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中国汽车动力电池产业创新联盟2025年度大会开幕_我的网站
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[ 行业] 2025年5月29日，以“智能化、低碳化、全球化”为主题的中国汽车动力电池产业创新联盟2025年度大会（简称“联盟年会”）于南京隆重开幕。中国机械工业联合会执行副会长罗俊杰、东风汽车集团原党委书记、董事长竺延风、中国工程院外籍院士、加拿大工程院院士、加拿大皇家科学院院士孙学良、江苏省南京市人民政府副市长蒋敏、工业和信息部装备工业发展中心副主任姚振智先生、工业和信息化部装备工业一司汽车发展处一级调研员陈春梅、江苏省工信厅、南京市工信局以及六合区政府相关领导出席会议，以及来自整车、电池、供应链、行业机构、高校等行业领域近千位专家代表汇聚一堂，共同探讨动力电池产业的发展。 
Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues.
WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey.
Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month.
According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research.
In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later.
According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them.
A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood.
The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said.
The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system.
Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger.
They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression.
Based on these findings, Lu’s team then developed a marker that can identify possible warning signs.
According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences.
According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise.
The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions.
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。Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues.
WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey.
Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month.
According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research.
In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later.
According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them.
A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood.
The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said.
The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system.
Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger.
They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression.
Based on these findings, Lu’s team then developed a marker that can identify possible warning signs.
According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences.
According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise.
The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions.
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中国汽车动力电池产业创新联盟理事长董扬主持会议
中国机械工业联合会执行副会长罗俊杰在致辞中回顾了动力电池产业的创新性成果并提出接下来的发展方向。2024年我国动力电池技术创新呈现“多点开花”的特点，实现多项突破。针对动力电池产业在促进“双碳”目标达成方面的重要作用，罗副会长提出了三点建议，一是强化技术引领，赋能产业创新发展；二是加快低碳布局，推动产业绿色发展；三是扩大开放合作，构建合作共赢发展。中国机械工业联合会执行副会长罗俊杰致辞
东风汽车集团原党委书记、董事长竺延风对动力电池技术与汽车行业应用方面取得的成果给予了充分肯定，并指出纯电、混动、氢燃料等多种技术路线的结合将形成行业发展的重要力量，将极大促进商业模式和经济模式的良性循环；同时，针对动力电池回收利用，提出继续建立完善的政策法规体系，发挥产业链上下游的协同作用，形成动力电池全生命周期闭环，促进产业绿色及低碳化发展。东风汽车集团原党委书记、董事长竺延风致辞
中国工程院外籍院士、加拿大工程院院士、加拿大皇家科学院院士孙学良对现阶段动力电池行业面临的问题进行了总结，一是资源性问题仍然突出，锂、钴等关键金属价格波动剧烈，对外依存度高，回收体系还不够完善；二是技术瓶颈问题，现有锂离子电池在追求更高能量密度的同时，往往伴随着安全性下降、快充性能下降的问题，而全固态电池的界面阻抗、批量一致性、操作压力、成本控制等问题仍需攻克；三是国际政策压力，尤其是欧盟《新电池法案》要求2027年起，所有动力电池出口必须持有“电池护照”，记录碳足迹、原材料来源等信息，这将接影响我国出口成本和竞争力。
B | 面对未来的发展方向，孙院士提出了加快全固态电池关键性突破、强化钠离子电池将与锂电互补、推动电池制造绿色化及智能化、完善回收体系并建立闭环产业链、拓展应用领域等五方面的建议。
中国工程院外籍院士、加拿大工程院院士、加拿大皇家科学院院士孙学良致辞
许艳华秘书长在联盟年度工作报告中表示，联盟工作将围绕中国动力电池产业研究报告、低空经济应用场景的动力电池产业研究、碳足迹核算方法研究与平台搭建、动力电池及关键材料知识产权风险研究与预警等方面展开；同时也将在组织团体标准制定、联合行业搭建动力电池全生命周期检测验证公共服务平台、组织企业开展技术交流、加强行业自律及强化体系建设等方面开展工作。
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中国汽车动力电池产业创新联盟秘书长许艳华作年度工作报告
联盟年会以“1+6”方式展开，即1个主题峰会和6个平行分论坛。主题峰会聚焦“固态电池技术发展与挑战”，对国内外固态电池产业链上下游的发展现状、技术创新成果及发展趋势、面临的挑战和企业案例等方向进行系统性成果分享。
D | 平行分论坛围绕“锂电池在低空经济的应用”、“AI技术赋能电池产业高质量发展”、“储能电池及安全防护高质量发展”、“动力电池先进材料新发展”、“低碳化智能拆解及回收再利用”、“电动汽车智能极速充电”等问题，从智能、安全、低碳、绿色等角度对动力电池发展中的热点性及焦点性话题进行了全面解读和分享。
当前，新能汽车行业发展处于变革的关键时期，而动力电池发展事关我国能源安全与产业的竞争力，为此，亟需加强在动力电池产业链技术水平上的持续突破，形成产业化及商业化闭环，持续增强我国在国际能源安全方面的领先地位和话语权。联盟年会的召开厘清了动力电池行业发展现状、趋势及问题，并提供了动力电池发展的中国方案，这对于动力电池领域参与国际化竞争，巩固领先地位具有重要的指导性意义，同时，也为全球实现碳达峰、碳中和目标贡献智慧和力量。
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