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Special Issue "Feature Papers in Electrical and Autonomous Vehicles"

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 3606

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Special Issue Editors

Prof. Dr. Salvador Alepuz

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Guest Editor

Tecnocampus Mataró-Maresme, Universitat Pompeu Fabra, 08302 Mataró, Spain
Interests: multilevel converters; renewable energy systems; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Sergio Busquets-Monge

E-Mail Website
Guest Editor

Electronic Engineering Department, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
Interests: power electronics; multilevel converters; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Section “Electrical and Autonomous Vehicles” is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our Section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish insightful and influential original articles or reviews, discussing key topics in the field. We expect these papers to be widely read and highly influential. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted.

Topics of interest include but are not limited to:

Electric vehicle chargers (on-board, off-board, and wireless power transfer);
Converters for electric drives and power trains;
Battery management systems;
New battery technologies;
Power supplies for auxiliary systems;
Power and energy management strategies;
Self-driving cars/autonomous driving/vehicles;
Artificial intelligence applications for vehicles and traffic;
Electric vehicles and smart cities/smart grids/smart homes;
Vehicle-to-grid (V2G), vehicle-to-home (V2H), vehicle-to-everything (V2X).

Prof. Dr. Salvador Alepuz
Dr. Sergio Busquets-Monge
Guest Editors

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 2000 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

electric vehicle
autonomous vehicle
power and energy management
battery
power train
EV auxiliary systems
artificial intelligence
traffic
V2G, V2H, V2X

Published Papers (6 papers)

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Research

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Open AccessArticle

State Parameter Estimation of Intelligent Vehicles Based on an Adaptive Unscented Kalman Filter

Yu Wang

Yushan Li

and

Ziliang Zhao

Electronics 2023, 12(6), 1500; https://doi.org/10.3390/electronics12061500 - 22 Mar 2023

Viewed by 251

Abstract

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Open AccessArticle

Thermal Analysis of Water-Cooling Permanent Magnet Synchronous Machine for Port Traction Electric Vehicle

and

Electronics 2023, 12(3), 734; https://doi.org/10.3390/electronics12030734 - 01 Feb 2023

Viewed by 451

Abstract

To further increase the torque/power density of a permanent magnet synchronous machine (PMSM) employed for a port traction electric vehicle, improving the thermal dissipation capacity of the cooling system used in the PMSM has become more and more important. This paper focuses on the thermal analysis of a water-cooling 200 kW PMSM for a port traction electric vehicle. First, the size parameters of the machine and the thermal property parameters of the materials used for each component are given. Based on the heat transfer theory, a fast evaluation method for a transient temperature rise in the water-cooling machine under multiple operating conditions is proposed. A lumped parameter thermal network (LPTN) model is established, and the temperature distributions of the machine at different operating conditions are analyzed. Second, under the same conditions, based on computational fluid dynamics (CFD), a three-dimensional (3D) CFD model is constructed. The influence of different cooling structures on temperature distribution is studied. The validity of the proposed fast evaluation method for a transient temperature rise in water-cooling machines under multiple operating conditions is verified. Finally, the results of the CFD and LPTN calculation are verified by experiments; the maximum temperature deviation of the rated speed/rated power operating condition is 8.5%. This paper provides a reference for the design and analysis of port traction electric vehicle machines. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Open AccessArticle

Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification

and

Electronics 2023, 12(3), 511; https://doi.org/10.3390/electronics12030511 - 18 Jan 2023

Viewed by 541

Abstract

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Open AccessArticle

Electric Vehicle Powertrains with Modular Battery Banks Tied to Multilevel NPC Inverters

Sergio Busquets-Monge

Salvador Alepuz

Gabriel García-Rojas

and

Josep Bordonau

Electronics 2023, 12(2), 266; https://doi.org/10.3390/electronics12020266 - 04 Jan 2023

Viewed by 776

Abstract

Nowadays, the internal combustion engine in vehicles is being replaced by electric motors, giving way to the electric vehicle, which results in reduced environmental impact, higher efficiency and lower emission of greenhouse gases. The powertrain of an electric vehicle is its most prominent subsystem, with the batteries and traction inverter being key components. Thus, due to their relevance, advances in the design of both components are of paramount importance. In this paper, the potential benefits achieved through a powertrain design approach based on combining a modular battery bank with multilevel NPC traction inverter topologies were analyzed, in comparison to a conventional two-level powertrain design. Several aspects were analyzed: modularity, complexity, battery-pack state-of-charge balancing, inverter loss, motor ac voltage harmonic distortion, motor common-mode voltage and reliability. Particularly, from the comparison study developed under the selected design scenario, the proposed design approach, based on modular battery packs and multilevel technology, shows a potential reduction of up to 55% in inverter losses, up to 65% in motor ac-voltage total harmonic distortion, and up to 75% in rms common-mode voltage. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Open AccessArticle

Research on a Random Route-Planning Method Based on the Fusion of the A* Algorithm and Dynamic Window Method

Yicheng Sun

Xianliang Zhao

and

Yazhou Yu

Electronics 2022, 11(17), 2683; https://doi.org/10.3390/electronics11172683 - 26 Aug 2022

Cited by 1 | Viewed by 767

Abstract

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths and solves barrier avoidance issues. Combining the A* algorithm with the dynamic window method, a robo-assisted random barrier avoidance method is used to resolve the issues caused by collisions and path failures. Improving the A* algorithm requires analyzing and optimizing its evaluation function to increase search efficiency. The redundant point removal strategy is then presented. The dynamic window method is utilized for local planning between each pair of adjacent nodes. This method guarantees that random obstacles are avoided in real-time based on the globally optimal path. The experiment demonstrates that the enhanced A* algorithm reduces the average path length and computation time when compared to the traditional A* algorithm. After fusing the dynamic window method, the local path is corrected using the global path, and the resolution for random barrier avoidance is visualized. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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Review

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Open AccessReview

A Synthesis of Algorithms Determining a Safe Trajectory in a Group of Autonomous Vehicles Using a Sequential Game and Neural Network

Józef Lisowski

Electronics 2023, 12(5), 1236; https://doi.org/10.3390/electronics12051236 - 04 Mar 2023

Viewed by 342

Abstract

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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