Charging Strategy Research Articles

Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

Effect of fast charging on degradation and safety characteristics of lithium-ion batteries with LiFePO4 cathodes

Fast charging compatibility is a desirable feature for batteries to shape a promising future in our rechargeable world. Enabling fast charging of advanced energy-dense Li-ion batteries for growing electric vehicle (EV) markets depends on the breakthrough of material chemistry and optimization of charging strategy. Lithium iron phosphate (LiFePO4, or LFP) is a pivotal cathode material in state-of-the-art EV batteries due to the merits of high thermal stability, long cycle lifetime, and high-temperature performance. However, degradation-safety interactions of LFP-based Li-ion batteries under fast charging conditions and low temperatures remain elusive. In this study, we cycle LFP cells under different fast charging strategies and thermal environments to understand their effects on degradation pathways, where the capacity, voltage, temperature, coulombic inefficiency, internal resistance, and impedance spectroscopy are evaluated. Half-cell studies are implemented to assess electrode-level capacity retentions. Post-mortem analyses are conducted to reveal physicochemical changes in electrodes. Accelerating rate calorimeter tests are carried out to measure evolutions of cell-level thermal instabilities after fast charging tests. This research article highlights the significant roles of graphite-centric lithium plating and LFP-centric transition metal dissolution in driving substantial electrochemical degradations, suggesting that a mild low temperature does not necessarily reduce the LFP cell lifetime.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

A Comprehensive Review of Smart Charging Strategies for Electric Vehicles and Way Forward

A Comprehensive Review of Smart Charging Strategies for Electric Vehicles and Way Forward

Multiobjective Battery Charging Strategy Based on Deep Reinforcement Learning

Multiobjective Battery Charging Strategy Based on Deep Reinforcement Learning

Plug-in charging or electric roads? Powering U.S. long-haul heavy-duty trucks in 2050

Abstract Pervasive plug-in fast chargers and/or electrified roadways (eRoads) might address the limited range, long recharging times, and reliance on greenhouse gas (GHG)-intensive, costly, and heavy batteries associated with electrifying long-haul heavy-duty trucks (HDTs). While these large-scale interventions shift environmental and cost burdens onto infrastructure, there is a lack of studies investigating how eRoads affect system-level GHG emissions, costs, material use, and peak electric grid power demands. We compare these aspects for the case of electrifying U.S long-haul HDTs (Class 8) in 2050 powered by combinations of plug-in fast chargers and eRoads. Our model accounts for battery downsizing, energy consumption, and truck operation patterns in quantifying life cycle impacts of batteries, plug-in chargers, eRoads, and hourly truck electricity demand. We find that plug-in fast chargers and eRoads reduce annualized 2050 HDT life cycle GHG emissions by 8% to 14% compared to using long-range batteries, which in turn have at least 50% lower emissions than diesel trucks. Conductive rails, overhead lines, and wireless eRoads (amortized across light- and heavy-duty vehicles) have lower system-wide costs than long-range batteries, plug-in fast chargers, or diesel trucks. Cost savings from smaller batteries, lower energy use and avoided recharging time offset high eRoads capital costs. While eRoads can reduce both system-level GHG and costs compared to diesel trucks, these reductions are sensitive to eRoads capital costs and losses from wireless power transfer and air resistance. eRoads require less lithium (87%) and copper (67%) than long-range batteries but increase regional peak power demands by up to 32%. Efficient wireless power transfer and aerodynamic pantographs enhance eRoads’ GHG and cost advantages, which may diminish if future batteries are more energy-dense, cheaper, or less GHG intensive. If successfully deployed, eRoads present opportunities for tighter integration between the transportation and electricity infrastructure systems, enabling optimized charging strategies to lower GHG emissions and costs.

