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Predicting the fuel economy capability of hybrid electric vehicle (HEV) powertrains by solving the related optimal control problem has been available for a few decades. Dynamic programming (DP) is one of the most popular techniques implemented to this end. Current research aims at integrating further powertrain modeling criteria that improve the fidelity level of the optimal HEV powertrain control behavior predicted by DP, thus corroborating the reliability of the fuel economy assessment. Dedicated methodologies need further development to avoid the curse of dimensionality which is typically associated to DP when increasing the number of control and state variables considered. This paper aims at considerably reducing the overall computational effort required by DP for HEVs by removing the state term associated to the battery state-of-charge (SOC).

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.

Active tire pressure control (ATPC) is an automatic central tire inflation system (CTIS), designed, prototyped, and tested at the Politecnico di Torino, which is aimed at improving the fuel consumption, safety, and drivability of passenger vehicles. The pneumatic layout of the system and the designed solution for on board integration are presented. The critical design choices are explained in detail and supported by experimental evidence. In particular, the results of experimental tests, including the characterizations of various pneumatic components in working conditions, have been exploited to obtain a design, which allows reliable performance of the system in a lightweight solution. The complete system has been tested to verify its dynamics, in terms of actuation time needed to obtain a desired pressure variation, starting from the current tire pressure, and to validate the design.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

A four wheel steer control logic is described. A first control logic release, obtained during previous research activity, is based only on feed forward (F.F.) but is here upgraded merging closed loop control (C.L.). Integration between F.F. and C.L. is described. Rear steering electromechanical actuator frequency response is analyzed, in order to consider its not ideal behaviour during control logic design. Several simulation are performed to qualitatively evaluate the error committed considering an ideal actuator during the control logic design. Specific manoeuvres are chosen to investigate about active system influence on vehicle handling; a 14 degrees of freedom vehicle model is validated in order to compare simulation results with experimental data.

This paper presents the methodology adopted by Politecnico di Torino Vehicle Dynamics Research Team to obtain objective indices for the evaluation of the comfort during the gear change process. Some test drivers and different passengers traveled on a test vehicle and assigned marks on the basis of their subjective feeling of comfort during the gearshifts. The comparison between the most significant subjective evaluations and the experimental values obtained by the instruments located on the vehicle is presented. As a consequence, some indices (based on physical parameters) to evaluate the efficiency and the comfort of the gearshift process are obtained. They are in good agreement with the subjective evaluations of the drivers and the passengers. The second part of the paper presents a driveline and vehicle model which was conceived to reproduce the phenomena experimented on the vehicle. The experimental validation of the model is presented.

The paper presents the main results of a study on the simulation of energy efficient management of on-board electric and thermal systems for a medium-size passenger vehicle featuring a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. A set of advanced technologies has been considered on the basis of very aggressive fuel economy targets: base-engine downsizing and friction reduction, combustion optimization, active thermal management, enhanced aftertreatment and downspeeding. Mild-hybridization has also been added with the goal of supporting the downsized/downspeeded engine performance, performing energy recuperation during coasting phases and enabling smooth stop/start and acceleration. The simulation has implemented a dynamic response to the required velocity and manual gear shift profiles in order to reproduce real-driver behavior and has actuated an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM).

The sustainable use of energy and the reduction of pollutant emissions are main concerns of the automotive industry. In this context, Hybrid Electric Vehicles (HEVs) offer significant improvements in the efficiency of the propulsion system and allow advanced strategies to reduce pollutant and noise emissions. The paper presents the results of a simulation study that addresses the minimization of fuel consumption, NOx emissions and combustion noise of a medium-size passenger car. Such a vehicle has a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. The simulation reproduces real-driver behavior through a dynamic modeling approach and actuates an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). Typical characteristics of parallel hybrid technologies, such as Stop&Start, regenerative braking and electric power assistance, are implemented via an operating strategy that is based on the reduction of total losses.

The aim of this work is the assessment of the predictive capabilities of fast running models, obtained through an appropriate reduction and simplification process from detailed 1D fluid-dynamic models, for a turbocharged s.i. engine under highly transient operating conditions. Simulations results have been compared with experimental data for different types of models, ranging from fully detailed 1D fluid-dynamic models to map-based models, quantifying the degradation of the model accuracy and the reduction in the computational time for different kinds of driving cycles, from moderately transient such as the NEDC to highly dynamic such as the US06.

