Charging Strategy Research Articles

An Economical Light Plug-in Electric Vehicle (LPEV) with On-Board Single-Stage Battery Charging System

This work proposes an efficient configuration for light plug-in electric vehicles (PEVs) with a cost-effective and compact charging system. In this work, an integrated isolated DC-DC converter is investigated for a light PEV that employs a brushless DC (BLDC) machine as a traction motor to lower the component cost of the motor drive. The designed converter has the ability to function competently in all operating situations. The proposed converter has a minimum element because of the multi-functional operation of the converter. The designed converter can function as both maximum power point tracking (MPPT) and power factor correction (PFC) converters in solar and grid charging. In propulsion and regenerative braking operations, the proposed converter has buck/boost conversion capability to adjust the DC bus voltage according to vehicle speed to lower the inverter switching power losses and the traction motor's current ripple. As an outcome, the developed charging scheme becomes condensed and effective, making it a viable alternative for industry applications of light plug-in vehicles.

Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs' system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs' system-level impact and that a naïve aggregation of individual profiles leads to an overestimation of the fleet's flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2kWh/EV, only 10% of the result of a naïve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.

Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.

Count the charging load forecast of the battery remaining life and ownership

In order to solve the accuracy of EV charging load prediction, the residual life reduction and the exact value of EV ownership caused by battery aging are taken into consideration. The charging load curve of the EV is simulated based on the Monte Carlo simulation method. Finally, based on the actual data of a certain region, the charging load of electric vehicles within a day is obtained. The case analysis shows that the proposed charging load prediction model of electric vehicles has high accuracy, which also shows that the large-scale grid connection of electric vehicles will bring new challenges to the power system. This method provides data support for the subsequent formulation of electric vehicle charging strategy and load side peak shifting and valley filling.

Analysis and mitigation of the impact of electric vehicle charging on service disruption of distribution transformers

The transition to electric mobility could overload distribution transformers to unprecedented levels. Sustained overloads produce excessive heat within the transformer and hasten insulation deterioration. As EV deployment rises and the severity of overloads increases, the focus shifts from long-term effects that reduce transformer life to more immediate impacts that can disrupt normal functioning. The latter typically manifest as the blowing of protection fuse, the triggering of pressure relief device, or the breakdown of winding insulation, each of which could result in transformer outage. The transformer’s vulnerability can be monitored in terms of its apparent power flow, top-oil temperature and hottest-spot temperature. The IEEE C57.91 standard specifies the permissible range for these parameters, beyond which the transformer operation could get interrupted. A study to determine the range of EV penetration that can be accommodated by a distribution transformer without any discontinuity in its operation is presented in this paper. The non-linear nature of EV charging is incorporated in the analysis to account for the effect of higher-order harmonics on the transformer’s internal temperature. The effectiveness of a controlled charging strategy, which seeks to regulate transformer loading by minimizing its variance, in preventing service disruption is also validated in this simulation study.

Design and Modeling of CLLC Converter for Bidirectional on-Board Charger

With the proposal of double carbon goals in various countries around the world, the development of new energy vehicles has received more and more attention as one of the important measures to reduce carbon emissions, and battery electric vehicles are the main research and application direction. In order to alleviate the impact of the influx of a large number of electric vehicles on the existing power grid, the construction of a flexible V2G system has become a future development trend, and the bidirectional on-board charger is an indispensable device. Combined with this application background, this paper designs a CLLC converter for the bidirectional on-board charger which has fully symmetrical parameters. Based on the three-stage charging strategy of lithium-ion batteries, the platform characteristics of lithium-ion batteries are transformed into converter parameters, ensuring higher efficiency during the charging process. The small-signal model of the converter is established and verified, and the closed-loop PFM control is realized. Finally, a planar transformer with integrated leakage inductor is designed and a 1.2kW experimental prototype is built.