A multi-objective coordinating model for distribution network with EVs, energy storage, and reactive power compensation devices

Promoting electric vehicles (EVs) is regarded as an effective solution for reducing carbon emissions and addressing growing environmental concerns. However, the increasing number of EVs has significantly heightened the demand for electric power charging, posing challenges to distribution systems. Studies have shown that a coordination strategy combining various compensation devices, such as energy storage systems and reactive power compensation devices, can enhance the integration capability of EVs. Furthermore, EVs can be managed as controllable loads that can be shed at low costs, contributing to the flexibility of the distribution network. This paper explores the uncertainties of EV load sizes and the coordination of active and reactive power. A multi-objective optimization model was developed to augment the flexibility of the distribution network and mitigate the risk of load shedding. The Monte Carlo method is utilized to simulate scenarios with EV load uncertainties. To simplify the power flow calculations in the proposed convex optimization model, the Distflow equation was adopted. Case study results demonstrate that managing EV loads in the distribution network can significantly reduce load shedding costs by 80 %, and the proposed EV charging strategy can effectively eliminate load shedding costs in most scenarios.

Charge and discharge strategies of lithium-ion battery based on electrochemical-mechanical-thermal coupling aging model

The capacity degradation is unfavorable to the electrochemical performance and cycle life of lithium-ion batteries, but the systematic and comprehensive analysis of capacity loss mechanism, and the related improvement measures are still lacking. Considering the aging mechanism of solid electrolyte interphases (SEI) growth, lithium plating, active material loss, and electrolyte oxidation, an electrochemical-mechanical-thermal coupling aging model is developed to investigate the lithium-ion battery capacity degradation. The results show that as the charge and discharge rates increase, all degradation losses of the battery get serious. The loss of positive active material is more sensitive to the discharge rate. The lithium plating loss is more susceptible to the charging rate. The increased charge cut-off voltage and the reduced discharge cut-off voltage both accelerate the battery aging. The charge cut-off voltage plays great roles in the electrolyte oxidation, loss of negative active material, and loss of lithium plating, while the discharge cut-off voltage greatly influences the loss of positive active material. Finally, the battery charging and discharging process is optimized and analyzed to obtain better anti-aging and safety performance. By clarifying the degradation mechanism and proposing effective measures, it is of great benefit to the design and operation of battery management system.

An interaction-enhanced co-evolutionary algorithm for electric vehicle routing optimization

Recently, the electric vehicle routing problem (EVRP) has emerged as a significant challenge in the transportation field. Unlike traditional vehicle routing problems, EVRP requires optimizing not only the customer route but also the charging scheme. This implies that routes must be planned while considering the availability of charging stations, and charging decisions must be made based on the specific route structure. Although the dual-population-based co-evolutionary algorithm (DPCA) has been proposed to address this coupling relationship, there is still room for further performance improvement. Therefore, this paper proposes an interaction-enhanced co-evolutionary algorithm (IECA) that facilitates a more effective collaborative search between routing and charging optimizations. For routing optimization, we propose an improved ant colony optimization method with a novel pheromone update approach to generate diverse route solutions. Specifically, we utilize information from the charging population to guide the pheromone update process. In contrast to DPCA, which solely relies on the best solution, we establish interaction among multiple solutions from both the routing and charging populations, which can better contribute the information interaction between two populations. Furthermore, we devise a novel population update strategy that enhances the selection of promising solutions. Experimental results validate the effectiveness of the proposed algorithm by demonstrating its ability to avoid local optima. Moreover, it achieves a reduction of approximately 4% in the average route distance across two public test suites. The source code is available at https://github.com/ShouLiang-Z/IECA.

Electrochemical and aging model of li-ion batteries and estimation of total number of cycles during the lifecycle of the battery

Fast charging is a critical factor for the widespread adoption of electric vehicles (EVs), yet it poses challenges to the durability of lithium-ion batteries (LiBs). This study develops an aging model for a 10 Ah LiB cell using COMSOL Multiphysics, integrating a one-dimensional electrochemical model with a zero-dimensional thermal model. The model is constructed using cell design and electrochemical parameters derived from existing literature, tailored to the specific electrode and electrolyte materials utilized in this study. Key aging phenomena, such as the Solid Electrolyte Interface (SEI) growth and lithium plating, are embedded within the model to simulate the aging patterns and the interplay between these factors across various C-rates and temperatures. Simulation results suggest that while higher charging rates can reduce charging time, they also lead to increased degradation, with lithium plating becoming more severe at these elevated rates. The model also indicates that temperature management during cell operation can influence cycle life, with strategic temperature increases potentially mitigating some negative effects of fast charging. The study also explores the concept of total charging time as an alternative metric for battery wear, examining its relationship with the charging current profile and temperature. Results indicate that while fast charging offers convenience, it significantly impacts cycle life, underscoring the need for optimized charging strategies. The insights from this work enhance the understanding of Li-ion cell aging mechanisms, shedding light on the complex trade-offs between charging speed, temperature management, and lifespan. This contributes to the development of more sophisticated battery management systems and charging protocols that aim to extend the operational life of LiBs while accommodating the demands of fast charging.

