Electronics

Research

13 pages, 2245 KiB

Open AccessArticle

Electrochemical Impedance Spectrum Equivalent Circuit Parameter Identification Using a Deep Learning Technique

by Asier Zulueta, Ekaitz Zulueta, Javier Olarte, Unai Fernandez-Gamiz, Jose Manuel Lopez-Guede and Saioa Etxeberria

Electronics 2023, 12(24), 5038; https://doi.org/10.3390/electronics12245038 - 18 Dec 2023

Cited by 1 | Viewed by 1779

Abstract

Physical models are suitable for the development and optimization of materials and cell designs, whereas models based on experimental data and electrical equivalent circuits (EECs) are suitable for the development of operation estimators, both for cells and batteries. This research work develops an [...] Read more.

Physical models are suitable for the development and optimization of materials and cell designs, whereas models based on experimental data and electrical equivalent circuits (EECs) are suitable for the development of operation estimators, both for cells and batteries. This research work develops an innovative unsupervised artificial neural network (ANN) training cost function for identifying equivalent circuit parameters using electrochemical impedance spectroscopy (EIS) to identify and monitor parameter variations associated with different physicochemical processes that can be related to the states or failure modes in batteries. Many techniques and algorithms are used to fit a predefined EEC parameter, many requiring high-human-expertise support work. However, once the appropriate EEC model is selected to model the different physicochemical processes associated with a given battery technology, the challenge is to implement algorithms that can automatically calculate parameter variations in real time to allow the implementation of estimators of capacity, health, safety, and other degradation modes. Based on previous studies using data augmentation techniques, the new ANN deep learning method introduced in this study yields better results than classical training algorithms. The data used in this work are based on an aging and characterization dataset for 80 Ah and 12 V lead–acid batteries. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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25 pages, 3423 KiB

Open AccessArticle

Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction

by Fanyuan Chu, Ce Shan and Lulu Guo

Electronics 2023, 12(24), 4951; https://doi.org/10.3390/electronics12244951 - 9 Dec 2023

Cited by 1 | Viewed by 1409 | Correction

Abstract

The burgeoning utilization of lithium-ion batteries within electric vehicles and renewable energy storage systems has catapulted the capacity prediction of such batteries to a pivotal research frontier in the energy storage domain. Precise capacity prognostication is instrumental not merely in safeguarding battery operation [...] Read more.

The burgeoning utilization of lithium-ion batteries within electric vehicles and renewable energy storage systems has catapulted the capacity prediction of such batteries to a pivotal research frontier in the energy storage domain. Precise capacity prognostication is instrumental not merely in safeguarding battery operation but also in prolonging its operational lifespan. The indirect battery capacity prediction model presented in this study is based on a time-attention mechanism and aims to reveal hidden patterns in battery data and improve the accuracy of battery capacity prediction, thereby facilitating the development of a robust time series prediction model. Initially, pivotal health indicators are distilled from an extensive corpus of battery data. Subsequently, this study proposes an indirect battery capacity prediction model intertwined with health feature extraction, hinged on the time-attention mechanism. The efficacy of the proposed model is assayed through a spectrum of assessment metrics and juxtaposed against other well-entrenched deep learning models. The model’s efficacy is validated across various battery datasets, with the Test Mean Absolute Error (MAE) and Test Root Mean Squared Error (RMSE) values consistently falling below 0.74% and 1.63%, respectively, showcasing the model’s commendable predictive prowess and reliability in the lithium-ion battery capacity prediction arena. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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28 pages, 13103 KiB

Open AccessArticle

Li-Ion Battery Immersed Heat Pipe Cooling Technology for Electric Vehicles

by In-Taek Oh, Ji-Su Lee, Jin-Se Han, Seong-Woo Lee, Su-Jong Kim and Seok-Ho Rhi

Electronics 2023, 12(24), 4931; https://doi.org/10.3390/electronics12244931 - 8 Dec 2023

Cited by 1 | Viewed by 2882

Abstract

Lithium-ion batteries, crucial in powering Battery Electric Vehicles (BEVs), face critical challenges in maintaining safety and efficiency. The quest for an effective Battery Thermal Management System (BTMS) arises from critical concerns over the safety and efficiency of lithium-ion batteries, particularly in Battery Electric [...] Read more.

