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Fuel consumption is an essential factor that requires to be minimized in the design of a vehicle powertrain. Simple energy models can be of great help - by clarifying the role of powertrain dimensioning parameters and reducing the computation time of complex routines aiming at optimizing these parameters. In this paper, a Fully Analytical fuel Consumption Estimation (FACE) is developed based on a novel GRaphical-Analysis-Based fuel Energy Consumption Optimization (GRAB-ECO), both of which predict the fuel consumption of light- and heavy-duty series hybrid-electric powertrains that is minimized by an optimal control technique. When a drive cycle and dimensioning parameters (e.g. vehicle road load, as well as rated power, torque, volume of engine, motor/generators, and battery) are considered as inputs, FACE predicts the minimal fuel consumption in closed form, whereas GRAB-ECO minimizes fuel consumption via a graphical analysis of vehicle optimal operating modes.

With the increase of heavy-duty transportation, more fuel efficient technologies and services have become of great importance due to their environmental and economical impacts for the fleet managers. In this paper, we first develop a new analytical model of the heavy-truck for its dynamics and its fuel consumption, and valid the model with experimental measurements. Then, we propose a bi-level optimization approach to reduce the fuel consumption, thus the CO2 emissions, while ensuring several safety constraints in real-time. Numerical results show that important reduction of the fuel consumption can be achieved, while satisfying imposed safety constraints.

Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature.