Charging Duration Research Articles

Innovative Feature Analysis of Electric Vehicle Technology, Charging Infrastructure, Power management, and Control Methods

Lowering the dependence on fossil fuels and reducing pollution from greenhouse gas (GHG) emissions is incredibly achievable through electric vehicle (EVs) technology. EV technology is an innovation that uses electricity, rather than fossil fuels, to power and refuel (recharge) vehicles. The adoption and development of EVs should lead to a decline in future demand for fossil fuels, which are finite in supply and exhaustible. Inherent challenges in EV technology, such as inadequate supply of critical minerals, power grid overload, battery technology constraints, extended charging durations, insufficient charging infrastructures, high initial costs, and limited driving range, must be addressed. The technology of charging infrastructures cannot be over-emphasized in EV technology. EV technology, charging infrastructures, vis-à-vis the impact of their integration into the grid is investigated. Effective control strategies and power management systems (PMSs) are required to optimize energy use to improve EVs' efficiency and lifetime. This research uses comprehensive analysis methods to assess various control strategies, PMSs, and their effects on EV integration into the grid.

Layered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions

AbstractElectric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en‐route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP‐NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state‐of‐the‐art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods.

Assessment of thermofluidic behaviour of a Medium-High-Temperature shell and tube latent heat storage through experimental and computational investigation

The study envisages a holistic experimental methodology encompassing the entire spectrum of formulation, characterization, and thermal response evaluation of a medium–high-temperature (MHT) phase change material (PCM). The heating rate in melting is significantly less than the cooling rate during solidification until PCM reaches phase transition temperature. For q“ = 1000 W/m2, charging duration of vertical domain (θ = 90°) is 15 % slower than horizontal domain (θ = 0°). However, with increase in flux to 2000 W/m2 the charging duration is only 6 % slower for θ = 90° than θ = 0°. Melting/charging duration for thermocouple positioned at A (r = 18 mm, top side of prototype) is 15.38 % lower than at F (r = 18 mm, bottom side of prototype) and positioned at C (r = 38 mm, top side) is 8 % lower than at D (r = 38 mm, bottom side) for horizontally orientated LHTES system. A substantial decrease (by 17.64 %) in discharging duration was observed with increase in the inlet flow rate and the inlet temperature by 2.5 times and 3.3 times, respectively. The total energy accumulated in the LHTES demonstrates an approximately 11 % increase with the escalation of electric flux from 1000 W/m2 to 2000 W/m2 during the charging process for θ = 0° orientation. In contrast to charging, faster solidification rate is observed in the bottom half of the prototype (points D, E, F) compared to the top half (points A, B, C) which has implications for the design and operation of LHTES systems.

The Embedded System of Battery Charging with Constant Current System using Microcontroller and IC LM338

The battery has the function of an electrical energy storage system. However, charging the battery is prone to damage. Damage to the battery is often caused by overcharging. The time used during the battery charging process is an important factor that must be considered when charging. thus, efficient battery charging is important and very necessary to avoid damage. An another important aspect of a battery charging method is the current and voltage values supplied during its charging process. The presence of inappropriate values damages and reduce battery life, while the maintenance of a constant current is an important charging process step that extends service life. This research is expected to create a safe and optimal battery charging system with a simple low-cost circuit based on constant current by utilizing the LM388 Integrated Circuit (IC) regulator. This study discusses a 12 V battery charging system with monitored and controlled constant current using an R-Shunt sensor. Voltage monitoring while charging was carried out with voltage sensors using a constant current of 0.1 to 1.3 Ampere. Furthermore, voltages and currents were displayed through a Liquid Crystal Display (LCD) screen controlled with Arduino Uno during battery charging. The measurement results showed that the circuit reached voltages and currents of 14.07 V and 0.19 A respectively for a charge duration of 8 hours.

Hydrothermally etched MXene-based nanocomposite electrode for supercapattery

The requirements of energy storage devices have only been ever-increasing, from greater charge storage to faster charging to boost the energy and power densities. However, existing lithium-ion batteries are limited by their power density due to long charging duration while supercapacitors have limited energy density and higher power density. A supercapattery, a device that possesses the qualities of a battery and a supercapacitor, is being developed to achieve higher energy and power densities in a single device. MXenes are a group of promising materials to be used as electrodes in supercapatteries for their exceptional pseudocapacitive properties. Herein, Ti3C2Tx MXene was synthesized using hydrothermal-assisted etching with various in situ HF etchants, and the effects of using different cationic intercalants were explored. This approach boosts the etching efficiency of weaker etchants, ensuring the proper exfoliation of MXene precursors whilst maintaining the structural integrity of MXenes and removing the need to delaminate and intercalate them post-synthesis. Combined with graphene by sonication, the resulting wrappage of graphene around MXene could increase the electrical conductivity, promote the electrode electrolyte interaction, and improve the electrochemical performance of MXene-based electrode signification and demonstrating synergistic effects. A supercapattery was fabricated by pairing activated carbon as the capacitive electrode and optimized MXene-graphene composite (MG-21) as the pseudocapacitive electrode. The device achieved a specific capacitance of 662.02F/g and an energy density of 20.58 Wh kg−1 at 3 A/g and exhibited an excellent energy and power density of 23.02 Wh kg−1 at 0.5 A/g and 13.1 kW kg−1 at 10 A/g, respectively.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Advances in EV wireless charging technology – A systematic review and future trends

