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This paper is part of a broader research project aiming at studying, designing, and prototyping a hydrogen-powered internal combustion engine to achieve fast market implementation, reduced greenhouse gas emissions, and sustainable costs. The ability to provide a fast market implementation is linked to the fact that the technological solution would exploit the existing production chain of internal combustion engines. Regarding the technological point of view, the hydrogen engine will be a monofuel engine re-designed based on a diesel-powered engine. The redesign involves specific modifications to critical subsystems, including combustion systems, injection, ignition, exhaust gas recirculation, and exhaust gas aftertreatment. Notably, adaptations include the customization of the cylinder head for controlled ignition, optimization of camshaft profiles, and evaluation of the intake system.

The 2023 FISITA White Paper (for which the author was a contributor) on managing in-service emissions and transportation options, to reduce CO2 (CO2-e or carbon footprint) from the existing vehicle fleet, proposed 6 levers which could be activated to complement the rapid transition to vehicles using only renewable energy sources. Another management opportunity reported here is optimizing the vehicle’s life in-service to minimize the life-cycle CO2 impact of a range of present and upcoming vehicles. This study of the US vehicle fleet has quite different travel and composition characteristics to European (EU27) vehicles. In addition, the embodied CO2 is based on ANL’s GREET data rather than EU27 SimaPro methodology. It is demonstrated that in-service, whole-of-life mileage has a significant influence on the optimum life cycle CO2 for BEVs and H2 fuelled FCEVs, as well as ICEs and PHEVs.

​Composites made of continuous fibers generally have higher strength-to-weight ratios in fiber directions as compared to those made of discontinuous fibers. However, the latter tend to display quasi-isotropic properties which can be of advantage when directions of mechanical loading can vary. For many real-world applications such as robust design of vehicle body components for crashworthiness, impact loads are stochastic in nature both in terms of magnitude and direction. Hence, in order to realize the true potential of laminated composites with continuous fibers, instead of orthotropic laminates which are most common due to the ease of design and manufacturing, angle-ply laminates are necessary.

The need for eco-friendly vehicle powertrains has increased drastically in recent years. The most critical component of an electric vehicle is the battery pack/cell. The choice of the appropriate cell directly determines the size, performance, range, life, and cost of the vehicle. Lithium-ion batteries with high energy density and higher cycle life play a crucial role in the progress of the electric vehicle. However, the packaging of lithium-ion cells is expected to meet lots of assembly demands to increase their life and improve their functional safety. Due to their low mechanical stability, the lithium-ion cell modules must have external pressure on the cell surface for improved performance. The cells must be stacked in a compressed condition to exert the desired pressure on the cell surface using compression foam/pads. The compression pads can be either packaged between each cell or once in every set of cells based on the cell assembly requirements.

Battery terminal voltage modelling is crucial for various applications, including electric vehicles, renewable energy systems, and portable electronics. Terminal voltage models are used to determine how a battery will respond under load and can be used to calculate run-time, power capability, and heat generation and as a component of state estimation approaches, such as for state of charge. Previous studies have shown better voltage modelling accuracy for long short-term memory (LSTM) recurrent neural networks than other traditional methods (e.g., equivalent circuit and electrochemical models). This study presents two new approaches – sequence training and data shuffling – to improve LSTM battery voltage models further, making them an even better candidate for the high-accuracy modelling of lithium-ion batteries. Because the LSTM memory captures information from past time steps, it must typically be trained using one series of continuous data.

Since entering the 21st century, the world has faced extremely serious environmental pollution and energy crises. In this context, new energy vehicles have been vigorously developed. Lithium-ion batteries have gradually become one of the most important energy sources for electric vehicles due to their excellent performance. State of charge (SOC) is one of the important indicators of the battery. Accurate estimation of SOC is of great significance to establishing a safe and accurate Battery management system (BMS). Among related methods for SOC estimation, observation-based methods have been widely used. However, this type of method has the disadvantages of being susceptible to disturbance and requiring high accuracy of the battery model. The traditional equivalent circuit model cannot meet the needs. This paper proposes a battery SOC estimation method based on LSSVM-UKF through research on lithium-ion battery modeling and SOC estimation methods.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

The study and application of Topology Optimization (TO) has experienced great maturity in recent years, presenting itself as a highly influential and sought-after design tool in both the automotive and aerospace industries. TO has experienced development from single material topology optimization (SMTO) to multi-material topology optimization (MMTO), where material selection is simultaneously optimized with material existence. Today, MMTO for standard structural optimization responses are well supported. An additional and vital response in the design of structures is that of stress. Stress-driven or stress-controlled optimization techniques for SMTO are well understood and have been well-documented, evidenced by both published works and its availability in multiple commercial solvers. However, its integration into MMTO frameworks has not yet achieved reliable levels of accuracy and flexibility.

In recent years, with the advent of the Fourth Industrial Revolution and the COVID-19 pandemic, people's lives worldwide have undergone significant changes. Additionally, the emergence of a new generation of consumers known as the millennial generation has led to a high demand for multipurpose family cars. The perspective is shifting towards choosing premium products that enhance the quality of life and pursue their own happiness and comfort through technology, rather than simply selecting a midsize SUV based on the increase in family size. We aim to meet the needs of these global customers by conducting research and developing various new features that were not previously available in midsize SUVs. In this study, we defined the actual target users for midsize SUVs and established UX concepts by analyzing their characteristics. Based on this, we employed an optimal design approach by analyzing the evaluation results by country for the various features implemented within the vehicle.

This work aims to perform the optimization of the iron-aluminum lightweight body frame of a commercial electric bus orienting the static performance (e.g., strength and stiffness), side-impact safety, and possible reduction in mass. Firstly, both the static and side-impact finite element (FE) models are established for the electric bus body frame. The body frame is partitioned according to the deformation and the thickness of the square tube beams, and the contribution is analyzed by the relative sensitivity and the Sobol index methods. The thickness of the tube beams in the nine regions is selected as the design optimization variables. After data sampling by the Hamersley method and conducting design of experiments (DOE), the surrogate models for optimization are fitted by the least square method.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.

This study employs TRIZ, the Theory of Inventive Problem Solving, to optimize a 2.2-liter automotive diesel engine facing challenges from system technology upgrades in the fuel injection system. This system requires the common rail pump. Two pumps were chosen and based on fuel quantity balance (QB) and drive ratio, one pump was finalized as the technical option, and it was studied in a detailed manner to identify the improving and worsening parameters with the help of a contradiction matrix and the 40 TRIZ principle, which are the main core ideas of TRIZ. The worsening parameters (drive torque) are reduced by 21.36%, and the chain load in the 0.5% worn chain condition also fulfills the system requirement. The chosen pump is further studied. This also helped to identify and categorize the system components of the main engineering system into subsystems and supersystems.

In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements.

Ambient temperature is a very sensitive use condition for electric vehicles (EVs), so it is imperative to ensure the maintenance of suitable temperature. This is particularly important in regions characterized by prolonged exposure to unfavorable temperature conditions. In such cases, it becomes necessary to implement insulation measures within parking facilities and allocate energy resources to sustain a desired temperature level. Solar energy is a renewable and environmentally friendly source of energy that is widely available. However, the effectiveness of utilizing solar energy is influenced by various factors, such as the time of day and weather conditions. The use of phase change material (PCM) in a latent heat energy storage (LHES) system has gained significant attention in this field. In contrast to single-phase energy storage materials, PCM offer a more effective heat storage capacity.

The National Highway Traffic Safety Administration (NHTSA) has been leading U.S. efforts related to the rulemaking process for Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory, a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool called Autonomie that has become one of the industry standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Interagency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation to support NHTSA CAFE rulemaking. This paper presents an innovative approach focused on large-scale simulation processes spanning standard regulatory driving cycles, diverse vehicle classes, and various timeframes. A key element of this approach is Autonomie’s capacity to integrate advanced engine technologies tailored to specific vehicle classes and powertrains.

With the widespread adoption of fuel cell electric vehicles, electrical insulation resistance is required for driver safety. However, there are two ways in which resistance decreases: the first is electrical shorts because of failure of high-voltage components, and the second is increased conductivity of fuel cell coolant because of depletion of ion exchange filter. In the conventional solution, since these two decreases could not be distinguished due to noise in the resistance value, a vehicle alerted customers without determining the cause and severity when the resistance value falls below a certain threshold. As a corrective maintenance, when an alert occurs, the vehicle is forced to be immediately delivered to the service center. However, in most cases where the alert came on, the cause was low-risk ion filter depletion. This resulted in customers complaining that they were startled and considering the alert to be non-threatening.

This paper presents an energy-optimal plan for the allocation of a heterogeneous fleet of delivery vehicles in a dynamic network of multiple distribution centers and fulfillment centers. Each distribution center with a heterogeneous fleet of delivery vehicles is considered as a hub connected with the fulfillment centers through the routes as spokes. The goal is to minimize the overall energy consumption of the fleet while meeting the demand of each of the fulfillment centers. To achieve this goal, the problem is divided into two sub-problems that are solved in a hierarchical way. Firstly, for each spoke, the optimal number of vehicles to be allocated from each hub is determined. Secondly, given the number of allocated delivery vehicles from a hub for each spoke, the optimal selection of vehicle type from the available heterogeneous fleet at the hub is done for each of spokes based on the energy requirement and the energy efficiency of the spoke under consideration.

Amid escalating concerns over climate change and emissions, this study presents a novel approach to develop sustainable fuels, leveraging advanced process modeling that uses waste CO2 streams from the biological ethanol fermentation process to produce e-methanol. Using Aspen Plus software, this research focuses on the conversion of biomass such as sugar cane and sugar beet to reduce reliance on fossil fuels and fortify energy resilience in a sustainable manner. In the first phase, bagasse, a byproduct of sugar production that is rich in carbon is used as a precursor for gasification and as a fuel to generate high-pressure steam. Oxygen obtained from electrolysis of water using renewable energy is used to preheat the biological exothermic fermentation phase. The CO2 captured during the fermentation phase is mixed with hydrogen obtained from the electrolysis process to synthesize e-methanol.

The concept of the vehicle has changed in accordance with the technological innovations on last decade. Today we can call these changes basically as "CASE" (Connected, Autonomous/Automated, Shared, and Electric). The ease of product access on the user side and the mass production related works have increased worldwide production volumes. This issue has resulted in a greater demand for manpower in the sector. In addition, management, productivity, and profitability related difficulties have occurred. In this project, improvements were made mainly around the productivity through the automation of "vehicle transfer operations in plant operations", which is one of a major problem and a manpower/hour consuming task. This system named as Remote-Control Auto Driving System (RCD). The advance technology used system enabling unmanned, secured operations, were implemented in mass production environment earlier than the rest of the world.