Charging Strategy Research Articles

The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region

The synergetic benefit of air quality improvements together with greenhouse gas (GHG) mitigations from fleet electrification can be maximized if the power used for electric vehicles (EVs) is from renewables. The worth-noting mismatch between renewable power generation and EV fleet charging demand requires appropriate coordination strategy. Here, we analyze the environmental benefits from increased EVs penetration in Beijing-Tianjin-Hebei (BTH) regions by integrating various scenarios of fleet electrification with coordinated charging strategies to examine the air quality improvement and GHG abatement. The study found that fleet electrification could bring substantial reduction on urban PM2.5 in BTH, especially in December by 0.8 ± 0.5 μg/m3. The coordinated charging strategy could further improve the air quality in BTH, albeit smaller than that of fleet electrification itself. PM2.5 reduction benefit from EV adoption could be significantly more pronouncing when ammonia emission reduction was considered, by more than 0.3 μg/m3 in both December and July, validating the great significance of vehicle NH3 emission control and the necessity of prioritizing the electrification of high ammonia emitting fleet. The outcome of this study helps to formulate the effective on-road transportation pollutant abatement strategies and offers technological support for policy makers to conduct more sensible sequence of future fleet electrification process.

Online Detection of Lithium Plating Onset for Lithium-Ion Batteries Based on Impedance Changing Trend Identification During Charging Processes

Lithium-ion batteries (LIBs) with a graphite anode are currently the most popular in electric vehicles. Lithium plating may occur in such LIBs due to an inappropriate charging strategy, worsening safety and cycle life. So far there is still a lack of effective online detection methods for the lithium plating onset. In the paper, the broadband impedance in the charging process was measured with a proposed simultaneous and fast measurement method. By comparing with the electrochemical impedance spectra measured in the quasi-steady state, the correlation between the impedance changing trend and the lithium plating onset was revealed. And a novel mechanism for the online detection of lithium plating onset was constructed based on the impedance changing trend identification, which is considered the main contribution of the paper. The proposed method can identify the lithium plating onset within a short delay. The maximum errors of the detection method are within 4.5% represented in state of charge, which is beneficial for optimal charging strategies to minimize the harm of lithium plating.

Effect of ignition delay runaway of traveling charge on gun firing safety

In order to promote the engineering application of dense traveling charge technology, a structure scheme of dense traveling charge with central hole was proposed. Aiming at this scheme, the interior ballistic model was established, and the interior ballistic performance advantages of the new traveling charge scheme and the effect of inconsistent ignition delay time on the interior ballistic performance were analysed. The calculated results show that the central hole combustion traveling charge scheme is a feasible scheme. Compared with the end face burning traveling charge scheme, the burning rate of the traveling charge is reduced and the mass ratio of the traveling charge to the total charge is increased, which can effectively increase the muzzle velocity of the projectile and improve the performance of the gun. While the control precision of ignition delay time is at 0.01ms level, which puts forward higher requirements for the design and processing of the slow-burning layer. The ignition delay runaway of the traveling charge will have great influence on the interior ballistic performance of the gun, especially if the delay time is too small, it will cause premature ignition and combustion of traveling charge, resulting in high secondary pressure peak, which is not conducive to the safety of artillery launch. This study can provide guidance for the design of delayed ignition mechanism of traveling charge based on slow burning material.

Gaps between Attitudes and Behavior in the Use of Disposable Plastic Tableware (DPT) and Factors Influencing Sustainable DPT Consumption: A Study of Hong Kong Undergraduates

Total waste from human activities, including waste plastics, is huge in Hong Kong. In particular, as a result of the prevention and control measures implemented during the COVID-19 pandemic, take-away meals increased tremendously in Hong Kong, generating disposable plastic tableware (DPT). Although Hong Kong has a charging scheme for plastic bags, it does not have a scheme for plastic tableware. Therefore, this study aimed to understand the attitudes and behavior of people in Hong Kong toward DPT. Our study focused on undergraduate students in Hong Kong, given that they will play a significant role in the future of environmental sustainability. The attitudes and behavior of Hong Kong undergraduate students toward DPT were examined through an online survey with 385 respondents. A multiple stepwise regression was conducted to investigate whether cognitive attitude formation factors could explain the sustainable attitudes formed by undergraduate students in Hong Kong. The survey results revealed that most undergraduates considered DPT to be one of the major causes of environmental damage in Hong Kong; however, many of them, particularly those who strongly agreed with this statement, said that the problem of DPT did not affect their quality of life. The regression analysis showed that imposing a DPT charge would be the most significant driver to reduce its use. The research findings identified gaps between attitudes and behavior regarding the use of DPT and the factors influencing sustainable DPT consumption.

