Battery Management and Ultrafast Charging Systems for Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7888

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Guest Editor
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80138 Naples, Italy
Interests: electrical machines; electrical drive and power electronics in the field of electrification transportation; distributed generation and energy storage systems

Special Issue Information

Dear Colleagues,

The growing interest in the commercialization of electric vehicles (EV) by the main “car-makers”, together with the technical development of battery storage system and the incentive policies by states for the use of electric mobility, is causing a change in the perception of the use and convenience of electric vehicles by drivers. It implies an expected increase in the penetration EV that has never been seen in the last twenty years. However, the actual extent of EVs is still limited. This is, on the one hand, due to a lack of charging infrastructures capable of reducing the EV charge time below five minutes when frequent long-range travel is involved, and, on the other hand, , of battery storage devices with high C-rates/energy density, ensuring a high-expected lifetime at a system level. Actually, for the most part, battery manufacturers declare a high performance in terms of C-rates (up to 10 C) and life cycles (up to 10,000) at a cell level.

Innovative solutions need to be evaluated and developed to allow EV drivers to have a similar or even better mobility experience than with conventional fossil fuel vehicles in terms of the availability, convenience, performance, and cost of the necessary charging infrastructures.

The authors are invited to address scientific papers on following topics:

  • Design criteria and management of ultrafast charging systems taking into account all technical possibilities for optimization, both on the vehicle (like temperature preconditioning), and for energy demand rationalization (e.g., local renewable power support for solar panels; battery storage for peak shaving and other grid services; demand control by interconnected route management systems for incoming vehicles, while taking into account the electricity grid availability; and voltage and frequency control constraints in real-time).
  • Scalable charging infrastructure for the ramp-up of expected electric mobility needs in terms of power levels and the number of charging posts at one site, adequately managing the impact on the grid.
  • Integration of energy storage systems into the current charging infrastructure.
  • Battery management and prediction lifetimes at system level.
  • Assessment of aggregate daily power demand curve based on daily/hourly distribution for long-range travel.
  • Attractive and convenient charging infrastructure access with connected vehicle systems, avoiding waiting times (through, for instance, charging facility reservation and scheduling and integration with the route planning of multiple vehicles). User preferences like the use of renewable energy and the avoidance of the frequent handling of heavy cables have to be considered. Automated conductive or wireless solutions are expected with highly reliable and interoperable devices. Optionally, a further extension of the developed stationary wireless charging technology towards urban and peri-urban "electric road" applications, with the aim of creating an installed base of wireless-ready vehicles, to provide the critical mass needed for the deployment of electrified roads at a later stage.

Prof. Dr. Diego Lannuzzi
Guest Editor

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Keywords

  • ultrafast charging
  • battery
  • management
  • control
  • design

Published Papers (2 papers)

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Research

16 pages, 5300 KiB  
Article
Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations
by Jon Anzola, Iosu Aizpuru and Asier Arruti
Electronics 2021, 10(3), 260; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10030260 - 22 Jan 2021
Cited by 15 | Viewed by 2826
Abstract
This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are [...] Read more.
This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are introduced and proposed as attractive strategies to improve the performance of this type of applications. Furthermore, through a series of simulations, it is observed that partial power processing based converters obtain reduced processed power ratio and efficiency results compared to conventional full power converters. So, with the aim of verifying the conclusions obtained through the simulations, two downscaled prototypes are assembled and tested. Finally, it is concluded that, in case galvanic isolation is not required for the charging unit converter, partial power converters are smaller and more efficient alternatives than conventional full power converters. Full article
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24 pages, 11810 KiB  
Article
State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach
by M. S. Hossain Lipu, M. A. Hannan, Aini Hussain, Afida Ayob, Mohamad H. M. Saad and Kashem M. Muttaqi
Electronics 2020, 9(9), 1546; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091546 - 22 Sep 2020
Cited by 50 | Viewed by 4561
Abstract
The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state [...] Read more.
The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences. Full article
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