Research on Interior Design of New Energy Vehicles under the Concept of Information Materialization

This research focuses on the interior designing of electric vehicles (EVs), considered as new energy vehicles (NEVs), within the information materialization framework. The idea of information materialization refers to the quantifiable demonstrations of given data within the vehicle interior space, enhancing user interactions and experiences. Considering the State of Charge (SoC) as the crucial designing parameter, the research investigates the several implications of SoC on NEV interior designing. SoC, a critical parameter that reflects the battery's charging level, profoundly influence the merging of SoC data into the vehicle's dashboard or infotainment systems, ensuring seamless accessibility and intuitive interpretations for the driver. Instant alerts and notifications are designed based on SoC levels to provide real-time feedback and guidance to end users, optimizing driving ranges and strategies for charging. Furthermore, the study emphasizes the significance of customization options in SoC displaying formats and accommodates varying users preferences and requirements of visibility. Analysis of empirical and feedback from users, the research confirms the effectiveness of SoC-driven designing intervention in enhancing user experiences, safety, and overall functionalities interiors of NEV. The findings underscore the pivotal role of SoC as a fundamental designing parameter in shaping the next generations of NEV interiors and highlight the need for complete consideration of energy management aspects in future vehicle design endeavors.

Charging Strategies for Electric Vehicles from Renewable Hybrid Systems

Objective: The aim of this study is to analyze charging strategies for electric vehicles (EVs) in a hybrid system connected to the conventional electricity grid, to optimize the use of renewable energies. Theoretical Framework: This paper presents the concepts of hybrid systems, photovoltaic power generation, and electric vehicle energy demand. It emphasizes the importance of integrating renewable energy into the transportation sector to reduce greenhouse gas emissions and promote sustainability. Method: The research was based on obtaining load curves and photovoltaic generation in different climatic conditions. The energy available for charging electric vehicles was calculated, considering the limitations of the hybrid system. Different charging power configurations were studied. Results and Discussion: The results demonstrate the feasibility of recharging EVs with renewable energy and highlight the operational flexibility provided by storage systems. Different climate scenarios affect energy availability for charging, highlighting the importance of adaptive strategies. Research Implications: This research contributes to developing sustainable strategies in the transportation sector by providing insights into integrating electric vehicles and renewable energy. Practical implications include reducing carbon emissions and decreasing dependence on fossil fuels. Originality/Value: This study stands out for its specific analysis of EV charging strategies in a hybrid context, providing new approaches for the efficient use of renewable energy in transportation. The integration of storage systems and adaptation to climatic conditions are innovative aspects that add value to the research.

SoC Profile Reconstruction in Residential EV Charging: Assessing the Degradation of V1G and V2G in Denmark

The development of Smart Charging (SC) strategies for Electric Vehicles (EVs) is on the rise, driven by the potential benefits for both customers and grid operators. While several studies have explored the impact of these strategies on battery degradation, there is a lack of studies based on real-world data. To overcome the limited information provided by the current EV-EVSE (EV Supply Equipment) communication protocols, a methodology to reconstruct State of Charge (SoC) profiles based on the charging power is proposed and used to reconstruct the profiles and analyse the battery degradation for three residential EVSEs in Denmark, comparing degradation trends in under Uncontrolled Charging (UC) mode, with the SC ones. Results show that the economic profits from unidirectional (V1G) SC strategies based on price, offer significant cost savings (10-22% annually) and reduce calendar ageing by up to 4% over a decade. V2G strategies entail modest increases in cost savings at the cost of a minimal additional degradation.

