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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.

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.

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

Custom functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. Typical examples include mathematical functions, filtering routines and lookup tables. The custom routines are very efficient and have been in production for many years [ 1 ]. These hand-crafted functions can be reused in new control algorithm designs being developed using Model Based Design (MBD) tools. The next generation of vehicle control software may contain a mix of both automatically generated software and manually developed code. At Ford Motor Company, the code is automatically generated from control algorithm models that are developed using The MathWorks tool chain. Depending on the project-specific needs, the control algorithm models are automatically translated to efficient C code using either The Math Works Real-Time Workshop Embedded Coder (RTW-EC) or dSPACE TargetLink production code generators.

Hybrid electric vehicle (HEV) powertrains have become key to developing environmentally friendly and fuel efficient vehicles. As such, companies are continually investing in developing new hybrid powertrain architectures. Ford Motor Company has developed a new “Dual-Drive” full hybrid electric vehicle that overcomes some attribute deficiencies of existing hybrid powertrain architectures due to the kinematic arrangement of the engine, motors and driveline components. This hybrid powertrain is comprised of conventional powertrain components as its base with an electric motor on the rear axle, and a crank integrated starter generator, engine and transmission on the front axle. It forms a complex configuration which provides fuel economy improvement over a conventional powertrain.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

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.

With the introduction of new technologies ranging from developing new alternative energy vehicles to passive and active safety systems, the automakers are responding to the increased complexity of the control system by embracing Model Based Design (MBD) and Auto-code Generation (ACG) tools for control system design. This translates into lower development costs, higher quality and faster time-to-market. The Ford Motor Company production hybrid group launched a pilot project to study the feasibility of using MBD to speed up the development and testing of the next generation Torque Monitor software. This software uses a custom data storage format, called Double Store Variable (DSV) format, for all the critical signals. Each variable contains two fields, one for storing the actual data and the second for storing a transformed copy (e.g. one's complement) of the data. This allows the software to detect run-time corruption of the data in real-time.

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.

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.

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.

Ford Motor Company has developed an Integrated Modeling Environment (IME) for hybrid electric vehicle (HEV) control system development. This paper presents the Integrated Modeling Environment which facilitates the design and development methodology for the production control algorithms to seamlessly move from simulation to the embedded microcontroller environment. The IME encompasses requirement management, system analysis and verification testing at multiple levels of the Systems Engineering V. In addition, the application of this environment for developing HEV control system (production algorithms and code) is also presented.

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.