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.

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.

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 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.