A novel framework for low-temperature fast charging of lithium-ion batteries without lithium plating

This paper proposes a novel framework for low-temperature fast charging of lithium-ion batteries (LIBs) without lithium plating. The framework includes three key components: modeling, constraints, and strategy design. In the modeling phase, a new electro-thermal coupled model is introduced, which integrates both frequency-domain and time-domain electro-thermal coupled models. They optimize the bidirectional pulsed current (BPC) heating parameters and charging current, respectively. Terminal voltage boundaries and lithium plating serve as constraints. Terminal voltage is confined within upper and lower limits to prevent battery overcharge and overdischarge. Lithium plating avoidance entails setting a minimum frequency for BPC heating. During charging, lithium plating is mitigated by maintaining negative potential above 0 V. To obtain calculated negative electrode potential, a three-electrode battery is employed to calibrate the electro-thermal coupled model parameters. Subsequently, a novel low-temperature fast charging strategy is devised. This strategy enables intelligent switching between BPC heating and charging, while also synergistically optimizing BPC heating parameters and charging current. The strategy also achieves optimization of both charging speed and energy consumption. Charging the battery SOC from 0.2 to 0.9 in 42 min at −10 °C, without triggering lithium plating, is feasible with this proposed strategy. Compared to strategies focusing solely on current amplitude optimization, heating followed by charging, and traditional methods, this heating strategy exhibits the highest charging speed.

Modeling the Impact of Microstructure Variation on Charging Capability in Lithium-Ion Batteries

Battery models can be used to predict conditions ripe for lithium metal plating in the anode during rapid charging. One common procedure is to simulate a rapid charge using a pseudo-two-dimensional (P2D) model and determine if and when the minimum local potential reaches 0.0 V with respect to metallic lithium in the anode. This normally occurs at higher states of charge during a DC fast charging or rapid regenerative charging event. However, we have recently determined that three-dimensional (3D) models, while broadly agreeing with P2D results, demonstrate much variation in the in-plane and through plane direction, which P2D models cannot resolve. Because P2D models employ effective medium approximations, any “local” value in the P2D space along the through-plane coordinate represents an average across a slice of the domain. Therefore, it may be insufficient to use minimum anode potentials from P2D models as indicators of local plating and may only be representative of global lithium plating onset.The results of rapid-charge 3D microstructure simulations are compared to P2D results. This allows us to verify whether rapid charging induces negative local potentials at the anode active material surface that are left undetected in a P2D model. The expected result implies that the 3D microstructure model is necessary to predict the onset of lithium plating. We then extend our previous study to look at the impact of design changes on the microstructure performance, and then link the microstructure performance changes to performance metrics at the battery cell, module, pack, and vehicle level. Finally, the impact of variation in the microstructure domain considering particle size variation is quantified to understand tolerances that may be necessary when developing charging schemes.

Extending battery life in CubeSats by charging current control utilizing a long short-term memory network for solar power predictions

Recently, there has been a surge in small satellites and CubeSats. A crucial factor limiting the duration of their missions is the lifespan of their batteries. Typically, batteries are charged immediately when there is sufficient power generated from the solar panels. However, this practice results in additional charging stress and degradation due to unnecessarily high current amplitudes. In this work, a distributed charging strategy based on solar power prediction is proposed to mitigate charging stress and thereby extend battery life, ensuring sufficient charging without jeopardizing spacecraft operation. The proposed method for power generation prediction relies on a Long Short-Term Memory (LSTM) network, trained on GOMX-4A satellite telemetry data. The proposed LSTM method performed an order of magnitude better, with a root mean square error (RMSE) of 0.2274 W, while a baseline prediction utilizing a Seasonal Auto-Regressive Moving Average has an RMSE of 1.2406 W. Using the predicted power generation from the LSTM method, the current is distributed using a proposed charging multiplier control, resulting in 72.0882% reduction in the median charging current. A direct possible impact on lithium-ion batteries was evaluated by employing an electrochemical model from the literature, confirming that the proposed strategy effectively reduces degradation caused by lithium plating. Moreover, the capacity fade in the example scenario at 25 °C was reduced by 0.0849%. The extent of degradation reduction will vary according to the required mission profile, the operational conditions, the specific chemistry, and the type of battery in use.

A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC's staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

Dynamic Operations of a Mobile Charging Crowdsourcing Platform

This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms. Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .