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Constant current, constant voltage (CCCV) charging with and without charge plans. The charge plan specifies an upper bound for the EV's charging current.
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The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging p...
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The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware ch...
Citations
... In the last few years, using ML methods has become increasingly interesting in many areas related to EVs. For example, the paper in [40] introduces a data-driven smart charging strategy for fleets of EVs, utilizing ML methods such as XGBoost, linear regression, and NNs. The study focuses on maximizing energy usage and presents a comprehensive approach for training regression models, data preparation, and integration into a smart charging algorithm. ...
Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector.
... Various approaches have been proposed to model EV charging [8]. These include deterministic techniques for EV charging modeling [9], Monte Carlo simulation (MCS) approaches [10], fuzzy methods [11], hybrid Fuzzy-MCS methods [12], linear programming approaches [13], and other techniques [14,15]. Accurate EV charging models can also significantly impact the demand forecasts of electrical grids. ...
... The authors in [23] used a Gaussian Mixture Model (GMM) to model the probability of EV charging, effectively capturing charging profiles by considering factors like battery capacity, consumption, charging infrastructure, day of the week, and settlement structure. The authors in [15] proposed a data-driven regression model to predict EV charging demand from a large historical dataset of charging processes. The authors in [24] presented a forecasting model for estimating EV charging demand using big data technologies, employing clustering analysis to classify traffic patterns, relational analysis to identify influencing factors, and a decision tree to establish criteria for determining EV charging speed and power. ...
This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.
... The key parameters of these EVs are shown in Table 2. According to [32], the exact charging profiles of all different EV models are not needed to ensure accurate modeling of charging loads. Instead, modeling should consider the charging profiles of different EVs with different maximum charging powers [32]. ...
... According to [32], the exact charging profiles of all different EV models are not needed to ensure accurate modeling of charging loads. Instead, modeling should consider the charging profiles of different EVs with different maximum charging powers [32]. The considered EVs represent well the most distinctive charging power groups seen in [29], thus giving reliable basis for the simulations. ...
Controlled electric vehicle (EV) charging at commercial locations has been seen as the key solution to mitigate the negative effects of uncontrolled charging on the power grid. In the scientific literature, EV users’ willingness to participate in charging control has been analyzed, and various control algorithms have been studied. However, there is a gap regarding the best practices to encourage users to participate in charging control and the potential influences of the EV users’ decisions on charging site operator's profits. In this article, the EV users’ perspective on charging control is assessed to form a user‐friendly charging control approach and compensation scheme for commercial charging locations. Then, simulations are carried out using real charging session data to analyze the potential influences of EV users’ decisions on charging site operator's profits. According to the results, the profits of the charging site operator are more heavily dependent on the number of customers than the optimality of the charging control. Hence, charging site operators should carefully consider the attractiveness of the implemented control strategy to maximize profits.
... Stochastic and robust optimisation techniques also play a crucial role in developing smart charging strategies. The authors in [68] propose a data-driven approach to predict arbitrary charging profiles during smart charging events. In [69], a hierarchical mixed-variable optimisation problem is developed to address the EV charging scheduling issue. ...
The adoption of electric vehicles (EVs) continues to break records yearly while governments promote their widespread use to decarbonize the transportation sector, contributing to mitigating climate change. EVs offer numerous benefits, including reduced air pollution and enhanced energy efficiency. However, there are also significant challenges associated with grid stability, fleet operation, and charging infrastructure due to the rapid dissemination of EVs in urban centers. This chapter explores these challenges and provides a discussion on potential solutions to address them. The primary focus is the integration of EVs in modern power grids, exploring key components such as smart charging, vehicle-to-grid technology, EV aggregators, and dynamic pricing schemes. These factors are crucial for the deployment of reliable charging infrastructure and efficient operation of EVs. Additionally, a roadmap is presented toward a net-zero transportation system incorporating renewable energy sources and responsible battery production and disposal. In this context, this chapter provides a comprehensive overview of the most critical aspects of the integration of EVs in the grid, aiming to contribute to a more sustainable transportation future.
... Research on optimizing charging schedules has several goals. Frendo et al. [13] introduced a data-driven approach based on a model that predicts the state of charge of vehicle batteries. This optimization, using an XGBoost algorithm, takes into account the decreasing state of charge to minimize inefficiencies in battery load utilization. ...