Parallel Operation of Integrated Battery Chargers for All Wheel Drive Electric Vehicles

In this paper, an integrated charging scheme for All Wheel Drive (AWD) Electric Vehicles (EVs) is proposed. The proposed scheme utilizes the existing driving hardware for charging the battery pack with minimum external components. As the driving hardware of AWD EVs include two motors and two inverters, an Integrated Battery Charger (IBC) with parallel configuration is proposed. In the existing topology of three phase IBC using split phase machine, the grid currents were not balanced. But in the proposed parallel IBC topology, the two split phase machines are proposed with different phase configurations, namely R-Y-B and Y-R-B, which will cancel the unbalance caused by each IBC, and make the grid currents balanced. The mathematical proof for the same is also included in this paper.The proposed parallel topology of IBCs has higher power rating and hence provides faster charging compared to conventional single IBCs. Circulating current mitigation for parallel IBCs is also presented in this paper. Exhaustive simulation and experimental results are included to validate the proposed scheme.

Compact Design of 40 kV 100 A High-Voltage Pulsed-Power Modulator for Driving X-Band Magnetrons

This paper describes the design of a very compact high voltage pulsed power modulator for application in a medical linear accelerator (LINAC). This modulator is manufactured with a modular cell structure based on a semiconductor switch, and the heat dissipation structure is minimized through a simple and efficient charge-and-discharge circuit design. In addition, an equalized charging strategy for multiple capacitors and a high voltage electric field noise reduction strategy were applied. Thus, a high voltage pulsed-power modulator with a very compact size (310 (W) mm × 480 (H) mm × 230 (D) mm) and a high peak power density of 111 kW/L was realized without the use of an external cooling device such as a chiller. The modulator was built and experiments were conducted on high-voltage non-inductive resistive load outputs. The results show that the modulator had-a stable pulse voltage under various conditions, such as an output voltage of up to -40 kV, an output current of up to 100 A, a variable pulse width of 2∼10 μs, and an output repetition rate of up to 1 kHz. In addition, the high-voltage insulation reliability of the proposed modulator and its applicability to magnetron loads were verified.

Characterising the flexibility of electric vehicle charging strategies: a systematic review and assessment

ABSTRACT The increasing uptake of electric vehicles (EVs) has raised concerns about the impact a large fleet could have on electricity markets and distribution grids alike. Charging strategies have emerged as a means to provide flexibility, especially to electricity distribution grids, by controlling the EV charging process. This paper presents a typification of charging strategies and introduces a conceptual framework for appraising their flexibility in distribution grids. This is underpinned by data collected through an aggregative systematic literature review. The framework is derived from an exploratory qualitative content analysis of the sampled data and encompasses four flexibility dimensions: time, duration, quantity, and location of charging. Structural elements of a charging strategy are also explored, complementing the framework. Finally, the paper also presents a quantitative data analysis assessing the level of flexibility provided by each charging strategy. Results show that flexibility dimensions are not equally exploited, direct control strategies do not strictly outperform other control alternatives and innovative charging structures are yet to thrive for flexibility supply to increase. These findings contribute to better-informed, evidence-based policy interventions.

Pharmacophore Oriented MP2 Characterization of Charge Distribution for Anti-SARS-CoV-2 Inhibitor Nirmatrelvir

Quantum mechanical second order Møller–Plesset (MP2) perturbation theory and density functional theory (DFT) Becke, 3-parameter, Lee-Yang-Parr (B3LYP) and Minnesota 2006 local functional (M06L) calculations were performed to optimize structure of nirmatrelvir and compute the Merz-Kollman electrostatic potential (MK ESP), natural population analysis (NPA), Hirshfeld, charge model 5 (CM5), and mulliken partial charges. The mulliken partial charge distribution of nirmatrelvir exhibits a poor correlation with the MK ESP charges in MP2, B3LYP, and M06L calculations respectively. The NPA, Hirshfeld, and CM5 partial charge scheme of nirmatrelvir indicate a reasonable correlation with MK ESP charge assignments in B3LYP and M06L calculations. The above correlations were not improved by the inclusion of implicit solvation model. The MK ESP and CM5 partial charges show a strong correlation between the results of MP2 and two DFT methods. The three optimized structures present a certain degree of differences from the crystal bioactive conformation of nirmatrelvir, suggesting the nirmatrelvir-enzyme complex is formed in the induced-fit model. The Reactivity of warhead electrophilic nitrile is justified by the relatively weaker strength of π bonds in the MP2 calculations. The nirmatrelvir hydrogen bond acceptors consistently show strong delocalization of lone pair electrons in three calculations, whereas hydrogen bond donors are found to have high polarization on the heavy nitrogen atoms in MP2 computations. This work helps to parametrize the force field of nirmatrelvir and improve accuracy of molecular docking and rational inhibitor design.

