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It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

Significant exhaust enthalpy is wasted in gasoline turbocharged direct injection (GTDI) engines; even at moderate loads the WG (Wastegate) starts to open. This action is required to reduce EBP (Exhaust Back Pressure). Another factor is catalyst protection, placed downstream turbine. Lambda enrichment is used to perform this. However, the conventional turbine has a temperature drop across it when used for energy recovery. Catalyst performance is critical for emissions, therefore the only location for any additional device is downstream of it. This is a challenge for any additional energy recovery, but a smaller turbine is a design requirement, optimised to work at lower operating pressure ratios. A WAVE model of the 2.0L GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated with steady state dynamometer data to estimate drive cycle benefit.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance.

Full hybrid electric vehicles are usually defined by their capability to drive in a fully electric mode, offering the advantage that they do not produce any emissions at the point of use. This is particularly important in built up areas, where localized emissions in the form of NOx and particulate matter may worsen health issues such as respiratory disease. However, high degrees of electrification also mean that waste heat from the internal combustion engine is often not available for heating the cabin and for maintaining the temperature of the powertrain and emissions control system. If not managed properly, this can result in increased fuel consumption, exhaust emissions, and reduced electric-only range at moderately high or low ambient temperatures negating many of the benefits of the electrification. This paper describes the development of a holistic, modular vehicle model designed for development of an integrated thermal energy management strategy.

Powertrain thermal encapsulation has the potential to improve fuel consumption and CO2 via heat retention. Heat retained within the powertrain after a period of engine-off, can increase the temperature of the next engine start hours after key-off. This in turn reduces inefficiencies associated with sub-optimal temperatures such as friction. The Ambient Temperature Correction Test was adopted in the current work which contains two World-wide harmonised Light duty Test Procedure (WLTP) cycles separated by a 9-hour soak period. A coupled 1D - 3D computational approach was used to capture heat retention characteristics and subsequent warm-up effects. A 1-D powertrain warm-up model was developed in GT-Suite to capture the thermal warm-up characteristics of the powertrain. The model included a temperature dependent friction model, the thermal-hydraulic characteristics of the cooling and lubrication circuits as well as parasitic losses associated with pumps.

Market trends for increased engine power and more electrical energy on the powergrid (3kW+), along with customer demands for fuel consumption improvements and emissions reduction, are driving requirements for component electrification, including turbochargers. GTDI engines waste significant exhaust enthalpy; even at moderate loads the WG (Wastegate) starts to open to regulate the turbine power. This action is required to reduce EBP (Exhaust Back Pressure). Another factor is catalyst protection, where the emissions device is placed downstream turbine. Lambda enrichment or over-fueling is used to perform this. However, the turbine has a temperature drop across it when used for energy recovery. Since catalyst performance is critical for emissions, the only reasonable location for an additional device is downstream of it. This is a challenge for any additional energy recovery, but a smaller turbine is a design requirement, optimized to operate at lower pressure ratios.

This paper surveys publications on automotive powertrain control, relating to modern GTDI (Gasoline Turbocharged Direct Injection) engines. The requirements for gasoline engines are optimising the airpath but future legislation suggests not only a finely controlled airpath but also some level of electrification. Fundamentals of controls modelling are revisited and advancements are highlighted. In particular, a modern GTDI airpath is presented based on basic building blocks (volumes, turbocharger, throttle, valves and variable cam timing or VCT) with an example of a system interaction, based on boost pressure and lambda control. Further, an advanced airpath could be considered with applications to downsizing and fuel economy. A further electrification step is reviewed which involves interactions with the airpath and requires a robust energy management strategy. Examples are taken of energy recovery and e-machine placement.

Due to the increasing complexity of modern systems, demands for a reduced time to market, lower costs and more rapid product evolution use is made of simulation methods in engineering development. An executable dynamic simulation model may be used to define a complex system from which engineers can observe system behavior and make decisions based on better quality information thus coordinating development efforts more effectively. This work presents two models, both real time capable; a test cell model and a vehicle and driver model with a well defined architecture that helps facilitate Simulation Based Development (SBD) efforts relating to powertrain and drivetrain development. The models are created with a well defined architecture (Flexible Architecture for Simulation Based Development) and run (simulated) through the NEDC and US06 drive cycles.

The teaching of engine mapping and calibration provides a unique challenge to Universities and Technical institutes the world over. The engine test cell facilities required for such tuition is prohibitively expensive for many organizations, for those fortunate enough to have the facilities their use is often already oversubscribed. In any case it is not desirable to have untrained operatives experimenting with expensive and potentially dangerous equipment without very close time intensive supervision. In the School of Engineering Design and Technology at the University of Bradford, although fortunate enough to have a number of state-of-the-art transient engine test cells, these safety concerns are contrasted against students' frustrations at a lack of practical experience in this area for the above reasons.

The strict regulation of environmental laws, the oil price and restricted resources has made the vehicle manufacturers to use other energy resources instead of fuel oil. Iran is recognized as the second holder of gas reservoirs in the world and can use hydrocarbon gases broadly in particular compressed natural gas (CNG) as the fuel for vehicles specifically in its public transportation fleet and thereby reduce the consumption of diesel fuel and gasoline. This will bring about the reduction of environmental pollutants and reduce the economic costs of transportation sector. With regard to the climatic situation of Iran and concerning the existence of broad network of gas distribution, CNG is a suitable alternative for other fuels. Therefore, developing bi-fuel engine (gasoline and CNG) in the short and middle term strategy for achieving this important subject will be necessary.

Low frequency longitudinal vibrations resulting from driver throttle inputs are a common problem in modern passenger cars. This phenomenon, which is commonly referred to as shuffle or shunt, is due to sudden changes in the engine torque exciting torsional oscillations in the driveline. This paper presents a dynamic model of a vehicle driveline for the optimization of low frequency torsional vibration. The model used is first validated against experimental tests. Parameter sensitivity studies have been carried out using the model to identify the important components affecting shuffle. Three key parameters have been chosen from the parameter study. To optimize these key factors, Genetic Algorithms (GAs) have been used in this multi-parameter optimization problem. The results obtained from GAs have been compared with the calculus based optimization techniques.