To tackle the potential grid overloading issue induced by excessive Electric Vehicles (EV) charging demand, a Low Voltage (LV) grid congestion management algorithm with three centralised EV charging management schemes is proposed in this study. The developed algorithm integrates grid information and aims at tackling the foreseen congestion issues by operating on the EV charging processes. This is done through linear programming (LP) or iterative calculations. While the first charging scheme aims at managing the congestion by only affecting the elements with the greatest influence on the congestion, the other two aim at ensuring impartiality towards all users and the overall energy transfer to the EVs, respectively. The simulated results are compared in terms of performance criteria such as grid impact, user satisfaction and fulfilment of charging energy demand. Overall, this study shows that the first scheme brings the best results from a grid perspective. On the other hand, the last scheme leads to competitive results from a grid point of view and the best overall results from a user perspective.

Low-carbon technologies (LCTs) such as Electric vehicles (EVs), heat pumps (HPs), and PV systems increase phase unbalance due to uneven phase distribution. Phase unbalance can lead to overheating, suboptimal capacity utilization, and power losses. This work analyzes the unbalance impact inflicted by the grid integration of PVs, HPs, and EVs under different combinations and penetration levels. The main novelty of this study is the use of different types of real-world distribution grids (rural, suburban, and urban) for the LCT unbalance impact comparison, while simultaneously considering the influence of several unbalance factors; LCT phase connections and grid distributions, the seasonal effect, and the power and consumption levels, the latter of which have been evaluated as mitigation strategies. The results showed that the combined integration of PVs, EVs, and HPs can cause high voltage unbalance, especially in grids with high existing loading. The seasonal effect was the most impactful unbalance factor, intensifying unbalance by the integration of PVs-HPs and PVs-EVs combinations during Winter and Summer, respectively. Furthermore, reductions in the power and consumption levels of the LCTs decreased the unbalance total violation duration in a range between 11% and 25% for all distribution grids. Reducing the LCT consumption levels also decreased the unbalance magnitude, which reached up to 15% for the urban grid under 100% HP and PV penetration. Finally, it was found that consumption duration enhances unbalance, such as the peak power levels, because it increases the simultaneity of technologies operating in different phases.

The study presents Electric Vehicle (EV) Point-of-View (POV) Vehicle to Grid (V2G) optimisation, where the system knows the trips and energy demand of the EV. This allows it to be less conservative in the departure State of Charge (SoC) of the EV battery. The peak and cost reduction of Vehicle POV optimisation are compared with state-of-the-art Location Point-of-View optimisation, this is, the car can do V2G but it has to depart each location with at least 80% SoC as the next trip energy demand is unknown. A Mix Integer Linear Programming (MILP) V2G optimisation approach is presented to reduce peak demand and total system cost of a fleet of EVs travelling between several residential locations and one common workplace. Simulations were conducted with 26 EVs unevenly distributed between five residential locations and commuters to a common workplace. The results indicate little differences in the reduction of peak demand between both POVs of V2G optimisation. However, the system cost can be further reduced with Vehicle Point-of-View optimization, saving an additional 200 euros annually per household by transporting cheaper energy from workplaces to residential locations.

Distributed generation, such as photovoltaics (PVs), and electrification of heating and transportation with heat pumps (HPs) and electric vehicles (EVs) will play a major role in the energy transition. However, these low-carbon technologies (LCTs) do not come without side effects such as voltage violations, power loss increase, component overloading, higher energy consumption, power peaks, and power quality issues, e.g., harmonics and phase unbalance. This work constitutes a review analysis and summary of all the important findings concerning the various grid impact issues that can appear due to the grid integration of these 3 LCTs. The work also encapsulates various research characteristics such as grid topology, seasons, simultaneous operation under various LCT combinations, penetration levels, etc. Moreover, it incorporates a qualitative analysis of the impact level of the most investigated grid issues and quantitative comparisons between the different grid types and LCTs. It has been shown that the combined integration of PVs-EVs and PVs-HPs can result in mitigation effects without extra solutions. Moreover, voltage deviations and unbalance affect more the rural grids while component overloading is more hazardous for suburban grids. Finally, proposed mitigation solutions, such as energy storage, smart charging, etc., are correlated with their respective grid impact issues.

