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This study attempts to clarify expectations for electric vehicle (EV) users and the charging providers to provide information relevant for the development of the charging network. The study is based on a new and large-scale data collection among electric and conventional car users in collaboration with IDA, FDM, and FDEL as well as expert interviews with key players in the automotive and charging industry. The user survey gives relevant information on the satisfaction and expectations of both electric and conventional car users when it comes to electric cars and the charging infrastructure. We compare the results with data collected on the same indicators in a previous DTU project five years ago in order to analyse how these have developed. We find that the satisfaction with the EV charging infrastructure is not very good but has increased for all types of chargers, except for fast charging. Furthermore, our results suggests that the importance level of some EV attributes (e.g., price, range, environmental performance) have remained very high in the last years, both for EV users and non-users Finally, we compare this development with results from a series of expert interviews with key players in the automotive and charging industry.

The battery energy storage system is an essential component in the modern energy system with the development of renewable energy, transportation electrification, and carbon-neutral goals. Battery degradation has been the most challenging issue of energy storage. This work presents a data-driven battery degradation model powered by long short-term memory (LSTM) recurrent neural network (RNN). Utilizing the battery dataset with more than 100 batteries exposed to different operations, the proposed model gives a precise prediction of full-discharge capacity and internal resistance (IR) with the root-mean-square error (RMSE) of 0.008 Ah and 0.00017 Ohm in 100 cycles, respectively. Instead of a single capacity or state of health (SOH) value projection, our model predicts the full-discharge capacity-voltage trajectory of the following cycles, addresses the capacity and energy content in different voltage ranges, and improves the accuracy and applicability of the SOH prognosis in industrial applications.

This deliverable (D1.1 External drivers) documents the work carried out in work package 1, Task 1.1 “Drivers: policies, framework conditions and key stakeholders in EV integration”. The main external conditions, trends and motivations driving the electrification of transportation which are relevant to the FLOW project are going to be reviewed. In addition, the key stakeholders, with a special attention towards the EV users, are listed. The European "Clean energy for all Europeans" and "Fit for 55" packages are identified as some of the most significant drivers for electrifying the transportation sector. The emphasis is put on achievingambitious CO2 emission reductions – including an “ad-interim” 55% target reduction on the emissions of newly sold vehicles by 2030 compared to 2021, and a 100% reduction by 2035. Moreover, the "Fit for 55" package specifically refers to the charging infrastructure, as its proactive development is seen as key in order to achieve the EV diffusion targets: 30 million zero-emissions vehicles by 2030. A number of key EU directives, regulations and initiatives have been proposed in order to achive the transportation sector electrification targets. D1.1 lists a number of these and states their relevance to the FLOW project. Among the most significant for smart charging, and ultimately V2G, are the Energy Performance of the Buildings Directive (EPBD) and Alternative Fuels Infrastructure Directive (AFID), which will help drive the deployment of a smart and sufficient charging infrastructure. Furthermore, the Common Rules for the Internal Market of Electricity Directive will help support the smart integration between transportation and the power system. The countries represented in FLOW are taking steps and initiatives to support both EV diffusion, strengthening the charging infrastructure and facilitating market and grid integration. This is done in accordance with EU directives and regulations – but also by taking s

Task T1.3 provides a list of the challenges and potentials that the rest of FLOW addresses and explores, serving as an important point of synchronization between partners in the project. To establish the known barriers for full support of EV integration, it summarises the main barriers and links to the WPs which address them in FLOW, namely User requirements and acceptance (WP2), Cyber security, standardization, and Interoperability (WP3), Scalability of local solutions (WP4), Business models, grid codes and regulations for system integration (WP5). T1.3 also creates a shortlist service catalogue to provide an overview of applicable services for different market conditions and stakeholders. This provides a starting point to WP5.

Based on a state-of-the-art review, T1.2 establishes the internal assumptions, scenarios and definitions on which FLOW bases its work. This is an important step in giving FLOW partners a common and synchronized framework. T1.2 starts by providing VGI definitions and terminology used in FLOW and promoted as commonly accepted in all the relevant fields. High-level EVs mass deployment scenarios are listed according to literature review/roadmaps - and serves as an input to WP5. T1.2 also focuses on charging technologies and forecasts to establish the state-of-the-art of charging technologies from partner experiences which includes charging power, efficiency, technical capabilities, battery loss (DTU PowerLab, IREC SmartEnergy lab, BMW), etc. The task touches upon disruptive charging technologies including ultrafast charging, inductive public charging which may change charging patterns significantly. Finally, T1.2 presents scenarios definition for EV charging demand (per location type) in terms of energy demand and flexibility quantification.

This paper proposes an autonomous distributed control design for coordinating the charging process of parking lots for electric vehicles (EVs). The focus of this paper is to investigate the performance of the modeled architecture. The model simulates for 24 hours an office parking lot scenario with real input data from 16 EVs. The primary objective is to quantify the fulfillment of the demand and the guaranteed amount of energy for each user. The secondary objective is to analyze the effects of restricting the power capacity of the parking lot from 88 kW to 43 kW. In the constrained charging scenario, the system guarantees a minimum energy of 12.2 kWh (roughly 61 km) to each car connected for at least 5 hours and 54 minutes. In the unconstrained charging scenario 12 EVs reach maximum state-of-charge (SOC), while 11 EVs reach it in the constrained one. Demand fulfillment is only marginally different between the two scenarios because the final SOC values of the EVs are nearly the same. On the other hand, constraining connection capacity reduces significantly the idle state of six chargers.

This study models the coincidence factor (CF) of EV charging given driving and plug-in behaviors by combining data sources from travel surveys and recorded EV charging data. From these we generate travel and plug-in behaviors by using a Monte Carlo approach to derive coincidence factors. By varying EV battery size, rated charging power and plug-in behavior, their influence on the coincidence factor is examined. The key results show that the coincidence factor decreases to less than 25% when considering more than 50 EVs with a charging level of 11 kW, with the coincidence factor strongly depending on the number of EVs considered. By contrast, driving behavior and battery size have a minor influence on the coincidence factor. Further, when mixing the parameters, such as EV battery size and rated charging power, then especially the active power drawn by the feeder does not change linearly. Ultimately, the study aims to add to the state-of-the-art by solely and systematically focusing on the CF and its sensitivity to a number of key factors. For planning and design, distribution system operators may use this study as a part of their planning for integration of electric vehicles in the electrical grid.

Battery degradation is the main uncertainty that hedges the development of battery projects. In this work, we build a data-driven battery degradation model assessing the impact of the complex state of charge (SOC) operation condition. Both cycle life and calendar life are incorporated, based on the available lab testing data of battery cells. Various degradation modeling functions are compared to acquire the best fitting results under different depths of discharge (DOD) ranges calculated by rainflow counting algorithm. The statistical relation between shallow cycles and calendar time is used to address the calendar degradation in the cycling scope. A battery frequency regulation service case study is carried out based on a one-year frequency record in the Nordic synchronous area. Our work bridges the battery cell testing datasets to the battery degradation modeling in grid applications and proposes a new perspective to address the calendar life by the analysis of the shallow cycles for the state of health (SOH) estimation, which improves applicability and accuracy.

The uptake of electric vehicles (EVs) may pose a challenge to power distribution networks (PDNs). While smart charging can be deployed to relieve stress on the grid, user-centric smart charging strategies could also exacerbate peak power demand due to synchronization when optimizing charging with regard to different objectives, such as charging costs. In this paper, we assess the charging demand emerging from a large fleet of EVs, with models for the decision to charge and distribution of the steady-state state-of-charge (SoC). These are applied to the municipality of Frederiksberg, Denmark, using data from the Danish national travel survey. Home and workplace charging are mapped to the urban 10 kV medium voltage PDN of Frederiksberg considering different charging behaviors and degrees of synchronization. Results indicate that the likelihood of severe congestion in the power distribution network is low and that it can be attributed to rare scenarios in which high synchronization is observed, particularly when maintaining the normal steady-state demand. Despite the low likelihood, preventive measures should be devised to mitigate such scenarios, especially if additional high-power consumers are connected.

De-coupling transport sector from the use of petroleum is giving way to the rise of electric mobility. As compromising the user’s comfort is not an option managing the power system becomes a tall challenge, especially during peak hours. Thus, having a smart connection to the power system, such as an electric vehicle (EV) smart charger, is considered part of the solution. This paper focuses on assessing the capabilities of smart chargers in the context of helping the electrical network without compromising the user’s comfort. By using a Tesla Model S P85, Renault Zoe, and Nissan LEAF, the paper first evaluates differently controlled (centralized and distributed) smart chargers against the IEC 61851 standard. Second, it tests smart features such as peak-shaving, valley-filling, and phase balancing. Being representatives of the state-of-the-art, both chargers exceed standard requirements and offer new grid service possibilities. However, the bottleneck for providing faster grid services remains the EV on-board charger. The results from this article can help to better simulate the dynamic charging behaviors of EVs.

