Ultra-fast charging of electric vehicles: A review of power electronics converter, grid stability and optimal battery consideration in multi-energy systems

Mateen, Suwaiba; Amir, Mohammad; Haque, Ahteshamul; Bakhsh, Farhad Ilahi

doi:10.1016/j.segan.2023.101112

Cited by 28 publications

(11 citation statements)

References 157 publications

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“…Nevertheless, the potential lies in improving algorithms, including sophisticated sensors for data gathering, and using cloud-based computing to boost the precision of models and their capacity to react in real-time. [30]- [32] Research Gaps and Future Directions: The existing literature highlights the need of doing more research to enhance prediction models for achieving optimum energy management in sustainable transportation. Subsequent research should prioritize enhancing the precision of models, verifying their effectiveness in various real-life situations, and formulating adaptive control systems that consistently acquire knowledge and adjust to evolving circumstances.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive machine learning for optimal energy management in sustainable transportation systems

Ivanovich Vatin,

Manasa

2024

MATEC Web Conf.

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This study explores the use of predictive machine learning techniques to enhance energy management in sustainable transportation systems, with a specific emphasis on electric vehicles (EVs). The analysis of EV specifications has shown a wide variety of battery capacities, ranging from 55 kWh to 75 kWh. These capacities have a direct impact on the energy storage capacity and the possible driving range of the vehicles. The range of vehicle weights, ranging from 1400 kg to 1700 kg, emphasized the possible effects on energy consumption rates and overall efficiency. The performance capabilities were shown with maximum speeds ranging from 160 km/h to 200 km/h. The energy consumption rates ranged from 0.18 kWh/km to 0.25 kWh/km, suggesting different levels of efficiency. An analysis of energy management data revealed that the lengths traveled varied from 180 km to 220 km, while the average speeds ranged from 50 km/h to 60 km/h. These variations directly affected the rates at which energy was used. The vehicles exhibited higher efficiency metrics by attaining energy consumption rates of 4.0 km/kWh to 5.6 km/kWh. The analysis of ambient variables indicated temperature fluctuations ranging from 20°C to 30°C, as well as a variety of terrain types that impact driving conditions and energy requirements. Predictive machine learning models demonstrated high accuracies, with Mean Absolute Error (MAE) values ranging from 0.13 to 0.18 kWh/km, Root Mean Squared Error (RMSE) values ranging from 0.18 to 0.22 kWh/km, and R-squared (R^2) scores ranging from 0.80 to 0.88. These results emphasize the need of using predictive machine learning to estimate energy consumption, optimize energy management systems, and address sustainable transportation concerns in order to improve the efficiency and sustainability of electric vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive machine learning for optimal energy management in sustainable transportation systems

Ivanovich Vatin,

Manasa

2024

MATEC Web Conf.

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“…Both of them can completely refuel an EV in 2 to 8 h, which may not be optimal for users requiring a driving range beyond the maximum capacity of their vehicles. Lastly, level 3 stations are considered super-fast stations and have a charging power greater than 50 kW [39]. While these may be the preferred option for users in need of quicker recharges, it is essential to consider that such high-speed charging could potentially lead to a reduction in the lifespan of the battery, posing a trade-off between charging speed and long-term battery health.…”

Section: Battery Logisticsmentioning

confidence: 99%

Battery Management in Electric Vehicle Routing Problems: A Review

Martin,

Escoto,

Guerrero

et al. 2024

Energies

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The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.

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“…To minimize switching losses, soft-switching power electronic switches are being introduced [147,148]. These converters, used in both on-board and off-board chargers, include buck, boost, buck-boost, SEPIC, Cuk, Zeta, and Super-lift Luo converters [24][25][26].…”

Section: Status Of DC Fast Charging Stations and Dc-dc Converters Cla...mentioning

confidence: 99%

“…Bidirectional chargers can feed power back into the grid through the grid-to-vehicle, V2G, mode [10]. Bidirectional charging is increasingly recognized for its potential to balance loads and integrate renewable energy, and reduce power losses in the grid [21][22][23][24]. This has sparked an increased interest in bidirectional chargers among researchers as a growing option for future EV applications.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology

Zentani,

Almaktoof,

Kahn

2024

Applied Sciences

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Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.

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Ultra-fast charging of electric vehicles: A review of power electronics converter, grid stability and optimal battery consideration in multi-energy systems

Cited by 28 publications

References 157 publications

Predictive machine learning for optimal energy management in sustainable transportation systems

Predictive machine learning for optimal energy management in sustainable transportation systems

Battery Management in Electric Vehicle Routing Problems: A Review

A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology

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