1月23日，以"致敬匠心，同心致远"为主题的机械工业出版社汽车分社二十周年庆典暨优秀作者峰会盛大召开。 这场凝聚着二十年深厚情谊的盛会，不仅是对分社过往发展的深情回望，更是一场联结优秀作者、共话未来的温馨相聚。汽车行业协会领导、高校科研 翘楚、企业领军人物及长期携手的优秀作者们齐聚一堂，共忆并肩奋斗的珍贵时光，共绘出版与汽车产业协同发展的美好蓝图。 世界汽车工程师学会联合会终身名誉主席，清华大学汽车产业与技术战略研究院院长赵福全；中国汽车维修行业协会会长张延华；中国汽车工业协会副 秘书长陈旭；中国汽车工程学会副秘书长陆丽俐；中国贸促会汽车行业分会副秘书长柴占祥；北京汽车博物馆副馆长王静；中国汽车工程研究院有限公司总 监总法律顾问抄佩佩；清华大学车辆与运载学院院长王建强；北京航空航天大学交通科学与工程学院书记杨世春；吉林大学汽车工程学院副院长王军年先 生；北京理工大学机械与车辆学院副院长王铎；同济大学汽车与能源学院电动汽车工程中心副主任朱建功；湖南大学机械与运载学院车辆系主任张冠军；机 械工业信息研究院党委副书记周宝东；机械工业信息研究院副院长机械工业出版社社长缪立进；机械工业出版社副社长丁诚；汽车分社首任社长 ...

Core Insights - Since 2025, multiple automotive companies have chosen to collaborate with Huawei and Momenta, although some self-research companies also partner with them, the core intelligent driving systems remain primarily self-developed [2] - The intelligent driving landscape is dominated by Huawei and Momenta, while self-research companies maintain significant market presence due to their terminal sales advantages [2] User Base Distribution - In the domestic intelligent driving market, Huawei, Momenta, and self-research companies lead in user numbers, while other factions have relatively dispersed and low user concentration [3] - From January to November 2025, Huawei's HI model achieved a deployment of 134,100 vehicles, accounting for 19.76% of third-party suppliers [3] - By the end of 2025, Huawei's QianKun intelligent driving system had accumulated over 5.42 billion kilometers of assisted driving, successfully avoiding 2.12 million potential collisions [3] - Momenta's city NOA deployment reached 414,400 vehicles, representing 61.06% of third-party suppliers during the same period [3] Market Share and Collaboration - From January 2023 to October 2024, Momenta achieved a market share of 60.1% in the city NOA third-party intelligent driving market, with 8 out of the top 10 global automotive companies collaborating with Momenta [4] - Self-research companies include BYD, Li Auto, Xiaomi, Tesla, and NIO, with BYD's "Tianshen Eye" system activated in 94.13% of its models [4][11] Technological Pathways - Huawei's intelligent driving system boasts a full-stack self-research capability, integrating chips, software algorithms, and sensors into a cohesive technology ecosystem [7] - Huawei's ADS system features a "chip-algorithm-cloud" architecture, enhancing data processing efficiency and system stability [7] - Momenta employs a data-driven innovation model, utilizing an "end-to-end large model" for scalable production, achieving significant breakthroughs in technology [10] Business Strategies - Huawei adopts a dual-track strategy of binding with leading automotive companies while expanding into the mid-to-low-end market [8] - Momenta's open cooperation strategy lowers the entry barrier for automotive companies, with over 130 models in mass production [10] Technical Route Characteristics - Different technical routes exist within the intelligent driving ecosystem, each with its advantages and disadvantages, leading to a diverse market landscape [14] - The pure vision route is cost-effective but struggles with recognizing unusual obstacles and performs poorly in extreme weather [14] - The multi-sensor fusion route, while providing precise measurements, incurs high hardware costs and complex data calibration [14] - End-to-end and modular routes reduce cumulative errors but face challenges in model interpretability and training costs [15]