Battery energy storage system (BESS) is key for future renewable energy systems, as it can provide various grid support functionalities, facilitate the participation of renewable energy sources in electricity markets, and increase grid stability. However, battery degradation is a major factor hindering the BESS implementation for grid applications. Battery state of health (SOH) is a key performance indicator of the BESS, and data-driven models powered by machine learning techniques are among the most promising solutions for the BESS degradation estimation. In this paper, a novel taxonomy of BESS services is proposed based on battery usage. Besides, the data-driven techniques for battery SOH modeling and data-driven SOH estimation applications for BESS providing grid services are reviewed and discussed. Further, a comprehensive discussion is presented regarding the challenges in the area of data-driven SOH modeling methods for the BESS providing grid services in practical applications.

The mass penetration of electric vehicles (EVs) could develop grid stability problems due to the increase of peak loads created by coincident charging factors. Smart charging is the control of the EV charging loads and has long been identified as a potential solution. Smart charging could also contribute to grid stability by mitigating the intermittent nature of renewable energy generation. This paper describes the current status of EV flexibility services at the distribution level. The analysis of the smart charging status is done considering the technological, economic and regulatory frameworks, and presenting what the different barriers of each of these aspects are. Additionally, the paper introduces the ACDC project (Autonomously Controlled Distributed Charger), which aims at developing an EV clustering method based on distributed smart charging control logic for flexibility services. For divulgation purposes, the scheduled test case scenario of the parking lot at the Technical University of Denmark is described. The paper concludes on some of the most relevant actions to overcome the most imminent barriers and to push further the rollout of EV charging infrastructure towards the target EV penetration planned by policymakers.

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world’s first commercial operation, have been used for V2G services for five years. The EVs have, together with ±10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh.

As part of a sustainable power system, a synergy between electric mobility and renewable energy sources (RESs) can play a crucial role on mitigating the nature of RESs and defer costly grid upgrades via smart-charging. This paper presents a distributed autonomous control architecture for electric vehicle (EV) chargers and a clustering method for charging coordination. The architecture framework is detailed depending on the number of chargers and specific location properties. Moreover, the framework unveils the communication, measurement and power flow. The aforementioned approach aims at simplifying the overall charging experience for the EV owners while coupling it with a healthy grid behavior. The proposed control architecture is simulated on a prosumer case with two EVs. The performance of the controller is considerably affected by observability capabilities of current smart-meters. Faster measurement cycles of smartmeters can reduce the overshoot time span but not prevent it.

This paper demonstrates a control strategy for providing primary frequency regulation from electric vehicles using unidirectional charging. The control architecture developed is tested and deployed in multiple locations using both commercial and test chargers relying on IEC 61851 communication with commercially available electric vehicles. The tests are conducted in a university campus area using commercial chargers and at a test-site at the Danish island of Bornholm. The results from Lyngby shows that the control architecture is able to control the charging of multiple unknown electric vehicles simultaneously in order to provide aggregated primary frequency regulation according to the tender requirements of the Danish Transmission System Operator, Energinet, for the Nordic region DK-2. The results from Bornholm shows a faster response of the control architecture, as the communication flow is optimized and the overall response time is lowered.

This paper presents a quantitative investigation of the inconvenience of electric vehicle (EV) charging relative to internal combustion engine vehicle (ICEV) fuelling in terms of the time penalty likely to be experienced by drivers. A heuristic approach to deriving idealised charging schedules from over 39,000 week-long travel diaries from the UK National Travel Survey is presented in order to quantify the expected convenience parity — the point at which EV charging and ICEV fuelling are of comparable convenience — for combinations of battery capacity, charger power and access to charging at different locations (home, workplace and public destinations). It was found that although the majority — up to 95% — of individuals who can charge at home are expected to be able to reach convenience parity with battery sizes currently available in EV models at the ‘affordable’ end of the market, this is significantly less likely for those who rely on workplace or public charging — and particularly for those who must rely solely on en route charging. These individuals are expected to suffer considerable inconvenience associated with EV charging relative to ICEV fuelling, and although greater battery capacities and charger power ratings are expected to lessen this inconvenience, there remains a significant gap in the convenience of EV ownership between those who can charge while parked at home and those who cannot. Further analysis is carried out to long journeys that cannot be made on a single charge; ‘range anxiety’ being a major obstacle to widespread EV adoption. It was found that if drivers are compliant with the UK Highway Code in taking regular breaks on long journeys, fewer than 0.01% of trips are expected to be delayed by charging when using battery capacities of 40–60 kWh.