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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 The transition of vehicle propulsion technologies away from conventional internal combustion engines toward more electrically dominant systems such as plug-in hybrid electric vehicles (PHEV) poses new challenges for vehicle thermal management systems. Especially at low ambient temperatures, consumer demand for cabin comfort as well as legislatively imposed safety considerations significantly reduce the electric driving range because only electric energy can be used for heating during emissions-free driving modes. Recent developments to find energy efficient thermal management systems for electric and plug-in electric vehicles have led to the implementation of automotive heat pump systems. As an alternative approach to meet dynamic heating demands and safety regulations, these systems use heat at a low temperature level, for example the waste heat of electric drivetrain components, to heat the passenger compartment efficiently and therefore increase the electric driving range.

Abstract Investigation of vehicle resistant forces and power losses is of crucial importance owing to current state of energy consumption in transport sector. Meanwhile, considerable portion of resistant forces in a ground vehicle is traced back to tires. Pneumatic tires are known to be a source of energy dissipation as a consequence of their viscoelastic nature. The current study aims to provide a modification to tire resistance by considering the power loss in a tire due to longitudinal slip. The modified tire resistance is comprised of rolling resistance and a newly introduced resistance caused by tire slip, called slip resistance. The physical model is chosen for parameters sensitivity study since the tractive force is described in this model via tangible physical parameters, e.g. tire tangential stiffness, coefficient of friction, and contact patch length.

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 Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. This is due to the fact that DP that offers the global optimal solution requires the pre-known knowledge of the driving condition for the whole drive cycle, which makes the real-time implementation of the strategy more challenging. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied.

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 From the fact that a propulsor consumes less power for a given thrust if the inlet air is slower, simulations are conducted for a propulsor imposed behind an airfoil as ideal boundary layer ingestion (BLI) propulsor to stand on the benefits of this configuration from the point of view of power and efficiency and to get a closer look on the mutual interaction between them. This interaction is quantified by the impact on three main sets of parameters, namely, power consumption, boundary layer properties, and airfoil performance. The position and size of the propulsor have great influence on the flow around the airfoil. Parametric studies are carried out to understand their influence. BLI propulsor directly affects the power saving and all of the pressure-dependent parameters, including lift and drag. For the present case, power saving reached 14.4% compared to the propeller working in freestream.

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 This study investigates methods to precisely control a coolant pump in an internal combustion engine. The goal of this research is to minimize power consumption while still meeting optimal performance, reliability and durability requirements for an engine at all engine-operating conditions. This investigation achieves reduced fuel consumption, reduced emissions, and improved powertrain performance. Secondary impacts include cleaner air for the earth, reduced operating costs for the owner, and compliance with US regulatory requirements. The study utilizes mathematical modeling of the cooling system using heat transfer, pump laws, and boiling analysis to set limits to the cooling system and predict performance changes.

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 Catalytic converters, a primary component in most automotive emissions control systems, do not function well until they are heated substantially above ambient temperature. As the primary energy for catalyst heating comes from engine exhaust gases, plug-in hybrid electric vehicles (PHEVs) that have the potential for short and infrequent use of their onboard engine may have limited energy available for catalytic converter heating. This article presents a comparison of multiple hybrid supervisory control strategies to determine the ability to avoid engine cold starts during a blended charge-depleting propulsion mode. Full vehicle and catalytic converter simulations are performed in parallel with engine dynamometer testing in order to examine catalyst temperature variations during the course of the US06 City drive cycle. Emissions and energy consumption (E&EC) calculations are also performed to determine the effective number of engine starts during the drive cycle.

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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