A SOC Correction Method Based on Unsynchronized Full Charge and Discharge Control Strategy in Multi-Branch Battery System

LiFePO4 batteries exhibit voltage plateau and voltage hysteresis characteristics during charging and discharging processes; however, the estimation of state-of-charge relies on voltage detection. Thus, the estimation accuracy of SOC is low in a traditional method. In this paper, a full charge and discharge SOC correction method is proposed; i.e., the SOC is corrected to 100% when the battery is fully charged and to 0% when fully discharged, and the actual usable capacity is corrected using the fully discharged capacity after being fully charged. Thereby, the cumulative error of the ampere-hour integration method is dynamically corrected. In engineering applications, however, the battery systems do not always undergo full charge and discharge cycling due to the operating conditions. By making full use of the distributed control characteristics of the multi-branch topology battery system, the present work proposes an optimized system control strategy to realize the unsynchronized full charge and discharge cluster by cluster, which extends the application of the full charge and discharge SOC correction method. The experimental results verify the accuracy of the proposed SOC correction method and the feasibility of the control strategy. A more reliable and efficient battery management scheme is provided for LFP battery system, which has high practical value in engineering.

A technical feasibility study of a liquid carbon dioxide energy storage system: Integrated component design and off-design performance analysis

Liquid carbon dioxide (CO2) energy storage (LCES) system is emerging as a promising solution for high energy storage density and smooth power fluctuations. This paper investigates the design and off-design performances of a LCES system under different operation strategies to reveal the coupling matching regulation mechanism of the charging and discharging processes. An off-design performance prediction model is developed based on preliminary designs of turbomachinery components and heat exchangers to evaluate the feasibility of the LCES system. The performances of the charging and discharging processes are analyzed under different load levels with two operation strategies: constant pressure charging and constant pressure discharging (CP-CP) operation strategy, and constant pressure charging and sliding pressure discharging (CP-SP) operation strategy. Results show that the LCES system has a round trip efficiency of 61.83% and an energy storage density of 21.92 kW·h·m−3 under the rated condition. As the input load level increases from 80% to 120%, the maximum charging time of the system decreases from 5.15 h to 3.36 h under the constant pressure operation strategy. When the output load level increases from 70% to 120%, the maximum discharging time of the system decreases from 5.48 h to 3.31 h under the constant pressure operation strategy and from 5.60 h to 3.30 h under the sliding pressure operation strategy. Besides, the round trip efficiency of the LCES system increases first and then decreases as both input and output load levels rise, while the energy storage density of the system follows a parabolic curve only with an increase in the output load levels. For better system performance, the CP-SP operation strategy and CP-CP operation strategy are more suitable for the system with output load levels of 70–100% and 100–120%, respectively. These achievements will provide a valuable reference for the stable operation of the LCES under off-design conditions.

Lithium-ion battery utilization in various modes of e-transportation

The electrification of the transportation sector leads to an increased deployment of lithium-ion batteries in vehicles. Today, traction batteries are installed, for example, in electric cars, electric buses, and electric boats. These use-cases place different demands on the battery. In this work, simulated data from 60 electric cars and field data from 82 electric buses and six electric boats from Germany are used to quantify a set of stress factors relevant to battery operation and life expectancy depending on the mode of transportation. For this purpose, the open-source tool SimSES designed initially to simulate battery operation in stationary applications is extended toward analyzing mobile applications. It now allows users to simulate electric vehicles while driving and charging. The analyses of the three means of transportation show that electric buses, for example, consume between 1 and 1.5 kWh/km and that consumption is lowest at ambient temperatures around 20 °C. Electric buses are confronted with 0.4–1 equivalent full cycle per day, whereas the analyzed set of car batteries experience less than 0.18 and electric boats between 0.026 and 0.3 equivalent full cycles per day. Other parameters analyzed include mean state-of-charges, mean charging rates, and mean trip cycle depths. Beyond these evaluations, the battery parameters of the transportation means are compared with those of three stationary applications. We reveal that stationary storage systems in home storage and balancing power applications generate similar numbers of equivalent full cycles as electric buses, which indicates that similar batteries could be used in these applications. Furthermore, we simulate the influence of different charging strategies and show their severe impact on battery degradation stress factors in e-transportation. To facilitate widespread and diverse usage, all profile and analysis data relevant to this work is provided as open data as part of this work.

Atomic Charge Schemes Comparison for Fe Single Atom in Graphitic Carbon: Insights from Quantum Simulations and Machine Learning

Atomic Charge Schemes Comparison for Fe Single Atom in Graphitic Carbon: Insights from Quantum Simulations and Machine Learning

Sustainability analysis framework for on-demand public transit systems

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.

