WCX 2026 (The WCX\u2122 2026 World Congress Experience), organized by the Society of Automotive Engineers (SAE), is a premier global conference and exhibition for automotive technology. SAE, a highly influential academic organization in the automotive, aerospace, and marine industries worldwide, serves as a critical resource for technical information in transportation machinery. SAE WCX2026 provides an exceptional platform for discussing cutting-edge insights into consumer metrics, regulatory standards, and technological advancements within the global automotive ecosystem. The event was held in Detroit, Michigan, USA, from April 14 to April 16, 2026.<\/p>\n\n\n\n
Paper Introduction<\/strong><\/h3>\n\n\n\n01 From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities<\/strong><\/h4>\n\n\n\nThis study proposes an efficient deep learning framework for simulating last-mile energy behavior in campus communities. By distilling large language model decisions into a lightweight neural network, the method achieves high-fidelity behavior prediction with significantly reduced computational cost.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/2-2-1024x939.png\")
<\/figure>\n\n\n\nCitation:<\/strong> Yang, Z., Chen, Y., and Ou, S., “From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0461<\/a>.<\/p>\n\n\n\n02 <\/strong>Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations<\/strong><\/h4>\n\n\n\nThis study proposes a data-driven framework for analyzing the impact of user behaviors on EV battery degradation in real-world conditions. Using over 15 million operational records, it shows that aggressive driving, frequent fast charging, and deep charge\u2013discharge cycles significantly accelerate battery SOH decline.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/3-1-1024x461.png\")
<\/figure>\n\n\n\nCitation:<\/strong> Liu, T., Jing, H., Zhu, J., Chen, Y., et al., “Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0458<\/a>.<\/p>\n\n\n\n03 <\/strong>Enabling Sustainable E-Mobility: An Edge<\/strong>\u2013<\/strong>Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles<\/strong><\/h4>\n\n\n\nThis study proposes an edge-cloud collaborative intelligent framework for accurate battery SOH estimation and RUL prediction in electric vehicles. By combining a Transformer-based architecture with pruning and knowledge distillation, the method achieves high prediction accuracy and low-latency in-vehicle deployment.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/4-1024x768.jpg\")
<\/figure>\n\n\n\nCitation:<\/strong> Gao, W., Lv, Z., and Ou, S., “Enabling Sustainable E-Mobility: An Edge\u2013Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0460<\/a>.<\/p>\n\n\n\n04 <\/strong>Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior<\/strong><\/h4>\n\n\n\nThis paper proposes the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim) to model human decision-making in sustainable mobility and energy systems. By combining LLM-driven personality-based agents with a hierarchical simulation architecture, the framework enables realistic and computationally efficient large-scale community simulations with strong fidelity to real-world mobility and energy patterns.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/5-2.png\")
<\/figure>\n\n\n\nCitation:<\/strong> Chen, Y., Yang, Z., and Ou, S., “Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0465<\/a>.<\/p>\n\n\n\n05 <\/strong>Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe<\/strong><\/h4>\n\n\n\nThis paper reviews major approaches for projecting plug-in electric vehicle (PEV) market shares and examines how policy stability shapes market outlooks across China, the United States, and Europe. The results suggest that stronger policy consistency is associated with greater convergence and confidence in PEV market projections, highlighting the critical role of regulatory predictability in transport electrification.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/6-2-1024x476.png\")
<\/figure>\n\n\n\nCitation: <\/strong>Luo, W., Ou, S., Zhou, P., Wang, T., et al., “Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0455<\/a>.<\/p>\n\n\n\n06 <\/strong>A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain<\/strong><\/h4>\n\n\n\nThis study proposes an AI-driven methodology for building a dynamic and comprehensive database of the global EV battery supply chain under geopolitical uncertainty. By combining automated data crawling, semantic deduplication, and LLM-based information extraction, the framework significantly improves supply chain visibility and supports more resilient, data-driven decision-making.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/7-1-1024x381.png\")
<\/figure>\n\n\n\nCitation:<\/strong> Zhu, J., Luo, W., Zhang, X., Yang, Z., et al., “A Large Language Model-Based Database for Analyzing the Battery Critical Minerals Supply Chain,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0471<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"The SAE WCX2026 has recently announced its paper accept […]<\/p>\n","protected":false},"author":1,"featured_media":1544,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[70],"tags":[],"class_list":["post-1552","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-en"],"blocksy_meta":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/\u56fe\u72471.jpg","_links":{"self":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/comments?post=1552"}],"version-history":[{"count":1,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552\/revisions"}],"predecessor-version":[{"id":1553,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552\/revisions\/1553"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media\/1544"}],"wp:attachment":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media?parent=1552"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/categories?post=1552"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/tags?post=1552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
