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Deep Learning Applications for Electric Vehicles
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Deep Learning Applications for Electric Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 9358

Special Issue Editor

Dr. Ahmed F. Ebrahim

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: power electronics control; renewable energy integration; smart grid; energy management; grid-connected converters; electrical drive applications

Special Issue Information

Dear Colleagues,

As consumers look for greener transportation options, interest in electric vehicles has increased around the world. Deep learning has proven to be an effective approach for enhancing the functionality and effectiveness of electric cars. The latest knowledge in electric vehicle battery management systems, energy optimization, and autonomous driving enabled by deep learning is the focus of this Special Issue.

This Special Issue will feature a wide range of articles discussing the use of deep learning in battery management systems. In these sections, we will investigate the possibility of using deep neural networks to monitor the health and life of EV batteries. Following this, we will also explore deep learning's potential for energy optimization, with the ultimate aim of increasing EV range and performance.

Deep learning's use in autonomous driving for electric vehicles is also covered here. Recent advances in training deep neural networks to recognize and respond to a wide variety of road conditions and obstacles will be presented in these articles, paving the way towards fully autonomous driving.

Overall, this Special Issue is an excellent forum for presenting novel applications of deep learning within the subject of electric vehicles. We hope to shed light on how deep learning has the potential to significantly improve the longevity and efficiency of the electric car sector.

Dr. Ahmed F. Ebrahim
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • deep learning
  • electric vehicles
  • battery management systems
  • energy optimization
  • autonomous driving
  • neural networks
  • machine learning
  • predictive control
  • sustainable transportation
  • EV industry

Published Papers (7 papers)

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Research

21 pages, 6392 KiB  
Article
End-to-End Differentiable Physics Temperature Estimation for Permanent Magnet Synchronous Motor
by Pengyuan Wang, Xinjian Wang and Yunpeng Wang
World Electr. Veh. J. 2024, 15(4), 174; https://doi.org/10.3390/wevj15040174 - 21 Apr 2024
Viewed by 343
Abstract
Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we [...] Read more.
Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we propose a framework for estimating the temperature of a permanent magnet synchronous motor by combining neural networks with the differentiable physical thermal model, as well as utilizing the simulation results. In detail, we first implement a differentiable thermal model based on a lumped parameter thermal network within an automatic differentiation framework. Subsequently, we add a neural network to predict thermal resistances, capacitances, and losses in real time and utilize the thermal parameters’ optimized empirical values as the initial output values of the network to improve the accuracy and robustness of the final temperature estimation. We validate the conceivable advantages of the proposed method through extensive experiments based on both synthetic data and real-world data and then provide some further potential applications. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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15 pages, 6961 KiB  
Article
Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision
by Yixiao Zhang, Yuanming Gong and Xiaolong Chen
World Electr. Veh. J. 2024, 15(2), 62; https://doi.org/10.3390/wevj15020062 - 11 Feb 2024
Viewed by 1311
Abstract
Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these [...] Read more.
Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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20 pages, 2930 KiB  
Article
A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization
by Mohamed H. Al-Meer
World Electr. Veh. J. 2024, 15(2), 38; https://doi.org/10.3390/wevj15020038 - 24 Jan 2024
Viewed by 1367
Abstract
The precise determination of the state of health (SOH) of lithium-ion batteries is critical in the domain of battery management systems. The proposed model in this research paper emulates any deep learning or machine learning model by utilizing a Look Up Table (LUT) [...] Read more.
The precise determination of the state of health (SOH) of lithium-ion batteries is critical in the domain of battery management systems. The proposed model in this research paper emulates any deep learning or machine learning model by utilizing a Look Up Table (LUT) memory to store all activation inputs and their corresponding outputs. The operation that follows the completion of training is referred to as the LUT memory preparation procedure. This method’s lookup process supplants the inference process entirely and simply. This is achieved by discretizing the input data and features before binarizing them. The term for the aforementioned operation is the LUT inference method. This procedure was evaluated in this study using two distinct neural network architectures: a bidirectional long short-term memory (LSTM) architecture and a standard fully connected neural network (FCNN). It is anticipated that considerably greater efficiency and velocity will be achieved during the inference procedure when the pre-trained deep neural network architecture is inferred directly. The principal aim of this research is to construct a lookup table that effectively establishes correlations between the SOH of lithium-ion batteries and ensures a degree of imprecision that is tolerable. According to the results obtained from the NASA PCoE lithium-ion battery dataset, the proposed methodology exhibits a performance that is largely comparable to that of the initial machine learning models. Utilizing the error assessment metrics RMSE, MAE, and (MAPE), the accuracy of the SOH prediction has been quantitatively evaluated. The indicators mentioned above demonstrate a significant degree of accuracy when predicting SOH. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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15 pages, 4325 KiB  
Article
Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus
by Xinxin Zhao, Ming Zhang and Guangyu Xue
World Electr. Veh. J. 2023, 14(12), 329; https://doi.org/10.3390/wevj14120329 - 29 Nov 2023
Viewed by 1313
Abstract
The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature [...] Read more.
The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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26 pages, 1862 KiB  
Article
Purchasing Intentions Analysis of Hybrid Cars Using Random Forest Classifier and Deep Learning
by Ardvin Kester S. Ong, Lara Nicole Z. Cordova, Franscine Althea B. Longanilla, Neallo L. Caprecho, Rocksel Andry V. Javier, Riañina D. Borres and Josephine D. German
World Electr. Veh. J. 2023, 14(8), 227; https://doi.org/10.3390/wevj14080227 - 18 Aug 2023
Cited by 3 | Viewed by 1964
Abstract
In developed or first-world countries, hybrid cars are widely utilized and essential in technological development and reducing carbon emissions. Despite that, developing or third-world countries such as the Philippines have not yet fully adopted hybrid cars as a means of transportation. Hence, the [...] Read more.
In developed or first-world countries, hybrid cars are widely utilized and essential in technological development and reducing carbon emissions. Despite that, developing or third-world countries such as the Philippines have not yet fully adopted hybrid cars as a means of transportation. Hence, the Sustainability Theory of Planned Behavior (STPB) was developed and integrated with the UTAUT2 framework to predict the factors affecting the purchasing intentions of Filipino drivers toward hybrid cars. The study gathered 1048 valid responses using convenience and snowball sampling to holistically measure user acceptance through twelve latent variables. Machine Learning Algorithm (MLA) tools such as the Decision Tree (DT), Random Forest Classifier (RFC), and Deep Learning Neural Network (DLNN) were utilized to anticipate consumer behavior. The final results from RFC showed an accuracy of 94% and DLNN with an accuracy of 96.60%, which were able to prove the prediction of significant latent factors. Perceived Environmental Concerns (PENCs), Attitude (AT), Perceived Behavioral Control (PBC), and Performance Expectancy (PE) were observed to be the highest factors. This study is one of the first extensive studies utilizing the MLA approach to predict Filipino drivers’ tendency to acquire hybrid vehicles. The study’s results can be adapted by automakers or car companies for devising initiatives, tactics, and advertisements to promote the viability and utility of hybrid vehicles in the Philippines. Since all the factors were proven significant, future investigations can assess not only the behavioral component but also the sustainability aspect of an individual using the STPB framework. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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21 pages, 6494 KiB  
Article
Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning
by Shuai Liu, Fan Ren, Ping Li, Zhijie Li, Hao Lv and Yonggang Liu
World Electr. Veh. J. 2023, 14(8), 208; https://doi.org/10.3390/wevj14080208 - 04 Aug 2023
Viewed by 1055
Abstract
Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios for autonomous driving through deep [...] Read more.
Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios for autonomous driving through deep unsupervised learning. Firstly, US DAS2 NDD are leveraged, and the selection of data variables representing the vehicle state and surrounding environment is conducted to formulate the segmentation criterion. The isolation forest (IF) algorithm is then employed to segment the data, yielding two distinct types of datasets: typical scenarios and extreme scenarios. Secondly, a one-dimensional residual convolutional autoencoder (1D-RCAE) is developed to extract scenario features from the two datasets. Compared to four other autoencoders, the 1D-RCAE can effectively extract crucial information from high-dimensional data with optimal feature extraction capability. Next, considering the varying importance of different features, an information entropy (IE)-optimized K-means algorithm is employed to cluster the features extracted using 1D-RCAE. Finally, statistical analysis is performed on the parameters of each cluster of scenarios to explore their distribution characteristics within each class, and four typical scenarios are identified along with five extreme scenarios. The proposed unsupervised framework, combining IF, 1D-RCAE, and IE-improved K-means algorithms, can automatically identify typical and extreme scenarios from NDD. These identified scenarios can then be applied to test the performance of autonomous driving systems, enriching the library of automated driving test scenarios. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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13 pages, 2613 KiB  
Article
Creating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic Data
by Markel Azkue, Eduardo Miguel, Egoitz Martinez-Laserna, Laura Oca and Unai Iraola
World Electr. Veh. J. 2023, 14(7), 197; https://doi.org/10.3390/wevj14070197 - 24 Jul 2023
Cited by 2 | Viewed by 1290
Abstract
Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining [...] Read more.
Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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