... Therefore, sophisticated forecasting of EV demand patterns and considerate charging scheduling are essential for stable and economical power system operations. During the operation of EVs, the collection of various data, including vehicle location, energy usage, and battery energy remaining, through black box systems plays a crucial role in predicting power demand and optimizing charging schedules [6]. For instance, Frendo et al. [7] have successfully developed a deep neural network-based model for predicting energy consumption using GPS and battery management system (BMS) data collected from vehicle operations, achieving an error rate within 3.5%. ...
... The integration of these technologies in the proposed system not only dramatically improves the technical limitations of existing EV data management infrastructures but also creates a reliable information-sharing foundation for optimizing energy consumption and stabilizing power grids. Most importantly, by implementing the principles of data 3A (availability, accountability, auditability) [6], it facilitates collaborative decision-making among various stakeholders within the EV ecosystem and fosters a virtuous cycle of datadriven innovation. This is expected to contribute to enhancing battery grid efficiency, expanding the integration of renewable energy, and ultimately addressing the societal challenge of achieving carbon neutrality. ...
... Especially, establishing institutional foundations to dispel privacy concerns associated with data provision and to encourage active participation is urgent [5]. This involves integrating research outputs into regulatory frameworks through standardization and regulatory sandboxes, protecting personal information using anonymized tokens and differential privacy techniques [6], and design-ing dynamic incentive mechanisms linked to the quantity and quality of data provided [7]. Additionally, policy efforts to improve EV users' perceptions and promote ecosystem participation through technology education and acceptability assessments are required. ...
In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.
... This projection poses three main challenges: the accessibility of charging for EV users, the availability of electric grid capacity to support the charging of electric vehicles, and the use of EV batteries as a source of flexibility for grid services [2]. To avoid a systematic reinforcement of the electricity grid due to the enormous power demand of EVs, several studies have been carried out in recent years on smart charging to manage the charging infrastructure for electric vehicles [1, [3][4][5][6][7][8][9]. With smart charging management, EVs can be charged outside electricity market restrictions, so that local or global electricity markets and grids become more stable. ...
This paper proposes a new approach to the design of smart charging systems. It aims to separate the role of the Smart Charging Service Provider (SCSP) from the role of the Charge Point Operator (CPO) to provide real flexibility and efficiency of mass deployment. As interoperability is required for this purpose, the challenge is to use standard equipment and protocols in the design of the smart charging Energy Management System (EMS). The use of an Open Charge Point Interface (OCPI) is crucial for an interface between the EMS and the Charge Point Operator. The smart charging EMS developed has been implemented and successfully tested with two CPOs, with different use cases: (1) EV charging infrastructure at office buildings, and (2) EV charging infrastructure installed at a public car park facility.
... Urban agglomerations in Europe (e.g., Paris, Stockholm) and in Canada (e.g., Toronto) are encouraging the off-peak hour B2C deliveries as a way to achieve several goals: (a) reduce the uncertainty of not finding the customer home (and associated extra kilometers) (b) driving in less rushed hours to help reduce traffic congestion [94][95][96][97][98]. Electric vehicles are ideally suited for these off-peak hours thanks to their reduced noise footprint. ...
Background: literature on last mile logistic electrification has primarily focused either on the stakeholder interactions defining urban rules and policies for urban freight or on the technical aspects of the logistic EVs. Methods: the article incorporates energy sourcing, vehicles, logistics operation, and digital cloud environment, aiming at economic and functional viability. Using a combination of engineering and business modeling combined with the unique opportunity of the actual insights from Europe’s largest tender in the automotive aftermarket electrification. Results: the Last Mile Logistics (LML) electrification is possible and profitable without jeopardizing the high-tempo deliveries. Critical asset identification for a viable transition to EVs leads to open new lines of research for future logistic dynamics rendered possible by the digital dimensions of the logistic ecosystem. Conclusions: beyond the unquestionable benefits for the environment, the electrification of the LML constitutes an opportunity to enhance revenue and diversify income.
... A typical isolated load problem is a constrained infrastructure problem, where the local charging infrastructure poses a charging limit for the charging network. Frendo, Graf, Gaertner, and Stuckenschmidt (2020) set the infrastructure bottleneck to 30 kW, and their smart charging algorithm makes the infrastructure sufficient for roughly 40 EVs at a workplace. The applied smart charging algorithm belongs to the sorting-based methods where it prioritizes the charging based on an urgency measure. ...
... Though PHEVs use one charging phase, BEVs use three. Charging methods typically take 7 hours and 17 minutes that add 7.01 kWh to the total energy charged (Frendo et al., (2020)). ...
