Charger Placement Research Articles

Optimal battery electric bus system planning considering heterogeneous vehicles, opportunity charging, and battery degradation

Battery electric buses (BEBs) have been seen as a viable alternative for sustainable mobility. The charger placement and fleet configuration play a crucial role in the operational efficiency of BEBs. In this study, a joint optimization model is developed to strategically plan the charger placement and fleet configuration for a BEB system. Distinct from existing research, we simultaneously incorporate opportunity charging, heterogeneous vehicles, and battery degradation to integratively minimize the costs of charger installation, BEB purchase, energy consumption, and battery degradation. Based on a model reformulation, a heuristic-exact hybrid solution method is customized to solve the model. A real-world case study from Oslo, Norway is provided to demonstrate the optimization model and solution method. The results indicate that the proposed model can reduce the total costs by 21.27% in comparison to the conventional model. Sensitivity analyses are further conducted to provide insightful findings and managerial implications for BEB systems.

Electric Vehicle Battery Charger with IOT System

The accelerating adoption of electric vehicles (EVs) has elicited the requirement for modern and developed charging solutions that promise efficiency, as well reliability. In this paper, we propose an Internet of Things (IoT) enabled DC electric vehicle charger to meet these requirements. The solution offers IoT functionalities for full and remote monitoring, diagnostics roadside assistance as well as charging management adapting to the available power. Making use of top-level sensors and connectivity, the charger system optimizes energy usage by intelligent grid balancing as well as enables dynamic load balancing techniques for installation-wise benefits such that it is possible to implement predictive maintenance. Meanwhile, IoT integration ensures a seamless user experience through mobile apps so that users can check the charging status of their vehicle and set up sessions at different times of the day while receiving important notifications. DC EV chargers are faster at charging than normal Alternating Current (AC) ones. They transform electricity from the grid into a form that can be directly applied in the vehicle battery. This ability translates to highly reduce charging times, which explains why they are well-suited for along highways and in commercial applications or urban settings where fast turnaround is critical. These trends are identified by service providers to improve charger placement and availability for drivers

Reinforcement learning-based charging cluster determination algorithm for optimal charger placement in wireless rechargeable sensor networks

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

Robust Fault-Tolerant Placement of Wireless Chargers for Directional Charging

Robust Fault-Tolerant Placement of Wireless Chargers for Directional Charging

Charger Placement with Wave Interference

Charger Placement with Wave Interference

Practical Charger Placement Scheme for Wireless Rechargeable Sensor Networks with Obstacles

Benefitting from the maturation of Wireless Power Transfer technology, Wireless Rechargeable Sensor Networks have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this article, we study a fundamental issue of W ireless ch A rger placement w I th obs T acles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1-1/e-ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

<div class="section abstract"><div class="htmlview paragraph">As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.</div></div>

Modelling of Electric Bus Operation and Charging Process: Potential Contribution of Local Photovoltaic Production

The transition from diesel to electric buses allows the reduction of greenhouse gas emissions. However, the impacts of charging strategies on the quality of bus services and the utility grid must be assessed to ensure the feasibility of the energy transition in the public transportation sector. This study investigates the performances of different locations and sizes of charging infrastructures by presenting the comprehensive modelling of a bus network. It also estimates the potential benefits of a local photovoltaic (PV) production to reduce negative impacts on the utility grid. The presented approach is used for modelling one urban bus line in Compiègne, France, and simulations are performed for various case studies. The results demonstrate that the proposed method allows analysing the impact of the charging process on the quality of bus services by determining the delays of arrivals. The simulations also show the impacts of charger placement on bus on-board battery capacity, total peak power demand of battery charging, and PV self-consumption ratio. The amount of PV energy used directly to charge buses remains low, although it varies between scenarios. PV energy during winter is not sufficient to fully charge buses; however, it can be enough with additional stationary storage in the summer.

Hybrid quantum-classical solution for electric vehicle charger placement problem

Hybrid quantum-classical solution for electric vehicle charger placement problem

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers

Based on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and optimization of charging vehicles. Designing the charging infrastructure for EVs has many impacts such as stress on the power network, increase in traffic flow, and change in driving behaviors. Therefore, the optimal placement of charging stations is one of the most important issues to address to increase the use of electric vehicles. In this regard, the purpose of this study is to present an optimization method for choosing optimal locations for electric car charging stations for Campus charging over long-term planning. The charger placement problem is formulated as a complex Multi-Criteria Decision Making (MCDM) which combines spatial analysis techniques, power network load flow, traffic flow models, and constrained procedures. The Analytic Hierarchy Process (AHP) approach is used to determine the optimal weights of the criteria, while the mean is used to determine the distinct weights for each criterion using the AHP in terms of accessibility, environmental effect, power network indices, and traffic flow impacts. To evaluate the effectiveness of the proposed method, it is applied to a real case study of Qatar University with collected certain attributes data and relevant decision makers as the inputs to the linguistic assessments and MCDM model. The Ranking of the optimal locations is done by aggregating four techniques: Simple Additive Weighting Method (SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II). A long-term impact analysis is a secondary output of this study that allows decision-makers to evaluate their policy impacts. The findings demonstrate that the proposed framework can locate optimal charging station sites. These findings could also help administrators and policymakers make effective choices for future planning and strategy.

