Development of Predictive Powertrain State Switching Control for Eco-Saving ACC Hitachi, Limited

Format:

Conference/Event

Author/Creator:

Imanishi, Imanishi, author.

Contributor:

Iihoshi, Yoichi

Okada, Takashi

Tashiro, Naoyuki

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

In recent years, improvement of in-use fuel economy is required with tightening of exhaust emission regulation. We assume that one of the most effective solutions is ACC (Adaptive Cruise Control), which can control a powertrain accurately more than a driver. We have been developing a fuel saving ADAS (Advanced Driver Assistance System) application named "Sailing-ACC". Sailing-ACC system uses sailing stop technology which stops engine fuel injection, and disengages a clutch coupling a transmission when a vehicle does not need acceleration torque. This system has a potential to greatly improve fuel efficiency. In this paper, we present a predictive powertrain state switching algorithm using external information (route information, preceding vehicle information). This algorithm calculates appropriate switching timing between a sailing stop mode and an acceleration mode to generate a "pulse-and-glide" pattern. In addition, future behavior of a preceding vehicle is predicted with a probabilistic algorithm to optimize switching timing. This algorithm uses a stereo camera and digital map information. As a result of driving experiments in a test course, fuel efficiency was improved by 43.4% in case of following travel around 40km/h. Additionally we evaluated practical performance of Sailing-ACC by simulation which simulated actual environment including a preceding vehicle and planimetric features. A result of following simulation was shown that the developed algorithm improved fuel efficiency by 38.8% during driving in a city area

Notes:

Vendor supplied data

Publisher Number:

2017-01-0024

Access Restriction:

Restricted for use by site license