Search Results

Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

Governments worldwide are taking actions aiming to achieve a sustainable transportation system that can comprise of minimal pollutant and GHG emissions. Particular attention is given to the real-world emissions, i.e. to the emissions achieved in the real driving conditions, outside of a controlled testing environment. In this framework, interest in vehicle fleet electrification is rapidly growing, as it is seen as a way to simultaneously reduce pollutant and GHG emissions, while on the other hand OEMs are facing a significant increase in the number of tests which are needed to calibrate this new generation of electrified powertrains over a variety of different driving scenarios.

Nowadays, the 48 V vehicle architecture seems to be the perfect bridge between the 12 V system and the costly High Voltage (HV) electrification towards the crucial goal of CO2 and pollutants emissions reduction in combination with enhanced performance. However, this approach leads to an increased complexity in the interaction between different sub-systems targeting the optimization of the Energy Management System (EMS). Therefore, it becomes essential to perform a preliminary hardware assessment, exploring the interactions between the different components and quantifying the cost vs benefit trade-off. To this purpose, an integrated experimental/numerical methodology has been adopted: a comprehensive map-based Hybrid Electric Vehicle (HEV) model has been built, allowing the simulation of a variety of hybrid architectures, including both HV and 48 V systems.

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.

Hybrid Electric Vehicles (HEVs) represent a powerful technology to save fuel and reduce CO₂ emissions, through the synergic use of a conventional internal combustion engine and one or more electric machines. However their performance strongly depends on the control strategy that shares the power demand among the engine and the electric motors at each time instant, with the objective of minimizing a pre-defined cost function over an entire driving cycle, and satisfying, at the same time, any additional constraints. The aim of this work is therefore the definition of a methodology to develop, through numerical simulation, a sub-optimal hybrid powertrain controller: starting from the problem definition, the ideal performance for a case study hybrid architecture was analyzed through a global optimization algorithm in order to point out information which can be used to define new control laws.

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.

Nowadays the increasing demand for sustainable mobility has fostered the introduction of innovative propulsion systems also in the public transport sector in order to achieve a significant reduction of pollutant emissions in highly congested urban areas. Within this context this paper describes the development of the HYBUS, an environmental friendly hybrid bus for on-road urban transportation, which was jointly carried out by Pininfarina and Politecnico di Torino in the framework of the AMPERE project. The first prototype of the bus was built by integrating an innovative hybrid propulsion system featuring a plug-in series architecture into the chassis of an old IVECO 490 TURBOCITY. The bus is 12 meters long and has a capacity of up to 116 passengers in the original layout. The project relied on a modular approach where the powertrain could be easily customized for size and power depending on the specific application.

The high level of electric power available on a Hybrid Electric Vehicle (HEV) enables the introduction of electrical auxiliaries in addition or in substitution to the ones currently available on a conventional powertrain. Among these auxiliaries, electric Superchargers (eSC) for the improvement of the vehicle performance or electrically heated catalysts for the reduction of the light-off time of the after-treatment may dramatically affect the Energy Management System (EMS) of an HEV. Moreover, since these devices are only fluid-dynamically, but not mechanically, linked to the powertrain, they are traditionally neglected in the optimization of the powersplit between internal combustion engine and electric machines by the EMS. The aim of the current work is the development of an EMS that is able to consider in real time the overall electric energy consumption of the entire powertrain.

Plug-in Hybrid Electric Vehicles (PHEVs) are one of the main technology options for reducing vehicle CO2 emissions and helping vehicle manufacturers (OEMs) to meet the CO2 targets set by different Governments from all around the world. In Europe OEMs have introduced a number of PHEV models to meet their CO2 target of 95 g/km for passenger cars set for the year 2021. Fuel consumption (FC) and CO2 emissions from PHEVs, however, strongly depend on the way they are used and on the frequency with which their battery is charged by the user. Studies have indeed revealed that in real life, with poor charging behavior from users, PHEV FC is equivalent to that of conventional vehicles, and in some cases higher, due to the increased mass and the need to keep the battery at a certain charging level.