A Plating-Free Charging Scheme for Battery Module Based on Anode Potential Estimation to Prevent Lithium Plating

Since fast charging schemes for lithium-ion batteries are known to lead to a reduction in battery capacity, there is a need to avoid lithium plating during the charging process. This paper designed an anode potential observer and a plating-free charging scheme for a battery module to avoid the risk of lithium plating for all cells in the module. The observer was designed using an electrochemical cell model and an electrical busbar model to estimate the anode potential of all cells within a parallel connected battery module. Due to its simplicity and low computational loads, the observer was easy to implement in a charge management system. The results demonstrated that the designed observer and charging scheme can accurately estimate the anode potential of all cells in the module. The estimation results of the observer were used in the plating-free charging scheme. Compared to conventional charging methods, the proposed scheme added an additional stage to estimate and control the anode potential, therefore reducing the risk of lithium plating during charging. It also reduced the peak temperature of the battery by approximately 9.8% and reduced the overall charging time by 18%.

Multiobjective Optimization Charging Strategy Based on a Fast Charging Electrochemical Model and Safe Charging Boundary

The present expeditious charging approach for electric automobiles relies on provisional trial data and the technical proficiency of lithium battery producers, and it is deficient in systematic methodologies for assessing the safety threshold of charging. The present study is grounded on the utilization of an electrochemical fast-charging model for the purpose of determining the temperature limits for lithium deposition. A proposed approach for enhancing the charging strategy involves the consideration of discharging pulses and pulse widths, and the utilization of a genetic algorithm based on the lithium deposition boundary. The present approach endeavors to enhance the duration of charging and minimize the occurrence of irreversible thermal effects by employing the existing threshold as a safeguard threshold. The outcomes of the experiment indicate that the electrochemical rapid charging approach proposed in this study exhibits a significant level of simulation precision when subjected to high magnification and a wide range of temperatures. Furthermore, the implementation of an enhanced genetic algorithm-based optimized charging strategy has demonstrated the capability to efficiently balance the charging duration and irreversible heat, leading to a significant improvement in the charging performance in comparison to the conventional 1 C constant current charging approach.

A new MPPT design using arithmetic optimization algorithm for PV energy storage systems operating under partial shading conditions

Arithmetic optimization is a new metaheuristic algorithm that has shown great strength and good performance in handling and solving complex problems. In photovoltaic systems, and especially in the case of a partial shading condition, the power-voltage curve of the photovoltaic array exhibits multiple peaks of which only one is global, making the tracking of the global maximum power point more complex, and thus requiring a powerful metaheuristic maximum power point tracking algorithm. Therefore, to address this problem, this paper introduces a new design of an arithmetic optimization based maximum power point tracking controller for battery charging schemes through photovoltaic systems operating under partial shading conditions. The main objective of the arithmetic optimization based maximum power point tracking proposed here is to maximize the power extracted from the photovoltaic array under all types of climatic conditions in order to efficiently charge the battery through the maximization of its charging current. The feasibility and effectiveness of the proposed arithmetic optimization based maximum power point tracking method are verified through MATLAB simulations and processor-in-the-loop test using the low-cost STM32F429 embedded board. Moreover, a comparison of its performance with existing maximum power point tracking methods such as, hybrid of grey wolf optimization and particle swarm optimization, particle swarm optimization and perturb and observe is also carried out while considering real climatic conditions as well as uniform and partial shading scenarios. Simulation and processor-in-the-loop results have demonstrated the good performance of the proposed method in terms of battery charging efficiency as well as the ability to track the global maximum power point with an efficiency of 99.99% and a tracking time of 0.85 s.

Optimal Driving Model for Connected and Automated Electric Freight Vehicles in a Wireless Charging Scenario at Signalised Intersections

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value.

