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Faced with one of the greatest challenges of humanity – climate change – the European Union has set out a strategy to achieve climate neutrality by 2050 as part of the European Green Deal. To date, extensive research has been conducted on the CO2 life cycle analysis of mobile propulsion systems. However, achieving absolute net-zero CO2 emissions requires the adjustment of the relevant key performance indicators for the development of mobile propulsion systems. In this context, research is presented that examines the ecological and economic sustainability impacts of a hydrogen-fueled mild hybrid vehicle, a hydrogen-fueled 48V hybrid vehicle, a methanol-fueled 400V hybrid vehicle, a methanol-to-gasoline-fueled plug-in hybrid vehicle, a battery electric vehicle, and a fuel cell electric vehicle. For this purpose, a combined Life-Cycle Assessment (LCA) and Life-Cycle Cost Assessment was performed for the different propulsion concepts.

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.

A numerical investigation of a six-stroke direct injection compression ignition engine operation in a low temperature combustion (LTC) regime is presented. The fuel employed is a gasoline-like oxygenated fuel consisting of 90% isobutanol and 10% diethyl ether (DEE) by volume to match the reactivity of conventional gasoline with octane number 87. The computational simulations of the in-cylinder processes were performed using a high-fidelity multidimensional in-house 3D CFD code (MTU-MRNT) with improved spray-sub models and CHEMKIN library. The combustion chemistry was described using a two-component (isobutanol and DEE) fuel model whose oxidation pathways were given by a reaction mechanism with 177 species and 796 reactions.

Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

During driving conditions, when it is needed to transition from Electric Vehicle (EV) to Hybrid Vehicle operation, synchronization of the engine with the shaft and transmission is essential to enable clutch engagement and, subsequently, providing engine power to the wheels. Challenges arise when the engine must generate power to move itself and cannot rely on electric motors for precision. Cost-effective hybrid vehicle propulsion architectures which utilize small 12V belt-starter generators (BSGs) to initiate engine activation are inherently affected. In these situations, a speed profile that balance rapid response and control effort while considering system limitations to mitigate undesirable overshoots and delays, is required. This paper presents a Linear Quadratic Integral (LQI) approach to formulate a speed reference profile that ensures optimal engine behavior.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

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 proliferation of electric vehicles in the market, it has become important for Automotive OEMs (Original Equipment Manufacturers) to focus on delivering a higher driving range while also maximizing performance. One approach OEMs are actively considering in meeting this goal is to include a secondary drive axle disconnect into the powertrain which has the potential to improve the overall driving range by about 6-8.3% [4]. This paper outlines the need for a novel controls architecture to make the Powertrain controls software modular and to reduce the development time needed to provide robust powertrain control software. To do this, the electrified powertrain torque controls at STELLANTIS NV takes a decentralized controls architecture approach, by separating the axle disconnect controls subsystem (ADCS) from the primary path of torque controls. The ADCS takes in information such as the desired axle state and controls the axle disconnect actuators to achieve that state.

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.

Road transport is bound to play a major role in the imminent transition to green energy. India has pledged to reach net-zero greenhouse gas emissions by 2070 at the COP26 [1] and is committed to have 30% electric vehicle (EV) sales by 2030 [2]. The Indian government is promoting fleet electrification through initiatives like FAME–II. India’s EV market is expected to grow at an annual rate of 90% between 2022 and 2030 [3]. With this projection combined with climate targets, comes an anticipated exponential rise in renewable energy contribution to the national power grid, accompanied by a huge transport-related demand for electricity. NITI Aayog – India’s public policy think tank – and the Ministry of Power are already looking into the expansion of EV charging infrastructure in India as part of smart grid implementation. The deployment of Vehicle-to-Grid (V2G) technology as an extension of the smart charging initiative is essential for a smooth transition to renewable energy.

Climate change and global warming are one of the major challenges faced by the world today. A significant number of Indian cities rank among the most polluted globally, with vehicular emissions being the primary contributor. To address this issue, the Government of India is actively advocating for the adoption of zero-emission vehicles such as electric vehicles through policies and initiatives like FAME II [1], PMP and the National Mission for Transformative Mobility and Storage. The acceptance of electric vehicles is growing in the Indian market seeing more than 200% increase in sales in the year 2022 compared to 2021 with a large share of 2-wheelers, 3-wheelers and compact cars getting electrified. Further adoption of electrification on a much larger scale currently faces the major challenge of high overall vehicle cost compared to conventional vehicles, with the major contribution coming from the HV battery which is the costliest system on the electric vehicles.

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.

Automotive Heating Ventilation and Air Conditioning (HVAC) system is essential in providing the thermal comfort to the cabin occupants. The HVAC noise which is typically not the main noise source in IC engine vehicles, is considered to be one of the dominant sources inside the electric vehicle cabin. As air is delivered through ducts and registers into the cabin, it will create an air-rush/broadband noise and in addition to that, any sharp edges or gaps in flow path can generate monotone/tonal noise. Noise emanating from the HVAC system can be reduced by optimizing the airflow path using virtual tools during the development stage. This paper mainly focuses on predicting the noise from the HVAC ducts and registers. In this study, noise simulations were carried-out with ducts and registers. A Finite Volume Method (FVM) based 3-dimensional (3D) Computational Fluid Dynamics (CFD) solver was used for flow as well as acoustic simulations.

A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter. In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP.

The automotive industry is moving towards larger SUVs and also electrification is a need to meet the carbon neutrality target. As a result, we see an increase in overall gross vehicle weight (GVW), with the additional weight coming from the HV battery pack, electric powertrain, and other electrical systems. Tow-eye is an essential component that is provided with every vehicle to use for towing during an emergency vehicle breakdown. The tow-eye is usually connected to the retainer/sleeve available in the bumper system and towed using the recovery vehicle or other car with towing provision. Therefore, the tow-eye should meet the functional targets under standard operating conditions. This study is mainly for cars with bumper and tow-eye sleeves made of aluminum which is used in the most recent development of vehicles for weight-saving opportunities. Tow-eye systems in aluminum bumpers are designed to avoid any bending or buckling of the sleeve during towing for whatever the GVW loads.

The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.