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Improving the development of electrified vehicles requires finding efficient methods for the component sizing of complex powertrains, since they may require a control optimization (for the energy management) which, when added to the sizing optimization, significantly increases the design space. A methodology to estimate the fuel consumption with a closed-form expression is found in the literature, which can be used to reduce the control/plant co-optimization to a static optimization problem. This approach can be used by either estimating the consumption of an existing powertrain: the descriptive level; or by predicting how the consumption will vary with the sizing parameters of the powertrain components: the predictive level. In previous works, the descriptive level was applied to the Toyota Mirai, a Fuel Cell Hybrid Electric Vehicle, showing that it can be extended to vehicles with a fuel cell system.

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.

The main purpose of this paper will be to investigate if a small snowmobile gasoline Direct Injected (DI) two-stroke engine has the potential to be adapted for two other types of applications: as a range extender (REX) for electric vehicles and for a motorcycle application. For the REX application, the main requested specifications (NVH, lightweight, compactness, minimum production cost and easy maintenance), correspond well to the main features of DI 2-stroke engines. The potential of a modified production engine operating in part load ultra-low NOx Controlled Auto Ignition (CAI) to meet the Euro 6 emissions standards on the NEDC cycle has already been demonstrated in a previous paper. In the first part of this new paper, we will investigate which solutions can be used to maintain this potential with even stricter legislations based on Euro 6d, WLTP cycle and Real Driving Emissions (RDE).

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.

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.

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).

The paper discusses different alternative choices regarding the simulation and control of combined hybrid vehicles with a simple or compound power split device (PSD). These choices concern the causal representation of PSD both in a vehicle model and in the supervisory controller, the structure of the supervisory controller, and the pathway to generate the setpoints to the component-level controllers. Quasistatic and high-frequency simulations provide the example applications to assess the competing approaches.

The paper presents a new control strategy to compensate the mutual influence of multiple injections in diesel and HCCI engines. The approach is based on a control-oriented model of the process, which represents the dependencies between injection timing, rail pressure, and masses injected. The model is conveniently inverted to yield the injection timing required to obtain a desired mass pattern. The model-based compensator developed is calibrated against measurements taken both on a dedicated injection bench and on a HCCI engine test bench. The compensator is then implemented in the control unit of the latter and validated against measurements of fuel consumption.

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.

The Quasi-Propagatory Model (QPM), a new approach to simulate fluid transients in duct systems, is presented in this paper. The model fills the gap existing between lumped-parameter and distributed-parameter models, allowing the simulation of propagatory effects, even in a compact, lumped-parameter framework. The model, previously developed for homentropic flows, is extended here to include the presence of section variations, friction and heat flows. This is done without substantially altering the model architecture. A theoretical and an experimental validation of the QPM have been carried out. Test cases have been run and the results compared with those yielded by the Method of Characteristics, which was taken as a reference model. Moreover, the model predictions were also compared with experimental data measured in a pipe test rig.