Charging Schedule Research Articles

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.

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

This paper presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

Overnight Charging Scheduling Optimization for Electric Battery Buses with Controllable Charging Completion Levels

Overnight Charging Scheduling Optimization for Electric Battery Buses with Controllable Charging Completion Levels

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

Charging facility planning and scheduling problems for battery electric bus systems: A comprehensive review

The adoption of battery electric buses (BEBs) has gained significant momentum in the public transportation sector due to their environmental and energy-saving merits. Nonetheless, challenges such as limited driving range and battery degradation, to some extent, hinder the extensive deployment of BEBs. This paper provides a comprehensive review of BEB adoption and utilization over the past decade, categorizing the state-of-the-art development and our discussion on BEB charging facility planning (BEB-CFP) and BEB charging scheduling (BEB-CS). In this review paper, we first survey the prevailing charging technologies in the BEB market and evaluate their applicability and limitations. Subsequently, the paper synthesizes the development and application of these technologies in terms of related infrastructure planning and charging scheduling operation, focusing on their unique considerations, modeling approaches, solution algorithms, and practical applications. Last, the paper concludes by identifying avenues for future research. Our findings contribute to the literature by providing a holistic revisit of BEB-CFP and BEB-CS with different types of charging technologies, offering selection recommendations and insights for both practitioners and policymakers aiming to optimize the utilization and sustainability of BEB fleets.

Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.

Optimizing Electric Vehicle Range through Integrating Rooftop Solar on Vehicle

This paper includes the research work to investigate the optimization of electric vehicle (EV) range by integrating rooftop solar panels onto the vehicle. The primary motivation stems from the increasing power demand for EV charging, requiring substantial grid electricity production. The paper explores the installation of rooftop solar panels to augment the EV range with a single full charge, reducing the dependence on the grid. The simulations are conducted using MATLAB modeling, optimizing solar and grid charging schedules based on solar irradiation data. The outcomes showcase a 1.44 kWh battery integration with an EV equipped with a 1 kW BLDC motor, weighing 800 kg, including payload. The installation of Rooftop Solar Photovoltaic (RTSPV) improved the range of the vehicle with 18.65%, energy consumption is increased with 15.5%, Cost savings in charging the battery is up to 15.5% and travel distance with full charge is increased up to 19.6%. This analysis is made with the reference drive cycle shown in the paper. While the integration of solar panels significantly extends the vehicle range, the research emphasizes the potential for sustainable charging practices to mitigate grid dependence and reduce emissions.

Optimizing Electric Vehicle Range through Integrating Rooftop Solar on Vehicle

This paper includes the research work to investigate the optimization of electric vehicle (EV) range by integrating rooftop solar panels onto the vehicle. The primary motivation stems from the increasing power demand for EV charging, requiring substantial grid electricity production. The paper explores the installation of rooftop solar panels to augment the EV range with a single full charge, reducing the dependence on the grid. The simulations are conducted using MATLAB modeling, optimizing solar and grid charging schedules based on solar irradiation data. The outcomes showcase a 1.44 kWh battery integration with an EV equipped with a 1 kW BLDC motor, weighing 800 kg, including payload. The installation of Rooftop Solar Photovoltaic (RTSPV) improved the range of the vehicle with 18.65%, energy consumption is increased with 15.5%, Cost savings in charging the battery is up to 15.5% and travel distance with full charge is increased up to 19.6%. This analysis is made with the reference drive cycle shown in the paper. While the integration of solar panels significantly extends the vehicle range, the research emphasizes the potential for sustainable charging practices to mitigate grid dependence and reduce emissions.

The Electric Vehicle Scheduling Problem for Buses in Networks with Multi-Port Charging Stations

As more and more cities try to reduce their CO2 emissions, public transport fleets are undergoing a transition from conventional to electric vehicles. To complete this shift, there is a need to build the required charging infrastructure. When the first electric buses were adopted, the charging stations were mostly built in the locations of large bus depots. However, in recent years, there has been a crowding problem in the charging stations resulting in queuing and unnecessary delays. In this study, we explore the potential of replacing single-port charging stations with multi-port charging stations that can serve multiple vehicles at once with a reduced charging rate. Because the charging rate reduces with the number of ports, we develop a mixed-integer linear program to determine the charging schedules of bus fleets in order to reduce the overall delays in the bus network. The novel formulation is tested in benchmark instances of various sizes demonstrating the improvement potential.

