Lieselot Vanhaverbeke

As cities seek to reduce car dependency while maintaining individual accessibility, understanding the economic implications of switching transport modes has become increasingly important for both users and policymakers. This study investigates the total cost of ownership of a speed pedelec as the primary commuting vehicle. By integrating user profiles and one-year longitudinal cost data through 480 data points, this paper calculates the total cost of ownership (TCO) for each of the 13 respondents. Although based on a small sample, the study provides rare empirical insights into real-world speed pedelec ownership costs. The analysis includes costs for maintenance, repairs, accidents, extra costs, and accessories. The one-year TCOs of the 13 respondents are compared with the ownership costs of a small battery electric, mild hybrid, and petrol-powered car. The findings show that an SP may present a cost-competitive mobility option compared with owning a comparable petrol Internal Combustion Engine Vehicle (ICEV), Mild Hybrid Electric Vehicle (MHEV) or Battery Electric Vehicle (BEV). These findings can inform individual mobility choices, help policymakers to develop targeted policy interventions to encourage sustainable and inclusive transport and contribute to discussions on the economic feasibility of alternative mobility configurations.

The development of public charging infrastructure is crucial to support mass electric vehicle (EV) adoption. Although many cities worldwide have already installed an initial network of public chargers, it is often unclear whether the current supply of infrastructure is in line with demand and how many more charging stations are required to cope with future EV growth. In this sense, transactional charging data on the existing network can help answer these questions. We present a novel method that uses historical charging data as input to obtain answers to the following questions: (a) How many more chargers are required to meet future demand? and (b) Where should these new chargers be installed? By mining the individual charging behavior of EV drivers, we show that overflow dynamics can be found between charging stations. That is, when a preferred charging station is fully occupied, it is found that EV drivers divert to other charging stations nearby. Identifying these dynamics allows us to simulate the impact of a demand increase on the charging infrastructure network more accurately. We found the number of new chargers required to be significantly lower when considering overflow dynamics. Our simulations indicate that if demand is doubled, 30%–50% fewer charging points are needed compared with a situation in which overflow dynamics are neglected but the same failure rate is still maintained (i.e., percentage of failed charging sessions in the network). Determining the exact number of chargers will depend on the failure rate policymakers are willing to accept, reflecting the trade-off between charging convenience and utilization.

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.

A shared autonomous electric vehicle (SAEV) fleet and the vehicle-to-grid (V2G) strategy both have great potential to reduce GHG emissions. As these concepts have complementary value, they are even more promising combined. However, to the best of our knowledge, no research has yet been conducted on locating charging infrastructure for SAEVs with V2G feasibility. For this construction, the challenge lies in the fact that both mobility demand (mainly for SAEVs) and energy (for any installation of charging infrastructure) have a major influence on this problem. To find the optimal charging infrastructure (CI) allocation for SAEVs with V2G operations, both mobility requirements and grid constraints must be considered. In this paper, we find that optimization models are the most frequently used method to solve the CI allocation problem. We conduct separate examinations of the V2G and SAEVs location optimization models that have been formulated in the literature, for which objective functions are used, and which constraints are considered (with respect to mobility and the electric grid). We find that SAEV and V2G models have overlapping elements, but remain disjunct in their respective perspectives. CI allocation for SAEVs mainly takes mobility into account, but tends to ignore grid constraints or impacts. On the other hand, CI allocation for V2G focuses on the distribution network and the grid, forgetting about mobility demand. To take advantage of the SAEV-V2G potential, future research should combine mobility and grid aspects to find the optimal CI locations for SAEVs with V2G feasibility.

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle’s energy consumption is notably reduced.

The implementation of autonomous delivery solutions in last-mile logistics operations is considered promising. Autonomous delivery solutions can help in tackling urban challenges related to last-mile logistics operations. Urbanization creates higher mobility and transportation demand, which contributes to increased congestion levels, traffic, air pollution, and accident rates. Moreover, mega-trends, such as e-commerce, demand that logistics companies react to increased customer expectations in terms of delivery time and service. Concerning service, electrified autonomous delivery solutions have the potential to operate 24/7 and can help to overcome driver shortages. This paper conducts a systematic literature review. Based on the literature set, a snowballing procedure was applied. Complementary gray literature was included. This work discusses different autonomous delivery solutions such as Autonomous Delivery Robots (ADRs), Unmanned Aerial Vehicles (UAVs), two- or multi-tiered systems, and the concept of passenger and freight integration. The work presents advantages and disadvantages, enabling the comparison of solutions. Furthermore, a research agenda is provided, from which practical-managerial and theoretical implications can be derived. The research agenda can help researchers, manufacturers, businesses, and governmental institutions to prepare for the arrival and subsequent implementation of autonomous delivery services. Various implications related to energy demand, legislation, implementation strategy, training, and risk and safety are presented. The outcome of this work calls for collaboration among various stakeholders, encourages mutual learning, and hints at the importance of national and international development projects.

The race for road electrification has started, and convincing drivers to switch from fuel-powered vehicles to electric vehicles requires robust Electric Vehicle (EV) charging infrastructure. This article proposes an innovative EV charging demand estimation and segmentation method. First, we estimate the charging demand at a neighborhood granularity using aggregated cellular signaling data. Second, we propose a segmentation model to partition the total charging needs among different charging technology: normal, semi-rapid, and fast charging. The segmentation model, an approach based on the city’s points of interest, is a state-of-the-art method that derives useful trends applicable to city planning. A case study for the city of Brussels is proposed. Our demand estimation results heavily correlate with the government’s predictions under similar assumptions. The segmentation reveals clear city patterns, such as transportation hubs, commercial and industrial zones or residential districts, and stresses the importance of a deployment plan involving all available charging technologies.