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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.

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.

Due to more and more complex powertrain architectures and the necessity to optimize them on the whole driving conditions, simulation tools are becoming indisputable for car manufacturers and suppliers. Indeed, simulation is at the basis of any algorithm aimed at finding the best compromise between fuel consumption, emissions, drivability, and performance during the conception phase. For hybrid vehicles, the energy management strategy is a key driver to ensure the best fuel consumption and thus has to be optimized carefully as well. In this regard, the coupling of an offline hybrid strategy optimizer (called HOT) based on Pontryagin’s minimum principle (PMP) and an online equivalent-consumption-minimization strategy (ECMS) generator is presented. Additionally, methods to estimate the efficiency maps and other overall characteristics of the main powertrain components (thermal engine, electric motor(s), and battery) from a few design parameters are shown.

The paper describes the derivation of a real-time controller for the energy management of a series hybrid city bus. The controller is based on Optimal Control theory and on a control-oriented model of the propulsion system. The model is of the quasi-stationary, backward type, and it is derived from tabulated data of the single components provided by the manufacturers and basic, first-principle equations. The fuel consumption obtained with the optimal controller is compared with that yielded by a conventional controller tracking the battery state-of-charge.

In this paper, an integrated simulation-based methodology demonstrating feasibility and performance of several electric-hybrid concepts is developed. Several advanced tools are coupled to define the specifications of each component of the hybrid powertrain, to select the most promising hybrid architecture and finally to assess the proposed powertrain with regard to CO2 and pollutants emissions. Concurrent minimization of NOx and CO2 emissions enables to find the best compromise to fulfil Euro 6 standards while lowering fuel consumption. This stage consists in an iterative co-optimization of the power split strategies between the electric drive and the Diesel engine and of the engine settings (injection pressure, EGR rate, etc.). The methodology combines optimal control laws and optimization methodology based on global statistical models using single-cylinder design of experiments. After several iterations, this method allows to find the optimal NOx/CO2 trade-off curve.

The authors present the supervisory control of a parallel hybrid powertrain, focusing on several issues related to the real-time implementation of optimal control based techniques, such as the Equivalent Consumption Minimization Strategies (ECMS). Real-time implementation is introduced as an intermediate step of a complete chain of tools aimed at investigating the supervisory control problem. These tools comprise an offline optimizer based on Pontryagin Minimum Principle (PMP), a two-layer real-time control structure, and a modular engine-in-the-loop test bench. Control results are presented for a regulatory drive cycle with the aim of illustrating the benefits of optimal control in terms of fuel economy, the role of the optimization constraints dictated by drivability requirements, and the effectiveness of the feedback rule proposed for the adaptation of the equivalence factor (Lagrange multiplier).

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.