Smart home charging of electric vehicles using a digital platform

As the penetration of electric vehicles (EVs) grows, it is important to understand the implications of home charging technologies for grid operations and for the budget of users. We conduct an empirical study analyzing data on 438 EVs over a period of 3,687 consecutive hours, collected by an energy aggregator which operates a digital platform. We first develop an optimization model to compute an optimal schedule of charging for all EVs in the dataset at minimum cost. Then, we compare the realizations against this optimal solution, distinguishing householders who use a smart charging functionality of the digital platform from those who do not use it. Our findings indicate that the smart charging behaviour conduces to better results, and close to the optimal solution. The non-users tend to start charging as soon as they plug-in their EVs, often at peak consumption times. In contrast, the smart charging strategy usually shifts the charging schedules towards times where the consumption is cheaper and the grid is less congested, facilitating a higher load factor and lower power losses. These results highlight the positive role of energy aggregators and digital platforms in coordinating users to lower the cost and enhance efficiency of energy consumption.

Assessing the value of information for electric vehicle charging strategies at office buildings

Smart charging strategies for electric vehicles (EVs) require as input information such as energy requirement and dwell time. In practice, that information is often not available. However, estimations may be obtained from historical charging behavior. This paper investigates the added value of historical information for EV charging strategies based on real-world EV charging data collected in an office building parking lot with 125 chargers. Furthermore, it provides valuable insights into EV charging behavior at office building parking lots, based on a statistical analysis of the data. The added value of data availability in EV charging strategies for day-ahead planning and real-time operation of office building parking lots is assessed via a set of quality metrics that measure user satisfaction and impact on the local grid. Offline charging strategies under various degrees of available information are validated by comparing their performance with the real-time operation of the parking lot. Results show a power peak reduction of more than 50% using historical data and simple estimations of arrival times, dwell times, and energy requirement. A trade-off between power peaks and service quality (on average 4.4kWh energy not served) is observed. It was found that knowledge of individual average energy provides higher added value compared to knowing individual average dwell time in both offline planning and real-time operation of the parking lot.

Research on safe charging strategy of lithium-ion battery based on three-electrode equivalent circuit model

Under extreme conditions of low temperature or fast charging, the side reaction of lithium (Li) plating can occur on the anode surface of lithium-ion batteries (LIBs), which may cause causing capacity fade. Alarmingly, in severe cases, dendrites will grow and pierce the separator. As a result, thermal runaway will be induced, which seriously affects the safety performance of LIBs. Along these lines, to avoid Li plating during the charging process, the safe charging strategy was thoroughly investigated in this work based on the implementation of the three-electrode equivalent circuit model (ECM). More specifically, for the three-electrode LIB implanted with a reference electrode (RE), its ECM was improved based on the traditional ECM. The cathode and anode of the LIB were decoupled through the RE, whereas combined with the heat generation, transfer, and aging characteristics, a first-order RC electro-thermal coupled model of three-electrode was established, and the semi-empirical aging model of the LIB was combined to analyse the coupling characteristics between the parameters of each model. According to the acquired experimental data, the key parameters of modelling were identified and the accuracy of the model was verified. In addition, aiming at the contradiction between the fast charging and aging speed, and combined with the NSGA-II optimization algorithm, the self-adaptive multi-stage constant current constant voltage (SMCCCV) charging strategy was also proposed to avoid Li plating on the anode surface. An optimization model was thus established with the charging time and aging loss as the objective functions, resulting in three charging strategies, namely minimum charging time, minimum aging, and balanced charging. From the simulated outcomes, it was demonstrated that the anode potential was strictly maintained above the safety threshold for these three strategies. On top of that, the balanced charging strategy reduced the aging loss by 11.5 %, while the relatively equivalent charging speed compared with the traditional constant current constant voltage (CCCV) strategy was maintained. The proposed safe charging strategy based on multi-objective optimization provides some valuable insights for next-generation fast charging techniques.

Leveraging demand-capacity balancing to reduce air traffic emissions and improve overall network performance

Improvements in European air traffic management (ATM) constitute one of the key levers to reduce aviation emissions in the future to reach ambitious goals. In this paper, we evaluate the benefits of improved network-centric ATM with coordinated optimisation of capacity, delays, re-routings and emissions vs the present fragmented ATM which has generated high delays and extensive re-routing due to sub-optimal capacity provision. In addition, we analyse the impact on flight efficiency of a novel trajectory-independent airport-pair charging scheme, which makes shortest routes the cheapest for Airspace Users (AUs), which is not always the case with the current charging scheme in Europe. We test and evaluate proposed changes in ATM using a case study based on real data covering 3,000–4,000 flights in large parts of Western European airspace. We find that network-centric ATM can reduce variable costs borne by AUs by 21% and in particular re-routing cost (and associated emissions) by almost 64%. Furthermore, airport-pair charging can save almost 11% of costs for AUs and up to 320,000 tons of CO2 emissions — if accompanied by adequate capacity changes that reflect the shift in demand towards shorter trajectories. We conclude that various stakeholder initiatives to reduce the fragmentation in European ATM and to move towards a more network-centric approach have merits and should be pursued by the policy makers.

