NDLI: Predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles

Content Provider	IET Digital Library
Author	Yoon, DoHyun Daniel Ayalew, Beshah Ivanco, Andrej Loiselle, Keith
Abstract	This study presents a radar-based predictive kinetic energy management (PKEM) framework that is applicable as an add-on driver assistance module for a heavy vehicle with an internal combustion engine powertrain. The proposed framework attempts to minimise fuel consumption by estimating the motion of the leading vehicle from radar information and optimising the inputs to the ego vehicle in a predictive manner. The PKEM framework consists of a driver-pedal pre-filter, an interacting multiple model radar-based filter and predictor of traffic object states, and a non-linear model predictive controller. The framework is integrated with established human-driver car-following models representing various driving styles and evaluated over a set of standardised driving cycles. The authors found that the energy-saving benefits can be as much as 23% over the baseline driver-only case with minimal compromises on travel time in urban environments, while the benefits are nearly negligible on the highway cycle. The results included also show the potential trade-offs in accommodating driver-desired inputs.
Starting Page	1824
Ending Page	1834
Page Count	11
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 13, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2020.0380
Journal	IET Intelligent Transport Systems
Publisher Date	2020-11-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automobile Baseline Driver-only Case Driver Assistance Eco-driving Driver Assistance Module Driver Information System Driver-desired Input Driver-pedal Pre-filter Driving Styles Ego Vehicle Energy Consumption Engines Established Human-driver Car-following Model Fuel Economy Heavy Vehicle Hybrid Electric Vehicle Interacting Multiple Model Radar-based Filter Internal Combustion Engine Powertrain Internal Combustion Engines Leading Vehicle Mechanical Drive And Transmissions Nonlinear Model Predictive Controller Optimal Control Optimisation PKEM Framework Power Transmission Predictive Control Predictive Manner Radar Information Radar-based Predictive Kinetic Energy Management Framework Road Safety Road Traffic Road Vehicle Standardised Driving Cycles Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Eco-driving strategies in battery electric vehicle use – how do drivers adapt over time?

2-dimensional human-like driver model for autonomous vehicles in mixed traffic

Influence of hybridisation on eco-driving habits using realistic driving cycles

Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements

Study on control strategy for personalised lane-change on highway

Ecological Vehicle Control on Roads With Up-Down Slopes

Model-Based Eco-Driving and Integrated Powertrain Control for (Hybrid) Electric Vehicles

Curve speed model for driver assistance based on driving style classification

Predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles

Similar Documents

Eco-driving strategies in battery electric vehicle use – how do drivers adapt over time?

2-dimensional human-like driver model for autonomous vehicles in mixed traffic

Influence of hybridisation on eco-driving habits using realistic driving cycles

Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements

Study on control strategy for personalised lane-change on highway

Ecological Vehicle Control on Roads With Up-Down Slopes

Model-Based Eco-Driving and Integrated Powertrain Control for (Hybrid) Electric Vehicles

Curve speed model for driver assistance based on driving style classification

Predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles