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State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Open Journal of Applied Sciences 2022年第8期12卷 1366-1382页

作者： Inioluwa Obisakin chikodinaka vanessa ekeanyanwu Ingram School of Engineering Texas State University San Marcos USA Department of Geography and Environmental Studies Texas State University San Marcos USA

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

关键词： Support Vector Regression (SVR) Long Short-Term Memory (LSTM) Network State of Health (SOH) Estimation

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Merging GIS and Machine Learning Techniques: A Paper Review

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Journal of Geoscience and Environment Protection 2022年第9期10卷 61-83页

作者： chikodinaka vanessa ekeanyanwu Inioluwa Feranmi Obisakin Precious Aduwenye Nathaniel Dede-Bamfo Department of Geography and Environmental Studies Texas State University San Marcos TX USA Ingram School of Engineering Texas State University San Marcos TX USA

GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

关键词： GIS Machine Learning Landslide Susceptibility Random Forest Urban Development Flood Prediction Health GeoAI

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Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

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Journal of Geographic Information System 2022年第4期14卷 332-346页

作者： chikodinaka vanessa ekeanyanwu Priyanjali Bose Matthew Beavers Yihong Yuan Inioluwa Obisakin Department of Geography and Environmental Studies Texas State University San Marcos TX USA Ingram School of Engineering Texas State University San Marcos TX USA

As a hazard, flood is an extremely important indicator of how a city is resilient to waterborne diseases and epidemics. Over many decades, flood as a hazard has been a major factor in inducing displacement of marginalized section of the people. Austin city within Central Texas has been identified as one of the major hotspots for flooding in recent decades. Thus, the objectives of the paper are two folded: 1) Empirically, we analyzed and mapped out the susceptibility levels from the factors of physical environments to assess the risk of urban flooding (rainfall data, surface water bodies and topography);in Austin, Texas and 2) Methodologically, we created a re-useable ArcGIS scripting tool that can be used by researchers to automate the process of flood risk modelling with certain criteria. The paper showcases a novel time sensitive building of a tool which will enable better visibility of flood within the city of Austin.

关键词： Mapping Texas Flood Risk Susceptibility Vulnerability GIS ArcPy

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