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Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

Abstract This article applies a new method for the evaluation and estimation of real-life energy consumption of two different thermal management systems based on driving behavior in the course of the day. Recent attempts to find energy-efficient thermal management systems for electric and plug-in hybrid electric vehicles have led to using secondary loop systems as an alternative approach for meeting dynamic heating and cooling demands and reducing refrigerant charge. However, the additional layer of thermal resistance, which influences the system’s transient behavior as well as passenger compartment comfort during cool-down or heat-up, makes it difficult to estimate the annual energy consumption. In this article, the overall energy consumption of a conventional and a secondary loop system is compared using a new method for describing actual customers’ driving behavior in the course of the day.

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 The high-frequency noise issue is one of the most significant noise, vibration, and harshness problems, particularly in battery electric vehicles (BEVs). The sound package treatment is one of the most important approaches toward solving this problem. Owing to the limitations imposed by manufacturing error, assembly error, and the operating conditions, there is often a big difference between the actual values and the design values of the sound package components. Therefore, the sound package parameters include greater uncertainties. In this article, an uncertainty analysis method for BEV interior noise was developed based on an interval model to investigate the effect of sound package uncertainty on the interior noise of a BEV. An interval perturbation method was formulated to compute the uncertainty of the BEV’s interior noise.

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 proposed approach present the development of a computer tool that allows, in the first phase, the modeling of the electric vehicle power chain. This phase is based on a library developed under the Matlab-Simulink simulation environment. This library contains all the components of the power chain; it offers the selection of the desired configuration of each component. In the second phase, the tool solves the autonomy optimization problem. This problem is resolved by a program based on genetic algorithms. This program permits to optimize the configuration parameters maximizing the vehicle autonomy of the chosen chain. This tool is based on a graphical interface developed under the Matlab simulation environment.

Abstract Future SAE Level 4 and Level 5 autonomous vehicles (AV) will require novel applications of localization, perception, control, and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This article concentrates on low-speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of the campus as an initial AV pilot test route for the deployment of low-speed autonomous shuttles. This article presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

Abstract In the case of electric vehicles, due to the charging current limitation of lithium battery at low temperatures (below -20°C), it has been proposed to heat the battery pack up to a suitable temperature range before charging through a liquid-heating plate with PTC. However, at a low state of charge (SOC), there is a question which one could take the place of battery pack to supply power for PTC when heating. So that off-board charger (OFC) has been considered to supply power for PTC in this paper. In order to control the current charging into the battery pack as less as possible at low temperatures, three control schemes of battery management system (BMS) are proposed and compared. Scheme 1: BMS controls the value of charging current request close to the working current of PTC. Scheme 2: BMS controls the value of charging voltage request to reach a state of relative balance. Scheme 3: BMS disconnects the pack from the charger and keeps the connection between PTC and charger.

Abstract Knowledge of intelligent vehicle absolute position is a vital premise for the implementation of decision programming, kinematic and dynamics control. In order to achieve high accuracy positioning and reduce running cost as much as possible under all operating conditions, this paper proposed an integrated positioning method based on GPS and Ultra Wide Band(UWB) for intelligent vehicle’s navigation and position system. In this method, GPS and UWB are alternately active according to the confidence level of GPS signal. When the vehicle is traveling in a wide-open area and GPS signal is well received, the positioning results of Dead Reckoning system are corrected by the low frequency positioning output from GPS. During the correcting process, in order to realize the better fusion of measurement data, a simplified federal Kalman filter was designed by using indirect method.

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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 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 A detailed model for pneumatic S-cam drum brake systems is developed and integrated into a multibody dynamic model for a 33-ft A-double long combination vehicle (LCV). The model, developed in TruckSim®, is used to study the dynamics of LCVs during straight-line braking at various speeds. It includes the response delay in braking that occurs from the time of application to when the brakes are applied at the drum for all axles. Additionally, the model incorporates an accurate characterization of brake torque versus chamber pressure at different speeds, along with the anti-lock brake system (ABS) dynamics, to yield an accurate prediction of the vehicle’s deceleration during braking. The modeling results are compared with test results at speeds ranging from 20 mph to 65 mph on dry pavement. A close match between the model’s prediction and test results is observed.

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 Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10−8 probability of failure per hour of operation. We then define the geometry of the problem where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on.

Abstract In order to further improve the active safety protection of the vehicle’s active collision avoidance system for vulnerable road users, consider the limitations of on-board sensors, a pedestrian active collision avoidance control strategy based on vehicle-to-vehicle (V2V) communication technology is proposed for the blind-spot dangerous scenario where pedestrians pass through the front of a stationary obstacle vehicle and collide with the host vehicle. Firstly, the relative position relationship model between the host vehicle and the pedestrian is established according to the pedestrian information detected by the obstacle vehicle sensor and the global positioning system (GPS) position information of the obstacle vehicle and the host vehicle so that the host vehicle can obtain the state information of the pedestrian in front of the obstacle vehicle through V2V communication.