Christopher L. Magee

JongRoul Woo, Christopher L. Magee

Battery electric vehicles (BEVs) are now clearly a promising candidate in addressing the environmental problems associated with conventional internal combustion engine vehicles (ICEVs). However, BEVs, unlike ICEVs, are still not widely accepted in the automobile market but continuing technological change could overcome this barrier. The aim of this study is to assess and forecast whether and when design changes and technological improvements related to major challenges in driving range and battery cost will make the user value of BEVs greater than the user value of ICEVs. Specifically, we estimate the relative user value of BEVs and ICEVs resulting after design modifications to achieve different driving ranges by considering the engineering trade-offs based on a vehicle simulation. Then, we analyze when the relative user value of BEVs is expected to exceed ICEVs as the energy density and cost of batteries improve because of ongoing technological change. Our analysis demonstrates that the relative value of BEVs is lower than that of ICEVs because BEVs have high battery cost and high cost of time spent recharging despite high torque, high fuel efficiency, and low fuel cost. Moreover, we found the relative value differences between BEVs and ICEVs are found to be less in high performance large cars than in low performance compact cars because BEVs can achieve high acceleration performance more easily than ICEVs. In addition, this study predicts that in approximately 2050, high performance large BEVs could have higher relative value than high performance large ICEVs because of technological improvements in batteries; however low performance compact BEVs are still very likely to have significantly lower user value than comparable ICEVs until well beyond 2050.

Shuo Jiang, Jie Hu, Christopher L. Magee et al.

In large technology companies, the requirements for managing and organizing technical documents created by engineers and managers have increased dramatically in recent years, which has led to a higher demand for more scalable, accurate, and automated document classification. Prior studies have only focused on processing text for classification, whereas technical documents often contain multimodal information. To leverage multimodal information for document classification to improve the model performance, this paper presents a novel multimodal deep learning architecture, TechDoc, which utilizes three types of information, including natural language texts and descriptive images within documents and the associations among the documents. The architecture synthesizes the convolutional neural network, recurrent neural network, and graph neural network through an integrated training process. We applied the architecture to a large multimodal technical document database and trained the model for classifying documents based on the hierarchical International Patent Classification system. Our results show that TechDoc presents a greater classification accuracy than the unimodal methods and other state-of-the-art benchmarks. The trained model can potentially be scaled to millions of real-world multimodal technical documents, which is useful for data and knowledge management in large technology companies and organizations.