Core Viewpoint - The introduction of the "Interim Measures for the Recycling and Comprehensive Utilization of Waste Power Batteries for New Energy Vehicles" marks a significant regulatory shift aimed at establishing a comprehensive management framework for battery recycling, emphasizing lifecycle management and digital solutions to enhance the sustainability of the new energy vehicle industry [1][2]. Group 1: Regulatory Innovations - The "Vehicle-Battery Integrated Scrapping" system addresses industry pain points by mandating that scrapped vehicles must include their power batteries, preventing the illegal sale of batteries in black markets [1][2]. - The digital identity management system for power batteries introduces unique dynamic codes for each battery, enabling full-chain traceability and proactive regulatory measures, which can enhance consumer confidence and optimize resource allocation for companies [1][2]. - A comprehensive responsibility system and strict penalty mechanisms are established, holding all stakeholders accountable and imposing administrative penalties for non-compliance, with severe cases potentially leading to criminal liability [2][3]. Group 2: Challenges in Implementation - Market-level challenges include the need to address the knowledge gap and financial concerns of vehicle owners, particularly those in the ride-hailing and freight sectors, who may resist the new regulations due to perceived short-term financial losses [2][3]. - Small and medium-sized enterprises face significant compliance costs and technical adaptation challenges, which may lead to the elimination of potentially viable companies during industry consolidation [3][4]. - Regulatory challenges include regional disparities in enforcement capabilities and the need for improved data-sharing mechanisms among departments to effectively identify and address violations [3][5]. Group 3: Recommendations for Successful Implementation - The establishment of a market-based recycling pricing mechanism and incentives for compliant battery disposal can help balance interests and reduce the appeal of black market sales [4][5]. - Government support for small enterprises through subsidies, low-interest loans, and tax reductions can facilitate compliance and encourage collaboration among industry leaders to support smaller firms [4][5]. - Strengthening grassroots enforcement capabilities and enhancing inter-departmental collaboration are crucial for effective regulation, alongside the development of detailed technical standards for battery recycling and utilization [5].

Core Insights - The Ministry of Public Security, in collaboration with the Ministry of Commerce and other departments, has introduced a series of reform measures to facilitate cross-regional transaction registration, aiming to better promote the circulation of second-hand vehicles [1] Group 1: Industry Trends - In 2025, the national public security traffic management departments processed a total of 41.11 million motor vehicle transfer registration transactions [1] - Among these, 38.41 million were automobile transfer registrations, accounting for 93.44% of the total [1] - Since 2020, the volume of second-hand vehicle transaction registrations nationwide has exceeded that of new vehicle registrations for six consecutive years [1]

1月24日，以"123，GO！"为主题的玉柴集团2026全球合作伙伴大会在"中国动力之 都"广西玉林盛大召开，近3000名全球合作伙伴齐聚一堂，共襄行业盛举、共绘发展蓝图。 此次大会既是玉柴对"十四五"发展成就的集中展示，更是其面向"十五五"，向全球市场彰显 战略自信、科技实力与开放胸怀的全新亮相。玉林市市长张惠强、自治区国资委副主任陈 超、中国内燃机工业协会专家委员会主任邢敏等行业领导与嘉宾，携手玉柴集团董事长李汉 阳出席大会，共话行业绿色转型与全球合作新未来。 大会伊始，自治区国资委副主任陈超致辞表示，广西正迎来发展黄金期，玉柴作为广西 工业排头兵，要以创新为核锻造产业升级引擎，以开放为桥拓展全球合作空间，以协同为要 筑牢共赢发展支撑，持续发挥行业引领作用，带动全球合作伙伴共享发展机遇，为构建全球 产业新格局贡献广西力量。玉林市市长张惠强回顾了玉柴75年引领中国动力变革的奋斗历 程，高度肯定其卓越成就，他表示，玉林的发展离不开玉柴的龙头引领与全球伙伴的鼎力支 持，期待各界持续携手，为建设壮美广西注入更强动力。 中国内燃机工业协会专家委员会主任邢敏在讲话中，盛赞玉柴为中国内燃机工业发展作 出的巨大贡献，指出 ...

当前，新能源及储能市场对电池的高安全性、高能量密度、高循环寿命需求持续攀升，此次合作正 是双方应对市场需求的关键布局。通过产业链协同与技术攻关，LG化学与江苏英联将深化战略伙伴关 系，力争在锂电创新材料产业新赛道上持续领跑。 1月21日，LG化学与江苏英联复合集流体有限公司（简称"江苏英联"）正式达成战略合作，并共同 成立联合实验室。LG化学NCC/PO事业部部长李忠勳、江苏英联总经理翁伟嘉等嘉宾出席签约暨揭牌仪 式，双方将以此为起点，携手开发新一代复合集流体用高分子创新材料，加速布局全球锂电创新材料市 场。 根据合作规划，联合实验室将聚焦两大核心方向：一是新一代锂电池复合集流体用高分子聚合物基 材的研发，二是配套创新工艺技术的突破。合作中，LG化学将发挥其在高性能薄膜材料领域的技术积 淀，江苏英联则投入领先的复合集流体技术与高水平制造能力，双方协同推进高性能复合集流体的创新 研发与规模化应用，助力锂电材料升级。 作为锂电池新型关键材料，复合集流体较传统集流体优势显著：不仅可减重超40%，还能实现降本 与性能提升的双重突破；其"金属-高分子聚合物-金属"的"三明治"特殊结构，既保障良好导电性，又能 大幅提高电 ...

