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The objective of this article is to provide a comprehensive investigation of the opportunities and applications of using solar panels in electrified vehicles. The use of photovoltaic (PV) panels as an auxiliary energy source of on-board fuel in plug-in hybrid electric vehicles (PHEVs), full hybrid electric vehicles (FHEVs), and battery electric vehicles (BEVs) is investigated. The electrical architectures and the benefits of various possible applications are presented, such as active vehicle cabin ventilation, charging the low voltage battery, and charging the high voltage (HV) traction battery to extended driving ranges. In addition, the possibility of using PV panels to cool down the HV battery in extreme temperature environments is also investigated, supported by experimental tests used to properly model the thermal behavior of the HV battery and the effect of the cooling.

The automotive industry is heading towards the path of autonomy with the development of autonomous vehicles. An autonomous vehicle consists of two main components. The first is the software which is responsible for the decision-making capabilities of the system. The second is the hardware which encompasses all aspects of the physical vehicle which are responsible for vehicle motion such as the engine, brakes and steering subsystems along with their corresponding controls. This component forms the basis of the autonomous vehicle platform. For SAE Level 4 autonomous vehicles, where an automated driving system is responsible for all the dynamics driving tasks including the fallback driving performance in case of system faults, redundant mechanical systems and controls are required as part of the autonomous vehicle platform since the driver is completely out of the loop with respect to driving.

With the recent advancements in sensing and processing capabilities of consumer mobile devices (e.g., smartphone, tablet, etc.), they are becoming attractive choices for pervasive computing applications. Always-on monitoring of human movement patterns is one of those applications that has gained a lot of importance in the field of mobility and transportation research. Automatic detection of the current transportation mode (e.g., walking, biking, riding a shuttle, etc.) of a consumer using data from their smartphone sensors enables delivering of a number of customized services for multi-modal journey planning. Most accurate models for automatic mode detection are trained with supervised learning algorithms. In order to achieve high accuracy, the training datasets need to be sufficiently large, diverse, and correctly labeled.

As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed.

Electrified powertrains respond to driver demand for vehicle acceleration by producing power through either the electric drive system or an on-board combustion engine or both. In Plug-In Hybrid Vehicles (PHEVs), the powertrain provides the purest form of transportation when responding to driver demand through the electric drive system. We develop a method to size the electric drive system in PHEVs to provide zero emission driving in densely populated urban regions. We use real world data from Europe and calculate instantaneous wheel power during trips. Ray tracing is used to identify the regions where trips occur and the population density of these regions is obtained from an open source dataset published by Eurostat. Regions are categorized by their population density into urban and extra-urban regions. Real world data from these regions is analyzed to determine the wheel power required in urban and extra-urban settings.

Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences.

Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycles by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the target or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile.