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Abstract In recent years, several buses have ignited in some cities in China, causing numerous deaths and significant property damage. However, few research studies have been conducted to deal with such accidents. Therefore, in this work, a linear-shaped charge jet with rectangular cross sections was used to hew out evacuation routes for burning buses, and the parameter design for the shaped charge jet was improved according to asymmetry limitations and human tolerance. A numerical finite element simulation model of the behavior of a jet penetrating the jambs was established using ANSYS/LS-DYNA software. The asymmetrical characteristics of an arc segment in the structure of a rectangular-shaped charge were analyzed, in addition to the influence on the deviations of the jet penetration capacity and blast injuries to occupants caused by the side effects of detonation.

Abstract Making use of a specifically designed dynamical vehicle model, the authors here presented the results of an activity for the evaluation of energy consumption and CO2 emissions of buses for urban applications. Both conventional and innovative (series hybrid, and fully electric) vehicles were considered to obtain interesting comparative conclusions. The derived tool was used to simulate the dynamical behaviour of these vehicles on a number of kinematic profiles measured during real buses operation in different contexts, varying from really congested city centre routes to fast-lane operated services. It was so possible to evaluate the energetic performances of those buses on a Tank-to-Wheel (TTW) basis.

Abstract The development, analysis, and optimization of battery electric class-7 heavy-duty trucks equipped with multi-speed transmissions are discussed in this paper. The designs of five new traction motors-fractional-slot, concentrated winding machines-are proposed for use in heavy-duty electric trucks. The procedure for gear-ratio range selection is outlined and ranges of gear ratios for three-to six-speed transmission powertrains are calculated for each of the proposed electric traction motors. The simulation and gear-ratio optimization tasks for class-7 battery electric trucks are formulated. The energy consumption of the e-truck with the twenty possible powertrain combinations is minimized over the four driving cycles and the most efficient powertrain layouts that meet the performance criteria are recommended.

Abstract Recently, electric heavy-duty tractor-trailers (EHDTTs) have assumed significance as they present an immediate solution to decarbonize the transportation sector. Hence, to illustrate the economic viability of electrifying the freight industry, a detailed numerical model to estimate the battery capacity for an EHDTT is proposed for a route between Washington, DC, to Knoxville, TN. This model incorporates the effects of the terrain, climate, vehicular forces, auxiliary loads, and payload in order to select the appropriate motor and optimize the battery capacity. Additionally, current and near-future battery chemistries are simulated in the model. Along with equations describing vehicular forces based on Newton’s second law of motion, the model utilizes the Hausmann and Depcik correlation to estimate the losses caused by the capacity offset of the batteries. Here, a Newton-Raphson iterative scheme determines the minimum battery capacity for the required state of charge.

Abstract Electric heavy-duty tractor-trailers (EHDTT) offer an important option to reduce greenhouse gases (GHG) for the transportation sector. However, to increase the range of the EHDTT, this effort investigates critical vehicle design features that demonstrate a gain in overall freight efficiency of the vehicle. Specifically, factors affecting aerodynamics, rolling resistance, and gross vehicle weight are essential to arrive at practical input parameters for a comprehensive numerical model of the EHDTT, developed by the authors in a subsequent paper. For example, drag reduction devices like skirts, deturbulators, vortex generators, covers, and other commercially available apparatuses result in an aggregated coefficient of drag of 0.367. Furthermore, a mixed utilization of single-wide tires and dual tires allows for an optimized trade-off between low rolling resistance tires, traction, and durability.

Abstract The following article illustrates the detailed study of the development of a unique flywheel-based regenerative braking system (f-RBS) for achieving regenerative braking in a commercial electric bus. The f-RBS is designed for installation in the front wheels of the bus. The particular data values for modeling the bus are taken from multiple legitimate sources to illustrate the development strategy of the regenerative braking system. Mechanical components used in this system have either been carefully designed and analyzed for avoiding fatigue failure or their market selection strategies explained. The positioning of the entire system is decided using MSC Adams View®, hence determining a suitable component placement strategy such that the f-RBS components do not interfere with the bus components. The entire system is modeled on MATLAB Simulink® with sufficient accuracy to get various results that would infer the performance of the system as a whole.

Abstract The suspension system has been shown to have significant effects on vehicle performance, including handling, ride, component durability, and even energy efficiency during the design process. In this study, a new roll-plane hydraulically interconnected suspension (HIS) system is proposed to enhance both roll and lateral dynamics of a two-axle bus. The roll-plane stability analysis for the HIS system has been intensively explored in a number of studies, while only few efforts have been made for suspension tuning, especially considering lateral plane stability. This article aims to explore the integrated lateral and roll dynamics by suspension tuning of a two-axle bus equipped with HIS system. A ten-degree-of-freedom (DOF) lumped-mass vehicle model is integrated with either transient mechanical-hydraulic model for HIS or the traditional suspension components, namely, shock absorber and anti-roll bar (ARB).

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Abstract To reduce the driveline oscillations during the shifting process of electric delivery vans (EDVs), this article proposes a swift and smooth disengagement strategy for the clutch in a dual-speed transmission (DST) system. Firstly, a novel electromechanical clutch actuator (ECA) for the proposed DST is designed and modeled. Then the structure of the DST for EDVs is briefly introduced, and the mathematical model of the DST is derived using the Lagrange method. Since the driveshaft torque is essential and unmeasurable, a Kalman filter is designed to estimate this value. Then the clutch disengagement strategy is proposed based on the estimated torque. Simulation studies are conducted under both normal and disturbed conditions to test the performance of the proposed algorithm. In addition, the processor-in-the-loop (PIL) experiment verifies the real-time ability of the whole algorithm.

