Fleet Control Research Articles

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Scenarios for New Mobility Policies and Automated Mobility in Beijing

In this study, we consider the introduction of new mobility services and technologies into the megacity of Beijing, China, as per developed strategy and action plans, in order to investigate their potential contribution to sustainable mobility. This includes population relocation (decentralization), the construction of new rail lines, the introduction of shared bike services as a feeder to subway stations, the electrification of passenger vehicles and the adoption of automated and shared vehicles. The well-established, system dynamics-based MARS model is adapted to Beijing and further improved via the inclusion of these new services, technologies and policies. We find that decentralization can have a profound effect on overall sustainability if not considered in conjunction with other policies and that new rail lines and shared bikes may only have benefits in specific zones. Shared and automated vehicles could increase VKT by 60% and reduce active and public transport trips by a quarter. As such, nuanced integrated policy approaches will be required that are similar to those currently in place, such as imposed car shedding and taxi fleet control.

Optimal charge scheduling and on-board control of an urban electrified BRT fleet considering synthetic representative driving cycles

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet . Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution.

Robust collision-free formation control of quadrotor fleets: Trajectory generation and tracking with experimental validation

This paper addresses the problem of formation control of multiple quadrotor vehicles, focusing on ensuring inter-vehicle collision avoidance in the presence of time-varying external disturbances. The problem is divided into two sub-problems: (i) the generation of collision-free trajectories; and (ii) trajectory tracking. Initially, by employing the Lyapunov-based backstepping technique, a set of collision-free trajectories is generated based on a potential function. Additionally, a trajectory tracking controller is developed for each individual quadrotor, ensuring accurate tracking of their respective trajectories. Furthermore, to attain robust tracking performance, nonlinear disturbance observers are developed and incorporated into the control inputs to compensate for time-varying external disturbances. Comprehensive simulation and experimental results are provided and analyzed to demonstrate the effectiveness and performance of the proposed strategy.

Spatially-aware station based car-sharing demand prediction

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. Our study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations.

National-scale bi-directional EV fleet control for ancillary service provision

Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.

Load-Out and Hauling Cost Increase with Increasing Feedstock Production Area

The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.

Prototype of a System for Tracking Transit Service Based on IoV, ITS, and Machine Learning

The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control.

Cloud and Edge Computing for Smart Management of Power Electronic Converter Fleets: A Key Connective Fabric to Enable the Green Transition

The increasing grid penetration of renewable energy generation, energy storage, and controllable electronic loads [e.g., electric vehicle (EV) chargers, motor drives, and electrolyzers) is making power electronic converters (PECs)—their controllable grid connection interfaces—omnipresent. Therefore, fleets of PECs are becoming key players not only in coordinating the generation and storage but also in unlocking the potential for flexibility within each load. In this article, we discuss how cloud-based platforms and edge devices can allow for interfacing PECs and advanced control algorithms. We present seven different study cases using our own developed cloud-based platform that demonstrate the smart coordination of PECs in a variety of prototypes and real-world solutions. Moreover, the capabilities of cloud and edge computing are highlighted throughout the article at large, effectively characterizing cloud-based control of fleets of PECs as an essential part of the future energy sector.

A Mission Planning Framework for Fleets of Connected UAVs

Currently, several platforms offer solutions for the management and control of fleets of unmanned aerial vehicles (UAVs), addressing a wide range of scenarios, each with its own particular set of objectives. Some of these solutions are mission planning platforms for broader usage, without aiming to solve a single scenario. However, these often either do not support multi-UAV collaboration or generate a static flight trajectory, which does not facilitate the coordination of a fleet of several UAVs in a mission that may need to adapt to the current environment. Through the development of a domain-specific language (DSL) - EasyMission - for UAV mission definition and control, we introduce a mission planning framework that makes it possible to describe mission plans that also enable the user to define adjustments and constraints that may have to be taken into account in real-time according to sensor readings or other events. This framework enables inexperienced users to design missions with low or moderate complexity levels, while still being a useful tool for advanced users due to its versatility in addressing multiple scenarios through a single platform. We show how the mission framework is able to easily define differentiated mission examples with distinct purposes and scenarios, and with real-time decisions and constraints.