A real-time energy flow management of a grid-connected renewable energy sources-based EV charging station

ABSTRACT As the sales of electric vehicles (EVs) increase in India, there will be an increase in the number of charging stations in the future. Meanwhile, the demand for extra power from the grid and intermittent power from renewable energy presents significant challenges for charging infrastructure. This paper presents a real-time energy flow management strategy (EFMS) designed for an EV charging station (EVCS) that utilises power from renewable energy sources (RES), an energy storage battery unit (ESBU), and the grid. The primary objective is to increase the utilisation of renewable energy and minimise the quantity of electricity bought during the peak period of the grid by implementing direct electric vehicle load control. EVCS has been designed to charge up to 80 EVs using five DC chargers. The EFMS aims to enhance the scheduling of EV charging by considering grid time-of-use (ToU) pricing and efficient usage of ESBU in terms of battery degradation effect and maximising the use of RES. A multi-objective optimisation algorithm has been developed for EV scheduling. The baseline uncoordinated charge method is contrasted with the proposed approach. The EFMS effectiveness is tested in MATLAB, and the subsequent analysis and results are drawn and discussed.

Learning-based scheduling of integrated charging-storage-discharging station for minimizing electric vehicle users' cost

Charging cost is an important concern for electric vehicle (EV) users. The ordered charging behavior, such as the reasonable selection of charging period and charging power, can greatly decrease users' charging cost. Towards the integrated charging-storage-discharging station (ICSDS), a learning-based method is proposed in this paper to minimize EV users' cost. The physical constraints of ICSDS and the user's demand are first built, and the charging scheduling problem of ICSDS is formulated as a Markov Decision Process (MDP) with unknown transition probability. Second, in order to generate optimal schedule through the learning network, the deep features of the future electricity price are extracted by using a long short-term memory (LSTM) network. Third, a twofold deep deterministic policy gradient (TDDPG) algorithm is proposed to generate the continuous charging actions and avoid the Q value overestimation. In addition, the TDDPG-based scheduling strategy is designed on the basis of the extracted features of electricity price. Finally, the validations through the real-world data demonstrate that the features of electricity price are extracted with a satisfied accuracy. Moreover, compared with many benchmarked methods, the experimental results demonstrate that the charging scheduling by TDDPG possesses excellent performance in minimizing EV users' cost.

Planning dynamic wireless charging infrastructure for battery electric bus systems with the joint optimization of charging scheduling

To address the battery-related shortcomings of battery electric buses (BEBs), dynamic wireless charging (DWC) technology that allows BEBs to charge while in motion has emerged, thereby extending the driving range and reducing the size of onboard batteries. The introduction of DWC technology raises a critical problem, namely, the deployment of DWC facilities. To resolve the infrastructure planning problem, this study first proposes a (higher-level) strategic planning model that optimizes the deployment of DWC facilities and battery capacity of BEBs, and then establishes a (lower-level) tactical planning model of optimal charging scheduling under the time-of-use (TOU) tariff mechanism considering the interdependence between infrastructure design and charging activities of BEBs. Mixed-integer nonlinear programming (MINLP) is proposed to optimize the model, aiming to minimize the overall cost associated with charging facilities, batteries, and charging. The proposed optimization model is tested and evaluated using a real-world bus system network, and the results demonstrate that the model effectively determines the DWC facility locations and battery sizes and optimizes the charging scheduling considering the TOU tariff for BEBs. The proposed model in this study provides a comprehensive guideline for relevant practitioners and further promotes the application of DWC technology in BEBs.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.