Abstract An energy management strategy based on model predictive control (MPC) was proposed for the hybrid bus. For the series configuration, MPC was used for power distribution among transmission components. Real-time optimization of the control strategy was achieved, which improved the fuel economy. First, a rule-based energy management strategy was proposed, and the logical thresholds of the stage of charge (SOC) and the demand power were formulated to underlie the subsequent study of the control strategy. Second, an energy management strategy based on global optimization was established where the dynamic programming algorithm was used to determine the SOC optimal reference curve and the limitation of fuel economy. In this way, the target and reference can be provided for the subsequent control strategy. Third, a radial basis neural network speed prediction model based on wavelet transform was formulated.

Abstract A dynamic model of a multi-unit air conditioning (AC) system in a generic bus was developed to investigate different control strategies on the system performance and the cabin comfort level. In this study, a part-load condition was considered, where adopting a proper strategy for governing a multi-unit system is important. Simulink and Simscape toolbox from MATLAB (R2019a) were used to build up the real-time model by integrating a cooling system with a cabin sub-model. The cooling system consists of two independently controlled units, based on a Vapor Compression Cycle (VCC). The cabin is modelled using a moisture air network and is coupled with the cooling system to exchange heat with the refrigerant through the evaporators. Moreover, the sensible and latent loads are incorporated into the cabin by a thermal network.

Abstract As electric vehicles (EVs) begin to increase their market share in the transport sector, the efficiency of battery packs becomes critical to their performance. Within large battery packs, cell variations occur due to manufacturing processes but can also become prominent during operation due to ineffective thermal management and accelerated degradation of some cells. A battery management system (BMS) will generally account for variations in state of charge (SOC) for cells in series through balancing, but conventional BMSs do not tend to consider the imbalances of cells in parallel as their SOCs should eventually converge themselves. This can, however, lead to cells experiencing higher currents and therefore increased degradation compared to other cells within the pack.

Abstract Driving skills and, more in general, driver’s behavior may have a major impact on vehicle performances. They can affect not only the fuel consumption of the machine but, at the same time, also its productivity and the durability of many mechanical, electronic, and hydraulic components equipped on the vehicle. In this article, a model, able to reproduce different driver’s approaches to the machine, is shown. The longitudinal driver model has been developed in Matlab/Simulink and, firstly, employed on buses and trucks applications; then it has been also exported into a wheel loader plant model designed in Simcenter AMESim. The article is focused on how the driver model, integrated into the wheel loader plant model, can simulate custom cycles with a different driving style (high/low aggressiveness). It allows, on one hand, to emulate a real driver behavior and, on the other hand, to increase simulation repeatability and reproducibility.

Abstract The demand for autonomous electric public transport is increasing globally. The operational requirement of these autonomous vehicles differs widely. Hence, there is an increase in the demand for different vehicle sizes and configurations. This has led to a number of methods and improvements in the vehicle package development process. This article presents the development of a holistic parametric packaging tool for autonomous vehicles called Autonomous Electric Vehicle Tool (AEV tool). The tool is designed with MATLAB, and via a Graphical User Interface (GUI), the user can input parameter data, which directly adjusts a parametric Computer-Aided Design (CAD) model developed with CATIA software. The overall vehicle dimensions, as well as the size of single components, can be changed, and different topology configurations can be chosen.

Abstract This research develops a hierarchical control strategy to improve the stability of multi-axle electric vehicles with in-wheel motors while driving at high speed or on low adhesion-coefficient roads. The yaw rate and sideslip angle are chosen as the control parameters, and the direct yaw-moment control (DYC) method is employed to ensure the yaw stability of the vehicle. On the basis of this methodology, a hierarchical yaw stability control architecture that consists of a state reference layer, a desired moment calculation layer, a longitudinal force calculation layer, and a torque distribution layer is proposed. The ideal vehicle steering state is deduced by the state reference layer according to a linear two-degree-of-freedom (2-DOF) vehicle dynamics model.

Abstract Windshield glare at night is a safety concern for all drivers. Public transit bus drivers also face another concern about glare caused by interior lighting sources originally designed for passenger safety. The extent to which interior light reflections contribute to glare is unknown. Unique methods for measuring discomfort and disability glare during bus driving were developed. An initial simulation study measured windshield luminance inside of a New Flyer D40LF diesel bus parked in a controlled, artificial, totally darkened test environment. Findings indicated significant disability glare (from elevated luminance) in the drivers’ primary field of view due to interior reflections. Any reduction in contrast would result in less prominent glare if actual driving conditions differ. To assess this, levels of windshield glare were also measured with the bus parked on the roadside under the “background glow” of the urban environment.

Abstract The outbreak of COVID-19 pandemic in the beginning of 2020 has made it necessary to review many practices in countless areas, changing our lifestyles drastically. Humanity has put health issues in priority to deal with the disease effectively. While health systems are having difficult times in terms of patient care, vaccination, and treatment protocols, existing designs in many areas have proven to be inefficient in preventing or decelerating the pandemic. As the disease is transmitted mainly by particle transfer through coughing, sneezing, and even with speaking, wearing face masks and keeping a distance of 2 m as well as hygiene (especially hand) are shown to be effective methods. However, long exposure to indoor air populated with people is still a major risk due to the possibility of high concentration of virus-contaminated air.

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Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.