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Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

Plug-In Hybrid Vehicles (PHEV) have been of significant importance recently to comply with future CO2 and pollutant emissions limit. However, performance of these vehicles is closely related to the energy management strategy (EMS) used to ensure minimum fuel consumption and maximize electric driving range. While conventional EMS concepts are developed to operate in wide range of scenarios, this approach could potentially compromise the fuel consumption benefit due to the omission of route and traffic information. With the advancements in the availability of real-time traffic, navigation and driving route information, the EMS can be further optimized to extract the complete potential of a PHEV. In this context, this paper presents application of predictive energy management (PEM) functionalities combined with information such as live traffic data to reduce the fuel consumption for a P1/P3 configuration PHEV vehicle.

With the increasing focus on reducing CO2 emissions to combat global warming and climate change, the automotive industry is exploring near zero-emission alternative fuels to replace traditional fossil-based fuels like diesel, gasoline, and CNG. Methanol is a promising alternative fuel that is being evaluated in India due to its easy transportation and storage, as well as its production scalability and availability potential. This study focuses on the retro-fitment solution of M100 (pure methanol) SI port-fuel injection (PFI) mode of combustion. A heavy duty single-cylinder engine test setup was used to assess methanol SI combustion characteristic. Lean operation strategy has been investigated. At lean mixture conditions a significant drop in NOX and CO emissions was achieved. The fuel injection techniques and the impact of exhaust gas recirculation (EGR) on the conventional stoichiometric combustion process is highlighted.

Indian cities are among the most polluted in the world. The transportation sector is one of the major sources of gaseous pollutants. In recent years, also the effects of climate change and global warming have been felt across the globe. India has therefore committed at the CoP26 summit in 2021 to reduce its CO2 emissions by 45% till the year 2030. The Indian automotive sector is already addressing the problem with implementation of the Stage 2 BS VI norms, CAFÉ & Stage V standards and pursuing rapid electrification with application of zero emission vehicles. India also has the largest rail network of Asia, and a significant proportion of greenhouse gases is emitted by this sector. Deployment of zero emission fuel cell trains would be one of the solutions to meet India’s emission reduction targets.

Compliance with the future CO2 emission limits for the fleet of vehicles sold presents a major challenge for the automotive industry. To comply with these stringent limits, one solution is mobility using hydrogen as an energy carrier. In this context, the development of Proton Exchange Membrane (PEM) fuel cells for commercial vehicle applications, both on- and off-road, is of significant interest due to the non-existent CO2 emissions. However, performance of these devices is closely related to the control concepts that are used to ensure high efficiency, good transient performance, high reliability & durability as well as safe operation. To address these challenges, this paper presents a modular Fuel Cell Control Software which offers the potential to be used for different P&ID (Piping & Instrumentation Diagram) configurations of fuel cell systems.

Most of the vehicles with internal combustion engines worldwide use fossil fuels. The widely used fuels available on the market are gasoline, diesel, and CNG. These fuels are getting costlier every year while at the same time generating pollutants through exhaust gases. Hence in the market, electric vehicles are effectively providing pollution-free solutions in the passenger car and lightweight carrier vehicle segments. However, the off-road, heavy-duty, and stationary applications with high load factors, are in general less favorable for battery electric scenarios since frequent charging will be mandatory and time-consuming. Hence, for these applications, the replacement of an internal combustion engine is quite difficult. There are various renewable fuels like ammonia, methanol, and biodiesel under research tests and study. As these are renewable fuels, the cost of these fuels can be lowered during mass production.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

The current political push for e-mobility marked a major decline in the R&D interest to internal combustion engine (ICE). Following this global trend, Ford is committed to going 100% electric by 2030 for passenger cars and 2035 for light commercial vehicles. At the same time, many researchers admit that, due to many objective factors, vehicles powered by ICE will remain in operation for decades to come. Development of alternative carbon-neutral fuels can bring a renaissance in the ICE development as practical limitations of electric-only approach get exposed. Since a significant part of energy losses in the ICE comes from friction, engine tribology has been an important research topic over the past two decades and a significant progress in improving the engine efficiency was achieved.

Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

As the world strives toward the common goal of carbon neutrality by 2050, motorsport cannot be allowed to stand alone as an exception. A gradual energy transition is clearly underway in the automotive industry but has already begun in motorsport as well. Among other initiatives, the Dakar Rally and the FIA have created the T1 ultimate category for prototypes powered by low-carbon fuels, including hydrogen. The Dakar is the pinnacle of off-road endurance rally competitions. It offers a great opportunity to ORECA Magny-Cours and FEV to expose their jointly developed internal combustion engine (ICE), fuelled with hydrogen only, to extreme conditions. In addition, the racing environment imposes a unique pace of development which can serve as a catalyst for spurring the H2-ICE technology. Moreover, a hydrogen powered engine is an interesting fit for motorsports because it combines high power output, a relatively long driving range and driving pleasure with an excellent carbon footprint.