Novel approach for liquid-heating lithium-ion battery pack to shorten low temperature charge time

The charging time for Li-ion power battery in hybrid electric vehicles (HEVs) and pure electric vehicles (EVs) is elongated at low temperature compared to that at the room temperature, which will deteriorate the customer using experience. Therefore, shortening the low-temperature charging time of battery is the critical technology to promote the popularization of electric vehicles. Many techniques have been proposed to shorten the low-temperature charging time, but these technologies often lead to problems such as the increase of vehicle energy consumption, the high temperature of the battery pack system (BPS) at the end of charging and the poor BPS temperature uniformity during charging. To tackle these problems, this paper proposes an optimization simulation model for charging control strategy combined with thermal model (SCCS-ThM) and BPS offline parameters based on a liquid heating thermal management system (LHTMS). The BPS offline parameters determine the charging power threshold under different ambient temperature and the different state of capacity (SoC). The temperature rise and the temperature differences of the BPS during charging are calculated with the thermal model. The optimization model obtains the optimal charging strategy by simulating the potential feasible charging strategies. The battery pack charge time is reduced by introducing a pre-heating stage prior to charging. The pre-heating period which minimizes overall charge time, including the pre-heating time, is determined using the thermal model. The experimental results show that for an initial battery pack temperature of −10 °C, overall charge time is minimized by starting to charge after the battery pack has been heated to 1 °C. With the same charging energy, the optimal charging strategy shortens the charging time by 16 % and reduces battery coolant heater energy consumption by 15 %. Besides, the maximum temperature at the end of charging and the maximum temperature difference during the whole charging process are 36 °C and 6 °C respectively, which are better than the original charging strategy.

Multi-objective study on an innovative system for domestic hot water production: A pilot building in Southern Europe

The present study deals with a multi-objective analysis of an innovative decentralised system to produce and store domestic hot water (DHW), emphasising on the combined effects of the technological aspect, control strategy and user’s behaviour. The proposed system, by relying on thermal energy storage, decouples energy production and demand while shaves peaks in the energy demand and, at the same time, provides more autonomy to users through local storages. To identify subtle interactions in components of DHW system, dynamic simulations are carried out by establishing a coupled TRNSYS-MATLAB code, calibrated and validated by experimental measurements. The energy analysis implies that the proposed system cuts the required annual electrical energy in half, of which up to 82% of needed primary energy is supplied from renewable sources, compared to previous electrical-decentralised system. The optimisation of the results through applying control strategies indicates that adopting a three-time charging scheme is advantageous in terms of providing a more stable temperature profile as well as a higher hot water temperature. Compared to an available-by-demand operation, this scheme reduces the required total annual electricity by 5.2 % and enhances total thermal loss from components up to 4.0%. Furthermore, a sensitivity analysis on the results emphasises the striking role of the user behaviour in electrical energy consumption either via draw-off temperature or adjusting the pre-defined temperature for activation of the built-in auxiliary heater.

Estimating Travel Choice Probability of Link-Based Congestion Charging Scheme for Car Commuter Trips in Jakarta

The likely effects on car commuters of enforcing congestion charging using the link-based corridor method include that they may shift to public transport, divert their route of travel, or decide not to travel to the related area. However, most recent research has focused mainly on the choice of modes. This paper examined the travel choices of private car commuters resulting from the congestion charging scheme set to be implemented in Jakarta, Indonesia. The scheme is intended to replace the current odd-even strategy. It is imperative to study all possible mutually exclusive alternatives faced by car commuters. A web-based e-survey was used for data collection, employing the stated preference method. The discrete choice multinomial logit model was chosen to analyze the data. A total of 401 of the 2125 respondents to the e-survey questionnaire link, evenly distributed to all areas of Greater Jakarta, were sampled in this study. The sample respondents who traveled by car, passing through the eight designated corridors, were analyzed. NLOGIT6 software was used to analyze the parameter of attributes, the probability of alternatives chosen, and the marginal effects of congestion charging on such corridors, employing the multinomial logit model (MNLM). One surprising finding was that the load factor and taxi fares were not significant, indicating that the level of in-vehicle overcrowding is not a concern of respondents, and taxi services are not a substitute for car travel. Another surprising finding was that income variables and job type do not significantly influence travel behavior. In terms of the probability of commuters to continue to travel by car when link-based congestion charging is imposed, only around half of the car travelers were willing to pay the congestion levy and pass through congestion charging corridors. The probability of car travelers diverting onto alternative roads is high, i.e., around 16.82% to 22.88%, while the probability of car travelers shifting to mass transportation is 17.69%. When interpreting direct marginal effects, there is a change in the probability of all travelers choosing to use private cars through the congestion charging corridor of −0.0338, or a decrease of −3.38% for every IDR 1000 increase in the congestion charging levy rate, ceteris paribus.