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

Insight from data analytics in a facilities management company

AbstractFacilities management (FM) of large public services is commonly outsourced to a specialist provider. Extensive data is collected on a wide range of operational tasks. Often, limited use is made of this data beyond operational considerations. Increasing availability of sophisticated data integration systems makes it possible to develop bespoke data science solutions to improve the efficiency, speed and accuracy of commonly encountered tasks. A Knowledge Transfer Partnership (a funding scheme of Innovate UK) was set‐up between Newcastle University and a large FM provider. Within this project, data science has been effectively applied to improve service delivery in three different case studies: (1) designing routing algorithms for scheduling of staff allocation to jobs, (2) using natural language processing for automatically raising work orders from help desk emails, (3) development of algorithms to optimise the placement of charging points for the transition of fleets of commercial vehicles from diesel to electric powered. For each case study, we show how the data science work has contributed to improvement of FM by: (1) maximising the productivity of engineers, (2) reducing the time taken to process help desk emails, (3) creating a data‐driven method to provide objective decision support for charger placement, respectively. The research in this paper is based on a contract with a local authority where the FM company provides planned and reactive maintenance for a range of assets in corporate buildings, hospitals and schools. In addition to describing the data science solutions, we will also discuss issues around knowledge transfer from data scientist to operational staff and how change management processes were employed in this project to embed the new working practices.

A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

Transportation electrification is known to be a viable alternative to deal with the alarming issues of global warming, air pollution, and energy crisis. Public acceptance of Electric Vehicles (EVs) requires the availability of charging infrastructure. However, the optimal placement of chargers is indeed a complex problem with multiple design variables, objective functions, and constraints. Chargers must be placed with the EV drivers’ convenience and security of the power distribution network being taken into account. The solutions to such an emerging optimization problem are mostly based on metaheuristics. This work proposes a novel metaheuristic considering the hybridization of Chicken Swarm Optimization (CSO) with Ant Lion Optimization (ALO) for effectively and efficiently coping with the charger placement problem. The amalgamation of CSO with ALO can enhance the performance of ALO, thereby preventing it from getting stuck in the local optima. Our hybrid algorithm has the strengths from both CSO and ALO, which is tested on the standard benchmark functions as well as the above charger placement problem. Simulation results demonstrate that it performs moderately better than the counterpart methods.

Task-driven charger placement and power allocation for wireless sensor networks

Deploying wireless chargers is a promising way to provide continuous energy supply for battery-driven sensor nodes, and has attracted more and more attention recently. Existing works mainly focus on studying the charging utility maximization problem, that is, they aim to supply as much power as possible to all sensor nodes with a certain number of wireless chargers. However, these works ignore the task performing function of sensor nodes, supplying more power to sensor nodes cannot guarantee that tasks can be performed better. In this paper, therefore, we consider a more practical issue of deploying wireless chargers, where our objective is to maximize the total achieved task utility with a limited deployment cost budget. We formulate our problem as a mixed integer non-linear programming problem, and prove its NP-Hardness. To address this problem, we split it into two sub-problems, where the first sub-problem is about power allocation for sensor nodes, and the second sub-problem is about optimal wireless charger placement. We design an approximation algorithm for each sub-problem, and give theoretical analyses of our algorithms. By solving the two sub-problems accordingly, we get a feasible solution that has a 12(1−1∕e) performance guarantee for the original problem. Finally, we conduct extensive simulations to validate the performance of our algorithms. The results show that our designs significantly outperform the baseline algorithms, which demonstrates the effectiveness of our algorithms.

INCEPTS: Software for high-fidelity electric vehicle en route state of charge estimation, fleet analysis and charger deployment

