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On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements, but meanwhile, the first generation of a complete portfolio of truck sizes is developed and available on the market. In these early applications, many compromises were accepted to overcome component availability, but meanwhile, the supply chain can address the specific needs of electric trucks. With that, the optimization towards higher usability and lower costs can be moved to the next level. Especially for long-haul trucks, efficiency is a driving factor for the total costs of ownership. Besides the propulsion system, all other systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the driving range.

Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

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.

Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

This paper presents a numerical methodology to generate lookup tables that provide d- and q-axis stator current references for the control of electric motors. The main novelty with respect to other literature references is the introduction of the iron power losses in the equivalent-circuit electric motor model implemented in the optimization routine. The lookup tables generation algorithm discretizes the motor operating domain and, given proper constraints on maximum stator current and magnetic flux, solves a numerical optimization problem for each possible operating point to determine the combination of d- and q- axis stator currents that minimizes the imposed objective function while generating the desired torque. To demonstrate the versatility of the proposed approach, two different variants of this numerical interpretation of the motor control problem are proposed: Maximum Torque Per Ampere and Minimum Electromagnetic Power Loss.

This paper presents the analysis of an innovative braking system as an alternative and environmentally friendly solution to traditional automotive friction brakes. The idea arose from the need to eliminate emissions from the braking system of an electric vehicle: traditional brakes, in fact, produce dust emissions due to the wear of the pads. The innovative solution, called Zero-Emissions Driving System (ZEDS), is a system composed of an electric motor (in-wheel motor) and an innovative brake. The latter has a geometry such that it houses MagnetoRheological Fluid (MRF) inside it, which can change its viscous properties according to the magnetic field passing through it. It is thus an electro-actuated brake, capable of generating a magnetic field passing through the fluid and developing braking torque. A performance analysis obtained by a simulation model built on Matlab Simulink is proposed.

The dynamic model is built in Siemens Simcenter Amesim platform and simulates the performances on track of JUNO, a low energy demanding Urban Concept vehicle to take part in the Shell Eco-Marathon competition, in which the goal is to achieve the lowest fuel consumption in covering some laps of a racetrack, with limitations on the maximum race time. The model starts with the longitudinal dynamics, analysing all the factors that characterize the vehicle’s forward resistance, like aerodynamic forces, altimetry changes and rolling resistance. To improve the correlation between simulation and track performances, the model has been updated with the implementation of a Single-Track Model, including vehicle rotation around its roll axis, and a 3D representation of the racetrack, with an automatic trajectory following control implemented. This is crucial to characterise the vehicle’s lateral dynamics, which cannot be neglected in simulating its performances on track.

The design and testing of innovative components and control logics for future vehicular platform represents a challenging task in the automotive field. The use of scale model vehicles constitutes an interesting alternative for testing assessment by decreasing time and cost efforts with a potential benefit in terms of safety. The target of this research work is the development of a customized scale vehicle platform for verifying and validating innovative control strategies in safe conditions and with cost reduction. Consequently, the electrification of a radio-controlled 1:5 scale vehicle is carried out and a customized remote real-time controller is installed onboard. One of the main features of this commercial product is its modular characteristics that allows the modification of some component properties, such as the viscous coefficient of the shock absorbers, the stiffness of the springs and the suspension geometry.

In view of BS-VI emission norms implementation in Commercial Vehicle (CV) application, the emissions are not only confirmed in standard condition but also in non-standard condition including different combinations of ambient temperature and pressure especially for checking the emission in WNTE cycle. However, achieving the emissions in different environmental conditions require physical emission calibration to be performed in those conditions. Hence, engine must be calibrated in climatic test chambers to ensure emission in different climatic conditions leading to multifold increase in the calibration effort. With addition of BS-VI emission regulation, After Treatment System (ATS) is a mandatory requirement to reduce the tail pipe emissions. Efficient functioning of ATS requires enough heating to convert the engine out emissions. Vehicle level Real Drive Emission (RDE) measurement without Conformity Factor (CF) limitation are added as an important legislative requirement.

Today, the battery development process for automotive applications is relatively decoupled from the vehicle integration and system validation phase. Battery pack design targets are often disregarded at very early development phases even though they are thoroughly linked to the vehicle-level requirements such as performance, lifetime and cost. Here, AVL proposes a methodology guided by virtual testing techniques to frontload vehicle-level validation tasks in the earlier phase of battery pack testing. This paper focuses on the benefits of the methodology for both battery suppliers and automotive OEMs. Applications will be explained, based on a modular virtual testing toolchain, which involves the simulation platform and models as well as the generation of model parameters and test cases.

This paper gives insights in the theoretical measurement uncertainty of E-Drive rotor position dependent results, like Id and Iq calculations, done by a modern propulsion power analyzer (PA). The calculation of Id and Iqis fundamental to perform control optimization and application tasks for an E-Drive system. To optimize the E-Drive system application towards e.g., best efficiency, best performance, or improved NVH the importance of the testing toolchain is described: a power analyzer delivering the required results, an automation system, and a Design of Experiment tool to set improved target values. Consequently, inverters applications featuring field-oriented control (FOC) with permanent magnet synchronous machines (PMSM) are updated with a chosen control strategy. For achieving a certain behavior of an E-Drive, different degrees of freedom in the Inverter Control Unit are available; Lookup tables Id and Iq represent two fundamental application labels to be considered.

The major objective of this paper is to develop thermal management strategy targeting optimum performance of Selective Catalytic Reduction (SCR) catalyst in a Medium Duty Diesel Engine performing in BS6 emission cycles. In the current scenario, the Emissions Norms are becoming more stringent and with the introduction of Real Drive Emission Test (RDE) and WHTC test comprising of both cold and hot phase, there is a need to develop techniques and strategies which are quick to respond in real time to cope with emission limit especially NOx. SCR seems to be suitable solution in reducing NOx in real time. However, there are limitations to SCR operating conditions, the major being the dosing release conditions which defines the gas temperature at which DEF (Diesel Exhaust Fluid) can be injected as DEF injection at lower gas temperatures than dosing release will lead to Urea deposit formation and will significantly hamper the SCR performance.

Ammonia is a new type of carbon-free fuel with low cost, clean and safe. The research and application of zero-carbon fuel internal combustion engines has become the mainstream of future development. However, there still exist problems should be solved in the application of ammonia fuel. Due to the lower flame laminar speed and higher ignition temperature, ammonia may have unstable combustion phenomena. In this work, the characteristics of ammonia combustion have been investigated, based on controllable thermal activated atmosphere burner. The ignition delay has been used to analyze the ammonia combustion characteristics. With the increase in co-flow temperature, the ignition delay of ammonia/air has an obvious decline. In order to investigate the emission characteristics of ammonia, CHEMKIN is used to validate the different chemical reaction mechanisms and analyse the ammonia emissions.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

This study assesses the capabilities of dynamic wireless power transfer with respect to range extension and payload capacity of heavy-duty trucks. Currently, a strong push towards tailpipe CO2 emissions abatement in the heavy-duty transport sector by policymakers is driving the development of battery electric trucks. Yet, battery-electric heavy-duty trucks require large battery packs which may reduce the payload capacity and increase dwell time at charging stations, negatively affecting their acceptance among fleet operators. By investigating various levels of development of wireless charging technology and exploring various deployment scenarios for an electrified highway lane, the potential for a more efficient and environmentally friendly battery sizing was explored.

The current electrification trend involving hybrid and electric vehicles requires accurate tools to evaluate performance and reliability of electric powertrains’ control systems. Thanks to Hardware in the Loop (HiL) technique, verification, validation and virtual calibration of Electronic Control Systems can be performed without physical plants, addressing the need of frontloading, cost and time reduction of new vehicles control systems development. However, HiL applications with power electronics controllers brings several concerns due to the extremely low timestep needed for accurate simulation of electromagnetic phenomena, making FPGA-based simulation the only option. Moreover, thermal aspects of electric motors are very important from the control perspective as complex thermal management control strategies are implemented to improve the efficiency and to prevent overheating that can cause permanent damage to the electrical machine.

Lithium Ion (Li-ion) batteries have emerged as the dominant technology for electric mobility due to their performance, stability, and long cycle life. Nevertheless, there are emerging environmental and economic issues from Li-ion batteries related to depleting critical resources and their potential shortage. This paper focuses on developing the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) of a generic Li-ion battery pack with a Nickel-Manganese-Cobalt (NMC) cathode chemistry, being the most used, and a capacity of 95 kWh as an average between different carmakers. The LCA and LCC include all the relevant phases of the life cycle of the product. The costs related to the LCC assessment have been taken as secondary data. Lastly, the same system boundary has been chosen both for the LCA and LCC.

This works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called ego-vehicle with other vehicles and infrastructure using V2V and/or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them.