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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.

Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO2 emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demands of testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign carried out with this passenger car. The controller is implemented in a vehicle simulation model that is parametrized to replicate the operation of the vehicle.

In 2023, the European Union set more ambitious targets for reducing greenhouse gas emissions from passenger cars: the new fleet-wide average targets became 93.6 g/km for 2025, 49.5 g/km in 2030, going to 0 in 2035. One year away from the 2025 target, this study evaluates what contribution to CO2 reduction was achieved from new conventional vehicles and how to interpret forecasts for future efficiency gains. The European Commission’s vehicle efficiency cost-curves suggest that optimal technology adoption can guarantee up to 50% CO2 reduction by 2025 for conventional vehicles. Official registration data between 2013 and 2022, however, reveal only an average 14% increase in fuel efficiency in standard combustion vehicles, although reaching almost 23% for standard hybrids. The smallest gap between certified emissions and best-case scenarios is of 14 g/km, suggesting that some manufacturers’ declared values are approaching the optimum.

An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

Stoichiometric natural gas (CNG) engines are an attractive solution for heavy-duty vehicles considering their inherent advantage in emitting lower CO2 emissions compared to their Diesel counterparts. Additionally, their aftertreatment system can be simpler and less costly as NOx reduction is handled simultaneously with CO/HC oxidation by a Three-Way Catalyst (TWC). The conversion of methane over a TWC shows a complex behavior, significantly different than non-methane hydrocarbons in stoichiometric gasoline engines. Its performance is maximized in a narrow A/F window and is strongly affected by the lean/rich cycling frequency. Experimental and simulation results indicate that lean-mode efficiency is governed by the palladium’s oxidation state while rich conversion is governed by the gradual formation of carbonaceous compounds which temporarily deactivate the active materials.

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.

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.

Legislative challenges, changing customer needs and the opportunities opened-up by electrification are the major driving forces in today’s automotive industry. Fuel cell vehicles offer the potential for CO2 emission free mobility, especially attractive for heavy duty long-haul range application. The development of key components of fuel cell powered vehicles, namely the fuel cell stack itself as well as the related hydrogen/air supply and thermal management sub-systems, goes hand in hand with various challenges regarding performance, lifetime and safety. The proper layout and sizing of the stack and the related fuel and air supply system components, as well as the suitable dimensioning of the cooling system, are decisive for the overall system efficiency and achievable lifetime.

This paper investigates how different on-board energy management system (EMS) algorithms can affect the total energy consumption considering propulsion, heating, ventilation, and air conditioning (HVAC) operation and thermal comfort requirements. Firstly, an integrated plug-in hybrid electric vehicle (PHEV) powertrain and HVAC model including vehicle cabin has been developed as a demonstrator. Two different EMS algorithms - namely a rule-based and an equivalent consumption minimization strategy (ECMS) one - are applied to the integrated PHEV model and evaluated under different environmental conditions. The results showed that the HVAC system operation affects the total energy consumption benefits when ECMS algorithm is used over the rule-based. ECMS reduces the total energy consumption by 2.5% compared to rule-based without HVAC operation, while the total energy consumption reduction changes to 5.3% and 6.3% when HVAC provides heating and cooling power respectively.

To achieve low tailpipe NOX emissions in Heavy-Duty engines, the rapid warm-up of the exhaust aftertreatment system (EAS) needs to be assisted by the adoption of new technologies to reduce engine-out emissions and increase the EAS conversion efficiency. Engine measures like cylinder deactivation, retarded start of the main injection, late intake valve closing, intake throttling and elevated idle speed can substantially increase the available exhaust gas enthalpy and temperature at the expense of additional fuel as has been shown in the literature. On the other hand, the exhaust system can be optimized in terms of hardware and controls, which is nowadays strongly supported by simulation. However, these simulation studies typically assume a fixed engine hardware and calibration and thus fixed engine-out simulation boundary conditions. Moving forward to tougher and real-world oriented legislation, the fixed cycle and engine-out boundary condition becomes insufficient.

The upcoming Euro 7 regulation introduces the concept of continuous On-Board (emission) Monitoring (OBM), while On-Board Fuel/Energy Consumption Monitoring (OBFCM) is already an integral part of modern vehicles. The current work aims to assess whether on-board data could provide sufficient information to characterize real-world vehicle performance and emissions. Nine Euro 6d-ISC-FCM passenger cars were used, covering a wide range of powertrain technologies, from conventional gasoline and diesel to hybrid (HEV) and plug-in hybrid (PHEV) electric vehicles. Three vehicles were thoroughly tested in the laboratory and on the road, aiming at evaluating in detail the on-board data monitoring system. The evaluation concerned OBFCM device recordings of fuel consumed and distance travelled, as well as tailpipe NOx emissions and exhaust mass flow rate.

The current European fleet of vehicles is ageing and lifetime mileages are rising proportionally. Consequently, a substantial fraction of the vehicle fleet is currently operating at mileages well beyond current durability legislation (≤ 160,000 km). Emissions inventories and models show substantial increases in emissions with increasing mileage, but knowledge of the effect of emissions control system deterioration at very high mileages is sparse. Emissions testing has been conducted on matched pairs (or more) of diesel and gasoline (and CNG) vehicles, of low and high mileage, supplementing the results with in-house data, in order to explore high mileage emission deterioration factors (DF). The study isolated, as far as possible, the effect of emissions deterioration with mileage, by using nominally identical vehicle models and controlling other variables.

