报告人：Changfu Zou, PhD, Professor, Electrical Engineering, Chalmers University of Technology
报告时间：2026年6月8日(周一) 16:00—18:00
报告地点：西华大学郫都校区5D-216 腾讯会议ID：564-176-188
主办单位：汽车与交通学院、汽车测控与安全四川省重点实验室、新能源汽车智能控制与仿真测试技术四川省工程研究中心、四川智能及新能源汽车产业学院
报告人简介
Changfu Zou is a Professor and PI of the Energy Systems and Optimal Control (eSOC) group at Chalmers University of Technology, Gothenburg, Sweden. His research focuses on advanced modeling, monitoring, and automatic control of energy storage systems, with a particular focus on batteries. Much of his work is conducted in close collaboration with industry partners, such as Volvo, Scania, and ABB. As PI or Co-PI, he has received funding from the Swedish Research Council (including Starting Grant and Project Grant), European Commission, Swedish Energy Agency, Swedish Innovation Agency, Knut and Alice Wallenberg Foundation, and others. His work has been recognized through distinctions such as the IEEE VTS Best Vehicular Electronics Paper Award, IEEE TTE Prize Paper Award, and selection to the Royal Swedish Academy of Engineering Sciences (IVA)’s 100 List. He has received competitive funding from the Swedish Research Council, European Commission, and Knut and Alice Wallenberg Foundation. He also serves on editorial boards of several leading journals and acts as a review expert for multiple national and international funding agencies.
报告内容简介
Phase transitions in battery electrodes govern rate performance, safety and lifetime, yet their underlying thermodynamics are typically accessible only through specialized cells and costly operando characterization. Here, we introduce Bayesian model-integrated neural networks (BMINN) to reconstruct thermodynamically consistent electrode chemical potentials and Gibbs free energies directly from cycling data. Using BMINN, we recover detailed free-energy landscapes that accurately capture staging structures, energy barriers, and phase-separation dynamics. Applied to graphite electrodes, the reconstructed thermodynamics reveal transient phases and heterogeneous intercalation pathways that are otherwise experimentally elusive and remain inaccessible to existing models based on open-circuit potential fits or porous electrode theory.
