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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.

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.

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.

Due to the increasing concerns on energy and environmental issues, the automotive industry has seen increased growth and development of electric and electrified vehicles [1]. The power-split design is one of the most common drivetrain configurations of a hybrid or electrified vehicle. The propulsion system of a power-split hybrid vehicle typically comprises of an engine drive system in which the engine, drivetrain and generator are mechanically coupled on a planetary gear set driveline while the electric drive system consists of a high voltage battery and a traction motor [2]. In recent years, Hardware-in-the-Loop (HIL) simulation has become an increasingly common approach for controls rapid prototyping and validation as part of the automotive product development cycle [2, 3].

Plugin Hybrid Electric Vehicles (PHEV) have a large battery which can be used for electric only powertrain operation. The control system in a PHEV must decide how to spend the energy stored in the battery. In this paper, we will present a prototype implementation of a PHEV control system which saves energy for electric operation in pre-defined geographic areas, so called Green Zones. The approach determines where the driver will be going and then compares the route to a database of predefined Green Zones. The control system then reserves enough energy to be able to drive the Green Zone sections in electric only mode. Finally, the powertrain operation is modified once the vehicle enters the Green Zone to ensure engine operation is limited. Data will be presented from a prototype implementation in a Ford Escape PHEV

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.

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.

Boost converter is widely applied to hybrid electric vehicles (HEV). Typical control methods employ two proportional-integral (PI) regulators to fulfill DC bus voltage closed-loop control and inductor current closed-loop control, respectively. They have intrinsic performance limitations: 1) slow dynamic response of DC bus voltage regulation; 2) high overshoot voltage during transient state; 3) it is difficult to design four gains best fit all operational conditions. This paper proposes a model prediction based boost converter control method for HEV applications. The proposed control method employs model based instantaneous power prediction and dynamic optimization in real time by minimizing a defined cost function to overcome above issues. First of all, the issues of typical control methods are analyzed. Then, the proposed control method is presented in detail, followed by simulation verification and comparison with PI based control method.

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.

Chrysler, Ford, General Motors, the U.S. Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) have collaborated over the past two years to develop an efficiency test for mobile air conditioner (MAC) systems. Because the effect of efficiency differences between different MAC systems and different technologies is relatively small compared to overall vehicle fuel consumption, quantifying these differences has been challenging. The objective of this program was to develop a single dynamic test procedure that is capable of discerning small efficiency differences, and is generally representative of mobile air conditioner usage in the United States. The test was designed to be conducted in existing test facilities, using existing equipment, and within a sufficiently short time to fit standard test facility scheduling. Representative ambient climate conditions for the U.S. were chosen, as well as other test parameters, and a solar load was included.

Recent events in the world have refocused auto manufacturers to design and produce more fuel efficient and environmentally friendly vehicles. One method to improve the fuel efficiency of vehicles is the hybridization of the vehicle's powertrain. Ford Motor Company is developing a hybrid electric powertrain for the Escape SUV. To quickly develop a control system to smoothly manage two propulsion systems as if it were a conventional powertrain is a difficult challenge. To meet that challenge, extensive use of Computer Aided Engineering simulation and analysis is necessary to quickly design, develop and verify control algorithms ready for production. This paper will present the design and development methodology for the production control algorithms to seamlessly move from the simulation environment to the embedded microcontroller.

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.

Accurate determination of driveshaft torque is desired for robust control, calibration, and diagnosis of propulsion system behaviors. The real-time knowledge of driveshaft torque is also valuable for vehicle motion controls. However, online identification of driveshaft torque is difficult during transient drive conditions because of its coupling with vehicle mass, road grade, and drive resistance as well as the presence of numerous noise factors. A physical torque sensor such as a strain-gauge or magneto-elastic type is considered impractical for volume production vehicles because of packaging requirements, unit cost, and manufacturing investment. This paper describes a novel online method, referred to as Virtual Torque Sensor (VTS), for estimating driveshaft torque based on Machine-Learning (ML) approach. VTS maps a signal from Inertial Measurement Unit (IMU) and vehicle speed to driveshaft torque.

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.

There is a clear trend towards Hybrid Electric Vehicles (HEV) due to the environmental concerns. On the other hand, with increasing hotel and ancillary loads and replacement of more engine driven mechanical and hydraulic loads with electrical loads, automotive systems are becoming more electric. This is the concept of More Electric Cars (MEC) which necessitates going to a higher voltage such as 42V for conventional cars. Can the evaluation of the 42V MEC smoothly lead to the Hybrid Electric Vehicles (HEV) and More Electric Hybrid Vehicles (MEHV)? In this paper, we investigate the feasibility of 42V & 14+42V electrical power systems for MEHV. Technical issues of such a solution are explored in detail.

Diagnosing the Exhaust Gas Recirculation (EGR) Valve remains one of the most challenging problems in emissions control systems diagnostics. California Air Resources Board (CARB) has started imposing specific requirements on automotive companies since 2011 that required the integration of on-board diagnostics (OBD) monitor for the detection and reporting of this type of control malfunction. In this paper, some methodologies of EGR valve system monitoring are investigated and a novel approach is proposed that shows reliable detection capability compared to the other methods. The proposed method requires certain conditions during deceleration fuel shutoff events to intrusively reactivate the EGR system and determine the obstructed valve condition. The method was evaluated on a 2.5L iVCT engine in an experimental Ford Escape Full Hybrid Electric vehicle. Vehicle results are shown and discussed.

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting more attention in the automotive industry with the technology improvement and increasing focus on fuel economy. For EVs and HEVs, especially all-wheel drive (AWD) EVs with two electric motors powering front and rear axles separately, an accurate motor speed measurement through resolver is significant for vehicle performance and drivability requirement, subject to resolver faults including amplitude imbalance, quadrature imperfection and reference phase shift. This paper proposes a diagnostic scheme for the specific type of resolver fault, amplitude imbalance, in AWD EVs. Based on structural analysis, the vehicle structure is analyzed considering the vehicle architecture and the sensor setup. Different vehicle drive scenarios are studied for designing diagnostic decision logic. The residuals are designed in accordance with the results of structural analysis and the diagnostic decision logic.

Variable Voltage Converter (VVC) is adopted in Power-Split structure of hybrid electric vehicles (HEVs) to optimize the Electric-Drive (e-Drive) system performance. With the wider availability of Silicon Carbide (SiC) power semiconductor for automotive applications, there are new opportunities to further optimize and improve performance of VVC, e.g. lower power loss, smaller size, and lighter weight, comparing to use traditional Silicon (Si) IGBT and diode. In this paper, a SiC based VVC is designed, prototyped, and evaluated. In order to maximize the benefits of SiC power devices in VVC application, each key component is carefully designed and selected, including SiC power module, power capacitor, and power inductor. The characterization and evaluation results demonstrate the benefits of advanced SiC devices in VVC design optimization, and such benefits quantified in this paper.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

The recent automotive industry trend towards electrification has created new challenges for NVH engineers. These challenges stem from new powertrain architectures and their complex interactions, the governing control strategies which aim to optimize energy management, and new unmasked sources of excitation. Additionally, vehicle manufacturers are attempting to reduce hardware testing in order to rapidly satisfy increasing production demand and to minimize its costs. Hence, to meet the above-mentioned challenges up front in the development process of Hybrid Electrical Vehicles (HEVs) while balancing competing design objectives of drivability, durability and NVH, a simulation-led design and optimization is required. NVH problems are often the result of mechanisms that originate through complex interactions between different physical domains (flow, electromagnetic, structural/mechanical, control logic, etc.) and the assembly of individual components into a complete system.