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With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), the coordinated charging of PHEVs has become an important issue in power distribution systems. This paper employs a multi-objective optimization model for coordinated charging of PHEVs in the system, in which the problem of valley filling and total cost minimization are bot...
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... this subsection, power consumption data is used to show the desirable performance of our 427 algorithm. The simulation parameters of four PHEV models are given in Table 3, the data in which 428 was sources from [22,23]. The positive parameter ε is set to 0.35 in this example. ...Similar publications
Advances in wireless power transfer technology provide the possibility to construct electrified roads and charge electric vehicles driving on the road. Dynamic charging mode enables the contactless interaction of electric vehicles with the power grid and has a promising prospect, but it may also bring about potential challenges to the power grid su...
Logistics distribution, an industry with the characterics of service and profits at the same time in the society, of which goods delivery service time has a significant impact on the customers’ satisfaction level. Based on the above motivation, by considering the two stages: 1) distributing goods on the way, and 2) returning back to the depot after...
This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as players in the game, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to...
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... while enhancing the generation of renewable energies can help reduce the carbon footprint and prevent the depletion of fossil resources [5][6][7][8]. ...
The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.
... This is due to the persistent need for longer drive ranges to be covered, battery charging time to be reduced, and energy efficiency to be improved. These operations are focused on smart charging control systems, reduction in manufacturing costs, and new safety and security technologies for EV owners [4]. ...
The electric vehicles (EVs) could potentially have a significant impact on power quality parameters and distribution networks as they are non-linear loads and their charging might result in tremendous power demand. When connected to the utility grid, a large number of EV charging stations from different manufacturers might create significant harmonic current emissions, impact the voltage profile, and eventually affect the power quality. Nevertheless, practical examples of disturbances from charging stations have not been made public. This paper aims to clarify the characteristics of conductive disturbances and levels of current harmonics generated by charging station and their severity on the quality of electric energy. The analysis was based on tests of a prototype station of an EV charging station integrated with a LED street light. The tests concern the determination of current harmonics and the values of conductive electromagnetic disturbances in the 150 kHz–30 MHz range. The test results of the prototype charger with observed exceedances of current harmonics (25th–39th range) and conducted interference exceedances are comprehensively described. After applying filtering circuits to the final version of the station, retesting in an accredited laboratory showed qualitative compliance.
... These concerns led to the development of "smart charging" protocols [18]. These strategies allow PEV adoption to expand and manage electric grid levels within safe limits, reducing the need for utility investments to renovate the distribution network to accommodate larger demand loads [19][20][21][22]. Without smart charging protocols, vehicles immediately begin charging when plugged in, regardless of the time or demand, which is referred to as "uncontrolled charging" [23]. ...
This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.
... Due to their environmental benefits and high energy efficiency, electric vehicles are very popular among academics, automotive and government organisations (Wei Li, Lin, Cai, Zhou, & Yan, 2019). In a broader perspective, ecology, politics and economics can be identified as the main causes of support for electric vehicles. ...
In recent years, a number of legal solutions have been adopted in Poland aimed at reaching the number of 1 million electric cars in use in 2025. Therefore, an important research issue is the assessment of electric vehicles available on the Polish market and the identification of vehicles that meet consumers' expectations to the greatest extent. The assessment of electric vehicles is a multi-criteria issue, characterized by a number of uncertainties related to their operational parameters, as well as the preferences of individual users and, more broadly, the preferences of society. A novelty of the article is the application of the fuzzy multi-criteria decision aid method called NEAT F-PROMETHEE (New Easy Approach To Fuzzy PROMETHEE), combined with the Monte Carlo method and elements of the SMAA (Stochastic Multicriteria Acceptability Analysis) method for the assessment of vehicles under uncertainty conditions. The theoretical contribution of this research therefore includes the synthesis of a fuzzy and stochastic approach to decision-making support, supported by outranking and incomparability relationships. Such a set of approaches to uncertainty makes it possible to improve the accuracy of decision-making, because the approaches indicated verify each other's results. Moreover, the application of the NEAT F-PROMETHEE method has solved the problem of evaluating electric vehicles from the perspective of a single decision-maker, while the combination of NEAT F-PROMETHEE and the stochastic approach has made it possible to simulate preferences of the society. Although there are many uncertainties in the decision-making problem, the approach has allowed to identify almost unambiguously the electric vehicle that is likely to gain the highest acceptance. As a result of the conducted research it was found that the approaches to uncertainty based on fuzzy sets, outranking relations and stochastic analysis complement each other, allowing the decision-maker to conduct a wider analysis of the imprecision of the obtained solution.
... Global energy demand is almost steadily increasing, the only exception being the global lockdown in the first half of 2020, which caused a temporary drop in demand for energy production [1]. Together with increased energy production, the number of greenhouse gases emitted into the atmosphere is also increasing: at present, more than 25 billion tons of CO 2 are released annually as a result of human activity [2]. One of the most important options for reducing CO 2 emissions into the atmosphere is the development of electric vehicles [3], because, according to the International Energy Agency, around 14% of greenhouse gases in the world are produced by the transport sector [4]. ...
Increasing the popularity of electric vehicles is one way of reducing greenhouse gas emissions and making the economy more sustainable. In Poland, the use of electric vehicles is to be increased by the adoption of the Act on Electromobility and Alternative Fuels. This Act obliges local government units and state administration to expand the electric vehicle fleet. The expansion of the fleet should be carried out on a planned basis, based on rational decisions supported by economic analyses. Therefore, the aim of this article is to provide a recommendation of an electric vehicle that meets the needs of local and state administration to the greatest extent possible. The aim has been achieved using the multi-criteria decision analysis method called PROSA-C (PROMETHEE for Sustainability Assessment—Criteria) combined with the Monte Carlo method. The PROSA-C method allows promoting more sustainable vehicles with high technical, economic, environmental and social parameters. The Monte Carlo method, on the other hand, is a stochastic simulation tool that allows for taking into account the uncertainty of parameters describing vehicles. As a result of the research, the most and least attractive vehicles were identified from the perspective of the needs of local government units and state administration. Moreover, the conducted research allowed confirming the effectiveness and usefulness of the research methodology proposed in the article and the procedural approach combining the PROSA-C and Monte Carlo methods.
... In [6][7][8], PEV charging strategies were proposed to improve the power quality of distribution networks in terms of network loss, harmonic distortion and voltage regulation. Wei et al. [9] presented a multi-objective optimisation strategy for coordinated PEV charging, to minimise the load peak-valley difference and the user cost. In [10,11], dynamic pricing schemes were built to prompt users to charge PEVs when electricity prices are low. ...
... So, it can optimise the charging time of active power charging and reactive power discharging as well as the charging phase simultaneously. For example, if a PEV has a stay period of 7-time slots while requiring 4 slots to be charged to the specified level, DPSO will evaluate all the charging groups with the total charging slots at 4. From the example particle structure, the PEV is charged and discharged reactive power at phases A and B for the time slots 1 and 2, respectively while no charging and discharging for the time slot 3. The 0,1-charging sequence over the total 7-time slots obtained is then used as the results for the present moment with the objective function calculated by (9). Compared with other heuristic algorithms, DPSO has a superior computational performance and does not require preconditions such as convexity, continuity and differentiability of the OPF problem [33]. ...