Digital Twin Development and Operation of a Flexible Manufacturing Cell using ISO 23247

Digital twins are representations of real-world systems in the digital world, relying on physical system data to optimise, manipulate, detect, and interact. The requirements of digital twins diverge depending on the application in focus. The complexity ranges from simple to highly complex use cases, which increases setup time and reduces maintainability. Therefore, different representations and views of the digital twin are necessary, e.g., file type conversions or abstractions of geometry. Standards that try to solve these problems are ISO 21597, by providing containerisation and linkage of data, and ITU-T Y.3090 and ISO 23247, by providing frameworks to support the creation process of digital twins. Based on these standards, this paper aims to enable a holistic view of digital twin applications in flexible manufacturing cells to reduce the implementation effort when the system is installed or changes. We identify a digital twin's most relevant features, parameters, and assets. This feature set is categorised regarding the frequency of changes. The main observable manufacturing elements are machine tools, robots, peripheral devices like autonomous mobile robots (AMRs), manufacturing utilities like tools, and processes. Various information systems depend on or interact with these manufacturing cells, e.g., CAM, PLM, MES, tool management, fleet control, and cell control. While applying the framework of ISO 23247 to a flexible manufacturing cell, we found that life cycle changes are only considered for products and not for the digital twin itself. Therefore we try to emphasise life cycle changes by linking the manufacturing cell to a life cycle meta-layer, simplifying the design, deployment, and updates of digital twin applications. This linkage remains valid through changes, reducing application maintenance effort by allowing re-instantiation of the applications. Therefore, a multidimensional representation of the physical layer can address rapidly changing real-time data to hardly changing life cycle information.

Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services

The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important.

A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.

Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.

Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.

Online learning for distributed optimal control of an electric vehicle fleet

The management of electrical power systems requires the resolution of large-scale problems whose agents are linked by coupling constraints. Nevertheless, decomposition methods cannot provide an exact solution while dealing with temporal dynamics in a stochastic environment. Indeed, each agent would have to solve a local minimisation in which future quantities intervene. However, these quantities depend on other agents’ future decisions which are still unknown. In order to enhance the existing approximate approaches to this challenge, the proposed method involves Alternating Direction Method of Multipliers to overcome the large dimension by an iterative resolution of local coordinated minimisations. Uncertain temporal dynamics are handled by a stochastic dynamic programming approach. In order to make local problems solvable, the online learning of a Markov process is added. The agents can then anticipate future global variations in a local probabilistic way. The optimal charging of an electric vehicle fleet paired with a wind power plant is considered as a case study. The expected benefits are highlighted, both at the outset and after training the anticipative models. The discussion addresses the learning parameters allowing the fastest convergence.

Use of an Improved Car-Following Model to Explain the Influence of Traffic Composition on Saturation Headway at Signalized Intersections

Previous studies mainly used statistical methods to analyze the impact of traffic composition on saturation flow rate from the mesolevel, and there is insufficient research on how traffic composition affects driving behavior. Thus, the purpose of this paper is to establish a more accurate car-following model, establish the relationship between microbehavior and mesostatistical regularity, and explain the influence of vehicle composition on saturation headway. In this paper, an improved full velocity difference (FVD) model is proposed, which abstracts the driver characteristics of a heterogeneous flow into four scenarios: car-car, car-bus, bus-car, and bus-bus. The measured data are used to calibrate and verify the basic FVD and the improved FVD models. The performance of the improved model is significantly improved. The RMSE and RMSPE are reduced by 15.29% and 22.32%, respectively. Finally, through numerical simulation experiments, the variation of saturation headway with different proportions of buses is analyzed. The saturation headway increases with the increase of the proportion of heavy vehicles. Moreover, another important finding is that the saturation headway is not significantly influenced by the position of the buses but only by the proportion of the buses. The research results could provide theoretical support for the control and management of fleets composed of different vehicles at intersections.

