Charging Strategy Research Articles

Optimal sizing of an integrated renewable energy system and effective utilization of surplus energy in electric vehicle charging

Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renewable energy system (IRES) to electrify twelve villages in the Uttarakhand state of India. Four battery energy storage (BES) devices, namely Lead-Acid (LA), Sodium-Sulfur (NAS), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered for storage in this study. Using the Chimp optimization algorithm (ChOA) on the MATLAB© platform, eight different configurations consisting of solar photovoltaic (SPV) array, a micro-hydropower (MHP) plant, and a biogas generator (BGG) are modeled and optimized. The study reveals that the optimal IRES configuration with the lowest cost and highest performance comprises 676 SPV panels (260 kWp), one MHP plant (25 kW), one BGG (40 kW), and 648 NAS batteries (778 kWh). This configuration has a total system life cycle cost (LCC) of INR 68.77 million and cost of energy (COE) of 16.77 INR/kWh at 0 % loss of power supply probability. The optimization problem was run 1–50 times and found that the proposed ChOA algorithm is more robust compared to others, displaying the lowest Best, Worst, and Mean values of LCC (across all eight configurations), convergence rapidity (26th iteration), and least computational time (3481 sec). However, GWO (seven configurations), MFO (34th iteration), and GA (4154 sec) stand very close to ChOA performance in terms of providing minimum LCCs, convergence rapidity, and computational time, respectively. Furthermore, surplus energy (SE) is effectively utilized by incorporating electric vehicles (EVs) as dump load in the system. The proposed integrated charging (IC) strategy outperforms other charging strategies by energizing 134 EVs, utilizing 99.59 % of SE, and reducing the total COE to 10.57 INR/kWh. Finally, the proposed IC strategy results in a net saving of 94,479.39 tons of greenhouse gas emissions. These findings support the feasibility of implementing a standalone IRES to electrify the study area and provide electricity to EVs.

Photovoltaic energy based fast charging strategy for VRLA batteries in small electric vehicles for sustainable development

Photovoltaic energy based fast charging strategy for VRLA batteries in small electric vehicles for sustainable development

An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid

With the large-scale EV connected into the wind power grid, the intermittent, fluctuating and stability bring rigorous challenges for power quality and dispatch, thus wind curtailment becomes more serious. This paper proposes an advanced multi-objective collaborative scheduling strategy to improve the accuracy of the predictable wind grid and ensure the stable operation of the grid by optimizing wind power and EV synergistically. Firstly, a hybrid power prediction method is proposed based on density-based spatial clustering applied to noise (DBSCAN) and partial least squares regression (PLSR) combined with long and short-term memory neural network (LSTM). It establishes a linear component prediction mode to predict the nonlinear wind power efficiently. Secondly, a multi-objective optimization scheduling model is established according to the predicted wind power, considering the vehicle-to-grid (V2G) characteristics of EV, grid load fluctuations, wind curtailment capacity, EV charging costs, and grid losses. The global control aims to achieve overall energy balance and optimize the charging and discharging of EV in a time dimension under constrained conditions. Additionally, based on the total number of EV charging and discharging determined by the global level control, a spatial scheduling optimization is conducted for EV and wind power by the distribution network control, with the goal of minimizing network losses in the distribution network system. Finally, through simulation and comparison of four scenarios, the results demonstrate that the proposed method achieves synergistic optimization of wind power and EV. This paper provides an efficient solution for the optimized scheduling of large-scale EV connected to the predictable wind power grid.

Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations

Electric vehicle technology has recently drawn a lot of interest on a global scale due to improved performance in its efficiency and the capability to solve the problems of carbon emission. As such, electric vehicles are the key to achieving sustainable development goals. This review article analyzes deeply the previous technical developments of electric vehicles, focusing on important topics like battery management systems, technologies of power electronics, techniques of charging, and the relevant algorithms and improvements. In addition, several critical problems, and difficulties are presented in order to pinpoint the gaps in the literature. To address the analysis of battery behavior, battery condition monitoring, real-time control design, temperature control, fault diagnostics, and efficiency of battery model are considered. This study highlighted the estimation techniques that predict the internal battery conditions such as internal temperature, state of health, and state of charge, which are difficult to be directly monitored and determined. A lithium-ion battery, a super-capacitor, and related bidirectional DC/DC converters constitutes the infrastructure of a hybrid power system. This review offers useful and practical recommendations for the future development of electric vehicle technology which in turn help electric vehicle engineers to be acquainted with effective techniques of battery storage, battery charging strategies, converters, controllers, and optimization methods to satisfy the requirements of sustainable development goals. Accordingly, this review article will be a platform and future guide for those who are interesting in the field of energy management and its development.

