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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.

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.

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 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.

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.