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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.

As the market for electric vehicles grows, so does the demand for appropriate charging infrastructure. The availability of sufficient charging points is essential to increase public acceptance of electric vehicles and to avoid the so-called “charging anxiety”. However, the charging stations currently installed may not be able to meet the full charging demand, especially in areas where there is a general lack of grid infrastructure, or where the fluctuating nature of charging demand requires flexible, high-power charging solutions that do not require expensive grid extensions. In such cases, the use of mobile charging stations provides a good opportunity to complement the existing charging network. This paper presents a prototype of a mobile charging solution that is being developed as part of an ongoing research project, and discusses different use cases.

In the dynamic landscape of battery development, the quest for improved energy storage and efficiency has become paramount. The contemporary energy transition, coupled with growing demands for electric vehicles, renewable energy sources, and portable electronic devices, has underscored the critical role that batteries play in our modern world. To navigate this challenging terrain and harness the full potential of battery technology, a well-defined and comprehensive data strategy resp. knowledge management strategy are indispensable. Conversely, the imminent and rapid progression of artificial intelligence (AI) is poised to have a substantial impact on the forthcoming landscape of work and the methodologies organizations employ for the management of their knowledge management (KM) procedures. Conventional KM endeavors encompass a spectrum of activities such as the creation, transmission, retention, and evaluation of an enterprise’s knowledge over the entire knowledge lifecycle.

Focused on the permanent magnet synchronous motor (PMSM) used in electric, this paper proposes an online insulation testing method based on voltage injection under high-temperature and high-humidity conditions. The effect of constant humidity and temperature on the insulation performance has been also studied. Firstly, the high-voltage insulation structure and principle of PMSM are analyzed, while an electrical insulation testing method considered constant humidity and temperature is proposed. Finally, a temperature and humidity experimental cycling test is carried out on a certain prototype PMSM, taking heat conduction and radiation models, water vapor, and partial discharge into account. The results show that the electrical insulation performance of the motor under constant humidity and temperature operation environment exhibits a decreasing trend. This study can provide theoretical and practical references for the reliable durability design of PMSM.

Polymer electrolyte membrane (PEM) fuel cells will play a crucial role in the decarbonization of the transport sector, in particular for heavy duty applications. However, performance and durability of PEMFC stacks is still a concern especially when operated under high power density conditions, as required in order to improve the compactness and to reduce the cost of the system. In this context, the optimization of the geometry of hydrogen and air distributors represents a key factor to improve the distribution of the reactants on the active surface, in order to guarantee a proper water management and avoiding membrane dehydration.

The paper introduces two methods for controlling motor voltage. One method requires the implementation of boost hardware, while the other allows for voltage control in battery failure mode without any additional hardware requirements. The boost voltage strategy for the hybrid system is based on managing boost modes, determining target voltages, and implementing PI control. The boost mode control includes different modes such as initial mode, normal mode, shutdown mode, and fault mode. Determining the boost target voltage involves regulating the boost converter with variable voltages depending on the operating states of the motor and generator. The second voltage control method without boost hardware is proposed in order to ensure that the vehicle can still function like a traditional car even under abnormal conditions of high-voltage battery failure in micro-mixing systems.

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.

An analytic first-order fuel consumption model is developed for FWD 2-motor HEV vehicles which on average achieve 36% EPA Combined efficiency. The premise of this paper is that this is primarily the result of new functionality specific to HEV. Detailed benchmarking data show that in such an HEV the engine not only provides traction power but simultaneously charges the battery. This combined operation of engine and electric powertrain is unique to HEV and is studied using their linear transfer functions. Charging by the engine enables extended electric driving at low traction power, which reduces engine running time and the associated overhead. The analysis predicts an engine duty cycle proportional to the traction power and inversely proportional to the engine output power: the electric driving is limited by the engine’s ability to deliver the required traction work.

The hybrid system's thermal strategy is centered around controlling the cooling of the motor, inverter, DCDC and evaporator. In this electric drive circuit system, the water temperature sensor is positioned at the radiator outlet rather than within it. Consequently, when determining the required air volume for radiator cooling and water demand for sub-components of the electric drive circuit, an estimation of the inlet water temperature becomes necessary. This estimation relies on a heat transfer formula that converts heat released by circuit sub-components into their contribution to temperature rise within the circuit plus the outlet temperature from the previous round through the radiator to determine inlet water temperature. The inverter's heat transfer power depends on voltage and current levels. Adjusting motor torque leads to rapid changes in current flow while maintaining a low speed for optimal flow rate through the electric drive pump.

The Insurance Institute for Highway Safety (IIHS) introduced its updated side-impact ratings test in 2020 to address the nearly 5,000 fatalities occurring annually on U.S. roads in side crashes. Research for the updated test indicated the most promising avenue to address the remaining real-world injuries was a higher severity vehicle-to-vehicle test using a striking barrier that represents a sport utility vehicle. A multi-stiffness aluminum honeycomb barrier was developed to match these conditions. The complexity of a multi-stiffness barrier design warranted research into developing a new dynamic certification procedure. A dynamic test procedure was created to ensure product consistency. The current study outlines the process to develop a dynamic barrier certification protocol. The final configuration includes a rigid inverted T-shaped fixture mounted to a load cell wall. This fixture is impacted by the updated IIHS moving deformable barrier at 30 km/h.

In order to achieve seamless mode switching control for hybrid electric vehicles (HEVs) in the event of battery failure, we propose a motor voltage-controlled mode switching method that eliminates power interruptions. This approach is based on an analysis of the dual-motor hybrid configuration's mode switching. We analyze the overall vehicle operation when the high-voltage battery occurs in different hybrid modes. To ensure that the vehicle can still function like a conventional car under such circumstances, we introduce a novel "voltage control" mode. In this mode, instead of operating in its traditional torque control manner, the P1 motor adopts a voltage control strategy. The P1 controller's variable becomes "voltage," and VCU sends the motor's working mode switching request and PCM finishes the mode transition. During system operation, the P1 motor promptly responds to these target voltages to maintain bus voltage within a normal range.

