Daytime Charging Research Articles

Energy Communities and Electric Mobility as a Win–Win Solution in Built Environment

Recently, there has been an increasing effort to promote energy efficiency, using renewable energies and electric mobility to achieve a more sustainable future and even carbon neutrality by 2050. This paper aims to understand if combining these technologies leads to a win–win solution. For that, the system’s characteristics that will be used for the simulation were defined as a residential community consumption scenario with and without electric vehicles charging overnight. The simulation was completed in software, and eight scenarios were tested: high population density/low population density with/without electric mobility and hourly tariff/simple tariff. After these scenarios had been tested, the conclusion was that the low population density and hourly tariff without and with electric mobility were the best two cases economically (in terms of levelized cost of energy, net present costs, and savings) and environmentally, and the worst was high population density with hourly tariff and electric mobility. Other scenarios were then tested, including changes in the load curve, namely a commercial load curve, and changes in the load curve of electric vehicle chargers, mainly daytime charging. The conclusion was that even though the initial hypothesis did not lead to a win–win solution, with changes in the hypothesis, the integration of electric mobility in energy communities might lead to that.

Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

Electric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic dispatch model of 2035 generation. We find that peak net electricity demand increases by up to 25% with forecast adoption and by 50% in a stress test with full electrification. Locally optimized controls and high home charging can strain the grid. Shifting instead to uncontrolled, daytime charging can reduce storage requirements, excess non-fossil fuel generation, ramping and emissions. Our results urge policymakers to reflect generation-level impacts in utility rates and deploy charging infrastructure that promotes a shift from home to daytime charging.

Adjustment of bus departure time of an electric bus transportation system for reducing costs and carbon emissions: A case study in Penghu

As a response to the worldwide problems of global warming and environmental pollution, electric vehicles have become the main direction of development in the automobile industry. Taking the bus system of Penghu Islands as the subject, this study explores the switching of all the original diesel buses to electric buses, and it adjusts the departure time of all the buses, with the purpose of reducing the costs of the construction and electricity used in an electric bus system. Plug-in and battery-swapping buses are used as examples in the study, and the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and Simulate Anneal Arithmetic (SA) algorithms, as well as an algorithm that combines the above, is used to optimize the departure times, in order not to affect the volumes and passenger demands in units of five minutes, the shift starts within the range of 15 minutes before or after the scheduled time. After each new schedule is prepared, batteries are used to optimize the daytime charging schedule of electric buses, to ensure the lowest cost of each new schedule. The results show that, regardless of which algorithm is used to optimize the departure time, all the minimum costs are lower than the best results before the adjustment, especially for the PSO-GA algorithm. Hence, the proper adjustment of the departure time can really reduce the construction and electricity costs and carbon emissions of the electric bus system.

Latent heat storage integration into heat pump based heating systems for energy-efficient load shifting

Integrating latent heat thermal energy storage (LHTES) units into building heating systems has been increasingly investigated as a heat load management technology. A conventional LHTES integration method for heat pump based heating systems is to connect the heat pump’s condenser for charging the LHTES unit. This integrating layout however usually leads to increased electricity input to the heating system. To underline this issue and provide solutions, this paper presents three new LHTES integrating layouts where the LHTES unit is connected with the de-superheater of the main heat pump (Case 2), the condenser of a cascaded booster heat pump cycle (Case 3), or a combination of using both the de-superheater and the booster cycle (Case 4). In the context of a multi-family house in Stockholm, a quasi-steady state heating system model was developed to evaluate the new integrating layouts, which were benchmarked against the baseline heating system without storage (Case 0) and the conventional integrating layout using the main heat pump condenser (Case 1). Hourly electric power input to the heating system was modelled for calculating the performance indicators including the heating performance factor, the operational expense and justifiable capital expense, and the indirect CO2 emissions. Two load shifting strategies were simulated for an evaluation period of Week 1, 2019: 1) charge during off-peak hours (8 pm to 6 am) and 2) charge during daytime hours (10 am to 7 pm). The simulation results of the off-peak charging strategy show that, in Cases 2–4, the heating performance factor is 22%-26% higher than Case 1 and the operational expense can be reduced by 2%-5% as compared with Case 0. The savings in the operational expense can justify the capital expense of 11 k-25 k Swedish Krona (SEK) for the LHTES systems in Cases 2–4 assuming a 15-year operation. Furthermore, the advantage of using the daytime charging strategy is principally the mitigation of CO2 emissions, which is up to 14% lower than the off-peak charging strategy. In summary, higher energy efficiency for heating is validated in the three new proposed integration layouts (Cases 2–4) against the condenser charging layout.

Hourly Power Grid Variations, Electric Vehicle Charging Patterns, and Operating Emissions.

Light-duty vehicles emit ∼20% of net US greenhouse gases. Deployment of electric vehicles (EVs) can reduce these emissions. The magnitude of the reduction depends significantly on EV charging patterns and hourly power grid variations. Previous US EV studies either do not use hourly grid data, or use data from 2012 or earlier. Since 2012, US grids have undergone major emission-relevant changes, including growth of solar from ∼1 to ∼20% of generation in California, and >30% reduction of coal power countrywide. This study uses hourly grid data from 2018 and 2019 (alongside hourly charging, driving, and temperature data) to estimate EV use emissions in 60 cases spanning the US. The emission impact of charging pattern varies by region. In California and New York, respectively, overnight EV charging produces ∼70% more and ∼20% fewer emissions than daytime charging. We quantify error from two common approximations in EV emission analysis, ignoring hourly variation in grid power and ignoring temperature-driven variation in fuel economy. The combined error exceeds 10% in 30% of cases, and reaches 50% in California, home to half of US EVs. A novel EV emission approximation is introduced, validated (<1% error), and used to estimate EV emissions in future scenarios.

Experimental Investigation and Energy Performance Simulation of Mongolian Ger with ETS Heater and Solar PV in Ulaanbaatar City

There are approximately 200,000 households living in detached houses and gers (yurts) with small coal stoves that burn raw coal in Ulaanbaatar city. A proper heating system and improvement of the energy efficiency of residential dwellings are vitally important for Ulaanbaatar city to reduce air pollution as well as for the operation of the current central energy system. This study shows the experimental results for two gers with two different heating systems and different thermal insulation, for investigating the merits of each. The technical feasibility of the system consisting of an electric thermal storage (ETS) heater with a daytime charging schedule and areal photovoltaic (PV) system was also examined by using a simulation with software developed in MATLAB (R2020a, MathWorks, USA). As a result of the experiment, the indoor comfort level and energy efficiency of the ger with added insulation and an ETS heater with nighttime charging were shown to be enhanced compared with those of the reference ger. The ger with added insulation and the ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation showed that the PV self-consumption rate is 76% for the Ger 2 with the ETS heater because of the daytime charging schedule of the ETS heater. The PV system supplied 31% of the total energy consumed, with the remaining 69% from the main grid.

A data driven typology of electric vehicle user types and charging sessions

The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure. While current literature provides charging choices and categorizations of charging behavior, these seem oversimplified and limitedly based on charging data.In this research we provide a typology of charging behavior and electric vehicle user types based on 4.9 million charging transactions from January 2017 until March 2019 and 27,000 users on 7079 Charging Points the public level 2 charging infrastructure of 4 largest cities and metropolitan areas of the Netherlands.We overcome predefined stereotypical expectations of user behavior by using a bottom-up data driven two-step clustering approach that first clusters charging sessions and thereafter portfolios of charging sessions per user. From the first clustering (Gaussian Mixture) 13 distinct charging session types were found; 7 types of daytime charging sessions (4 short, 3 medium duration) and 6 types of overnight charging sessions. The second clustering (Partition Around Medoids) clustering result in 9 user types based on their distinct portfolio of charging session types. We found (i) 3 daytime office hours charging user types (ii) 3 overnight user types and (iii) 3 non-typical user types (mixed day and overnight chargers, visitors and car sharing). Three user types show significant peaks at larger battery sizes which affects the time between sessions. Results show that none of the user types display solely stereotypical behavior as the range of behaviors is more varied and more subtle. Analysis of population composition over time revealed that large battery users increase over time in the population. From this we expect that shifts charging portfolios will be observed in future, while the types of charging remain stable.

Battery electric bus infrastructure planning under demand uncertainty

The electrification of city bus systems is an increasing trend, with many cities replacing their diesel buses with battery electric buses (BEBs). Due to limited battery capacities, and to random battery discharge rates—which are affected by weather, road and traffic conditions—BEBs often need daytime charging to support their operation for a whole day. The deployment of charging infrastructures, as well as the number of stand-by buses available, has a significant effect on the operational efficiency of electric bus systems. In this work, a stochastic integer program has been developed to jointly optimise charging station locations and bus fleet size under random bus charging demand, considering time-of-use electricity tariffs. The stochastic program is first approximated by its sample average and is solved by a customised Lagrangian relaxation approach. The applicability of the model and solution algorithm is demonstrated by applications to a series of hypothetical grid networks and to a real-world Melbourne City bus network. Managerial insights are also presented.

Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques.

Assessing electric mobility feasibility based on naturalistic driving data

In a context where electric mobility is gaining increasing importance as a more sustainable solution for urban environments, this work presents an analysis of electric mobility feasibility and adequacy based on private users' naturalistic driving data. Several scenarios were tested to evaluate different charging event opportunities and their impacts on electric mobility feasibility. In more detail, scenario 1 considered that vehicles would recharge whenever they are stopped for 2, 4 or 6 h, either on weekdays or weekend days; scenario 2 tested the hypothesis of recharging only during the night period; and scenario 3 assumed that vehicles would recharge during the day on weekdays. Furthermore, the potential energy impacts of electric mobility at a city level, by applying a driver and street level approach, were also estimated.Results revealed that electric mobility is highly feasible for weekday urban trips, while weekend trips due to their higher average distance are less suitable to be performed by EVs. Scenario 1, due to its higher recharging opportunities was found to be the best-case scenario. In this case, the percentage of eligible trips was found to be equal to or higher than 94% and 88% on weekdays and weekend days, respectively. Results showed also the lower electric mobility feasibility if considering only daytime charging, on weekdays (scenario 3). However, if considering night charging (scenario 2), the electric mobility eligibility was found to improve significantly. When considering a street level analysis, the potential reduction in energy consumption ranges in average from −60 to −70%, enabling the visualization of higher EV potential, with increasing potential for reducing energy consumption for increasing road grades.Concluding, since electric mobility is particularly suited for urban driving and most households detain 2 or more vehicles, there is a high potential to replace at least one ICEV by an EV. In this case, people may adapt their driving behavior, using the EV for their day-to-day urban driving while the ICEV would be used for longer trips. Nonetheless, the capacity to recharge during night plays a significant role on trips eligibility. Therefore, the availability of home-charge set-ups or a much higher deployment of public charging stations at residential locations are required in order to incentivize drivers to shift towards electric mobility.

Can battery electric light commercial vehicles work for craftsmen and service enterprises?

Battery Electric Light Commercial Vehicles (BE-LCVs) can reduce the environmental impacts of Craftsmen and Service (C&S) Enterprises transportation. These Enterprises produce vital services, using diesel vehicles for transportation of personnel, tools and materials to worksites, thus contributing to pollution and greenhouse gas emissions. Enterprises that have taken BE-LCVs into use report practical range challenges leading to a need to reorganize their transportation activities. The driving pattern of 7 C&S enterprises operating 115 vehicles, were logged over two weeks. The potential of using BE-LCVs can be evaluated by combining the real range of BE-LCVs in Norway, with these driving patterns. Although 42% of diesel LCVs (D-LCVs) could be replaceable by BE-LCVs with a range of 170 km. Many covered so short daily distances that the transport work would only be reduced by 13%. The replaceable vehicles and transport work can increase by redistributing vehicle assignments, daytime charging, or with longer range BE-LCVs. If all year range increases to 200 km, then almost all vehicles are potentially replaceable. Purchase incentives are required to unlock the potential, but may, not produce large effects until the range improves. BE-LCVs with 50% longer range enters the market in 2018, which should expand the market.

A Single-Phase Integrated Onboard Battery Charger Using Propulsion System for Plug-in Electric Vehicles

Plug-in electric vehicles (PEVs) are equipped with onboard level-1 or level-2 chargers for home overnight or office daytime charging. In addition, off-board chargers can provide fast charging for traveling long distances. However, off-board high-power chargers are bulky, expensive, and require comprehensive evolution of charging infrastructures. An integrated onboard charger capable of fast charging of PEVs will combine the benefits of both the conventional onboard and off-board chargers, without additional weight, volume, and cost. In this paper, an innovative single-phase integrated charger, using the PEV propulsion machine and its traction converter, is introduced. The charger topology is capable of power factor correction and battery voltage/current regulation without any bulky add-on components. Ac machine windings are utilized as mutually coupled inductors, to construct a two-channel interleaved boost converter. The circuit analyses of the proposed technology, based on a permanent magnet synchronous machine (PMSM), are discussed in details. Experimental results of a 3-kW proof-of-concept prototype are carried out using a ${\textrm{220-V}}_{{\rm{rms}}}$ , 3-phase, 8-pole PMSM. A nearly unity power factor and 3.96% total harmonic distortion of input ac current are acquired with a maximum efficiency of 93.1%.