Lithium-ion batteries, crucial in powering Battery Electric Vehicles (BEVs), face critical challenges in maintaining safety and efficiency. The quest for an effective Battery Thermal Management System (BTMS) arises from critical concerns over the safety and efficiency of lithium-ion batteries, particularly in Battery Electric Vehicles (BEVs). This study introduces a pioneering BTMS solution merging a two-phase immersion cooling system with heat pipes. Notably, the integration of NovecTM 649 as the dielectric fluid substantially mitigates thermal runaway-induced fire risks without requiring an additional power source. Comprehensive 1-D modeling, validated against AMESim (Advanced Modeling Environment for Simulation of Engineering Systems) simulations and experiments, investigates diverse design variable impacts on thermal resistance and evaporator temperature. At 10 W, 15 W, and 20 W heat inputs, the BTMS consistently maintained lithium-ion battery temperatures within the optimal range (approximately 27–34 °C). Optimized porosity (60%) and filling ratios (30–40%) minimized thermal resistance to 0.3848–0.4549 °C/W. This innovative system not only enhances safety but also improves energy efficiency by reducing weight, affirming its potential to revolutionize lithium-ion battery performance and address critical challenges in the field. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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18 pages, 3918 KiB

Open AccessArticle

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

by Iker Azurmendi, Ekaitz Zulueta, Jose Manuel Lopez-Guede and Manuel González

Electronics 2023, 12(23), 4719; https://doi.org/10.3390/electronics12234719 - 21 Nov 2023

Cited by 2 | Viewed by 2935

Abstract

Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we [...] Read more.

Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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18 pages, 4507 KiB

Open AccessArticle

A Novel Power Measurement Method Using Lock-In Amplifiers with a Frequency-Locked Loop

by Abdur Rehman, Kangcheoul Cho and Woojin Choi

Electronics 2023, 12(10), 2219; https://doi.org/10.3390/electronics12102219 - 12 May 2023

Cited by 2 | Viewed by 1918

Abstract

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods [...] Read more.

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods continues to decline. Precision in power measurement is an essential factor for the billing and management of power supply and demand. Moreover, it is challenging to build a supply plan for the power demand and to manage the billing for the power consumption. To solve these problems, this paper proposes a novel method based on Lock-in Amplifier (LIA) and Lock-in Amplifier Frequency-Locked Loop (LIA-FLL) to measure the power with high precision and accuracy. The proposed method first tracks the variations in the input signal frequency using LIA-FLL and generates the updated reference signals for LIA. After that, the LIA is used to extract the accurate amplitude of each frequency component. The proposed method results in accurate and precise measurement, even with harmonics and frequency fluctuations. The validity of the proposed method is verified by comparing the power measurement results with the classical method, FFT, and ZERA COM3003 (a commercially available power measurement reference instrument). Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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20 pages, 4932 KiB

Open AccessArticle

Simulation Study on Heat Generation Characteristics of Lithium-Ion Battery Aging Process

by Rui Huang, Yidan Xu, Qichao Wu, Junxuan Chen, Fenfang Chen and Xiaoli Yu

Electronics 2023, 12(6), 1444; https://doi.org/10.3390/electronics12061444 - 17 Mar 2023

Cited by 6 | Viewed by 2488

Abstract

Lithium-ion battery heat generation characteristics during aging are crucial for the creation of thermal management solutions. The heat generation characteristics of 21700 (NCA) cylindrical lithium-ion batteries during aging were investigated using the mathematical model that was created in this study to couple electrochemical [...] Read more.

Lithium-ion battery heat generation characteristics during aging are crucial for the creation of thermal management solutions. The heat generation characteristics of 21700 (NCA) cylindrical lithium-ion batteries during aging were investigated using the mathematical model that was created in this study to couple electrochemical mechanisms, heat transfer, and aging loss. These findings indicate that, at the same operating current, the heat generation power of the cell increased significantly with battery aging. This increase was primarily due to the energy loss caused by the growth of the solid–electrolyte interface (SEI) and a reduction in the negative porosity and other physical characteristics of the SEI, such as its ionic conductivity and molar volume, which also had an impact on the heat generation power. By investigating the variations in battery heat generation in different aging modes, the electrochemical mechanisms underlying the effects of aging on battery heat generation can be comprehended in depth. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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26 pages, 7607 KiB

Open AccessArticle

A Comprehensive Study on the Effect of Inhomogeneous Heat Dissipation on Battery Electrochemical Performance

by Yi Xie, Xingyu Mu, Zhongwei Deng, Kaiqing Zhang, Bin Chen and Yining Fan

Electronics 2023, 12(6), 1266; https://doi.org/10.3390/electronics12061266 - 7 Mar 2023

Cited by 1 | Viewed by 1634

Abstract

In this paper, the unbalanced discharge of lithium-ion battery module caused by heat dissipation is studied. The battery pack is composed of 12 batteries, which are divided into four modules in series, and three batteries in each module are in parallel. The three-dimensional [...] Read more.

In this paper, the unbalanced discharge of lithium-ion battery module caused by heat dissipation is studied. The battery pack is composed of 12 batteries, which are divided into four modules in series, and three batteries in each module are in parallel. The three-dimensional electrochemical-thermal model of a single battery and a battery pack is established by the polynomial approximation pseudo-two-dimensional (PP2D) method in ANSYS fluent 16.0, and the correctness of the model is verified by simulation and experiment. On this basis, the non-uniform temperature distribution and the coupling relationship between electrical parameters and electrochemical parameters in the battery pack under inhomogeneous heat dissipation were studied. The mechanism of how the temperature difference affects the distribution of current and state of charge (SOC) is also given. According to the research results, the control of the average temperature of the battery pack and the control of temperature difference are incompatible and need to be traded off. Enhanced cooling can reduce the average temperature, but it produces a large temperature gradient, resulting in a greater internal temperature difference of the battery pack. The large temperature difference enlarges the difference of the branch current and aggravates the unevenness of SOC in the battery pack. In addition, the temperature difference most suitable for SOC uniformity is not the traditional 5 °C but increases with the increase of the ambient temperature and the cooling medium temperature. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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14 pages, 3151 KiB

Open AccessFeature PaperArticle

Study on Thermal Runaway Propagation Characteristics of Lithium Iron Phosphate Battery Pack under Different SOCs

by Minghao Zhu, Jiajie Yao, Feiyu Qian, Weiyi Luo, Yin Chen, Luyao Zhao and Mingyi Chen

Electronics 2023, 12(1), 200; https://doi.org/10.3390/electronics12010200 - 31 Dec 2022

Cited by 5 | Viewed by 2683

Abstract

Thermal runaway (TR) of lithium-ion batteries (LIBs) has always been the most important problem for battery development, and the TR characteristics of large LIBs need more research. In this paper, the thermal runaway propagation (TRP) characteristics and TR behavior changes of three lithium [...] Read more.

Thermal runaway (TR) of lithium-ion batteries (LIBs) has always been the most important problem for battery development, and the TR characteristics of large LIBs need more research. In this paper, the thermal runaway propagation (TRP) characteristics and TR behavior changes of three lithium iron phosphate (LFP) batteries (numbered 1 to 3) under different states of charge (SOCs) were studied. The main parameters discussed include temperature, temperature rise rate, mass, mass change rate, and TRP flue gas ejection behavior. The experimental results indicate that with the increase in SOC, the TRP behavior of the battery is more obvious. The higher the temperature, more blocked temperature rise rate, mass loss rate, and greater mass loss, the shorter the TRP time that can be observed. The TRP interval of 100% SOC battery 1 to 2 is 71.4% smaller than that of 50% SOC, while the TRP interval of battery 2 to 3 is reduced by 87.2%. In addition, a 100% SOC battery pack exhibits spark ejection, while 50% SOC and 0% SOC battery pack exhibit flue gas generation. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles)

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