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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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17 pages, 6453 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

by Jinhao Meng, Guangzhao Luo, Mattia Ricco, Maciej Swierczynski, Daniel-Ioan Stroe and Remus Teodorescu

Appl. Sci. 2018, 8(5), 659; https://doi.org/10.3390/app8050659 - 25 Apr 2018

Cited by 245 | Viewed by 19394

Abstract

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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15 pages, 2546 KiB

Open AccessEditor’s ChoiceReview

Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications

by Jing Chen, Jiahong Zheng, Qinwu Gao, Jinjie Zhang, Jinyong Zhang, Olatunji Mumini Omisore, Lei Wang and Hui Li

Appl. Sci. 2018, 8(3), 345; https://doi.org/10.3390/app8030345 - 28 Feb 2018

Cited by 213 | Viewed by 20580

Abstract

There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived. Full article

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16 pages, 2249 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Inkjet-Printed and Paper-Based Electrochemical Sensors

by Ryan P. Tortorich, Hamed Shamkhalichenar and Jin-Woo Choi

Appl. Sci. 2018, 8(2), 288; https://doi.org/10.3390/app8020288 - 14 Feb 2018

Cited by 104 | Viewed by 13072

Abstract

It is becoming increasingly more important to provide a low-cost point-of-care diagnostic device with the ability to detect and monitor various biological and chemical compounds. Traditional laboratories can be time-consuming and very costly. Through the combination of well-established materials and fabrication methods, it is possible to produce devices that meet the needs of many patients, healthcare and medical professionals, and environmental specialists. Existing research has demonstrated that inkjet-printed and paper-based electrochemical sensors are suitable for this application due to advantages provided by the carefully selected materials and fabrication method. Inkjet printing provides a low cost fabrication method with incredible control over the material deposition process, while paper-based substrates enable pump-free microfluidic devices due to their natural wicking ability. Furthermore, electrochemical sensing is incredibly selective and provides accurate and repeatable quantitative results without expensive measurement equipment. By merging each of these favorable techniques and materials and continuing to innovate, the production of low-cost point-of-care sensors is certainly within reach. Full article

(This article belongs to the Special Issue Printed Electronics 2017)

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12 pages, 1727 KiB

Open AccessEditor’s ChoiceArticle

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

by Zhengjun Qiu, Jian Chen, Yiying Zhao, Susu Zhu, Yong He and Chu Zhang

Appl. Sci. 2018, 8(2), 212; https://doi.org/10.3390/app8020212 - 31 Jan 2018

Cited by 221 | Viewed by 9979

Abstract

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis. Full article

(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)

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15 pages, 3070 KiB

Open AccessEditor’s ChoiceArticle

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

by Yue Hu, Weimin Li, Kun Xu, Taimoor Zahid, Feiyan Qin and Chenming Li

Appl. Sci. 2018, 8(2), 187; https://doi.org/10.3390/app8020187 - 26 Jan 2018

Cited by 200 | Viewed by 11455

Abstract

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs. Full article

(This article belongs to the Section Energy Science and Technology)

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