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 e...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.展开更多
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 marginal...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.展开更多
文摘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.
文摘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.