The demanding CO2 emission targets are fostering the development of downsized, turbocharged and electrified engines. In this context, the need for high boost level at low engine speed requires the exploration of dual stage boosting systems. At the same time, the increased electrification level of the vehicles enables the usage of electrified boosting systems aiming to exploit the opportunities of high levels of electric power and energy available on-board. The aim of this work is therefore to evaluate, through numerical simulation, the impact of a 48 V electric supercharger (eSC) on vehicle performance and fuel consumption over different transients. The virtual test rig employed for the analysis integrates a 1D CFD fast running engine model representative of a 1.5 L state-of-the-art gasoline engine featuring an eSC in series with the main turbocharger, a dual voltage electric network (12 V + 48 V), a six-speed manual transmission and a vehicle representative of a B-SUV segment car.

The Vehicle Energy Consumption calculation Tool (VECTO) is used in Europe for calculating standardised energy consumption and CO2 emissions from Heavy-Duty Trucks (HDTs) for certification purposes. The tool requires detailed vehicle technical specifications and a series of component efficiency maps, which are difficult to retrieve for those that are outside of the manufacturing industry. In the context of quantifying HDT CO2 emissions, the Joint Research Centre (JRC) of the European Commission received VECTO simulation data of the 2016 vehicle fleet from the vehicle manufacturers. In previous work, this simulation data has been normalised to compensate for differences and issues in the quality of the input data used to run the simulations. This work, which is a continuation of the previous exercise, focuses on the deeper meaning of the data received to understand the factors contributing to energy and fuel consumption.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

In order to meet the CO2 emission reduction targets, downsizing coupled with turbocharging has been proven as an effective way in reducing CO2 emissions while maintaining and improving vehicle driveability. As the downsizing becomes widely exploited, the increased boost levels entail the exploration of dual stage boosting systems. In a context of increasing electrification, the usage of electrified boosting systems can be effective in the improvement of vehicle performances. The aim of this work is therefore to evaluate, through numerical simulation, the impact of different voltage (12 V or 48 V) electric superchargers (eSC) on an extremely downsized 1.0L engine on vehicle performance and fuel consumption over different transient manoeuvres.

Nowadays, injection rate shaping and multi-pilot events can help to improve fuel efficiency, combustion noise and pollutant emissions in diesel engine, providing high flexibility in the shape of the injection that allows combustion process control. Different strategies can be used in order to obtain the required flexibility in the rate, such as very close pilot injections with almost zero Dwell Time or boot shaped injections with optional pilot injections. Modern Common-Rail Fuel Injection Systems (FIS) should be able to provide these innovative patterns to control the combustion phases intensity for optimal tradeoff between fuel consumption and emission levels.

H-ergo, a concept of light electric vehicle devoted to personal mobility, will here be presented. H-ergo is a low-noise, user-friendly, zero-emission vehicle, with a pleasant style. Its main features include high payload/vehicle mass ratio, electric energy supplied either by batteries or by fuel cell, ergonomic style in order to transport a driver or a person whit mobility problems, chassis design to minimize cost of production, variable wheelbase (through electric actuator). The paper presents the main ideas on which the vehicle design was based and summarizes the most important results obtained.

This paper presents a novel strategy for the control of the motor torques of a rear wheel drive electric vehicle with the objective of improving the lap time of the vehicle around a racetrack. The control strategy is based upon increasing the size of the friction circle by implementing torque vectoring and tire slip control. A two-level nested control strategy is used for the motor torque control. While the outer level is responsible for computing the desired corrective torque vectoring yaw moment, the inner level controls the motor torques to realize the desired corrective torque vectoring yaw moment while simultaneously controlling the wheel longitudinal slip. The performance of the developed controller is analyzed by simulating laps around a racetrack with a non-linear multi-body vehicle model and a professional human racing driver controller setting.

Reducing computational time has become a critical issue in recent years, particularly in the transportation field, where the complexity of scenarios demands lightweight controllers to run large simulations and gather results to study different behaviors. This study proposes two novel formulations of the Optimal Control Problem (OCP) for the Energy Management System of a Plug-in Hybrid Electric Vehicle (PHEV) and compares their performance with a benchmark found in the literature. Dynamic Programming was chosen as the optimization algorithm to solve the OCP in a Matlab environment, using the DynaProg toolbox. The objective is to address the optimality of the fuel economy solution and computational time. In order to improve the computational efficiency of the algorithm, an existing formulation from the literature was modified, which originally utilized three control inputs.

The paper presents a simulation methodology created to support an integrated safety system development process which was tested for the side impact collision load case. The methodology is based on the coupled and complementary use of two software packages: PreScan and Madymo. PreScan was utilized for designing two traffic scenarios and the sensing and control systems for the side collision recognition, while Madymo was utilized for assessing the effects of pre-crash deployment of thorax airbag. The collision conditions from the scenarios were used as input to define a Madymo side collision model of the host vehicle and to investigate and optimize several airbag deployment parameters: pre-crash deployment time, airbag permeability, vent hole size and vent hole opening time.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.