Plug‐in electric vehicles (PEVs) are being widely deployed, especially in the residential premises. Random PEV charging causes negative impacts on the power grid, a coordinated PEV charging is essential. Currently, PEV charging studies mainly focus on the active power control of PEVs while there is a risk of worse operation, due to the transferring of charging load instead of eliminating. Besides, existing PEV charging studies are commonly based on a three‐phase balanced network model, but practical distribution networks, especially on the low‐voltage level, are unbalanced. This study proposes a novel coordination strategy of PEV charging in unbalanced distribution networks, with the support of reactive discharging and phase switching. The optimisation model of PEV charging is firstly proposed to minimise the network loss and charging cost while maximising users’ benefit from ancillary reactive service. The optimal power flow problem defined above is solved by a combined solver of discrete particle swarm optimisation and direct load flow. Then, a coordination strategy of PEV charging, reactive discharging and phase switching is presented to apply the proposed optimisation over 24 h. Finally, detailed simulations are conducted on a real Australian distribution network, to test the feasibility and effectiveness of the proposed optimisation and coordination strategy of PEV charging in unbalanced distribution networks.
... Vehicles 2020, 2 211 and Khan [8] pointed that the increasing use of PHEVs is an important issue to be taken into account in domestic energy power management, while Li et al. [9,10] investigated the advantages for the power distribution systems due to the optimum coordination of the PHEVs charging times. Furthermore, Mohammadi et al. [11] asserted that if the PHEVs are able to work in a bidirectional power mode (draining power from and supplying power to the grid), they can collaborate to regulate the grid voltage and frequency along a day. ...
... After the clutch fully opened (CPD = 100 mm), the gear ratio is changed in 0.2 s; then, the ICE is reconnected to the gearbox (CPD = 100 to CPD = 0 mm) in 0.5 s [32]. Therefore, the gearbox input torque T gb (Nm) is defined according to the rules presented in Equation (9) and the available traction torque at the vehicle frontal wheels T a f (Nm) is then defined by Equation (10). ...
For decades, drivers and fleet managers have been impacted by the instability of fuel
prices, the need to save resources and the duty to meet and attain environmental regulations and certifications. Aiming to increase performance and efficiency and reduce emissions and mileage costs, plug-in electric vehicles (PHEVs) have been pointed out as a viable option, but there are gaps related to tools that could improve the numerous existing conventional vehicles. This study presents the design of an aftermarket hybridization kit that converts a vehicle originally driven by a combustion engine into a PHEV. To achieve this goal, an optimization was conducted with the objective of decreasing the cost (regarding fuel consumption and battery charging) to perform a local driving cycle, while attenuating the tailpipe emissions and reducing the battery mass. The torque curves of
the electric motors, the battery capacity, the parameters for a gear shifting strategy and the parameters for a power split control were the design variables in the optimization process. This study used the Campinas driving cycle, which was experimentally obtained in a real-world driving scenario. The use of a local driving cycle to tune the design variables of an aftermarket optimization kit is important to achieve a customized product according to the selling location. Among the optimum solutions, the best trade-off configuration was able to decrease the mileage cost in 22.55%, and reduce the tailpipe emissions by 28.4% CO, 33.55% NOx and 19.11% HC, with the addition of a 137 kg battery.
... Energy storage is one the most promising proposed techniques that is mostly combined nowadays to AC/DC microgrids because offers several alternatives ranging from simple energy storage services to voltage stability [39] and energy management solutions [40]. In addition, optimal charging controllers for coordinated charging of EVs [41], decentralized control schemes in order to enhance AC/DC microgrids stability [42], as well as artificial intelligent methods (AI) such as machine learning (ML) open new horizons for the power systems management [43]. ...
Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid's capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.
An optimal strategy for simultaneous charging of electric vehicle battery packs is proposed as a multi-objective optimization problem in this paper. The charger consists of 'n' constant voltage current sources for charging n batteries, respectively. Considering the energy loss and the charging criteria, a battery charging issue for the charging mode is formulated as a multi-objective optimization problem and the
optimal solution is obtained with the application of quadratic programming. In addition, an adaptive momentum-based steepest
descent algorithm is propounded to minimize the simultaneous
charging time in the charging mode. Simulation and experimental
results show that the actual state of charge converges to the same
value within the theoretical shortest time for under the charging
mode.