Development of propulsion control systems frequently involves large-scale transient simulations, e.g. Monte Carlo simulations or drive-cycle optimizations, which require fast dynamic plant models. Models of the air-path—for internal combustion engines or fuel cells—can exhibit stiff behavior, though, causing slow numerical simulations due to either using an implicit solver or sampling much faster than the bandwidth of interest to maintain stability. This paper proposes a method to reduce air-path model stiffness by adding an impedance in series with potentially stiff components, e.g. throttles, valves, compressors, and turbines, thereby allowing the use of a fast-explicit solver. An impedance, by electrical analogy, is a frequency-dependent resistance to flow, which is shaped to suppress the high-frequency dynamics causing air-path stiffness, while maintaining model accuracy in the bandwidth of interest.

From April 2020 BS 6 phase 1 legislation has come into place in India. Further in the coming years from 2022 CAFÉ norms will be implemented targeting 122 g/km CO2 fleet emissions. Also, from year 2023 onwards BS 6 phase 2 emission legislation with RDE cycle will be in place. With the expensive exhaust after-treatment system needed for meeting BS 6 norms, the Diesel powertrain based vehicles cost has increased further creating even further price difference to it’s Gasoline fuel variants. Additionally, the price difference between Diesel and Gasoline fuel is always reducing. These reasons have changed the buying pattern of passenger cars in India, with vehicle powered by engine<1.5 L displacements have gradually shifted predominantly to Gasoline powertrain. The impact of this will further stress the fleet CO2 emissions for manufacturers.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of innovative drivetrain technologies including conventional and electrified propulsion systems is expected to play an increasingly important role in helping OEMs meet fleet CO2 reduction targets for 2025 and beyond. NVH development for vehicles with electrified powertrains introduces new challenges, which need to be understood and solved. The electrified vehicle space spans variants from micro and mild hybrids all the way through plug-in hybrids and fully electric vehicles. In addition to conventional NVH development methodologies, active sound design (ASD) can play a crucial role to enhance the interior sound perception of such vehicles and hence, improve customer acceptance of new technologies. This paper will begin with an introduction to the NVH challenges posed by electrified vehicles.

Experimental modal analysis (EMA) is a measurement technique to assess the dynamical properties of mechanical components and systems in various phases of their life cycle, e.g. for design, end-of-line testing and health monitoring. The most common EMA uses accelerometers, which provide high frequency acceleration measurements at a few discrete locations. However, attached accelerometers may alter the systems mass and damping properties and multiple tests are required to obtain spatially dense information. To overcome these issues, in this paper we use high-speed cameras and video processing algorithms. In fact, cameras as contact-less sensors do not modify the dynamics of the system under test. Furthermore, cameras provide full-field displacement data, allowing to obtain spatially dense transfer functions with a single excitation, which reduces the experiment duration.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

In modern vehicle acoustics, Active Sound Design (ASD) is a popular method to enhance the interior sound perception of a vehicle. In vehicles with a traditional internal combustion engine, the load dependency of the sound can be increased to give a positive dynamic feedback. Irritating small band resonances can be attenuated by boosting the surrounding frequency content, and downsized-engines with a small number of cylinders can be made to sound like the larger ones from former days. However, once a characteristic sound is designed for a specific vehicle, it is a tedious process to transfer this lead-sound to derivatives (e.g. same model but different exhaust system). So far, sound designers must adapt the ASD dataset manually - usually this takes several loops of measuring the vehicles interior sound on a track and adjusting the ASD settings back in the office.

The increase in efficiency is the focus of current engine development by adopting different technologies. One limiting factor for the rise of SI-engine efficiency is the onset of knock, which can be mitigated by improving the combustion process. HCCI/SACI represent sophisticated combustion techniques that investigate the employment of pre-chamber with lean combustion, but the effective use of them in a wide range of the engine map, by fulfilling at the same time the need of fast load control are still limiting their adoption for series engine. For these reasons, the technologies for improving the characteristics of a standard combustion process are still largely investigated. Among these, water injection, in combination with the Miller cycle, offers the possibility to increase the knock resistance, which in turn enables the rise of the engine geometric compression ratio.

The trend to simultaneously improve fuel economy and engine performance has led to industry growth of turbocharged engines and as a result, the need to address their undesirable airborne noise attributes. This presents some unique engineering challenges as higher customer expectations for Noise Vibration Harshness (NVH), and other vehicle-level attributes increase over time. Turbocharged engines possess higher frequency noise content compared to naturally aspirated engines. Therefore, as an outcome, whoosh noise in the Air Induction System (AIS) during tip in conditions is an undesirable attribute that requires high frequency attenuation enablers. The traditional method for attenuation of this type of noise has been to use resonators which adds cost, weight and requires packaging space that is often at a premium in the under-hood environment.