BATTPOWER application: Large-scale integration of EVs in an active distribution grid – A Norwegian case study

With the considerable increase of Distributed Energy Resources (DER), reliable and cost-effective operation of distribution grids becomes challenging. The efficient operation relies on computationally dependable and tractable optimisation solvers, which may handle: 1) non-linear AC power flow constraints, and 2) time-linking variables and constraints and objectives of DER, over the operational horizon. In this paper, we introduce an application of a high-performance MultiPeriod AC Optimal Power Flow (MPOPF) solver, called “BATTPOWER”, to simulate active distribution grids for a near-future scenario. A large-scale Norwegian distribution grid along with a large population of Electric Vehicles (EV) are here taken as the case-study. We suggest and analyse three operational strategies (in terms of control of charge scheduling fleet of EV) for the Distribution System Operator (DSO): (a) uncoordinated charge scheduling, (b) coordinated charge scheduling with the objective of energy cost-minimisation without operational constraints of the grid, and (c) coordinated charge scheduling with the objective of energy cost-minimisation along with the operational constraints of the grid. The results demonstrate that the uncoordinated charging would lead to: 1) overloading of lines and transformers when the share of EVs is above 20%, and 2) higher operational costs than the proposed control strategies of (b) and (c). In strategy (b) operational line/transformer limits are violated when the populations of EVs are growing above 36%. This implies that current market design must be altered to allow active control of a large proportion of DERs within grid operational limits to achieve cost minimization at system level. To our knowledge, the work presented in this paper is the first ever attempt to do a comprehensive analysis of the impact of EV charging demand on a real Norwegian distribution grid. Moreover, the inference of the analysis says that the Norwegian distribution networks are more prone to congestion problems than the voltage problems for the EV demand which includes a smart charging scheme accounting for grid conditions.

Research on accurate modeling of hydrodynamic interaction forces on AUVs operating in tandem

The hydrodynamic interaction force between autonomous underwater vehicles (AUVs) operating in tandem can adversely affect the hydrodynamic prediction of individual AUVs and the formation control of the tandem fleet. Accurate modeling of hydrodynamic interaction forces is crucial to the model-based controller design of AUVs. In this paper, we innovatively propose a modeling method that can describe the hydrodynamic interaction relationship accurately and continuously (when the spacing between adjacent AUVs changes). First, an accurate hydrodynamic model for individual AUVs in tandem fleets is established by modifying the typical model through the consideration of memory effects. Second, a model of hydrodynamic interaction forces is derived by subtracting hydrodynamic forces acting on a single AUV operating independently from those acting on an AUV operating in tandem. To enable this model to continuously describe the hydrodynamic interaction relationship when the spacing changes, this model is further derived by taking the spacing as an independent variable based on the parameter conversion method. Finally, planar motion mechanism (PMM) tests are carried out using the computational fluid dynamics (CFD) method to verify the accuracy and continuity of the proposed modeling method. The results show that compared with the typical model, the average error of hydrodynamic forces predicted by the proposed model is reduced by 5.36%, which verifies the feasibility and effectiveness of the proposed modeling method.

The Mediterranean Rhodes Gyre: modelled impacts of climate change, acidification and fishing

The Mediterranean Rhodes Gyre is a cyclonic gyre with high primary production due to local upwelling of nutrients and occasional deep overturning up to 1 km depth. This nutrient-rich state is in sharp contrast to other parts of the Eastern Mediterranean which are oligotrophic. Here we investigated the upwelling system central to the Rhodes Gyre and the impact of different stressors like meteorological changes, acidification and fishing pressure up to the year 2100. A water column model spanning the physical, chemical and biological system up to top predators (GOTM-ERSEM-BFM-EwE) was used to simulate the pelagic environment under single and combined stressors. Results show that due to increasing winter temperatures, deep overturning events will become more rare in the future until they stop occurring around 2060 under the Paris-agreement climate scenario (RCP4.5) or around 2040 under the business-as-usual climate scenario (RCP8.5). Stratification will become stronger as temperature effects outweigh salinity effects in the surface mixed layer. Together with the lack of deep overturning, this stronger stratification limits the nutrient supply to the euphotic zone, significantly reducing primary production. Phytoplankton species shift towards smaller species as nutrients become more scarce, mimicking the situation found currently on the edge of the gyre. Climatic changes and fishing pressure will affect higher trophic levels in an additive way for some species (sardines, dolphins), while in a synergistic way for others (anchovy, mackerel). Acidification impacts are negligible. Fish stocks will reduce significantly under all scenarios involving climate change effects: ~30% under scenarios imposing RCP4.5 and ~40% under scenarios imposing RCP8.5. The beneficial impact of maximum sustained yield-level fishing is very limited, indicating a need for mitigating measures beyond fleet control.