Coasting Technology for Real-World Fuel Economy Improvement of a Hybrid Vehicle

2020 ◽  
Author(s):  
Tomoya Yamaguchi
Author(s):  
Brian S. Fan ◽  
Amir Khajepour ◽  
Mehrdad Kazerani

Recent development of hybrid vehicles in the automotive industry has demonstrated the capability of reducing fuel consumption while maintaining vehicle performance. The purpose of this paper is to present a hybrid vehicle model created in MATLAB and ADAMS, and its fuel economy improvement over a conventional vehicle system. The hybrid vehicle model discussed in this paper utilizes the Honda IMA (Integrated Motor Assist) architecture. The powertrain components’ power output calculation and the control logic were modeled in MATLAB/Simulink, while the mechanical inertial components were modeled in ADAMS. Communication between MATLAB and ADAMS was established by ADAMS/Controls. The vehicle model created using MATLAB and ADAMS provides a more accurate, more realistic, and a highly flexible simulation platform. In order to evaluate the accuracy of the MATLAB/ADAMS hybrid vehicle model, simulation results were compared to the published data of ADVISOR. Fuel economy of hybrid and conventional vehicle models were compared using the EPA New York City Cycle (NYCC) and the Highway Fuel Economy Cycle (HWFET). The hybrid vehicle demonstrated 8.9% and 14.3% fuel economy improvement over the conventional vehicle model for the NYCC and HWFET drive cycles, respectively. The MATLAB/ADAMS vehicle model presented in this paper, demonstrated the fuel economy advantage of the hybrid vehicle over the conventional vehicle model, while offering a simulation platform that is modular, flexible, and can be conveniently modified to create different types of vehicle models.


Author(s):  
Daniel F. Opila ◽  
Xiaoyong Wang ◽  
Ryan McGee ◽  
R. Brent Gillespie ◽  
Jeffrey A. Cook ◽  
...  

Hybrid vehicle fuel economy and drive quality are coupled through the “energy management” controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using shortest path stochastic dynamic programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SP-SDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets.


2015 ◽  
Author(s):  
Deepak Sharma ◽  
Sreenath K Reghunath ◽  
Ashwini Athreya

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