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Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.

This paper examines the effect of pulse-and-glide (PnG) driving strategies on the fuel efficiency when applied on parallel HEVs. Several PnG strategies are proposed, and these include the electrical, mechanical, and combined PnG strategies. The electrical PnG strategy denotes the hybrid powertrain control tactics in which the battery is charged or discharged according to the power demanded while maintaining the constant vehicle speed. On the other hand, the mechanical PnG strategy denotes the powertrain control tactics in which the vehicle accelerates or decelerates according to the power load while minimizing the battery usage. The combined PnG strategy involves both electrical and mechanical strategies to find a balanced point in between them. Here, a tradeoff relationship between the fuel efficiency and the vehicle drivability related to the tracking performance of the desired target speed is revealed.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

Pressure variation during engine combustion generates torque fluctuation that is delivered through the driveline. Torque fluctuation delivered to the tire shakes the vehicle body and causes the body components to vibrate, resulting in booming noise. HKMC (Hyundai Kia Motor Company)’s TMED (Transmission Mounted Electric Device) type generates booming noises due to increased weight from the addition of customized hybrid parts and the absence of a torque converter. Some of the improvements needed to overcome this weakness include reducing the torsion-damper stiffness, adding dynamic dampers, and moving the operation point of the engine from the optimized point. These modifications have some potential negative impacts such as increased cost and sacrificed fuel economy. Here, we introduce a method of reducing lock-up booming noise in an HEV at low engine speed.

In this paper, the tradeoff relationship between the Air Conditioning (A/C) system performance and vehicle fuel economy for a hybrid electric vehicle during the SC03 drive cycle is presented. First, an A/C system model was integrated into Ford’s HEV simulation environment. Then, a system-level sensitivity study was performed on a stand-alone A/C system simulator, by formulating a static optimization problem which minimizes the total energy use of actuators, and maintains an identical cooling capacity. Afterwards, a vehicle-level sensitivity study was conducted with all controllers incorporated in sensitivity analysis software, under three types of formulations of cooling capacity constraints. Finally, the common observation from both studies, that the compressor speed dominates the cooling capacity and the EDF fan has a marginal influence, is explained using the thermodynamics of a vapor compression cycle.

The auto industry is responding to the environmental and energy conservation concerns by gradually producing mass-production of hybrid electric vehicles (HEV) as well as conducting development of fuel cell vehicles. One of many challenges of putting a customer-satisfactory HEV on the road is to develop “imperceptible” engine start-stops. This paper presents an investigation of engine start-stop NVH in a power split powertrain HEV. This investigation includes analyzing the root cause of engine start-stop NVH issues, developing the methodology and metric to gauge NVH improvement, and developing measures to resolve the NVH issues. The effectiveness of some proposed counter measures are presented and discussed through vehicle testing results. Finally, the overall NVH improvement of engine start-stop with implementation of practical counter measures is also presented.

A systematic process of optimization is suggested to obtain the best control maps for a parallel type hybrid electric vehicle. Taking the fuel consumption as the cost function and driving cycle as part of the constraints, an optimization problem for CVT pulley ratio control and motor torque control can be formulated. The change of the battery charge state between the start and end point of the given driving cycle also works as a constraint. In order to see the effect of various control strategies on system behavior and overall fuel consumption, a simulation model was built to accommodate the functional blocks representing hybrid powertrain subsystem components and corresponding control units.

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.

The automotive industry is experiencing a profound change due to increasing pressure from environmental and energy concerns. This leads many automakers to accelerate hybrid and electric vehicle development. Generally hybrid and electric vehicles create less noise due to their compact engines (or no engine). However, customer satisfaction could be negatively impacted by the peak whine emitted by electric motor. Unlike conventional gas vehicles, the strategy for reducing motor whine is still largely unexplored. This paper presents an analytical study on electric motor whine radiated from the transmission in a hybrid vehicle. The analysis includes two stages. Firstly, a detailed finite element (FE) model of the transmission is constructed, and case surface velocities are calculated utilizing motor electromagnetic force. Then a boundary element model is built for evaluating noise radiated from the transmission surface using acoustic transfer vector (ATV) method.

