and software to achieve desired functions and performance attributes. Over time, these control algorithms have matured and achieved optimum behavior. The companies have vast repositories of embedded software for a variety of control features that have been validated and deployed for production. These software functions can be reused with minimal modifications for future applications. The companies are also constantly looking for new ways to improve the productivity of the development process that may translate into lower development costs, higher quality and faster time-to-market. All companies are currently embracing Model Based Design (MBD) tools to help achieve the gains in productivity. The most cost effective approach would be to reuse the available legacy software for carry-over features while developing new features with the new MBD tools.

Legislative emissions requirements, customer expectation and environmental concerns are driving the introduction of Hybrid Electric Vehicle (HEV) technologies. In the European market, where diesel powertrain technology has high penetration, Micro Hybrid technology, featuring engine stop/start plus regenerative charging, is attractive due to system cost versus CO2 emission benefits. The availability of the engine stop/start feature in real world usage depends on the control logic taking account of, for example, safety, comfort or other factors. The research reported here involved developing tools to analyze the duration of automatic engine stop events in real world usage taking account of the situations where automatic engine stop would be unavailable. These tools help determine the durability requirements for key system components, in particular the battery, and estimation of the likely fuel savings as a function of the system calibration.

Ford Motor Company has developed a full hybrid electric vehicle with a dual-drive hybrid powertrain configuration that has great potential to overcome some attribute deficiencies in existing hybrid powertrain architectures. This paper first provides an overview of the dual-drive hybrid electric vehicle architecture and its primary operating modes. The paper then presents the energy management control system that selects operating modes and desired powertrain operating points to improve fuel efficiency. Finally, experimental results from a dual-drive hybrid prototype vehicle are used to demonstrate the fuel efficiency improvement.

The desire for improved vehicle fuel economy, driven by high gas prices and concerns over energy independence, have sparked interest and demand for hybrid electric vehicles. Hybrid electric vehicle propulsion systems exhibit complex interactions which need to be understood in order to maximize fuel economy over the range of operating modes. Model-based development processes which use vehicle system models capable of representing the functional behaviors with embedded controls are needed for fast, efficient design of vehicle control systems which manage overall energy usage. Model-based vehicle system development processes have been employed for a Dual Drive HEV system. The process for creating these vehicle system models is described along with an approach for using these models to develop HEV systems. Details of key subsystem models and the process for integration of full vehicle implementation level controls are discussed.

With increasing environmental awareness and higher fuel prices, Hybrid Electric Vehicles (HEVs) are receiving a lot of attention and gaining increased market acceptance. Until recently, technologies needed were not fully mature to make HEVs both viable and affordable. Customer usage varies significantly in terms of speed, acceleration and grades. In order to understand how to exploit the benefits of hybrids, these driving variations must be understood. Through analysis it is shown that each of the hybrid configurations has merits for a particular driving pattern and hence the usage pattern must be taken into account before determining the most suitable hybrid configuration for a customer segment / geographic region.

The technology used in hybrid vehicle concepts is significantly different from conventional vehicle technology with consequences also for the noise and vibration behavior. In conventional vehicles, certain noise phenomena are masked by the engine noise. In situations where the combustion engine is turned off in hybrid vehicle concepts, these noise components can become dominant and annoying. In hybrid concepts, the driving condition is often decoupled from the operation state of the combustion engine, which leads to unusual and unexpected acoustical behavior. New acoustic phenomena such as magnetic noise due to recuperation occur, caused by new components and driving conditions. The analysis of this recuperation noise by means of interior noise simulation shows, that it is not only induced by the powertrain radiation but also by the noise path via the powertrain mounts. The additional degrees of freedom of the hybrid drive train can also be used to improve the vibrational behavior.