Energy-efficient Neuro-fuzzy-based Multi-node Charging Model for WRSNs using Multiple Mobile Charging Vehicles

Mobile charging vehicles (MCVs) have played a significant role in advancing Wireless Rechargeable Sensor Networks (WRSNs). Recent research has primarily focused on on-demand charging in WRSNs using MCVs. However, minimal attention has been given to the simultaneous analysis of multiple MCVs with multi-node energy transfer capabilities. Additionally, when making scheduling decisions, most existing methods still need to consider various network parameters, while a few have overlooked the challenge of sensor nodes’ (SNs) poor charging responses to non-uniform energy expenditure rates. This article addresses the issues above and proposes an innovative charging-scheduling Algorithm based on Neuro-fuzzy with PSO model (ANFIS-PSO) to resolve these challenges. To ensure uniform workload allocation among MCVs and prevent interference during the charging process, we deploy MCVs strategically using the Optimal Fuzzy C-Means clustering method. We then optimize MCV visiting points to recharge energy-critical SNs, implying reduced energy consumption and charging delay. We next adopt the ANFIS-PSO model, which blends various network parameters for creating the charging schedules of the MCVs. We devise an expression to find the SNs’ adaptive charging threshold values based on their remaining lifetime. We also formulate another expression called the average charging weight function to assist the ANFIS-PSO model in selecting the next promising SN for recharging. Experimental simulations validates that our method is effective and outperforms baseline methods, with a substantial 11% reduction in charging delay, a 6% improvement in the life-survival ratio, and a 7% increase in energy utilization efficiency across all performance metrics.

Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

FCVM(i): Integrated FCNP-VWA-MCDM(i) Methods for On-Demand Charging Scheduling in WRSNs

On-demand charging schemes have been recently proposed to make efficient charging schedules of mobile chargers by introducing MCDM methods in wireless rechargeable sensor networks (WRSNs). However, most of the existing schemes use analytic hierarchy process (AHP) or fuzzy AHP (FAHP) of paired ratio scale (PRS) to exaggerate the actual paired difference between multi-criteria, thereby very likely producing misapplications such as inappropriately ranking the charging locations or inaccurately drawing the partial charging time in charging scheduling (CS) of WRSNs. In addition, in case of using FCNP of paired interval scale (PIS) for weight assignment of multi-criteria, weight compensation has not been considered. In particular, it is still unknown which is the best method for integrating FCNP with several MCDM approaches. This paper proposed novel CS methods by integrated FCNP-VWA-MCDM(i) called FCVM(i) which solves all of these problems. The proposed methods first assign the weights to multiple criteria discriminating charging request nodes (cRNs) using FCNP and make compensation of them to be relatively exact weights with VWA. Then, on the basis of these weights, MCDM(i) is used to elect the best proper next charging position. In this way, drawing up the recharging schedule, at the selected charging locations, we decide the reasonable partial charging time using the assigned weights with FCNP-VWA. Extended experiment results prove that the FCVM(1) using TOPSIS gives the best performance among FCVM(i) methods.

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

Developing an Efficient Support Vector Machine Learning Model for Mobile Charging Stations.

In the context of increasing global energy scarcity, optimizing electric vehicle (EV) mobile charging stations is critical for promoting sustainable transportation. This study introduces the use Artificial Neural Network (ANN) models, and Support Vector Machine (SVM) enhanced with the Adams optimizer, to address the challenge of efficient EV charging in energy-constrained environments. The models are designed to predict optimal charging station locations and schedules, with the ANN-Adams optimization fine-tuning the model parameters to improve accuracy and performance, while ensuring dynamic adaptation to fluctuating energy availability and demand patterns. This research contributes to the development of intelligent, adaptive systems for EV infrastructure, paving the way for more resilient and energy-efficient urban mobility solutions. The system employs supervised learning, where models were trained and tested on labeled datasets. The performance of the SVM models was compared to that of a Multilayer Perceptron Network (MLPN) with Adams optimization. The results showed that the MLPN with Adams optimization achieved an accuracy of 97.85%, while the SVM model had a prediction accuracy of 81%. The ANN recorded 91.29% accuracy. Both approaches significantly improved classification accuracy, model generalization on testing datasets, and reduced misclassification errors.

Transactive-Based Day-Ahead Electric Vehicles Charging Scheduling

Transactive-Based Day-Ahead Electric Vehicles Charging Scheduling