Current advancements in wireless power transfer systems have improved the viability of enhancing the scope of the driving journey of an Electric Vehicle. This paper addresses the prime aspects of wireless charging infrastructure using a systematic approach, such as compensation topologies, power converter circuit design, and power transfer methods. The exclusive wireless charging track on the road minimizes the size of the battery device and the charging duration of energy storage during driving. The ability to transmit high power through a coil placed on the road to the Electric Vehicle requires an appropriate design for the complete wireless power transmission module. This paper also presents advancements in the domain of wireless technologies and recommends upcoming opportunities and future research trends.

Effects of Electrochemical Hydrogen Charging Parameters on the Mechanical Behaviors of High-Strength Steel.

Extended exposure to seawater results in the erosion of the structural high-strength steels utilized in marine equipment, primarily due to the infiltration of hydrogen. Consequently, this erosion leads to a decrease in the mechanical properties of the material. In this investigation, the mechanical responses of Q690 structural high-strength steel specimens were investigated by considering various hydrogen charging parameters, such as the current density, charging duration, and solution concentration values. The findings highlighted the significant impacts of electrochemical hydrogen charging parameters on the mechanical behaviors of Q690 steel samples. Specifically, a linear relationship was observed between the mechanical properties and the hydrogen charging current densities, while the associations with the charging duration and solution concentration were nonlinear. Additionally, the fracture morphology under various hydrogen charging parameters was analyzed and discussed. The results demonstrate that the mechanical properties of the material degrade with increasing hydrogen charging parameters, with tensile strength and yield stress decreasing by approximately 2-4%, and elongation after fracture reducing by about 20%. The findings also reveal that macroscopic fractures exhibit significant necking in uncharged conditions. As hydrogen charging parameters increase, macroscopic necking gradually diminishes, the number of microscopic dimples decreases, and the material ultimately transitions to a fully brittle fracture.

Hydrogen trapping and embrittlement in a second-generation Ni-based single crystal superalloy

This study explores the hydrogen trapping capability and hydrogen embrittlement (HE) of a second-generation single crystal (SX) superalloy. Embrittlement susceptibility is greater as the duration of hydrogen charging is extended. The presence of hydrogen induces cleavage, micro-crack formation and denser slip traces. Moreover, hydrogen facilitates the formation of nano-voids and the activation of high-density dislocations, leading to denser slip bands which serve as initiation sites for micro-cracks. In addition, the desorption activation energy of hydrogen trapped at dislocations and soluble in the γ matrix is 33.7 kJ/mol, and hydrogen trapped at the γ/γ′ interface and vacancies is 42.4 kJ/mol. First-principles calculations have indicated that hydrogen reduces the binding strength at the γ/γ′ interface, which promotes the propagation of micro-cracks.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Optimal regulation strategy of energy storage combined with new energy stations participating in power market

Abstract Energy storage systems can efficiently address the challenges of inadequate power grid regulation capabilities and the escalating complexity of maintaining frequency stability due to a significant integration of renewable energy sources. Under the background of power marketization reform, it is highly important to establish a perfect power market mechanism and incorporate energy storage into it to stimulate energy storage advancements. Consequently, this paper proposes an optimization model for energy storage in conjunction with new energy stations participating in the power market, following the introduction of new energy storage technologies. It effectively verifies the optimization energy storage charge and discharge duration with examples. It achieves the realignment of output curves for coordinating the operation of new energy stations and energy storage facilities, enabling peak shaving and load shifting to minimize the peak-to-valley differential in the system load. It makes the overall output more ‘stable’ and improves the efficiency of the comprehensive operation of wind, solar, and energy storage. In the future, the engagement of energy storage will be essential in the market, and it is crucial to propose practical suggestions to enhance its participation.

Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework’s effectiveness, and the results demonstrate its proficiency in predicting the EVs’ plugged-in status, with XGBoost’s classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs’ power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Performance of a high-temperature transcritical pumped thermal energy storage system based on CO2 binary mixtures

Pumped thermal energy storage is a novel energy storage technology with features of high efficiency, geographical independence and suitable for bulk capacity energy storage. As a subset of pump thermal energy storage system, the transcritical CO2 arrangements have received widespread attention due to their excellent thermodynamic performance. However, the high cost and low efficiency of heat exchange progress caused by CO2 phase transition hinder the transformation of this system from scientific theory to practical application. CO2 binary mixtures is innovatively employed as working fluid in proposed system. The temperature of mixture is variable rather than remaining constant during the evaporation and condensation progresses. R134a, R290, R600 and R601 are selected respectively to mix with CO2 to form the new working fluids. Advanced thermodynamic and economic models are established to investigate the performance of novel systems. Results indicate that the system driven by CO2/R134a with a refrigerant mass fraction of 0.15 exhibits the optimal overall performance. At the optimal point of the 10 MW/8h system, the round trip efficiency and levelized cost of storage are 66.18 % and 0.146 $/kWh, respectively. Moreover, the investigation of system performance with various energy storage capacities is put forward. It is shown that when the energy system capacity reaches 300 MW, the levelized cost of storage can be reduced to 0.122 $/kWh. To adapt to different peak and valley periods in different regions, systems with different charge and discharge times are investigated and it is found that the charge duration should better be equal to the discharge duration.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries; most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches, followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Transformation Behaviors and Microstructure Modifications in Hydrogen-Charged Ti–Ni Shape Memory Alloy

AbstractThe effect of hydrogen charging duration on the transformation behavior, microstructural evolution, and dynamic microstructural changes associated with thermoelastic martensitic transformation in Ti–Ni shape memory alloy was investigated. Compared with the uncharged specimen, the martensitic transformation start (Ms) and reverse transformation finish (Af) temperatures increased with charging time, whereas the martensitic transformation finish and reverse transformation start temperatures remained almost unchanged. In situ SEM results were consistent with these behaviors. Upon cooling, the transformation progressed from the center to the surface in charged specimens, indicating a higher transformation temperature in the center than the surface. The latent heat of transformation decreased with increasing charging time, quantitatively attributed to an untransformed region consisting of hydrogen-induced martensite and a hydrogen-affected layer. The hydrostatic effect from those layers on the interior B2 phase was proposed as the origin of the increased Ms and Af temperatures.

Experiment-Based Determination of Optimal Parameters in Constant Temperature–Constant Voltage Charging Technique for Lithium-Ion Batteries Using Taguchi Method

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise.

Assessment of the Effectiveness of Energy Transfer for Shore-to-Ship Fast Charging Systems

Charging systems from shore to ship are typically designed based on a range of operational and design parameters, encompassing onboard power and propulsion needs, available charging durations, and the capacity of local power networks. In areas with weak grid infrastructure, onshore energy storage is often employed to facilitate high-power charging for vessels requiring short charging intervals. Nevertheless, incorporating on-shore energy storage adds complexity to the system, and the selection of system configuration can profoundly affect the efficiency of energy transfer from the grid to the vessel. This study presents a comparative analysis of energy efficiency among AC, DC, and Inductive shore-to-ship charging solutions for short-distance ferries utilizing both AC and DC-based propulsion systems. Findings illustrate that an increased reliance on onshore battery power correlates with decreased overall energy efficiency during the charging process. Thus, optimizing energy efficiency necessitates careful consideration when distributing the load between the grid and onshore battery. Results indicate that DC charging offers advantages over other solutions for AC-based propulsion systems in terms of energy efficiency. However, for DC-based propulsion systems, the most efficient solution may be either DC or AC charging, contingent upon the distribution of load between the grid and onshore battery. Furthermore, it is inferred that despite adding additional conversion stages and complexity to the system, the energy efficiency of inductive charging is comparable to wired schemes. Considering the added benefits of contactless charging, such as reliability, safety, and robustness, these findings advocate for the adoption of inductive charging as a promising solution.

Investigation of Lithium-Ion Battery Negative Pulsed Charging Strategy Using Non-Dominated Sorting Genetic Algorithm II

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions.

Solar Heating Modulated by Evaporative Cooling Provides Intermittent Temperature Gradients for Ionic Thermoelectric Supercapacitors

AbstractSolar heating is important for many applications but less attractive for concepts requiring intermittent heating, such as ionic thermoelectric supercapacitors (ITESCs). However, the heating process even at constant solar illumination can be converted to temperature oscillations through water infiltration and evaporation. Here, this process is demonstrated for a carbon nanotube‐cellulose membrane and used to induce temporally varying temperature gradients across an ITESC, which enables continuous operation through repeated charge and discharge cycles. A temperature variation of 10 K can be generated on the top electrode, which leads to a variation in the temperature difference across the ITESC of 7.5 K. Precise control over charge and discharge durations can be achieved by adjusting the volume and interval of the added water. The concept of temporarily adjusting temperatures by evaporative cooling may be extended to create intermittent heating also for other heat sources that are typically constant.