This study proposes an efficient deep learning framework for simulating last-mile energy behavior in campus communities. By distilling large language model decisions into a lightweight neural network, the method achieves high-fidelity behavior prediction with significantly reduced computational cost.<\/p>\n\n\n\n Citation:<\/strong> Yang, Z., Chen, Y., and Ou, S., “From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0461<\/a>.<\/p>\n\n\n\n This study proposes a data-driven framework for analyzing the impact of user behaviors on EV battery degradation in real-world conditions. Using over 15 million operational records, it shows that aggressive driving, frequent fast charging, and deep charge\u2013discharge cycles significantly accelerate battery SOH decline.<\/p>\n\n\n\n Citation:<\/strong> Liu, T., Jing, H., Zhu, J., Chen, Y., et al., “Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0458<\/a>.<\/p>\n\n\n\n This study proposes an edge-cloud collaborative intelligent framework for accurate battery SOH estimation and RUL prediction in electric vehicles. By combining a Transformer-based architecture with pruning and knowledge distillation, the method achieves high prediction accuracy and low-latency in-vehicle deployment.<\/p>\n\n\n\n Citation:<\/strong> Gao, W., Lv, Z., and Ou, S., “Enabling Sustainable E-Mobility: An Edge\u2013Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0460<\/a>.<\/p>\n\n\n\n This paper proposes the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim) to model human decision-making in sustainable mobility and energy systems. By combining LLM-driven personality-based agents with a hierarchical simulation architecture, the framework enables realistic and computationally efficient large-scale community simulations with strong fidelity to real-world mobility and energy patterns.<\/p>\n\n\n\n Citation:<\/strong> Chen, Y., Yang, Z., and Ou, S., “Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0465<\/a>.<\/p>\n\n\n\n This paper reviews major approaches for projecting plug-in electric vehicle (PEV) market shares and examines how policy stability shapes market outlooks across China, the United States, and Europe. The results suggest that stronger policy consistency is associated with greater convergence and confidence in PEV market projections, highlighting the critical role of regulatory predictability in transport electrification.<\/p>\n\n\n\n Citation: <\/strong>Luo, W., Ou, S., Zhou, P., Wang, T., et al., “Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0455<\/a>.<\/p>\n\n\n\n This study proposes an AI-driven methodology for building a dynamic and comprehensive database of the global EV battery supply chain under geopolitical uncertainty. By combining automated data crawling, semantic deduplication, and LLM-based information extraction, the framework significantly improves supply chain visibility and supports more resilient, data-driven decision-making.<\/p>\n\n\n\n Citation:<\/strong> Zhu, J., Luo, W., Zhang, X., Yang, Z., et al., “A Large Language Model-Based Database for Analyzing the Battery Critical Minerals Supply Chain,” WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https:\/\/doi.org\/10.4271\/2026-01-0471<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" The SAE WCX2026 has recently announced its paper accept […]<\/p>\n","protected":false},"author":1,"featured_media":1544,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[70],"tags":[],"class_list":["post-1552","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-en"],"blocksy_meta":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/04\/\u56fe\u72471.jpg","_links":{"self":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/comments?post=1552"}],"version-history":[{"count":1,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552\/revisions"}],"predecessor-version":[{"id":1553,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1552\/revisions\/1553"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media\/1544"}],"wp:attachment":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media?parent=1552"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/categories?post=1552"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/tags?post=1552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/figure>\n\n\n\n02 <\/strong>Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations<\/strong><\/h4>\n\n\n\n
<\/figure>\n\n\n\n03 <\/strong>Enabling Sustainable E-Mobility: An Edge<\/strong>\u2013<\/strong>Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles<\/strong><\/h4>\n\n\n\n
<\/figure>\n\n\n\n04 <\/strong>Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior<\/strong><\/h4>\n\n\n\n
<\/figure>\n\n\n\n05 <\/strong>Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe<\/strong><\/h4>\n\n\n\n
<\/figure>\n\n\n\n06 <\/strong>A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain<\/strong><\/h4>\n\n\n\n
<\/figure>\n\n\n\n