Design and application: Simplified electrochemical modeling for Lithium-ion batteries in whole lifespan

In order to expedite battery development cycles and optimize design, electrochemical model is widely employed to investigate battery performance. However, while these models have been proven efficient in reducing experimental costs, most of them do not sufficiently account for capacity degradation throughout the entire life cycle of the battery. To address this issue, a pseudo-two-dimensional model is developed to better characterize charge-discharge characteristics, taking into account capacity fading effects. Firstly, the impact of relevant design parameters on internal resistance is addressed, which is significant for optimizing battery design and reducing internal resistance. Secondly, to shorten battery charging time, a two-step constant current charging strategy with the negative electrode potential as the threshold is proposed and its efficacy is verified. Compared to constant current-constant voltage charging strategies, the designed novel approach reduces the charging time by 20 % and significantly reduces capacity fading caused by thickening solid electrolyte interphase films. Finally, the retired battery specific load profiles are input into the model to analyze changes in battery performance and estimate battery life for a second use.

Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model

In response to the increasing number of EVs(electric vehicles) being charged in a disorderly manner, this paper proposes a two-layer deep learning model based on aggregator pricing and EV user charging strategy. Firstly, the relationship linking the power grid, aggregator, and EVs is defined. Secondly, the pricing of the aggregator and the allocation of EV charging stations are divided into two stages. In the first stage, the aggregator sets the tariffs and sends them to the users, and the users receive the charging strategy. In the second stage, the aggregator adjusts the tariff on the basis of the charging strategy provided by the EVs and the actual situation of different charging stations and then releases it. Next, a two-layer deep learning model is built to solve the final pricing and EV charging strategy model, and the concept of humanity is introduced. The upper layer is the charging decision layer, which is solved by deep reinforcement learning. The lower layer is the charging station selection layer, which is solved by deep Q-learning. The method proposed in this paper can effectively improve the profits of the aggregator and reduce the charging cost of EVs.

Promotion and Resistance: The Difficulty and Resolution of Establishing News Paywall in China

The news paywall, an access mechanism for news providers to implement pay-per-read for online news and information content originated in the U.S. news industry, is seen as an essential part of the news industry’s profitability model in the Internet era, comparable in importance to advertising. However, the development of the mechanism in the Chinese news market has yet to be satisfactory, and its promotion has been repeatedly blocked. Few scholars have comprehensively evaluated the differences in developing and promoting the new paywall in Chinese news market from political, economic, and cultural aspects. Hence, the study adopts a case study method to analyze the difficulties in developing and promoting the news paywall system in China in terms of the composition and function of the news industry, and the public's understanding and perception of the news industry, and the news industry's adopting a pay-for-content strategy. Eventually, the paper aims to provide corresponding solutions by incorporating the U.S. paywall development experience. This study argues that balancing the development of news media's functions of guiding public opinion and providing professional information services, activating the social attributes of paywalls, and clarifying charging strategy can help with the development and popularization of the news paywall in China's news market.

Micro-fault identification for lithium-ion batteries in electric vehicles based on similarity measurement of the charging sequence

Battery fault diagnosis is critical to safeguard the safety of electric vehicles. Since lithium-ion battery cells are usually inconsistent and difficult to distinguish from faults, existing micro-fault identification methods are difficult to detect faults as early as possible based on real-world vehicle data. In contrast to the operation process, the voltage and temperature changes are more regular since the current relatively remains constant and the charging strategy is fixed, which is conducive to accurate micro-fault identification. According to the charging strategy of electric vehicles, charging data extraction and selection methods are introduced. Considering the practicality and the difficulty of threshold value determination, the micro-fault identification approach based on DBSCAN is proposed. Normalized IAVs and LIP distances are utilized as features, which come from charging sequences. The results of the thermal runaway vehicle show that the method can effectively find fault cells in voltage signals 18 days before thermal runaway occurs.

Development of a Testing System for High-efficiency Power Recovery Bag Accumulators

Currently, most of the bladder-type accumulator test benches used have problems such as large installed capacity, severe energy loss, and incomplete testing functions. This article focuses on the research of the testing requirements and power recovery issues of bladder-type accumulators. A new type of hydraulic schematic and electrical control system for power recovery bladder type accumulator testing based on mechanical compensation with dual output shaft motor transmission, differential charging and discharging of dual accumulators, and parallel addition of oil replenishing accumulators has been designed. A measurement and control software based on the LabVIEW platform has been developed. The experimental results show that the system’s power recovery efficiency can reach 60.5% by adopting a scheme of mechanical compensation type power recovery, differential charging and discharging of energy accumulator group, and parallel addition of oil replenishing energy accumulator. The testing system can accurately, efficiently, and conveniently complete various performance tests of hydraulic bladder accumulators.

