A detailed computational modelling and analysis of the effect of geometric dimensions for coil assembly to transfer power wirelessly in electric vehicles

With the electrification in transportation systems, Electric Vehicles (EVs) have developed rapidly in recent years. At the same time, with large-scale EV integration to power grids, the charging behaviours of EVs bring both challenges and opportunities to power grids operation. This thesis focuses on the EV energy management in smart grids, and the EV energy management problem is studied considering three stakeholders' interests, i.e. EV owner, aggregator and grid, respectively. First, the economic relationship between EV owners and the aggregator is studied (EV owners' and aggregator's interest). Two multi-objective optimisation methods are applied to investigate the economic relationship between these two stakeholders and the aggregator{owner economic inconsistency issue is presented. To mediate this issue, a rebate factor is proposed in the model. The results show that a signi cant reduction in the EV owners' charging fee from self-scheduling can be achieved while the aggregator pro t is maximised. Second, the EV aggregator bidding strategy in the electricity market is studied (aggregator's interest). By jointly considering the reserve capacity in the day-ahead market and the uncertainty of reserve deployment requirements in the real-time market, a scenario-based stochastic programming method is used to maximise the expected aggregator pro t. The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. In addition, an owner{aggregator contract is designed to mitigate the economic inconsistency issue between EV owners and the aggregator. The results show that the expected aggregator pro t is guaranteed by maximising reserve deployment payments and mitigating the penalties and thus the uncertainty of the reserve market is well managed. Third, the EV integration in a transmission system is studied (grid's interest) to achieve the coordination between generators and EVs. To tackle the challenge of large-scale EV integration problem, a bi-level scheduling strategy is proposed. The bi-level strategy clearly de nes the responsibility of transmission system operator and the aggregator. An EV information grouping method is designed, which could e ciently tackle the optimisation complexity problem. In addition, a detailed EV battery charging model is built. The results show that the total cost of the systems is minimised and EVs could shave the peak and ll the valley loads. This thesis discusses the EV energy management problem considering three stakeholders' interests, respectively. The proposed strategies in this thesis clearly evaluate and de ne the economic relationships and responsibility among EV owners, aggregator and the grid in managing EV charging and discharging behaviours. Based on three case studies conducted in this thesis, EV energy management could bene t the stakeholders as follows: (1) the EV owner charging fee is minimised while their driving requirements are satis ed; (2) the aggregator pro t is maximised by participation in the electricity market; (3) the cost of the system is minimised by achieving the coordination between EVs and generators.

The electrification of transport systems is essential for improved city air quality and reduced noise, may also contribute to enhanced energy security and decreased greenhouse gas emissions. The key enabler of the large-scale uptake of electric vehicles (EVs) are improved lithium-ion batteries (LIBs), offering higher mass specific energies, volumetric energy densities, potential differences and energy efficiencies. Most LIBs used in automotive applications combine nickel-cobalt-manganese (NCM) oxide cathodes with graphite (Gr) anodes intercalating lithium ions from organic electrolyte solutions of lithium salts. Two widely reported modifications include increasing the nickel content in cathodes and introducing silicon-graphite (SiGr) composite anodes, enabling increased energy storage capacities. Technological developments in EVs and LIBs have triggered a growing interest in using Life Cycle Assessment (LCA) to quantify the environmental burdens of electrified mobility (Ellingsen et al., 2014; Kim et al., 2016). Figure 1 compares the global warming potential (GWP) (kg CO2-eq. (battery kW h)- 1) of battery manufacturing at different locations, for reports that allowed the production footprint to be distinguished. The indication that despite the higher coal intensity in its electricity mix, China’s LIB manufacturing has a lower GWP production footprint than other regions is counter-intuitive and raises the need for more detailed analysis. An important additional aim of this work is to consider whether the high environmental burdens of producing LIBs can be counter-balanced by extended EV use periods and the parameters that affect these. Since nickel-rich cathodes and silicon-based anodes are considered the most promising modifications for next-generation LIBs in the near-term, their combined environmental performance is studied. This paper reports on the development of a detailed unit process-based Life Cycle Inventory model, built to assess the production of current and future NCM batteries in China. The definition of the studied product system and LCI model is followed by the introduction of four different battery production scenarios, which were developed to assess the impacts of producing batteries in China, study the introduction of silicon in anodes and examine the effects of two novel cathode chemistries with increased nickel content (NCM622, NCM811). A detailed presentation of the production phase impacts is provided, based on the ReCiPe 1.08 Midpoint characterisation method. The production phase analysis is complemented by the development of a gate-to-gate model, assessing the environmental impact of using a LIB in a passenger vehicle in China. The results indicate that the GWP of producing a LIB in China is 250 kg CO2-eq (battery kW h)-1, which is 40% higher than previously estimated (Ellingsen et al., 2014) and significantly higher than earlier reported values for China (Hao et al., 2017; Yu et al., 2018). The mismatch with the latter two studies is due to the fundamentally different assumptions made when modelling the production phase. This work provides the means to make sensible comparisons, using the same model and assumptions, and accurately benchmark the performance of different scenarios. It is shown that copper production for anode current collectors makes the most important contribution towards all human toxicity and ecotoxicity categories, with the next most important contribution coming from nickel sulfate production for mixed metal oxide cathodes. Furthermore, the manufacturing of next-generation LIBs is estimated to have a slightly increased impact intensity on a per battery pack basis, with the increased nominal energy capacity effectively reducing the impacts on a per kW h basis. The use of LIBs in China primarily affects the GWP, as a result of the high coal intensity of the local electricity mix. References Amarakoon, S., Smith, J., Segal, B., 2013. Application of life-cycle assessment to nanoscale technology: Lithium-ion batteries for electric vehicles. No. EPA 744-R-12-001. Ellingsen, L.A.W., Majeau-Bettez, G., Singh, B., Srivastava, A.K., Valøen, L.O., Strømman, A.H., 2014. Life Cycle Assessment of a Lithium-Ion Battery Vehicle Pack. J. Ind. Ecol. 18, 113–124. Hao, H., Mu, Z., Jiang, S., Liu, Z., Zhao, F., 2017. GHG Emissions from the production of lithium-ion batteries for electric vehicles in China. Sustain. 9. Kim, H.C., Wallington, T.J., Arsenault, R., Bae, C., Ahn, S., Lee, J., 2016. Cradle-to-Gate Emissions from a Commercial Electric Vehicle Li-Ion Battery: A Comparative Analysis. Environ. Sci. Technol. 50, 7715–7722. Majeau-Bettez, G., Hawkins, T.R., StrØmman, A.H., 2011. Life cycle environmental assessment of lithium-ion and nickel metal hydride batteries for plug-in hybrid and battery electric vehicles. Environ. Sci. Technol. 45, 4548–4554. Yu, A., Wei, Y., Chen, W., Peng, N., Peng, L., 2018. Life cycle environmental impacts and carbon emissions: A case study of electric and gasoline vehicles in China. Transp. Res. Part D Transp. Environ. 65, 409–420. Figure 1

Given the significance of the transportation sector to the economy of a country, major companies and government-linked entities have invested in infrastructure and transportation services. Nonetheless, the sector faces issues relating to traffic congestion, energy consumption, and environmental impacts such as air pollution and carbon emissions. To address and analyze these issues, the current study employed microscopic modeling using the AIMSUN software, which allowed for detailed modeling and simulation. The current study examined the impacts of different operating conditions, namely: internal combustion engine vehicles (ICEVs) and electric vehicles (EVs), on energy consumption, energy savings, cost savings, and emissions traveling on a total of six (6) routes: (i) long-distance highway travel, (ii) short-distance highway travel, (iii) long-distance urban travel, (iv) short-distance urban travel, (v) long-distance suburban travel, and (vi) short-distance suburban travel. The impacts of the traffic management systems, such as traffic lights, roundabouts, and road altitude, were also analyzed in this research. The current study discovered that, on average, EVs consumed 30 percent less energy than ICEVs and a 26 percent energy cost saving for long-distance highway travel. On long-distance urban travel, EVs experienced higher energy and cost savings than ICEVs, with 86 percent and 64 percent, respectively. In addition, EVs had lower carbon dioxide emissions than ICEVs. This study concludes that EVs offer positive impacts on energy cost savings and carbon dioxide emissions reduction for all six (6) simulated routes in Malaysia compared to ICEVs, thereby contributing to the existing literature on EVs in Malaysia.

