Evaluation of Longitudinal ADAS Functions for Fuel Economy Improvement of Class 8 Long Haul Trucks 2023-01-0217

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost.

With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication. Upcoming road grade, traffic lights and lead vehicle speeds can be utilized to optimize vehicle speed profile, energy management and thermal management strategies. While many studies have been conducted in the past to evaluate control strategy changes based on longer time horizon, limited studies have been conducted to evaluate shorter time horizon strategies that dynamically adjust vehicle speed (or suggest vehicle speed) for fuel efficiency. In this study, FEV North America, Inc. has applied a model-based approach to evaluate the fuel economy improvement potential of a connected electrified Class 8 long-haul truck. A system-level 1-D propulsion and thermal system model of an electrified Class 8 truck was simulated in real-world conditions including traffic lights, multiple lead vehicle and varying road grades using GT-SUITE. The look-ahead information on road grade, traffic light schedule and lead vehicle speeds were assumed to be available through GPS, V2X communication and long-range radar sensors. A system-level 1-D propulsion model of a Class 8 truck was developed and simulated in real-world driving conditions including traffic lights, multiple lead vehicles, and varying road grade using GT-SUITE. The look-ahead information on road grade, traffic light schedule, and lead vehicle states were assumed to be available through GPS, V2X communication, and long-range radar sensors. The connectivity information was used to implement ADAS features like Predictive Cruise Control (PCC), Advanced Adaptive Cruise Control (AACC), and Eco-Approach (EA) to optimize the vehicle target speed and evaluate their combined fuel economy benefit on a real-world drive cycle.