Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs
Mohammad Reza Amini, Qiuhao Hu, Hao Wang, Yiheng Feng, Ilya, Kolmanovsky, Jing Sun

TL;DR
This study experimentally validates eco-driving and eco-heating strategies for connected hybrid electric vehicles, demonstrating significant energy savings through V2I communication, predictive optimization, and thermal energy storage during real-world city driving at cold temperatures.
Contribution
The paper introduces a real-time predictive optimization framework integrating eco-driving and eco-heating strategies using thermal energy storage in a hybrid electric vehicle.
Findings
Eco-driving reduces fuel consumption by 14.5%.
Eco-heating achieves 4.7% energy savings.
Combined strategies save over 19% energy compared to baseline.
Abstract
This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the…
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Taxonomy
TopicsVehicle emissions and performance · Traffic control and management · Aerodynamics and Fluid Dynamics Research