Optimal Management for Parking-Lot Electric Vehicle Charging by Two-Stage Approximate Dynamic Programming

This paper targets the day-time charging scenario for plug-in electric vehicles at parking-lots near commercial places, where most vehicles have extended parking time. Compared with night-time charge scenarios for residential buildings, commercial building parking-lot charging during day-time feature significant stochastic vehicle arrival and departure, as well as highly dynamic electricity price. A two-stage approximate dynamic programming framework is proposed to determine the optimal charging strategy, utilizing the predicted short-term future information and long-term estimation from historical data. All the vehicles are desired to be charged to full prior to the departure time specified under constrained total charging capacity. The uncharged amount is subject to a significant penalty cost. Simulation scenarios are created by modeling the vehicle arrival behavior as Poisson process, including arrival time, departure time, and arrival state of charge. The simulation results show that the proposed method can significantly decrease the energy cost.

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system.

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

AbstractGreen technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the researches in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs. Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent ener...

Minimizing the costs of constructing an all plug-in electric bus transportation system: A case study in Penghu

The growth of worldwide environmental awareness has prompted numerous countries to focus on developing energy-conservation and carbon-reduction technology. The advancement of such technology enables the emergence of low-noise, low-polluting alternatives for bus systems, such as hybrid-electric, battery-electric, and fuel-cell electric buses (e-buses). For such buses to serve the existing schedules and lines operated by their conventional counterparts, reorganizing bus transportation systems is a major challenge and entails construction costs that comprise the costs of e-buses, battery capacity, chargers, and bus scheduling.To facilitate the development of environmentally friendly public transportation, this study proposes a model for simulating the operation and battery charging schedule of plug-in e-buses on the basis of an existing schedule and line network. The model was used to estimate the overall construction cost of converting the existent bus transportation system into an all plug-in e-bus one. Focusing on the bus transportation system in Penghu, an archipelago of Taiwan, this case study examined the effects of day- and nighttime charging requirements on the construction cost of an e-bus transportation system to improve the practicability of e-buses. It also applied a genetic algorithm to determine the minimum construction cost, which varied depending on the number of e-buses, level of battery capacity, number of chargers, and electricity costs. The optimized parameters involved the hourly residual battery capacity and battery charging times during the daytime operating hours. The results showed that although daytime charging involved electricity uses during peak hours and thus incurred additional costs, it contributed to the use of e-buses and an overall reduction in the construction cost. In summary, the proposed optimization method would successfully reduce the construction cost of the Penghu e-bus transportation system.

Electric Vehicle Charging Impacts by Increasing Demand and Varying Customer Charging Behaviors

This paper presents the impact evaluation of a possible massive implementation of EV charging facilities over the electric distribution grid of Bogota, Colombia. Chargers are represented using a model based on tests with real charging systems. Likewise, the topologies used are based on existing networks. The simulation method adopted offers the possibility of comparing different charging scenarios for each studied network. The main evaluation criteria are: voltage behavior in the nodes, voltage regulation and chargeability of the distribution transformers. Simulation results are given by comparing quantitative values obtained via progressive inclusion of chargers in the distribution network. The presented values depict three different scenarios: office use (slow daytime charging), residential use (slow nighttime charging) and commercial use (semi-fast daytime charging).The impact on the distribution network produced by massive EV recharge depends mainly on the behavior of users: start time and duration of recharge.

Optimal Day-Time Charging Strategies for Electric Vehicles considering Photovoltaic Power System and Distribution Grid Constraints

Electric vehicles (EVs) charging stations with a photovoltaic (PV) system for day-time charging have been studied. This paper investigates the issues such as how to coordinate the EVs customers for coordinated charging, maximize photovoltaic utilization, and reduce customers cost of EVs charging and operator electricity. Firstly, an ideal charging load curve was built through using the linear programming algorithm. This optimal curve, which realized maximum photovoltaic power and minimum electricity cost, was used as the objective curve. Secondly, a customer response model was utilized, to propose an optimization method and strategy for charging service tariffs. Particle swarm optimization algorithm was used for time-of-use tariffs and peak-flat-valley time division so that the charging load after price regulation was adjusted to best fit the objective curve, and both the EVs customers and the operator benefit from this. Finally, the proposed model and method have been verified by two cases.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

Day charging electric vehicles with excess solar electricity for a sustainable energy system

In order to reach significant CO2 emissions reductions, future energy systems will require a large share of renewable energies, such as wind and solar photovoltaic power. However, relying on such renewable energy sources is expected to generate considerable excess power during certain periods of the day, in particular during night time for wind and daytime for solar power. This excess power may be conveniently used to power electric mobility. This paper explores the possible complementarities between wind and solar power and electric vehicles charging, based on 2050 scenarios for the case study of Portugal. Model results show that CO2 emissions targets can only be achieved with high levels of photovoltaics penetration and electric vehicles, reinforcing the need for daytime charging infrastructures, presumably at or near work facilities.