Core Insights - The integration of AI inference chips is becoming crucial for automotive intelligence as autonomous driving approaches [2][10] - The demand for inference chips is expected to significantly increase by 2026 due to the rapid growth of automotive intelligence needs [3] Inference Demand Surge - AI model training has been a key growth driver for the AI chip market, with high-end chips like NVIDIA's H100 and H200 being highly sought after, often resulting in multi-million dollar orders [4] - Inference chips have now surpassed training chips in demand, becoming the new mainstay for data center computing power and smart driving applications, as companies focus on translating large models into practical applications [4][5] Automotive Intelligence Key to Success - Autonomous vehicles are evolving into highly integrated "smart mobile terminals" that require real-time decision-making capabilities, supported by the powerful computing power of inference chips [6] - A Level 4 autonomous vehicle can generate data volumes of several gigabytes per second, necessitating rapid processing and analysis for effective driving decisions [6][7] Performance and Efficiency of Inference Chips - Inference chips are designed for edge computing, allowing for immediate data processing without relying on cloud transmission, which is critical for timely decision-making in autonomous driving [7] - New generation inference chips utilize advanced architectures and manufacturing processes, such as 7nm technology, to provide high performance while significantly reducing energy consumption [8] Customization for Autonomous Driving - Inference chips must be tailored for core tasks in autonomous driving, such as visual recognition and decision control, through customized neural network accelerators to enhance processing efficiency and accuracy [9] Industry Transformation with Inference Chips - Inference chips represent a pivotal point in AI industry development, acting as a bridge from research to market application and playing an essential role in automotive intelligence [10] - Achieving automotive-grade certification is a significant hurdle for inference chips, requiring rigorous environmental testing to ensure reliability and stability throughout the vehicle's lifecycle [10][11] Challenges and Future Outlook - Algorithm adaptation is a key challenge for inference chips in automotive applications, necessitating close collaboration between chip manufacturers and automotive companies to optimize performance [11] - The rise of inference chips marks a new phase in the AI and autonomous driving industry, addressing core issues such as cost, latency, and privacy, and enabling deeper integration of AI technologies into operational contexts [11][12] - As AI technology and automotive hardware converge, the future application prospects for inference chips will expand, with increasing competition among automotive companies to develop more competitive autonomous driving solutions [12]

保时捷中国CEO潘励驰表示，保时捷是财务自足公司，每分钱花在刀刃上。在公司内部，保时捷降 本措施仍将持续，节省资金用于投资市场与未来研发，比如保时捷上海研发中心是重点。保时捷目前处 于重新校准和调整阶段，通过优化精简经销商网络、推出专属车型等举措赢回中国市场，不以销量判定 成功。2025年经销商数量已由150家缩减到114家，2026年目标缩减到80家。他还提到，本年代末保时捷 增加两款内燃机及插混新车型，包括B级和D级SUV内燃机和插混。此外，保时捷暂时没有国产化规 划。 ...

据德国《商报》，大众汽车首席执行官奥利弗·布卢默在接受该媒体采访时表示，只有在汽车关税 政策发生变化的情况下，大众汽车才能推进其在美国建设奥迪工厂的计划。布卢默表示："在关税负担 保持不变的情况下，进行大规模额外投资在财务上是不可行的。"他指出，大众汽车对于潜在的新工厂 有多种选址选择，包括其在南卡罗来纳州拥有的土地，同时其他州也表达了兴趣。布卢默称："一旦相 关条件明确，我们将做出决定。" 。 ...

音乐-SW涨5.508%，中国神华涨4.423%，紫金矿业涨4.354%，中国海洋石油涨4.011%，小鹏汽车- W跌4.167%，康师傅控股跌4.590%，信义光能跌5.850%。 ...