Quasi-resonant boundary-conduction mode (QR-BCM) and triangular current mode (TCM) have found widespread use in the literature and industry due to their good performance at relatively low complexity. However, additional control challenges occur when these modulations are applied to the four-switch buck-boost (FSBB) converter, due to a discontinuity in switching frequency in multimode operation. This article presents the first closed-loop operation of a variable-frequency, multimode, quasi-resonant BCM control scheme including smooth mode transitions. The proposed control utilizes feed-forward mode transition techniques, based on software interrupt handlers integrated into the digital control scheme. In contrast to most soft-switching schemes in the literature, the proposed digital control does not imperatively rely on high-frequency current measurements but uses dc measurements and high-frequency voltage measurements instead. A 10 kW prototype is developed with which the proposed modulation is compared with three other soft-switching modulation schemes. Our results indicate that the losses of FSBB converter can be reduced by up to 60% using the proposed modulation. Especially at partial powers and high voltages, significant efficiency gains can be achieved.

Airports and airlines are examining and committing to the electrification of Ground Support Equipment (GSE). In line with this trend, in this paper, we develop a model to simulate and optimize the GSE operations at airports. The aim is to estimate the required quantity of eGSE, the charging requirements of eGSE, the change in airport electricity requirements, and the scheduling possibilities of eGSE charging for the existing turnaround procedures. This is done by means of a Task Scheduling Problem (TSP), that is optimized using Mixed-Integer Linear Programming (MILP). A case study is performed on KLM's GSE fleet at Amsterdam Airport Schiphol. Based on this, it is concluded that daily operations can be sustained without increasing fleet size for GSE types capable of lasting a full day on a single charge, assuming vehicles can recharge overnight. This is the case at many airports due to nighttime curfews. The operational procedures used by the handler play a key role in achieving this outcome. The results confirm that the model is suitable for strategic decision-making and it is effective at the operational level. The model has the potential to lead to a more efficient use of resources in the operation.

Vehicle-to-Everything (V2X) is a promising solution to support the energy transition, but concerns about battery degradation and capacity loss remain a major barrier for electric vehicle (EV) users. A clear understanding of degradation caused by V2X is essential to increase user confidence and encourage participation in V2X services. Many V2X studies have researched battery degradation, but the results vary widely between articles, making it hard to draw conclusions. Existing review articles mention the different outcomes but do not discuss the contradictions. In this article, a large set of V2X degradation studies is compared using a quantitative analysis. The yearly added degradation due to V2X is extracted from 37 V2X degradation papers, resulting in a set of 97 data points. The dataset is analysed to compare degradation in different situations and highlight contradictions in similar situations. Results show that the average yearly added degradation is 0.87% (95% CI: 0.35–1.4%). When degradation is explicitly considered in V2X service optimisation, the added degradation is limited to just 0.9% per year. Moreover, under specific conditions, V2X can even help reduce overall battery degradation by reducing calendar ageing. Temperature and SoC are especially important in assessing the benefit of V2X on calendar ageing, but these factors are most often overlooked. This review has highlighted common shortcomings in V2X degradation literature that affect the assessment of the impact of degradation. The results can be used to clear up misconceptions about degradation in V2X and to guide future research directions.

The development of lithium-ion batteries has experienced massive progress in recent years. Battery aging models are employed in advanced battery management systems (BMSs) to optimize the use of the battery and prolong its lifetime. However, Li-ion battery cells often experience fluctuations in battery capacity and performance during cycling, which makes capacity prediction more difficult. Moreover, the reason for the capacity regeneration phenomenon occurring after resting periods is not clear yet, as well as the influence of cycling conditions on capacity regeneration. A relationship between this phenomenon to cycling state of charge (SoC) ranges and current rates was investigated in this article on a battery cell with lithium nickel manganese cobalt (NMC) oxide positive electrode. Experimental results show that the capacity increase is a consequence of decreased internal impedance after the resting period. The experiments also showed that a significant power drop and subsequent power regeneration after a resting period occurs only for specific SoC ranges, and applying a resting period after battery cycling can mitigate this power fading process. The semi-empirical model of battery degradation including capacity regeneration is proposed in this article based on physical processes inside of the cell retaining low computational requirements. The acquired results can be utilized in BMSs for more accurate state of health (SoH) estimation and to prolong battery lifetime.

Electric aircraft represent a promising low-emission alternative to fuel-powered aviation. As the energy source, the battery pack must guarantee key performance metrics such as energy density, power density, lifetime, and safety. Among these, energy density is particularly critical as it directly impacts the range and payload capacity. Additionally, the battery thermal management system (BTMS) of the battery pack is essential to maintain safety, efficiency, and lifetime. Hence, to design a battery pack with improved energy density and optimized thermal and aging performance, a complete electro-thermal-aging (ETA) model at both cell and pack levels is developed to predict pack behavior under operational conditions. Simulations demonstrate that the proposed model achieves an accurate thermal prediction accuracy within 0.87°C during an example all-electric aircraft (AEA) mission profile. Optimization based on the proposed model is conducted, focusing on geometric configurations of the battery pack and coolant flow parameters, including channel wall thickness (L_Al), inlet width (W_cl), cell spacing (D_cell), package wall thickness (L_enc), inlet flow temperature (T_cl,in), and flow velocity (U_cl,in). An optimized liquid-cooled battery module using Samsung 18650-35E cells is designed with [L_Al, W_cl, D_cell, L_enc] = [0.4, 1.6, 20, 0.5]mm, T_cl,in =35°C, and U_cl,in = 0.05ms^-1 during cruise and 0.02ms^-1 during takeoff, climb, and descent. This configuration achieves a maximum temperature of 41.76°C and a maximum cell-to-cell temperature difference of 3.11°C, improving thermal uniformity. The lifetime performance also demonstrates a 5.51% improvement in state-of-health (SOH) after 180 cycles. Based on the module-to-pack structure analysis, the battery pack exhibits energy densities of 227.01Wh kg^-1 gravimetrically and 353.67Wh L^-1 volumetrically. This study facilitates the guideline for compact and lightweight liquid-cooled battery pack design with improved thermal and aging performance for AEA applications.

The increasing number of electric vehicles (EVs) means both a challenge and an opportunity for the electric grid. Different charging algorithms have been proposed in the literature to tackle these specific challenges and make use of the potential services that EVs can provide. However, to properly investigate the conflicting objectives, a multi-objective approach is paramount. These algorithms provide a family of solutions instead of just one, so the decision-maker can see the connection and trade-offs between the objectives. This paper proposes a highly customisable multi-objective framework based on an expanded version of the augmented ε-constraint 2 method. Together with a mixed integer linear programming (MILP) formulation, it is used to solve a charging station scheduling problem. An energy management system (EMS) executes the calculated schedules to show the effect on the individual EVs. Numerical simulations based on market and EV data from the Netherlands demonstrate the adaptability and effectiveness of the proposed algorithm.

The growth of suburbs is a challenge for public transport, and new tools are needed to electrify suburban and intercity bus lines sustainably. In-motion-charging (IMC) buses combine the advantages of both trolleybuses and electric buses. This paper analyses a case of using IMC buses on a 17-km-long intercity route service between Arnhem and Wageningen in the Netherlands. The analysis covers different traction battery technologies, sizes, and charging strategies to find the economically optimal solution. The study was carried out using a numerical model of an IMC bus, which was validated and tuned based on year-round experimental recordings obtained from Arnhem trolleybuses. The model outputs were next used to analyse the batteries ageing under specific charging-discharging current profiles. The analysis shows that the most long-term cost-effective solution for the considered case consists of using merged IMC and opportunity charging as well as a 90 kWh LTO battery, whose expected lifetime would be more than 14 years.

Electric vehicles (EVs) with vehicle-integrated photovoltaics (VIPV) and vehicle-to-grid (V2G) technology can help address power grid challenges arising from the energy transition. While VIPV and V2G offer widespread benefits, their impact on EV battery life affects their economic viability. Many existing studies examining the impact of VIPV and V2G on EV battery life do not fully capture the complexity of real-world battery usage, often relying on less detailed battery data. This work models and combines detailed and validated EV battery data with validated battery ageing models to determine the impact of VIPV and V2G on EV battery life. First, a validated EV battery simulation model is used to generate realistic, per-second battery data for an EV operating in The Netherlands and Spain. Following this, VIPV power profiles, V2G day-ahead energy trading power profiles, and V2G automatic frequency restoration reserve power profiles are integrated with the battery data. Subsequently, battery datasets for different scenarios are implemented in both NMC and LFP-based semi-empirical ageing models to quantify calendar and cycling ageing capacity loss. The results show that gradual VIPV charging decreases the required annual grid charging frequency by 23% in The Netherlands and 44% in Spain, leading to lower SoC ranges, which can reduce NMC and LFP calendar ageing capacity loss by 9% in both countries. Additional cycling due to V2G day-ahead energy trading can shorten battery life by up to 12.5 years for NMC and up to 3.9 years for LFP. Moreover, the research indicates that ageing models based on tests with regular power profiles may not accurately estimate cycling ageing in power profiles with increased irregularity caused by VIPV and V2G.

Lithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy storage systems, due to their effect on performance and lifespan, safety, and environmental footprint. Linear degradation models can be inaccurate in capturing the highly nonlinear behavior of LIB degradation caused by multiple simultaneous degradation mechanisms. Hence, this work first analyzes the main different mechanisms, their causes, and their interrelations. Secondly, the various single- and multi-mechanism physics-based (PB) and data-driven (DD) models for LIB degradation and knee-point identification are summarized and compared regarding their prediction performance on degradation and transition from stabilized to saturated aging. While single-mechanism PB models can be effective in the LIB first-life prediction, they can seriously undermine the knee-point and saturated aging. Moreover, the modeling of the different aging mechanisms can significantly increase the complexity of the multi-mechanism PB models. Finally, while DD models for LIB degradation have been developed, a DD model focused on knee-point identification and LIB second-life is still missing from the literature.

The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.

In the presence of a catenary infrastructure, the transition from fossil fuel-based bus fleets to electric-powered ones can be facilitated through conventional trolleybuses or In-Motion-Charging trolleybuses, offering environmentally friendly and cost-effective solutions. However, grid congestion at traction substations (TSs) can limit this transition as the grid operator is incapable or unwilling to provide more capacity. As grid connection contracts are typically tallied and billed in periods of 15 minutes, stationary energy storage devices can prove useful in short-term buffering of the power demand. Consequently, more electrification projects can be rolled out under the same, or minimally extended grid contract. In this aim, this paper looks at validating energy storage as a means of enabling bus fleet electrification. It presents a power management strategy that controls the power exchange between the energy storage system (ESS) within the TS, specifically to manage the 15-minute average power. This strategy also serves as a tool for sizing the ESS with the minimum capacity required for the application. A case study for the city of Bologna, Italy, has been considered to validate the proposed approach. The findings indicate that billing contract power can be reduced by up to 41.7% when a storage device actuates in high-energy-demand substations. Furthermore, different types of Lithium-ion cells, including their second-life versions, are compared to determine the most beneficial options under limited cost and volume constraints. Recommendations are drawn on the exact scenarios where each type of cell is most beneficial.

Power control of flexible loads will play a significant role in energy transition. This work has developed a mixed-integer linear power control (MILP) model that manages electric vehicle (EV) chargers, heat pumps (HPs), and PV rooftops. The power control was tested with and without vehicle-to-grid (V2G) capabilities in different grid types, namely residential, commercial, and mixed grids. Moreover, the effect of different seasons and charger efficiencies was investigated. It was shown that grid characteristics such as EV parking times and building occupations can affect significantly the power control, e.g. the amount of imported and V2G power. Moreover, while V2G power is rarely used due to current V2G round-trip efficiency, future efficiency improvement can lead to a significant increase in V2G use. Finally, the seasonal effect had also a significant impact with Summer being characterized by higher exported and lower imported energy due to the high and prolonged PV power availability.

The large Electric Vehicle (EV) fleet penetrations can provoke several grid impact issues if no EV smart-charging is implemented. However, many EV smart-charging works assume an accurate prediction of input data, such as the EV driving patterns, which are highly uncertain. This paper addresses the impact and potential management of several uncertainties related to EV smart charging, such as photovoltaic (PV) generation, load demand, arrival state-of-charge (SOC), requested energy, and arrival and departure time of the EVs. The application of different levels of uncertainty budgets is proposed to account for the gradual impact of every uncertainty on smart charging performance. Moreover, potential uncertainty management is investigated with the use of robust optimization (RO) in predictive receding-horizon EV smart charging under the worst-case uncertainty level, and the ''price of robustness"is calculated. The results show that the EV driving uncertainties are more hazardous for the provided charging energy. In contrast, PV generation and load demand uncertainties have a significant impact mostly on the charging cost. Moreover, the price of robustness is very low for EV charging under every uncertainty case.

This paper proposes a shared multi-stakeholder PV system for traction substations and nearby residential loads to reduce the need for storage, AC grid exchange, and curtailment. The residential stakeholders offer both the base electrical load and the solar panels installation space needed by the traction stakeholder, who brings the peak load and investments to the former. Two case studies were conducted for one year in the city of Arnhem, The cy=Netherlands, using comprehensive and verified simulation models: A high-traffic and a low-traffic substation. The results showed a positive, synergetic benefit in reducing the PV system's excess energy and size requirement for any type of traction substations connected to any number of households. In one detailed example, the multi-stakeholder system suggested in this paper is shown to reduce curtailment by up to 80% in moments of zero-traction load. Generally, the direct load coverage of a PV system is increased by as much as 7 absolute percentage points to the single-stakeholder system when looking at energy-neutral system sizes. This multi-stakeholders system offers then an increase in the techno-economic feasibility of PV system integration in urban loads.

The transportation industry is a significant source of greenhouse gas emissions, with freight transport emerging as one of the main contributors owing to its extensive mileage and substantial weight. As a result, electrification of road transportation has become a vital step in reducing direct CO₂ emissions. While the adoption of passenger electric vehicles has gained notable traction, the landscape for Heavy-Duty Electric Vehicles (HDEVs) is still in its early stages of development. Accelerating the advancement and adoption of HDEVs hinges on prioritizing the installation of their charging infrastructure. This requires a deep understanding of HDEVs' energy and power requirements while also considering grid limitations. Meeting the high demand for charging necessitates exploring on-site renewable energy generation and stationary batteries as viable solutions. Recognizing this imperative, a multiobjective sizing model has been developed, tailored specifically to address the requirements of HDEV charging stations. These objectives include minimizing investment costs, penalizing undercharged or rejected HDEVs' charging demand, reducing idle charger time, and managing expenditures within a charging station. The key outcomes of the model encompass various critical factors essential for designing and implementing charging infrastructure for HDEVs. These factors include determining the optimal number of PV panels and wind turbines to harness renewable energy, specifying the capacity of the battery energy storage system, and identifying the necessary number and rated power of chargers in alignment with the grid contract limit.

The report examines the role of Standalone Microgrids (SMs) in electrification and emissions reduction, focusing on the comparison of HOMER Pro and iHOGA PRO+ software. It assesses these tools using 22 criteria and three case studies, highlighting differences in optimization results and the importance of selecting software based on specific requirements. Despite minor discrepancies in sizing, the comparison underscores each software’s strengths and weaknesses in designing efficient and cost-effective SMs. The main authors have identified the following 3 Key Takeaways from the report: HOMER PRO and iHOGA PRO+ are publicly available software tools for the microgrid optimization process during the microgrid pre-design phase. Task 18 has defined 22 criteria (quantitative and qualitative) to help software users evaluate which software tool fits better their needs. Based on the quantitative criteria, Task 18 has found that the simulation results from both software tools are equivalent, both when simulating an existing microgrid with real measurement data and when simulating a new microgrid from scratch. However, based on the qualitative criteria, both software tools have some uniqueness in terms of their features.