The fast advances in autonomous driving technology prompt the question of suitable operational models for future autonomous vehicles. A key determinant of the viability of such operational models is the competitiveness of their cost structures. Using a comprehensive analysis of the respective cost structures, this research shows that public transportation (in its current form) will only remain economic in situations which allow substantial bundling of demand. In all other cases, shared and pooled vehicles serve the travel demand more efficiently. In contrast to the general opinion, shared fleets may not be the most efficient alternative due to higher efforts for vehicle cleaning. Moreover, a substantial share of vehicles may remain in private possession and use.

Cost-based analysis of autonomous mobility services

Status and future perspectives of reliability assessment for electric vehicles

Hybrid renewable energy applications in zero-energy buildings and communities integrating battery and hydrogen vehicle storage

Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.

Dynamic Autonomous Vehicle Fleet Operations: Optimization-Based Strategies to Assign AVs to Immediate Traveler Demand Requests

Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)

Shared autonomous electric vehicles (SAEVs) are a promising car-sharing service expected to be implemented in the near future. However, existing studies on the optimization of SAEV systems do not consider uncertainties in the SAEV systems, which may interfere with the achievement of the desired performance or objective. From the perspective of the company, a SAEV system should be designed to minimize the total cost while securing the targeted wait time of the customer, but uncertainties in the SAEV system can cause variation in the customer wait time, which can lead to inconveniences to customers and damage to the reputation of the company. Therefore, this study considers the uncertainties in a SAEV system and applies reliability-based design optimization (RBDO) to the design of the SAEV system to minimize the total cost of system design while satisfying the target reliability of the customer wait time. A comparison of the optimization results of various wait time constraints and probabilities of failure provides observations on applying RBDO to the design of a SAEV system. Furthermore, several insights can be obtained through various parametric studies. From this study, it is verified that RBDO can be successfully applied to the design of a SAEV system and a design framework for the SAEV system that can both lower the cost and ensure the reliability of the customer wait time is proposed.

Shared autonomous electric vehicle design and operations under uncertainties: a reliability-based design optimization approach

Reliability-based design optimization (RBDO) allows decision-makers to achieve target reliability in product performance under engineering uncertainties. However, existing RBDO studies assume the target reliability as a given parameter and do not explain how to determine the optimal target reliability. From the perspective of the market, designing a product with high target reliability can satisfy many customers and increase market demand, but it can generate a large cost leading to profit reduction of the company. Therefore, the target reliability should be a decision variable which needs to be found to maximize the company profit. This paper proposes a reliability-based design for market systems (RBDMS) framework by integrating RBDO and design for market system (DMS) approaches to find the optimal target reliability. The proposed RBDMS framework is applied to electric vehicle (EV) design problems to validate effect of the target reliability on company profit—or market share—and engineering performances of EV. Several observations about the optimal target reliability are presented from the case study with various scenarios. From the EV design case study, it is verified that the proposed RBDMS framework is an effective way of finding the optimal target reliability that maximizes the company profit, and the optimal target reliability varies depending on the situation of market and competitors.

Selection of optimal target reliability in RBDO through reliability-based design for market systems (RBDMS) and application to electric vehicle design

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services.

Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization.

Shared autonomous vehicles (SAVs), which combine autonomous driving technology and car-sharing service, are expected to become essential parts of transportation systems in the near future. Existing studies related to SAV system design and optimization have focused on shared autonomous battery electric vehicle (SABEV) systems using battery electric vehicles (BEVs) as SAVs. As fuel cell electric vehicles (FCEVs) emerge as alternative fuel vehicles, the SAV system needs to be extended to shared autonomous fuel cell electric vehicle (SAFCEV) systems employing FCEVs as SAVs. This study newly presents a design framework of an SAFCEV system based on a proton-exchange membrane fuel cell (PEMFC) model. Optimization is conducted for SABEV and SAFCEV systems to minimize the total cost while satisfying the customer wait time constraint. Compared to the SABEV system, SAFCEV system shows a decrease of system fleet size of 9.8% (9 fleets) and an increase of driving range of 108.8% (148.6 km). Total cost of SAFCEV system is 1.6% ($54,857) lower than that of the SABEV system, indicating that SAFCEV can be more cost effective than SABEV. Several observations on various operating environments of SABEV and SAFCEV systems are obtained from parametric studies. In the case of a hybrid system that simultaneously operates SABEV and SAFCEV, the total cost is reduced by 0.8% ($25,852) compared to when only SAFCEV is operated. This study verifies the potential of FCEV as SAV and presents design directions for SABEV and SAFCEV systems. 

Design for shared autonomous vehicle (SAV) system employing electrified vehicles: Comparison of battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)

Ungki Lee

Papers

Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)

Shared autonomous electric vehicle design and operations under uncertainties: a reliability-based design optimization approach

Selection of optimal target reliability in RBDO through reliability-based design for market systems (RBDMS) and application to electric vehicle design

Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization.

Design for shared autonomous vehicle (SAV) system employing electrified vehicles: Comparison of battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)