While battery electric vehicles (EVs) are on the advance, the broad customer basis is still concerned about battery electric range, a phenomenon commonly known as range anxiety. To tackle these concerns, the functionality of in-vehicle navigational systems must adapt to the new propulsion technology with a limited battery capacity. Central aim is to consider charging infrastructure in route planning. Furthermore, detailed powertrain models are required to accurately forecast an EV's energy consumption. On the other hand, such detailed models are hardly applicable to large scale road networks that are usually handled by routing services for vehicle navigation. This study proposes a two-staged approach to compute time optimal routes for EVs. To this end, a reduced road network is obtained from a leading routing service. Subsequently, a detailed consumption model is applied and the resulting multiobjective shortest path problem is solved using an adapted Moore-Bellman-Ford algorithm. Within an experimental study, the consumption forecast is validated against measurement data and query times of the proposed methodology are assessed for generic routing problems. The former shows significant improvement of consumption forecast accuracy compared to state-of-the-art models while the latter indicates potential for application in car manufacturers vehicle backend services.

In times of ever increasing power feed-in from fluctuating renewable power sources like wind and solar power it increases the need for more storage capacity to compensate such fluctuations. Electric vehicles (EVs) are often described as a possible way to provide this storage capacity. To make the storage of EVs available in a reasonable manner an energy management system is needed. In our research projects RegModHarz and Harz.EE-mobility it is analysed how EVs can support higher renewable energy integration for the rural region Harz. This model region is also subject of the presented work. In this paper an agent based simulation model for EVs is presented to show the impact of managed EV charging. The model has three main focuses. At first the model must reproduce reasonable user behaviour like driving characteristics. The second focus is to model the electrical characteristics of the EV. In last focus is the simulation of a decentralized EV energy management.We present a modular and extendable simulation framework with which we are able to model a precise simulation of a great amount of EVs for the region Harz. With a detailed EV simulation model we are able to display that EVs will produce seasonal dependent energy consumption. We show that a decentralized load management of these EVs reduces residual load fluctuations and therefore helps to increase the integration of renewable energy into the grid.

New electrified powertrains are increasingly entering the vehicular fleet and therefore their energy response to traffic management measures that have been designed for conventional vehicles is under consideration. The present paper examines the effect of traffic signals' adjustment on the energy consumption and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions of various types of modern powertrains, such as hybrids and electric vehicles, estimating the effect of signal settings on vehicular energy consumption. This examination follows a modeling approach, where vehicular speed profiles for various signal setting scenarios were evaluated in energy terms, with the use of detailed instantaneous powertrain models of hybrid and electric vehicles. The evaluation reveals the formed trends on the energy performance of modern vehicles when an adjustment of traffic signal settings is applied to traffic. The recognition of these trends is essential as traffic streams will be increasingly penetrated by new electrified powertrains.

One of the main challenges of agriculture 4.0 is the development of autonomous vehicles that manage to carry out complex tasks in agricultural environments with greater efficiency and energy autonomy. Additionally, electric vehicles in agriculture must meet the demands of mechanization and robustness that irregular topography and heavy work involved in agricultural processes. Therefore, carry out preliminary studies that allow obtaining, in a simulated way, the physical parameters, i.e., geometric dimensions, construction materials, weight, engine torques, maximum and minimum current and voltage values of the batteries, etc., necessary for the construction of electric agricultural vehicles efficiently is more and more necessary in engineering projects. This paper, presents the development and implementation of a simulation environment that allows obtaining the physical parameters through different dynamic testing conditions. Thus, starting from the semantic and computational basis, the kinematic and dynamic models used for the construction of the simulation environment of the agricultural vehicle and the methodological development for its construction, are presented. A comprehensive analysis of the results and their implication in the actual construction of an efficient electric vehicle is also presented.

Large-scale development of electric vehicles (EVs) will have a significant impact on the power grid with their great demand for electric power consumption. It is necessary to design the optimal charging scheme for EVs. In this paper, we propose a centralized and a decentralized scheme of optimal decision-making on charging of EVs based on a dynamic pricing model. The objective of both schemes is to minimize the costs of EVs by establishing EV charging optimal schedules that fills the overnight load valley.The centralized scheme,whose objective is to minimize the total charging costs of all EVs, determines the charging schedules of EVs through solving a global optimization problem by the utility.The decentralized scheme is based on congestion game theory,under which the charging schedules are determined locally and directly by EVs to minimize their own costs. Detailed mathematical models and solution procedures are presented for both the centralized and decentralized schemes. Results show that both schemes have good performance on valley-filling. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4098