Biofuels are a promising alternative to fossil fuels as their availability has been reduced during the last decades and they are the main sources of greenhouse gases emissions. Moreover, the targets of the international regulations include reduction of fossil fuels consumption, and improvement of the sustainability of the vehicle fleet. Blending gasoline with biofuels will result in changes in fuel blending procedures and combustion process especially for the gasoline direct injection (GDI) engines. In this article, flame visualization using chemiluminescence techniques in a Single Cylinder Optical Research Engine (SCORE) is presented, with an adjusted intake pressure of 850 mbar and early intake single injection (280 CAD BTDC), by using 100% hydrocarbon-based gasoline, E10 (90% gasoline - 10% ethanol) and ETBE20 (80% gasoline - 20% ethyl tert-butyl ether). ETBE20 is a potential alternative for E10, as it contains the same amount of renewable fuel and has low water solubility.

In this paper, the multi-physical simulation workflow from electromagnetics to structural dynamics for a squirrel-cage induction machine is explored. In electromagnetic simulations, local forces and rotor torque are calculated for specific speed-torque operation points. In order to consider non-linearities and interaction with control system as well as transmission, time-domain simulations are carried out. For induction machines, the computational effort with full transient numerical methods like finite element analysis (FEA) is very high. A novel reduced order electro-mechanical model is presented. It still accounts for vibro-acoustically relevant harmonics due to pulse-width modulation (PWM), slotting, distributed winding and saturation effects, but is substantially faster (minutes to hours instead of days to weeks per operation point).

In Plug in hybrid electric vehicles (PHEVs), the management of the main drivetrain components and the shift between pure electric and hybrid propulsion is decided by the on-board energy management system (EMS). The EMS decisions have a direct impact on CO2 emissions and need to be optimized to achieve as low emissions as possible. This paper presents optimization methods for EMS algorithms of a parallel P2 PHEV. Two different supervisory control algorithms are examined, employing simulations on a validated PHEV platform. An Equivalent Consumption Minimization Strategy (ECMS) algorithm is implemented and compared to a rule-based one, the latter derived by back-engineering of available experimental data. The different EMS algorithms are analyzed and compared on an equal basis in terms of distance, demanded energy and state of charge levels over different driving cycles.

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Eliminating toxic exhaust emissions, amongst them particulate matter (PM), is one of the driving factors behind the increasing use of electrified vehicles. However, it is frequently overseen that PM arise not only from combustion, but from non-exhaust traffic related causes as well; in particular from the vehicle brakes, tires and the road surface. Furthermore, as electrified vehicles weigh more and typically exhibit higher torques at low speeds, their non-exhaust emissions tend to be higher than for comparable conventional vehicles, especially those generated by tires. Fortunately, tire related emissions are directly related to tire wear, so that limiting tire wear can reduce these emissions as well. This can be accomplished by intelligently modulating the vehicle torque profile in real time, to limit the operation in conditions of higher tire wear.

The latest generation of internal combustion engines may emit significant levels of sub-23 nm particles. The main objective of the Horizon 2020 “DownToTen” project was to develop a robust methodology and provide policy recommendations towards the particle number (PN) emissions measurements in the sub-23 nm region. In order to achieve this target, a new portable exhaust particle sampling system (PEPS) was developed, being capable of measuring exhaust particles down to at least 10 nm under real-world conditions. The main design target was to build a system that is compatible with current PMP requirements and is characterized by minimized losses in the sub-23 nm region, high robustness against artefacts and high flexibility in terms of different PN modes investigation, i.e. non-volatile, volatile and secondary particles.

A vital element of any vehicle-certification test is the use of representative values for the vehicle resistance forces. In most certification procedures, including the WLTP recently adopted by the EU, the latter is achieved mainly through coast down tests. Subsequently, the resistance values measured are used for setting up the chassis-dyno resistances applied during the laboratory measurements. These reference values are obtained under controlled conditions, while a series of corrections are applied to make the test procedure more repeatable and reproducible. In real driving, the actual vehicle road loads are influenced by a series of factors leading to a divergence between the certified fuel consumption values, and the real-world ones. An approach of calculating representative road loads during on-road tests can help to obtain a more unobstructed view of vehicle efficiency and, when needed, confirm the officially declared road loads.

Fuel injection is a key process influencing the performance of Gasoline Direct Injection (GDI) Engines. Injecting fuel at elevated temperature can initiate flash boiling which can lead to faster breakup, reduced penetration, and increased spray-cone angle. Thus, it impacts engine efficiency in terms of combustion quality, CO2, NOx and soot emission levels. This research deals with modelling of flash boiling processes occurring in gasoline fuel injectors. The flashing mass transfer rate is modelled by the advanced Hertz-Knudsen model considering the deviation from the thermodynamic-equilibrium conditions. The effect of nucleation-site density and its variation with degree of superheat is studied. The model is validated against benchmark test cases and a substantiated comparison with experiment is achieved.