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Ford Motor Company has investigated a series hybrid electric vehicle (SHEV) configuration to move further toward powertrain electrification. This paper first provides a brief overview of the Vehicle System Controls (VSC) architecture and its development process. The paper then presents the energy management strategies that select operating modes and desired powertrain operating points to improve fuel efficiency. The focus will be on the controls design and optimization in a Model-in-the-Loop environment and in the vehicle. Various methods to improve powertrain operation efficiency will also be presented, followed by simulation results and vehicle test data. Finally, opportunities for further improvements are summarized.

Through the use of hybrid technology, Ford Motor Company continues to realize enhanced vehicle fuel economy while meeting customer performance and drivability targets. As is characteristic of all Ford Hybrid Electric Vehicles (HEVs), the basis for resolving these competing requirements resides with its Vehicle System Control (VSC) strategy. This strategy implements complex high-level executive controls to coordinate and optimize the desired operational state of the major HEV powertrain subsystems. To ensure that the VSC software meets its intended functionality, a software validation process developed at Research and Advanced Engineering has been integrated as part of the vehicle controls development process. In this paper, this VSC software validation process implemented for a next generation hybrid powertrain is presented. First, an overview of the hybrid powertrain application and the VSC software architecture is introduced.

The increasing trend toward electric and hybrid-electric vehicles (HEVs) has created unique challenges for NVH development and refinement. Traditionally, characterization of in-vehicle powertrain noise and vibration has been assessed through standard operating conditions such as fixed gear engine speed sweeps at varied loads. Given the multiple modes of operation which typically exist for HEVs, characterization and source-path analysis of these vehicles can be more complicated than conventional vehicles. In-vehicle NVH assessment of an HEV powertrain requires testing under multiple operating conditions for identification and characterization of the various issues which may be experienced by the driver. Generally, it is necessary to assess issues related to IC engine operation and electric motor operation (running simultaneously with and independent of the IC engine), under both motoring and regeneration conditions.

The air-hybrid engine absorbs the vehicle kinetic energy during braking, puts it into storage in the form of compressed air, and reuses it to assist in subsequent vehicle acceleration. In contrast to electric hybrid, the air hybrid does not require a second propulsion system. This approach provides a significant improvement in fuel economy without the electric hybrid complexity. The paper explores the fuel economy potential of an air hybrid engine by presenting the modeling results of a 2.5L V6 spark-ignition engine equipped with an electrohydraulic camless valvetrain and used in a 1531 kg passenger car. It describes the engine modifications, thermodynamics of various operating modes and vehicle driving cycle simulation. The air hybrid modeling projected a 64% and 12% of fuel economy improvement over the baseline vehicle in city and highway driving respectively.

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.

The future of the Hybrid Electric Vehicle (HEV) is very promising for the automotive industry. In order to take a full advantage of this concept, a better thermal performance of the electric motor is required. In this study, Computational Fluid Dynamics (CFD) model was first verified through several prototypes testing and then is going to be used to execute a series of design of experiment via simulation. Based on the thermal studies in this paper, the integrated coolant jacket design has a better performance than that of separated one. The thermal performance of the stator with the 3M coating is better than the one with paper liner. In addition, using 3M coating reduces the packaging size of the stator.

Increased cooling demands in Hybrid Electric Vehicles (HEVs), compactness of engine compartment, and the additional hardware under the hood make it challenging to provide an effective cooling system that has least impact on fuel economy, cabin comfort and cost. Typically HEVs tend to have a dedicated cooling system for the hybrid components due to the different coolant temperatures and coolant flow rates. The additional cooling system doubles the hardware, maintenance, cost, weight and affects vehicle fuel economy. In addition to the cooling hardware, there are several harnesses and electronics that need air cooling under the hood. This additional hardware causes airflow restriction affecting the convective heat transfer under the hood. It also affects the radiation heat transfer due to the proximity of hardware close to the major heat sources like the exhaust pipe.

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.

A hybrid electric vehicle (HEV) can utilize the electromechanical path to optimize the ICE operation and implement the regenerative brake, the fuel economy of a vehicle therefore gets improved significantly. Bi-directional Boost converter is usually used in an electric drive system to boost the high voltage (HV) battery voltage to a higher dc-link voltage. The main advantages for a system with Boost converter is that the traction inverter is de-coupled from battery voltage variations causing it to be over-sized. When designing this Boost converter, the switching frequency is a key parameter for the converter design. Higher switching frequency will lead to higher switching loss of power device (IGBT +diode), moreover, it has significant impact on inductor ripple current, HV battery ripple current and input capacitor current. Therefore, the switching frequency is one of the most important parameters for the design and selection of both active and passive components.

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.

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.

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.

In recent times, the development of alternate-fuel vehicles, including those fueled by hydrogen, has become relatively common. While there are potential safety related issues with any combustible fuel, these have been resolved over the last 100+ years. The comfort level with gasoline fuel has resulted from the widespread application of simple safety procedures followed at every stage of gasoline refinement and handling. It is important to have analogous procedures for handling hydrogen-fueled vehicles safely and with confidence. The characteristics of hydrogen, including: a) wide flammability range, b) very low ignition energy, c) odorless and difficult to detect, d) high diffusion rate, e) high buoyancy, f) invisible flame, etc., bolster the need for safe practices and procedures.

Regenerative braking in hybrid electric vehicles is an essential feature to achieve the maximum fuel economy benefit of hybridization. During vehicle braking, the regenerative braking recuperates its kinetic energy, otherwise dissipated into heat due to friction brake, into electrical energy to charge the battery. The recuperation is realized by the driven wheels propelling, through the drivetrain, the electric motor as a generator to provide braking while generating electricity. “Rigid” connection between the driven wheels and the motor is critical to regenerative braking; otherwise the motor could drive the input of the transmission to a halt or even rotating in reverse direction, resulting in no hydraulic pressure for transmission controls due to the loss of transmission mechanical oil pump flow.

The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles.

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.

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.

Electric vehicles (EV) and hybrid electric vehicles (HEV) require high torque/acceleration ability and wide speed range. To meet both of them, the traction machines usually have to be oversized, which results in high volume and weight, high cost, and low efficiency. In practical application, high speed motors combining with gear box provide the expected torque and speed capability. If pole-changing machines are employed to achieve wide torque and speed ranges, gear box and motor size can be reduced in EVs/HEVs. This paper presents a pole-phase modulation motor drive which changes both of poles and phases simultaneously, as a result that the motor extends its torque/speed capability in a flexible way. Simulation results verify the principle and control method for this kind of motor drives.

The advancement of Machine-learning (ML) methods enables data-driven creation of Reduced Order Models (ROMs) for automotive components and systems. For example, Gaussian Process Regression (GPR) has emerged as a powerful tool in recent years for building a static ROM as an alternative to a conventional parametric model or a multi-dimensional look-up table. GPR provides a mathematical framework for probabilistically representing complex non-linear behavior. Today, GPR is available in various programing tools and commercial CAE packages. However, the application of GPR is system dependent and often requires careful design considerations such as selection of input features and specification of kernel functions. Hence there is a need for GPR design optimization driven by application requirements. For example, a moving window size for training must be tuned to balance performance and computational efficiency for tracking changing system behavior.

Hybrid Electric Vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with eAWD capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in co-simulation with CarSim. The paper proposes a control system which utilizes tire force optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal tire force targets to at each tire such that the targets avoid tire saturation.