Electric Vehicles (EVs) have gained large interest in the energy sector as a carrier to support clean transportation and green electricity. The potential to use battery storages of electric vehicles as a sink for excess electricity that may result from large integration of wind power, especially in countries like Denmark, is widely discussed and promoted. However, the wide-spread adoption of EVs requires the provision of intelligent grid and EV charging infrastructure. To analyse and understand the amount of EVs that could be integrated in the local distribution grids, within its existing capabilities, is absolutely essential for the system operators to plan and implement the levels of grid reinforcement and intelligence required. This paper investigates the local grid limitations to accommodate large amount of EVs of sizable power ratings in residential areas. The case study applied in this paper uses a detailed secondary distribution grid model in Denmark. Various EV integration scenarios are analysed in this work to understand the network operational flexibility and ruggedness. The simulation results show that level of EV integration varies with the strength of different feeders in the studied network. Simple grid reinforcement measures like adding new feeders in the existing network improves EV penetration levels.

A centralized approach to solve optimal operation of a practical sized distribution grid with a large number of electric vehicles (EVs) is a computationally challenging task. This work proposes a bi-level hierarchical optimization framework to solve the problem. In the hierarchy, optimal operation of the distribution grid is considered in one level, while the optimal operation of EVs is carried out in another level. The hierarchical optimization framework is based on information exchange among distribution grid control center, EV aggregators, individual EVs, and market operator. The proposed framework consists of detailed mathematical modelling of distribution system components, EVs, and operational constraints. The proposed framework is applied to coordinate charging of hundreds of EVs in the IEEE 34-node three-phase unbalanced distribution system. Case studies demonstrate that the proposed hierarchical optimization framework provides benefits to the distribution grid operations as well as to the individual EV customers.

The adverse climate change has posed an emergency for the world to shift to green energy systems. The largest contributor for air pollution, which is the primary cause for the adverse climate change is the fossil fuel driven conventional vehicles. Therefore, electric vehicles are going to be the future of the automotive industry. Due to the abundancy of lithium mineral and high-power density characteristics, lithium-ion powered electric vehicles are prominent choice. Similar to the vehicles driven by internal combustion engines, the electric vehicles should be able to travel long distances while carrying significantly heavy loads. This requires the battery to operate continuously for long duration under heavy loads, which results in increase of battery temperatures beyond the normal operating range. High battery temperature will degrade its performance and subsequently deteriorate battery life. Therefore, it is important to ensure that the battery temperature lies well within normal range for all possible operating conditions of the electric vehicle. This can be achieved by adopting a suitable Battery Thermal Management System (BTMS). In the present study, computational analysis of a single Lithium-ion (LiCoO­­­2 26650) cell designed based on Newman, Tiedemann, Gu and Kim (NTGK) formulation has been carried out to evaluate the maximum temperature attained by the cell. Furthermore, a BTMS is designed for the same using a novel liquid cooling plate (containing three channels) which is in contact with the cell surface. The maximum temperature attained by the cell with BTMS at different discharge rates of 0.5 C, 1.0 C and 1.5 C is determined. To enhance cooling, two fins are attached on top of the liquid cooling plate. Water is used as coolant and analysis is carried out for 0.01 m3/s flow rate. The maximum temperatures attained by the cell with and without BTMS are found to be is 313.72 K, 307.37 K, 302.31 K and 298.55 K, 298.52 K, 297.41 K at discharge rates of 1.5 C, 1.0 C and 0.5 C. Therefore, with BTMS the maximum cell temperature attained is 39%, 25% and 16% less compared to the bare cell, which indicates that the BTMS adopted in the present work is significantly effective in controlling rise in maximum cell temperature.

A smart contract and IPFS-based framework for secure electric vehicles synchronization at charging station

Investigations of High Efficiency Wireless Power Transfer Systems (WPTS) for Electric Vehicles (EVs)

Electric Vehicle numbers have rapidly increased over the past decade. There is a dire need to establish accurate simulation frameworks to model the Electric Vehicle performance. Multiple simulation model-based approaches have been proposed in literature which consider the impact of various drive cycles on vehicle characteristics. All the available Electric Vehicle simulation models available in literature use lumped battery parameters which do not capture the dynamic behaviour of the vehicle. A detailed battery model considering drive cycle dynamics In Electric Vehicle Digital Twins have not been explored by researchers. This paper aims to provide tangible EV performance characteristics accounting for detailed 2RC battery model. This paper has used a two wheeler drive characteristics driven by a 2 kW Brushless Direct Current Motor powered by a 3.2 kWh battery pack. This paper also compares the variations in battery parameters with the lumped battery model to highlight the advantages of the proposed approach.

Multi-Objective Optimization Technique for the Operation of Grid tied PV Powered EV Charging Station