Air‐cored axial flux permanent magnet generator for direct driven micro wind turbines

AbstractThis paper presents the development of an Air‐Cored Axial Flux Permanent Magnet Generator (ACAF PMG) integrated for direct battery charging scheme for urban and rural micro‐wind turbine applications rated at 1 kW. The proposed generator has a Single Stator Double Rotor configuration with a relatively short axial length. In this paper, the analytical model for generator sizing is presented and the main parameters are defined. Novel analytical model is presented to determine the required number of turns for each phase in order to produce a balanced voltage in the three phases. The generator is electromagnetically analyzed using Finite Element model and simulated using Matlab/Simulink to predict its performance under several operating conditions. The effect of retainment ring type on the generator performance is also demonstrated. The machine is harnessed to a battery bank via a three‐phase fully controlled converter using six bridge thyristors to regulate the output voltage delivered from the generator to the battery. Generator model and simulation results are validated via experimental tests. The preliminary experimental results showed a good match with the simulation results. Moreover, the proposed machine is compared favourably with other counter machines of similar configuration and power ratings in terms of machine size, power‐to‐weight ratio, and voltage regulation. This paper therefore demonstrates the concrete benefits of utilizing the axial flux PMG in scavenging energy to generate electrical power from micro wind turbines.

Recent advances in wireless power transfer technologies for electric vehicles

In today’s society where electric vehicles are widely used, it is particularly important to make electric vehicles more efficient and convenient to charge. Wireless power transfer technology is becoming increasingly popular as a new type of charging technology. Different from conventional charging strategies, wireless power transfer becomes a hot topic in industry and academia due to the application possibility. It can not be ignored that this technology can also effectively reduce energy consumption. And the technology contributes to improve some environmental issues. Without doubt that it is a kind of beneficial clean energy source in today’s society. In this paper, the state-of-the-art wireless power transfer technologies are comprehensively review in terms of operation characteristics and optimization strategies. Meanwhile, a comparative analysis is given with different application scenarios, including the cost and power conversion efficiency. At the same time, the future perspectives for developing the wireless power transfer techniques are given.

Cloud-Battery management system based health-aware battery fast charging architecture using error-correction strategy for electric vehicles

The health-aware battery fast charging (HABFC) strategy for electric vehicles (EVs) is predominantly dependent on the input conditions. These conditions are set using a set of various type of sensors (temperature, current, voltage, etc.), which can seldom be erroneous. Also, one of the primary challenges associated with physical battery management system (BMS) is the lack of memory required to execute intricate state estimation methodologies. Therefore, this paper proposes the Cloud-BMS based health-aware battery fast charging (HABFC) architecture with error correction strategy for reducing the charging time and increasing the cycle life of the EV battery. A cloud BMS has been integrated in the HABFC architecture to reduce the dependency on erroneous sensors. To design an effective cloud BMS, this paper reviews the seven most critical battery state estimation techniques (state of charge (SoC), state of health (SoH), state of power (SoP), state of energy (SoE), state of temperature (SoT), state of function (SoF), and state of safety (SoS)). Based on comparison of mathematical computational methods, its accuracy and challenges, the best computational method for each state estimation technique has been determined. The cloud BMS makes all state estimations based on parameters estimated using the virtual battery. Further, this paper presents an error correction strategy that helps correct the error in physical BMS employing a master-slave configuration. The proposed cloud BMS based HABFC architecture is validated using a cell cycling setup. It can be concluded that the proposed architecture produces 21.24% more cycle life as compared to fast charging without HABFC and 11% more cycle life as compared to cell charged using HABFC without cloud BMS.

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets.This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. The study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. The adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems. Program SummaryProgram Title: QMMM 2023.CPC Library link to program files:https://doi.org/10.17632/zmkc9db4zj.1.Licensing provisions: Apache-2.0 for the program and CC-BY-4.0 for the manual.Programming languages: Fortran90, Perl, and shell scripting.Nature of problem: Multiscale modeling and simulations by the combined quantum mechanical and molecular mechanical (QM/MM) method.Solution method: The program performs single-point calculations, geometry optimizations, transition state optimizations, and molecular dynamics (MD) by the QM/MM method by interfacing calls to binary executables in a QM package (GAMESS-US, Gaussian, or ORCA) and an MM package (TINKER) for the required calculations. QMMM 2023 supports various types of embedding schemes and multiple ways of treating a QM-MM boundary passing through a covalent bond.Additional comments including restrictions and unusual features: (1) QMMM 2023 supports polarizable-embedding and flexible-embedding schemes based on the electronegativity equalization principle and allowing charge transfer between the QM and MM regions. (2) For a QM-MM boundary passing through covalent bonds, all link-atom-based treatments preserve the charge of the MM region (and MM bond dipoles if desired) while effectively avoiding the over-polarization of the QM region. Further tuning is available to account for the charge penetration effects at the QM-MM boundary. (3) For dynamics simulations, Hamiltonian-based adaptive-partitioning algorithms allow on-the-fly reclassification of atoms or groups into the QM or MM regions without abrupt changes in the energy of the entire system or in the forces on all atoms.

