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Electronics
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Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles
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Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13703

Special Issue Editor

Dr. Erik Schaltz

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark
Interests: application of power electronics, electric machines, fuel cells, batteries, ultracapacitors, etc. in electric and hybrid electric vehicles; battery state-estimation, management (electric and thermal), and modelling (electric, thermal, and lifetime) of battery cells and packs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transportation sector is moving away from the internal combustion engine towards an electrified drive system. The electrification is done either partly, as in hybrid vehicles, or completely, as in electric vehicles.

The steady increased performance and reduced cost of lithium-ion batteries have resulted in electric vehicles, which from a user’s point of view are competitive with traditional combustion-based vehicles. Ultracapacitors offer a high specific power and cycle life, which make them suitable in applications with highly fluctuating energy exchange as in hybrid systems. The emerging lithium-ion capacitor provides an attractive alternative as it contains features from both the lithium-ion battery and ultracapacitor.

The unique performance of these energy storage devices can also be utilized in other types of vehicles like agricultural machines, busses, drones, ferries, and even aircrafts. The purpose of this Special Issue is therefore to explore the application of batteries or ultracapacitors in a wide range of different types of electric or hybrid vehicles.

Assoc. Prof. Erik Schaltz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Emerging battery, ultracapacitor, or lithium-ion capacitor technologies
  • State-of-charge, state-of-health or state-of-power estimation methods
  • Diagnostics and prognostics
  • Monitoring and equalizer circuits
  • Power electronic converters for batteries or ultracapacitors
  • Energy management strategies
  • Grid integration and vehicle-to-grid usage
  • Sizing and optimization methods
  • Demonstration of new concepts
  • Evaluation of field studies

Published Papers (6 papers)

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Research

14 pages, 3139 KiB  
Article
Comparing the Sustainability of Different Powertrains for Urban Use
by Fabio Cignini, Adriano Alessandrini, Fernando Ortenzi, Fabio Orecchini, Adriano Santiangeli and Fabrizio Zuccari
Electronics 2023, 12(4), 941; https://doi.org/10.3390/electronics12040941 - 13 Feb 2023
Viewed by 869
Abstract
The real environment impacts the fuel and energy consumption of any vehicle: technology, physical and social phenomena, traffic, drivers’ behaviour, and so on; many of them are difficult to quantify. The authors’ methodology was used to test the real impact of vehicles in [...] Read more.
The real environment impacts the fuel and energy consumption of any vehicle: technology, physical and social phenomena, traffic, drivers’ behaviour, and so on; many of them are difficult to quantify. The authors’ methodology was used to test the real impact of vehicles in “standard” urban conditions, and many generations of hybrid powertrains are compared. One of the latest performance indexes is the percentage of time the vehicle runs with zero emissions (ZEV). For example, the hybrid vehicle tested ran up to 80% with no emissions and fuel consumption below 3 L per 100 km. A few energy performance indicators were compared between five vehicles: one battery electric vehicle (BEV), two hybrid gasoline–electric vehicles (HEVs), and two traditional vehicles (one diesel and one gasoline). Their potential to use only renewable energy is unrivalled, but today’s vehicles’ performances favour hybrid power trains. This paper summarises the most sustainable powertrain for urban use by comparing experimental data from on-road testing. It also evaluates the benefits of reducing emissions by forecasting the Italian car fleet of 2025 and three use cases of the evolution of car fleets, with a focus on Rome. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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19 pages, 2563 KiB  
Article
Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles
by Daniel Kim, Seokjoon Hong, Shengmin Cui and Inwhee Joe
Electronics 2022, 11(12), 1850; https://doi.org/10.3390/electronics11121850 - 10 Jun 2022
Cited by 3 | Viewed by 1949
Abstract
Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split [...] Read more.
Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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33 pages, 16689 KiB  
Article
Global Sensitivity Analysis of Economic Model Predictive Longitudinal Motion Control of a Battery Electric Vehicle
by Matthias Braband, Matthias Scherer and Holger Voos
Electronics 2022, 11(10), 1574; https://doi.org/10.3390/electronics11101574 - 14 May 2022
Cited by 1 | Viewed by 1447
Abstract
Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential [...] Read more.
Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential of model predictive control (MPC) methods to reduce energy consumption. However, this results in a complex control system with many parameters that affect the energy efficiency. Thus, an important question remains: how do these partially uncertain (system or controller) parameters influence the energy efficiency? In this article, a global variance-based sensitivity analysis method is used to answer this question. Therefore, a detailed powertrain model controlled by a longitudinal nonlinear MPC (NMPC) is developed and parameterized. Afterwards, a qualitative Morris screening is performed on this model, in order to reduce the parameter set. Subsequently, the remaining parameters are quantified using Generalized Sobol Indices, in order to take the time dependence of physical processes into account. This analysis reveals that the variations in vehicle mass, battery temperature, rolling resistance and auxiliary consumers have the greatest influence on the energy consumption. In contrast, the parameters of the NMPC only account for a maximum of 5% of the output variance. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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15 pages, 950 KiB  
Article
An Algorithm to Predict E-Bike Power Consumption Based on Planned Routes
by Erik Burani, Giacomo Cabri and Mauro Leoncini
Electronics 2022, 11(7), 1105; https://doi.org/10.3390/electronics11071105 - 31 Mar 2022
Cited by 9 | Viewed by 3428
Abstract
E-bikes, i.e., bikes equipped with a small electrical engine, are becoming increasingly widespread, thanks to their positive contribution to mobility and sustainability. A key component of an e-bike is the battery that feeds the drive unit: clearly, the higher the capacity of the [...] Read more.
E-bikes, i.e., bikes equipped with a small electrical engine, are becoming increasingly widespread, thanks to their positive contribution to mobility and sustainability. A key component of an e-bike is the battery that feeds the drive unit: clearly, the higher the capacity of the battery, the longer the distances that the biker will cover under engine support. On the negative side, the additional weight incurred by the electric components is likely to ruin the riding experience in case the battery runs out of power. For this reason, an integrated hardware-software system that provides accurate information about the remaining range is essential, especially for older or “not-in-shape” bikers. Many e-bikes systems are already equipped with a small control unit that displays useful information, such as speed, instantaneous power consumption, and estimated range as well. Existing approaches rely on machine learning techniques applied to collected data, or even on the remaining battery capacity and the assistance level required by the drive unit. They do not consider crucial aspects of the planned route, in particular the difference in altitude, the combined weight of bike and biker, and road conditions. In this paper, we propose a mathematical model implemented in an application to compute battery consumption, and hence the presumed remaining range, in a more accurate way. Our application relies on external sources to compute the route and the elevation data of a number of intermediate points. We present the mathematical model on which our application is based, we show the implemented application in shape of an app, and we report the results of the experiments. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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17 pages, 21370 KiB  
Article
A Non-Isolated Hybrid Zeta Converter with a High Voltage Gain and Reduced Size of Components
by Padala Lakshmi Santosh Kumar Reddy, Yeddula Pedda Obulesu, Srinivas Singirikonda, Mosleh Al Harthi, Mohammed S. Alzaidi and Sherif S. M. Ghoneim
Electronics 2022, 11(3), 483; https://doi.org/10.3390/electronics11030483 - 07 Feb 2022
Cited by 5 | Viewed by 2480
Abstract
In this paper a novel non-coupled inductor-based hybrid Zeta converter with a minimal duty cycle is proposed. The converter’s potential benefits include buck and boost operation modes, easy implementation, continuous input current, and high efficiency. The converter provides a higher voltage gain than [...] Read more.
In this paper a novel non-coupled inductor-based hybrid Zeta converter with a minimal duty cycle is proposed. The converter’s potential benefits include buck and boost operation modes, easy implementation, continuous input current, and high efficiency. The converter provides a higher voltage gain than a conventional Zeta converter and is adapted to EV and LED applications due to the continuous input current. The proposed converter operates in three distinct operation modes via two electronic switches, each operated independently with a different duty ratio. This paper also analyzes the converter’s performance based on equivalent circuits, and analytical waveforms in each operating mode and design procedure are shown. The voltage gain and dynamic modelling are computed for both buck and boost operational modes for the hybrid Zeta converter. The efficiency and performance of the converter in both operating modes are validated using MATLAB/Simulink. Hardware in the loop (HIL) testing method on RT-LAB OP-5700 for both operation modes of the converter are performed. The peak efficiency of the proposed converter with an input voltage of 36 V is obtained at 95.2%. The proposed converter offers a wide voltage gain at a small duty cycle with fewer components and high efficiency. Simulations and experiments have been carried out under different conditions and the results proved that the proposed converter is a viable solution. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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30 pages, 10729 KiB  
Article
Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging
by Adel El-Shahat and Erhuvwu Ayisire
Electronics 2021, 10(22), 2842; https://doi.org/10.3390/electronics10222842 - 19 Nov 2021
Cited by 7 | Viewed by 2437
Abstract
Dynamic wireless power systems are an effective way to supply electric vehicles (EVs) with the required power while moving and to overcome the problems of low mileage and extensive charging times. This paper targets modeling and control for future dynamic wireless charging using [...] Read more.
Dynamic wireless power systems are an effective way to supply electric vehicles (EVs) with the required power while moving and to overcome the problems of low mileage and extensive charging times. This paper targets modeling and control for future dynamic wireless charging using magnetic resonance coupling because of the latter’s efficiency. We present a 3D model of transmitter and receiver coils for EV charging with magnetic resonance wireless power developed using ANSYS Maxwell. This model was incorporated into the physical design of the magnetic resonance coupling using ANSYS Simplorer in order to optimize the power. The estimated efficiency was around 92.1%. The transient analysis of the proposed circuit was investigated. A closed-loop three-level cascaded PI controller- was utilized for wireless charging of an EV battery. The controller was designed to eliminate the voltage variation resulting from the variation in the space existing between coils. A single-level PI controller was used to benchmark the proposed system’s performance. Furthermore, solar-powered wireless power transfer with a maximum power point tracker was used to simulate the wireless charging of an electric vehicle. The simulation results indicated that the EV battery could be charged with a regulated power of 12 V and 5 A through wireless power transfer. Fuzzy logic and neuro-fuzzy controllers were employed for more robustness in the performance of the output. The neuro-fuzzy controller showed the best performance in comparison with the other designs. All the proposed systems were checked and validated using the OPAL Real-Time simulator. The stability analysis of the DC–DC converter inside the closed-loop system was investigated. Full article
(This article belongs to the Special Issue Applications of Batteries and Ultracapacitors in Electric or Hybrid Vehicles)
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