Search Results

Real-world second-by-second vehicle driving cycle data is very important for vehicle research and development. A project solely dedicated to generating such information would be tremendously costly and time consuming. Alternatively, we developed such a database by utilizing two publicly available passenger vehicle travel surveys: 2004-2006 Puget Sound Regional Council (PSRC) Travel Survey and 2011 Atlanta Regional Commission (ARC) Travel Survey. The surveys complement each other - the former is in low time resolution but covers driver operation for over one year whereas the latter is in high time resolution but represents only one-week-long driving operation. After analyzing the PSRC survey, we chose 382 vehicles, each of which continuously operated for one year, and matched their trips to all the ARC trips. The matching is carried out based on trip distance first, then on average speed, and finally on duration.

Realistic vehicle fuel economy studies require real-world vehicle driving behavior data along with various factors affecting the fuel consumption. Such studies require data with various vehicles usages for prolonged periods of time. A project dedicated to collecting such data is an enormous and costly undertaking. Alternatively, we propose to utilize two publicly available vehicle travel survey data sets. One is Puget Sound Travel Survey collected using GPS devices in 484 vehicles between 2004 and 2006. Over 750,000 trips were recorded with a 10-second time resolution. The data were obtained to study travel behavior changes in response to time-and-location-variable road tolling. The other is Atlanta Regional Commission Travel Survey conducted for a comprehensive study of the demographic and travel behavior characteristics of residents within the study area.

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.

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 automotive industry is rapidly expanding its Hybrid, Plug-in Hybrid and Battery Electric Vehicle product offerings in response to meet customer wants and regulatory requirements. One way for electrified vehicles to have an increasing impact on fleet-level CO2 emissions is for their sales volumes to go up. This means that electrified vehicles need to deliver a complete set of vehicle level attributes like performance, Fuel Economy and range that is attractive to a wide customer base at an affordable cost of ownership. As part of “democratizing” the Hybrid and plug-In Hybrid technology, automotive manufacturers aim to deliver these vehicle level attributes with a powertrain architecture at lowest cost and complexity, recognizing that customer wants may vary considerably between different classes of vehicles. For example, a medium duty truck application may have to support good trailer tow whereas a C-sized sedan customer may prefer superior city Fuel Economy.

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.

The 2013 Ford Fusion will be launched with an optional automatic engine start-stop feature. To realize engine start-stop on a vehicle equipped with a conventional powertrain, there are two major challenges in the vehicle system controls. First, the propulsive torque delivery from a stopped engine has to be fast. The vehicle launch delay has to be minimized such that the corporate vehicle attributes can be met. Second, the fuel economy improvement offered by this technology has to justify the cost associated with it. In pursuing fuel economy, the driver's comfort and convenience should be minimally impacted. To tackle these challenges, a vehicle system control strategy has been developed to accurately interpret the driver's intent, monitor the vehicle subsystem's power demands, schedule engine automatic stop and re-start, and coordinate the fast and smooth torque delivery to the wheels.

Torque controls for the engine and electric motors in a Powersplit HEV are keys to the success of balancing fuel economy, driveability, and battery power control. The electric variable transmission (EVT) offers an opportunity to let the engine operate at system-optimal fuel efficient points independently of any load. Existing work shows such a benefit can be realized through a decentralized control structure that translates the driver inputs to independent engine torque and speed control. However, our study shows that the decentralized control structures have a fundamental limitation that arises from the nonminimum phase (NMP) zero in the transfer function from the driver power command to the generator torque change rate, and thus not only is it difficult to obtain smooth generator torque but also it can cause violations on battery power limits during transients. Additionally, it adversely affects the driveability due to the generator torque transients reflected at the ring gear.

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.

Environmental awareness and fuel economy legislation has resulted in greater emphasis on developing more fuel efficient vehicles. As such, achieving fuel economy improvements has become a top priority in the automotive field. Companies are constantly investigating and developing new advanced technologies, such as hybrid electric vehicles, plug-in hybrid electric vehicles, improved turbo-charged gasoline direct injection engines, new efficient powershift transmissions, and lighter weight vehicles. In addition, significant research and development is being performed on energy management control systems that can improve fuel economy of vehicles. Another area of research for improving fuel economy and environmental awareness is based on improving the customer's driving behavior and style without significantly impacting the driver's expectations and requirements.

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.

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.