Development of level three solar based fast charging station for electric vehicle

The whole research is based on the charging process of electric vehicle as this is the emerging future of Pakistan. to reduce the environmental pollution the whole world is transferring towards renewable energy, so the transportation sector is also a great source of causing environmental pollution so to reduce this problem the transportation section is moving toward pollution free vehicle as the electric vehicle is now a days in market. But the charging of electric vehicle is again must be pollution free to reduce pollution impact. In order to achieve this goal, the charging strategy must be pollution free so renewable energy is the best solution and to reduce the charging time the fast-charging station is presented to charge a battery in less time using renewable energy. The advanced used batteries are lithium-ion battery these batteries have two charging modes of charging combining these two will generate more effective results for charging in less time. By using two hybrid sources the system become reliable in case of weather dependent situation

An open-data based framework to estimate EV demand and attainable flexibility and application to the case of Singapore

The electrification of the transport sector will have consequences that go beyond the transportation system itself, for example by also impacting power distribution networks and their operation. This both comes with potential threats and opportunities, such as sharp stochastic load changes that further burden already stressed power transmission and distribution systems. But opportunities arise from the flexibility that the vehicles batteries charging process could provide, eventually helping in better managing such infrastructures. This paper presents a framework that leverages openly available vehicles mobility-related data sources to define and simulate scenarios of electric mobility adoption, quantifying load and available demand flexibility in such scenarios. In particular, demand flexibility potential is assessed according to different charging strategies, including vehicle to grid, and different vehicles stationing behaviours. The goal is to provide a framework that is potentially of use to a set of different stakeholders, from city planners to grid operators and aggregators, to assess flexibility sources towards several applications. These range from planning for resilience by evaluating flexibility sources for provision of services to the grid, to enabling participation in ancillary services markets. The framework leverages a set of data sources, particularly carparks’ occupancy data in the form of live availability information at high temporal resolution.The framework is applied to a test case in the city of Singapore with the goal to estimate the consumption flexibility available from electric vehicles in carparks throughout the city. Maps that visualize the availability of such flexibility both in space and time are generated based on this assessment.

GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

A semi-distributed charging strategy for electric vehicle clusters

Uncoordinated charging of electric vehicle (EV) clusters can lead to unanticipated surges in demand that strain the grid, hence reducing its reliability. To address this challenge, this paper proposes a semi-distributed control method for EV clusters charging where the decision-making process takes place locally, rather than relying on an external controller. The method ensures that charging demand can be fulfilled while achieving several goals, including following local renewable energy generation, pausing charging for transformer protection, and continuing charging progress in the absence of external information. The proposed method is validated through simulations and experimental tests during a public demonstration. The results demonstrate the functionality of the control method and show that it can effectively manage the charging of a cluster of EVs while reducing strain on the power grid.

The electric on-demand bus routing problem with partial charging and nonlinear function

The electric vehicle routing problems (EVRPs) with recharging policy consider the limited range of electric vehicles and thus include intermediate visits to charging stations (CSs). In general, minimizing the resultant charging costs such as charging duration or charging amount is also part of the objective of the EVRP. Accordingly, the EVRP has received considerable attention over the past years. Nevertheless, this type of problem in the domain of passenger transportation, a VRP variant, has been rarely studied in the literature, especially with time windows, a realistic nonlinear charging function or a partial charging policy. Hence this research extends the existing work on the EVRP to the on-demand bus routing problem (ODBRP) which transports passengers with the bus station assignment (BSA). The resultant problem is the electric ODBRP (EODBRP). Specifically, each passenger can have more than one stations to board or alight, and they are assigned to the ones with the smallest increase in the total user ride time (URT). In the EODBRP, frequent intermediate visits to CSs are considered. Moreover, a nonlinear charging function is used and the partial charging strategy is applied. To solve the EODBRP, a greedy insertion method with a ‘charging first, routing second’ strategy is developed, followed by a large neighborhood search (LNS) which consists of local search (LS) operators to further improve the solution quality. Experimental data are generated by a realistic instance generator based on a real city map, and the corresponding results show that the proposed heuristic algorithm performs well in solving the EODBRP. Finally, sensitivity analyses with divergent parameters such as the temporal distributions of passengers and bus ranges may provide practical guidance.

Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale.