让车 自己知道 下一秒会发生什么

Who we are

We are the New Energy & Intelligent Connected Vehicle research team at the School of Mechanical and Power Engineering, Zhengzhou University (a national "Double First-Class" university). The group is led by Prof. GUO Yaohua, a PhD in vehicle engineering from Jilin University and a former industry chief engineer with more than 20 years of experience; he has led over 100 vehicle technology programmes and the development of more than 10 chassis products for buses, light trucks, heavy trucks and mining trucks.

What we work on

Three interlocking directions: (1) intelligent tires and tire–chassis cooperative control, where in-tire multimodal sensing estimates pressure, temperature, wear, friction and tire forces for the chassis control loop; (2) intelligent X-by-wire chassis, including gas–hydraulic interconnected anti-rollover systems, low-latency AEBS, dual-redundant electro-hydraulic steering and full-frequency active suspension; (3) new-energy electric drive powertrains, integrating motor, multi-speed transmission and controller for heavy-duty duty cycles.

Verified results

The bus intelligent-chassis programme won a Second Prize of the SAE-China Science & Technology Progress Award (2025, first author ranking). Expert evaluation by academicians concludes that the overall technology reaches an internationally advanced level, while the gas–hydraulic interconnected anti-rollover technology and the low-latency AEBS reach an internationally leading level. Published benchmarks include a maximum roll angle of 1.84° in extreme manoeuvres (61.3% smaller than a high-end international competitor), an AEBS system latency of 174 ms (about 30% faster than the international market leader) and a peak drive-assembly efficiency of 95.3% (19.5% lower vehicle energy consumption than an international competitor).

AI-native research and this website

We are systematically bringing AI into the chassis loop: physics-informed neural networks for tire–ground mechanics, digital twins with virtual calibration, AI preview cooperative control, LLM agents for R&D workflows, on-vehicle lightweight inference under ISO 26262 / SOTIF safety argumentation, and privacy-preserving fleet data loops. This website itself runs entirely client-side AI features — a local knowledge-base assistant (retrieval-augmented), a command-palette search, a personalised briefing generator and an applicant–direction matching tool. No server, no tracking: the assistant can optionally be connected to your own OpenAI-compatible endpoint.

Collaboration and applications

We welcome joint research, commissioned development, technical consulting and joint student supervision. Prospective Master's/PhD students and postdocs are welcome — please send your CV, transcripts and representative outputs to guoyh@zzu.edu.cn, stating "Application – Master/PhD/Postdoc – Name" in the subject. Office: School of Mechanical and Power Engineering, Zhengzhou University, Kexue Avenue Campus.

Note: the English brief summarises the site; detailed engineering figures and photo captions remain in Chinese. Company names and commercial data have been deliberately neutralised on this site, and the interactive simulation is an illustrative teaching demo rather than measured data.