Efficient Multiple Green Energy Base Stations Far-Field Wireless Charging for Mobile IoT Devices

Powering a huge number of Internet of Things devices (IoTDs), necessitated in many IoT applications, is a dreadful problem in many circumstances, in terms of the cost of labor, time and so on. Far-field green energy wireless charging is a promising technique to remotely power IoTDs in a large-scale network. In order to improve the end-to-end energy efficiency, for the scenario with multiple green base stations (GBSs) wirelessly charging multiple IoTDs, our previous research work proposed a wireless charging scheme to aggregate the wireless power received by a static IoTD from multiple GBSs. In reality, in various emerging IoT applications, a large proportion of IoTDs are mobile. Hence, in this work, we focus on intelligently assigning multiple GBSs to wirelessly charge mobile IoTDs such that the IoTDs can be efficiently powered. We first propose the moving model and the charging model of IoTDs. Based on the non-linear wireless charging model and non-linear wireless energy conversion model, we then propose the Greedy dynAmic joInt chargiNg (GAIN) algorithm to efficiently power the mobile IoTDs. Through extensive simulations, we validate the performance of the proposed algorithm.

A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning

Reinforcement learning (RL) is popularly used for the development of an orderly charging strategy for electric vehicles (EVs). However, a new environment (e.g., charging areas and times) will cause EV users' driving behaviors and electricity prices to change, which leads to the trained RL-based charging strategy is not suitable. Besides, developing a new RL-based charging strategy for the new environment will cost too much time and data samples. In this paper, a deep transfer reinforcement learning (DTRL)-based charging method for EVs is proposed to realize the transfer of trained RL-based charging strategy to the new environment. Firstly, we formulate the uncertainty problem of EV charging behaviors as a Markov Decision Process (MDP) with an unknown state transfer function. Furthermore, an RL-based charging strategy based on deep deterministic policy gradient (DDPG) is well-trained by using massive driving and environmental data samples. Finally, an EV charging method based on transfer learning (TL) and DDPG is proposed to perform the knowledge transfer on the trained RL-based charging strategy to the new environment. The proposed method is verified by numerous simulations. The results show that the proposed approach can reduce the outliers to meet the user charging demands and shorten the EV charging strategy development time in the new environment.

Study on orderly charging strategy of EV with load forecasting

The development and popularization of electric vehicles (EVs) is of great significance to environmental protection, energy saving and emission reduction. With the wide popularization of EV, the EV's disorderly charging brings the security hidden trouble to the grid. Firstly, according to the safe operation of power grid and the charging requirements of EVs, an optimal scheduling model based on grid loss is established, then, the optimal scheduling model is transformed by second-order cone relaxation technology. Secondly, because the orderly charging schedule of EV is based on accurate charging load forecasting, this paper based on LSTM-XGBoost dynamic combination forecasting, the dynamic combination model of LSTM and XGBoost is optimized by using Bayesian optimization method, and more accurate charging load forecasting results are obtained. Finally, the accuracy of the prediction method and the effectiveness of the optimal scheduling strategy are verified by the charging data of the EV in the actual area.

A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems

In real-life, routing plans try to optimize multiple objectives without relying on just one. So, this paper introduces three multi-objective electric vehicle routing problems (MOEVRP) that consider different charging strategies and electric vehicle (EV) charger types while optimizing five conflicting objectives: a total minimization cost of recharging, the number of vehicles required, a total travel distance, load-dependent energy consumption, and the total number of charging stations required. We develop a new hierarchical approach consisting of two phases: a Hybrid Ant Colony Optimization (HACO) and an Artificial Bee Colony Algorithm (ABCA). In the first phase, an initial solution is obtained using a HACO that integrates local search algorithms and simulated annealing (SA) to reduce the solution time. Then in the second phase, the problem is solved using an ABCA considering the initial solution obtained from the first phase. Using the proposed HACO-ABCA as the search engine, two posteriors’ methods, namely the weighted-sum method (WSM) and the conic method (CM), are applied to scalarize the five objectives. The effectiveness of the proposed hierarchical approach is examined on well-known test-based instances and obtained the best new results in most instances. Additionally, the proposed solution is applied to a real-life case study. The results show that multi-objective traditional methods give more effective results than multi-objective evolutionary algorithms, regardless of the MOEVRP problem type. We can also conclude that the partial recharge and multiple recharge technology options can significantly improve the route decisions of logistic companies.

Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations

This work proposes a differential evolution algorithm to control a vehicle-to-grid (V2G) system based on photovoltaic generation and energy cost curves, and constraints associated with the power converters’ operation, battery charging strategy, and initial budgets. The algorithm is designed to trade off the batteries’ state of charge and the profits gained from selling energy to the grid. To achieve this balance, a fuzzy controller is employed and acts based on forecasts of the photovoltaic generation and the cost of electricity, within prediction windows of 120 min, adapting the batteries’ rate of charging or discharging. Simulation results show that for different curves and different initial budgets, the target state of charge is reached at the end of the time horizon. By evaluating the proposed scheme under different scenarios, the algorithm’s performance is proven to be suitable for future practical deployment.