Demonstration of Energy Consumption Reduction in Class 8 Trucks Using Eco-Driving Algorithm Based on On-Road Testing

2022-01-0139

03/29/2022

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
Vehicle to Everything (V2X) communication has enabled on-board access to information from other vehicles and infrastructure. This information, traditionally used for safety applications, is increasingly being used for improving vehicle fuel economy [1-5]. This work aims to demonstrate energy consumption reductions in heavy/medium duty vehicles using an eco-driving algorithm. The algorithm is enabled by V2X communication and uses data contained in Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) to generate an energy-efficient velocity trajectory for the vehicle to follow. An urban corridor was modeled in a microscopic traffic simulation package and was calibrated to match real-world traffic conditions. A nominal reduction of 7% in energy consumption and 6% in trip time was observed in simulations of eco-driving trucks. Next, track testing of representative velocity profiles was executed based on SAE J1321 recommended practices [6], which showed good agreement with simulation results. The team also went through an exercise to understand the achievable upper bounds on energy consumption benefits based on a drive cycle synthesized by National Renewable Energy Laboratory (NREL) for Port Drayage application [7]. The velocity trajectory generation using a calibrated traffic simulation, use of offline smoothing routines to understand upper bounds, and track testing based on J1321 procedures contribute towards the novelty of this work.
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DOI
https://doi.org/10.4271/2022-01-0139
Pages
9
Citation
Bhagdikar, P., Gankov, S., Frazier, C., Rengarajan, S. et al., "Demonstration of Energy Consumption Reduction in Class 8 Trucks Using Eco-Driving Algorithm Based on On-Road Testing," SAE Technical Paper 2022-01-0139, 2022, https://doi.org/10.4271/2022-01-0139.
Additional Details
Publisher
Published
Mar 29, 2022
Product Code
2022-01-0139
Content Type
Technical Paper
Language
English