Hybrid Electric Vehicle (HEV) prototypes are based off production platforms. Several new systems are added to the vehicle, either to perform hybrid functions or to support them and enhance vehicle performance and fuel economy. All these systems are electrically connected in the vehicle with overlay wiring harnesses. Architecture of overlay wiring harness for the HEV requires identification of new systems and working out their electrical connectivity requirements. This dictates the level of changes required in the vehicle electrical system. Harnesses are built based on the circuit design and location of these systems in the vehicle. EMI requirements, routing and packaging challenges are resolved during the overlay process and testing of the prototype. This paper presents the process of harness design, its architecture and integration challenges in the vehicle.

Ford's Hydrogen Hybrid Research Vehicle (H2RV) is an industry first parallel hybrid vehicle utilizing a hydrogen internal combustion engine. The goal of this drivable concept vehicle is to marry Ford's extensive hybrid powertrain experience with its hydrogen internal combustion engine technology to produce a low emission, fuel-efficient vehicle. This vehicle is seen as a possible bridge from the petroleum fueled vehicles of today to the fuel cell vehicles envisioned for tomorrow. A multi-layered hydrogen management strategy was developed for the H2RV. All aspects of the vehicle including the design of the fuel and electrical systems, placement of high-voltage subsystems, and testing, service, and storage procedures were examined to ensure the safe operation of the vehicle. The results of these reviews led to the design of the hydrogen sensing and mitigation system for the H2RV vehicle.

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.

Hardware-in-the-Loop (HIL) test system-based development has been used throughout Ford Motor Company for some time. The development of control strategies for Hybrid Electric Vehicle (HEV) Electronic Control Units (ECU) has benefited from HIL-based testing. It has proven to be an efficient tool for HEV software strategy development, implementation and validation. The Ford HEV HIL system is a very flexible tool able to simulate the whole HEV control system. Ford's HEV HIL system is based on the capabilities of MathWorks tools, dSPACE ControlDesk1, and ATI Vision2 tools and has demonstrated excellent performance. Comprehensive strategy validation is probably the most time consuming and the most routine task for mature vehicle programs. The goal of this paper is to demonstrate how this task is automated for HEV programs at Ford Motor Company.

Electrified vehicles including Battery Electric Vehicles (BEVs) and Plug-In Hybrid Vehicles (PHEVs) made by Ford Motor Company are fitted with a telematics modem to provide customers with the means to communicate with their vehicles and, at the same time, receive insight on their vehicle usage. These services are provided through the “MyFordMobile” website and phone applications, simultaneously collecting information from the vehicle for different event triggers. In this work, we study this data by using Big Data Methodologies including a Hadoop Database for storing data and HiveQL, Pig Latin and Python scripts to perform analytics. We present electrified vehicle customer behaviors including geographical distribution, trip distances, and daily distances and compare these to the Atlanta Regional Survey data. We discuss customer behaviors pertinent to electrified vehicles including charger types used, charging occurrence, charger plug-in times etc.

In recent years we have witnessed increased discrepancy between fuel economy numbers reported in accordance with EPA testing procedures and real world fuel economy reported by drivers. The debates range from needs for new testing procedures to the fact that driver complaints create one-sided distribution; drivers that get better fuel economy do not complain about the fuel economy, but only the ones whose fuel economy falls short of expectations. In this paper, we demonstrate fuel economy improvements that can be obtained if the driver is properly sophisticated in the skill of driving. Implementation of SmartGauge with EcoGuide into the Ford C-MAX Hybrid in 2013 helped drivers improve their fuel economy on hybrid vehicles. Further development of this idea led to the EcoCoach that would be implemented into all future Ford vehicles.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

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.

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.

Dc-link capacitor sizing considerations are discussed for HEV/EV e-Drive systems. The capacitance value of the dc-link in HEV/EV e-Drive systems affects numerous factors. Some of the most significant are the system stability and the maximum tolerable dc-bus transient voltage with operating point change or with worst-case energy dump into the capacitor. Also requiring attention is the equivalent series resistance and inductance of the capacitor module. The former affects thermal behavior of the capacitor module and the latter affects voltage spikes occurring at every turn-off of a power semiconductor switch. In addition, these factors are dependent on other power-stage component parameters, control structures and controller gains. Also such effects and cross-couplings are operating-point dependent.