As the electrification of the transportation industry and growing adoption of these electric vehicles continues, the demand for charging infrastructure is increasing concurrently. Properly deploying this infrastructure requires that there exist models which can adequately estimate the performance of these vehicles under real scenarios. While there has been significant work on estimating energy-consumption for realistic drive profiles, there is a need to systematically incorporate the conditions (e.g. wind, elevation) along a route for enabling efficient charger deployment. In this work, we have developed a software package known as INCEPTS, an agent-based simulation software that can predict high-fidelity electric vehicle performance and can capture the dynamics exhibited by the individual vehicles as they travel on their respective routes. The software then uses this to analyze fleets of vehicles which will provide the fleet performance statistics that result from various impacts exhibited by the fleet in the simulated area. These statistics can then be further used to inform decisions such as charger deployment to ensure that the placement of chargers adapts to the local needs of the electric vehicle fleet. Furthermore, this software is developed such that it is capable of simulating different kinds of electric vehicles (e.g. cars, trucks, eVTOLs) and different locations. To demonstrate the capabilities of the software, a variety of simulations are performed using Massachusetts as a target location. The first set of simulations show the importance of accounting for local conditions of individual vehicles. For example, the difference is state-of-charge could differ by up to 30% for the same distance traveled. The second set of simulations looked at the fleet performance within Massachusetts in which a bimodality of energy used per mile is observed, with means at 184 Wh/mi and 244 Wh/mi. Our work demonstrated that this bimodality results from a combination of both local conditions and geometric restrictions caused by the road network. These results are indicative of the unique capabilities of the INCEPTS platform and we plan to demonstrate additional use-cases in follow-up studies. • Coupling of environmental factors with models for battery, vehicle and fleet dynamics. • Scalable vehicle simulations for fleet statistics and complex systems analysis. • The software can simulate any type of electric vehicle fleet in any part of the world. • Massachusetts, U.S. case study used to demonstrate the software’s capabilities.

Electric Vehicle Charger Placement Optimization in Michigan Considering Monthly Traffic Demand and Battery Performance Variations

Limited charging infrastructure for electric vehicles (EVs) is one of the main barriers to adoption of these vehicles. In conjunction with limited battery range, the lack of charging infrastructure leads to range-anxiety, which may discourage many potential users. This problem is especially important for long-distance or intercity trips. Monthly traffic patterns and battery performance variations are two main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. Knowing this, the current study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations. This study incorporates a recently developed modeling framework to suggest the optimal locations of fast EV chargers to be implemented in Michigan. Considering demand and battery performance variations is the major contribution of the current study to the proposed modeling framework by the same authors in the literature. Furthermore, many stakeholders in Michigan are engaged to estimate the input parameters. Therefore, the research study can be used by authorities as an applied model for optimal allocation of resources to place EV fast chargers. The results show that for charger placement, the reduced battery performance in cold weather is a more critical factor than the increased demand in warm seasons. To support foreseeable annual EV trips in Michigan in 2030, this study suggests 36 charging stations with 490 chargers and an investment cost of $23 million.

Optimal wireless charger placement with individual energy requirement

Supply energy to battery-powered sensor devices by deploying wireless chargers is a promising way to prolong the operation time of wireless sensor networks, and has attracted much attention recently. Existing works focus on maximizing the total received charging power of the network. However, this may face the unbalanced energy allocation problem, which is not beneficial to prolong the operation time of wireless sensor networks. In this paper, we consider the individual energy requirement of each sensor node, and study the problem of minimum charger placement. That is, we focus on finding a strategy for placing wireless chargers from a given candidate location set, such that each sensor node's energy requirement can be met, meanwhile the total number of used chargers can be minimized. We deal with the problem under both omnidirectional and directional charging models, and prove its NP-hardness. For the omnidirectional charging case, we present two approximation algorithms which are based on greedy scheme and relax rounding scheme, respectively. We prove that both of the two algorithms have performance guarantees. For the directional charging case, we first extract the candidate orientation set for each candidate location to reduce the search space from infinite to a limited set, and then propose a greedy algorithm that also has a proved performance guarantee. Finally, we validate the performance of our algorithms by performing extensive numerical simulations. Simulation results show the effectiveness of our proposed algorithms.

Practical Heterogeneous Wireless Charger Placement with Obstacles

This paper considers the problem of practical H eterogeneous w I reless charger P lacement with O bstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies , on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of $\frac{1}{2} - \epsilon$ 1 2 - e . We conduct experiments to evaluate the performance. Results show that our algorithm outperforms the comparison algorithms by at least 33.49 percent on average.

On the Interaction of Charging-Aware Mobility and Wireless Capacity

Today's mobile battery-powered communication devices require that users access chargers via wired and, recently, wireless recharging facilities. For a device departing from a location with a given energy “budget”, a plausible strategy is to seek a charger location once the energy is exhausted. We present a model of mobile nodes that captures the paths followed by the nodes with depleted energy seeking, possibly via a detour, to reach a charger. The derived location-dependent mobile node density distribution is used to express the location-dependent congestion of a wireless network whose capacity is used by the mobile nodes. The boundaries, and the relative placement of the charger, create intriguing discontinuities in the probability density function of the nodes across space. We find that chargers are not always “hotspots” in terms of node density, and that the energy budget of the nodes at the beginning of their trip impacts that density and determines the hotspot. Moreover, upon energy depletion, to detour from one's path or not, results in a distinctly different relative impact of the charger placement on the ability of nodes to sustain communication, depending, again on the energy budget the nodes have upon departure from a waypoint.