Research on personalized charging strategy of electric bus under time-varying constraints

Research on personalized charging strategy of electric bus under time-varying constraints

Modelling driver's response to demand management strategies for electric vehicle charging in Australia

Electric vehicles (EVs) can help decarbonise transport as long as the energy sector successfully balances the electricity supply with the demand and maximises the use of renewable resources. For that, demand-side management strategies are necessary to encourage users to charge at certain times through time-of-use (ToU) tariffs or to control the load provided for charging (i.e., supplier-managed charging). However, adapting to different charging times can disrupt users' schedule flexibility. This study investigates consumer preferences for smart charging technology and control (user-managed or supplier-managed) and responses to progressive ToU tariff discounts for guiding changes in EV charging time in Australia. We analyse the potential of ToU tariffs in shifting demand to late-night hours and around midday when there is a peak in solar energy generation. Based on a sample of 994 drivers (including 97 EV owners), we estimated a multinomial choice model to identify key predictors of individual preference for smart charging management and a bivariate ordered model to investigate consumer response to time-of-use discounts. The results show that activity-travel behaviour is an important predictor of both demand-side management strategies. Consumers willing to change EV charging time in response to ToU tariffs are likely to have more flexible schedules, while those with more time constraints seek the practical benefits of smart charging. Current EV owners have higher propensities than potential adopters to choose supplier-managed smart charging and charge around midday in response to ToU tariffs. This indicates that trials with current EV owners may overestimate acceptance of these strategies. Our findings show that while monetary incentives can successfully shift an important share of consumers to night-time hours, these incentives are not very effective in shifting to midday charging. Synergy in formulating transport and energy demand strategies may be necessary to facilitate such a shift.

Thermal performance analysis of concentric-tube latent heat storage systems integrated with varying energy source under various charging strategies

This study presents a comprehensive assessment of the performance of a vertical concentric tube thermal storage system. The system is charged using constant and variable solar energy sources similar to real-world conditions. The initial part of the work focuses on identifying the suitable optimal phase change material for maximizing energy storage. It later evaluates the impact of using a variable energy source compared to a constant source for charging the system. A performance assessment of the system is carried out for two approximated constant energy source cases (constant time-averaged and constant peak fluid temperatures) and an actual variable energy source (unsteady fluid temperature). The findings indicate that constant time-averaged and peak charging temperatures can lead to under-prediction by up to 25.65 % and over-prediction by up to 32.72 % in the energy storage compared to the actual case considering variable charging temperature. Two charging strategies are proposed and compared with the established cascade methodology, revealing the potential for improving thermal energy storage. The dynamic charging strategy shows promising results with potential energy storage gains of up to 55.57 %. This study emphasizes the importance of considering the variable charging source and highlights effective strategies for enhancing energy storage in the thermal storage systems.

Li-ion battery charging strategy based on multi-state joint estimation model

This paper proposes a fast-charging strategy for lithium-ion batteries based on an intelligent optimization algorithm with multi-physics constraints. First, a thermoelectric coupling model and a battery degradation model have been established. Then an improved particle swarm optimization (PSO) is used to calculate the optimal charging strategy under different weighting factors. Finally, the Pareto boundary is drawn according to the optimization objective, and two charging strategies with different optimization objectives are developed. By comparing in different benchmark functions, the So-improved PSO proposed in this paper improves the convergence of the algorithm compared to the traditional PSO algorithm without increasing the computation time. The simulation results show that the multistage constant-current (MCC) charging strategy proposed in this paper can adjust the magnitude and order of charging current according to the change of weights, and reducing the loss of capacity and energy by only 0.017 % and 35.1 % while improving the charging speed by 24.9 % compared with the constant current-constant voltage (CCCV) charging strategy of 2C. Compared with the traditional CCCV charging strategy, the charging method proposed in this paper can have a broad application prospect.