Noise reduction is generally accomplished by applying appropriate noise control treatments at strategic locations. Noise control treatments consisting of poroelastic materials in layers are extensively used in noise control products. Sound propagation through poroelastic materials is governed by macroscopic material and geometric properties. Thus, a knowledge of material properties is important to improve the acoustical performance of the resulting noise control products. Since the direct measurement of these properties is cumbersome, these have been usually estimated indirectly from easily measurable acoustic performance metrics such as normal incidence sound transmission and/or absorption coefficient, measured using readily available impedance tube. The existing inverse characterization approaches fulfilled the estimation by curve fitting measured and predicted acoustic models.

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants.

The world is on a “take-make-waste,” linear-growth economic trajectory where products are bought, used, and then discarded in direct progression with little to no consideration for recycling or reuse. This unsustainable path now requires an urgent call to action for all sectors in the global society: circularity is a must to restore the health of the planet and people. However, carbon-rich textile waste could potentially become a next-generation feedstock, and the mobility sector has the capacity to mobilize ecologically minded designs, supply chains, financing mechanisms, consumer education, cross-sector activation, and more to capitalize on this “new source of carbon.” Activating textile circularity will be one of the biggest business opportunities to drive top- and bottom-line growth for the mobility industry.

Curbs are as key to automated driving system (ADS) navigation, operation, and safety as they are for human driven vehicles. The design, maintenance, and management of curbs and adjacent infrastructure can make the difference in whether ADS vehicles can pick up and deliver passengers and goods safely, efficiently, and effectively. Curbs may also be key to integrating ADS services with other forms of active and human-driven transportation. Benefits from accessibility, reduced emissions, and strong supply chains require that ADS vehicles be able to dock curbside in a manner that does not disrupt traffic or impede safe movement of people walking, biking, or using a mobility device. Automated Vehicles and Infrastructure Enablers: Curbs and Curbside Management addresses considerations regarding the curb with respect to pick up and drops for passengers and freight, as well as managing and designing both sides of the curb with respect to automated vehicles and other types of shared mobility.

The automobile is undergoing the biggest transformation of its 100-year history. Motivated by consumer desire for automobiles to integrate with their digital life and inspired by new electric vehicles (EVs) that routinely receive over-the-air software updates, traditional automakers are embarking on a journey to re-engineer the vehicle as a platform defined by software. The foundation of the shift is a complete re-design from a mechanical hardware-centric system to a cloud-connected, software-centric ecosystem where each function is executed via a service-oriented architecture. This is the basis of the software-defined vehicle (SDV). The Software-defined Vehicle and its Engineering Evolution: Balancing Issues and Challenges in a New Paradigm of Product Development examines the complex journey ahead for traditional manufacturers as they transition to this new software-defined system.

The safety of commercial aviation industry has come under extensive scrutiny and how the system safety process is applied. One specific system safety regulation concerns how unsafe system operating conditions are meeting regulatory requirements. Minimal regulatory guidance was available on this topic and an industry committee (American Society for Testing of Materials) decided to provide a consensus standard with input from a cross-section of airplane manufacturers, suppliers, and regulatory authorities on what is meant by an unsafe system operating condition and how compliance can be shown to the regulation(s). The committee determined that an unsafe system operating condition is when a failure condition severity increases (to hazardous or catastrophic) due to crewmember(s) inaction. For example, if a hazard has occurred it is possible the severity can increase to an unacceptable level as the crewmember(s) are not aware of the hazard.

The electric vehicle market in India has tremendous growth potential in the upcoming years and decades, attracting numerous automotive manufacturers, including Tier-1 suppliers, seeking to participate in this growth phase. Electric powertrains used in e-cars on Indian roads comply with BIS and AIS standards. However, these standards alone do not provide sufficient clarity on the complete list of tests required for developing an e-Axle through all stages of development. Developing the e-Axle in-house for the Indian market poses a significant challenge for OEMs and Tier-1 suppliers, as it will play a crucial role in overall profitability at high volumes in the long term. Adhering solely to the BIS and AIS standards may prove insufficient in fulfilling the developmental prerequisites of an electric axle (e-Axle) system.

Due to the transformation of the automotive industry from conventional vehicles to electric vehicles, new challenges have emerged concerning Electromagnetic Compatibility. Though the Radiated Emission limits in global regulation are the same for both types of powertrains of vehicles, however, due to the phenomena of conversion of high voltage to low voltage, rapid charging/discharging, and different components involved in electric powertrain, the Radiated Emission from electric vehicles give a strikingly different trend which is challenging to combat. When compared with the conventional Spark Ignition vehicle, many other electronic components of the electric vehicle stay in the mode of Power ON while in the “Ignition ON” steady state. This resulted in us observing a significant shift in the amplitude and frequency throughout the frequency band of Radiated Emission measurement.

Efficient cargo transportation plays a crucial role in logistics management and supply chain operations. Accurately detecting and utilizing cargo space within vehicles is vital for maximizing transport capacity, minimizing costs, and optimizing resource allocation and time management. This research paper focuses on enhancing cargo utilization using intelligent systems to improve logistics management. The major research is on developing a system that combines computer vision algorithms and intelligent systems to detect and implement a combination of features for efficient use of cargo space within vehicles and monitor cargo to reduce losses. The proposed approach will use and utilize image processing methods to get relevant features and identify cargo areas.