Changfu Zou
Changfu Zou is an Associate Professor in the Automatic Control research unit. His research focuses on modelling and optimal control of energy storage systems, particularly lithium-ion batteries. Many of his works are in collaboration with industry partners, such as Volvo Cars, Volvo Trucks, and CEVT AB. As the project manager, he has received funding from the European Commission, Swedish Research Council (incl. VR Starting Grant), Swedish Energy Agency, STINT (Swedish Foundation for International Cooperation in Research and Higher Education), Swedish Electromobility Center, etc. Specifically, he has hosted three Marie Skłodowska-Curie Individual/Postdoctoral Fellows with funding from the European Commission under the programme of Horizon Europe. Dr Zou joined Chalmers in 2017 as a Postdoctoral Researcher and became an Assistant Professor in 2019 in the same research unit at Chalmers. He was a visiting researcher at the University of California, Berkeley, USA. He obtained the B.E. degree in Vehicle Engineering from Beijing Institute of Technology, China, and the PhD degree in Automation and Control Engineering from the University of Melbourne, Australia.More information can be found at https://sites.google.com/view/changfu
Showing 50 publications
State of Power Prediction for Battery Systems with Parallel-Connected Units
A PDE Model Simplification Framework for All-Solid-State Batteries
Analysis and Estimation of the Maximum Switch Current during Battery System Reconfiguration
Sensitivity Analysis of the Battery System State of Power
Combining offline and online machine learning to estimate state of health of lithium-ion batteries
Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits
Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation
Model Order Reduction Techniques for Physics-Based Lithium-ion Battery Management: A Survey
Fast charging control of Lithium-ion batteries: Effects of input, model, and parameter uncertainties
Active Balancing of Lithium-Ion Batteries Using Graph Theory and A-Star Search Algorithm
Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control
Run-to-Run Control for Active Balancing of Lithium Iron Phosphate Battery Packs
Cell Balancing Control for Lithium-Ion Battery Packs: A Hierarchical Optimal Approach
Novel Mesoscale Electrothermal Modeling for Lithium-Ion Batteries
Next-Generation Battery Management Systems: Dynamic Reconfiguration
Near-Fastest Battery Balancing by Cell/Module Reconfiguration
Dynamic modeling and coordinate control for an engine-generator set
Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method
Load-responsive model switching estimation for state of charge of lithium-ion batteries
Model-based state of charge estimation algorithms under various current patterns
Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter
Random forest regression for online capacity estimation of lithium-ion batteries
Real-time monitoring of capacity loss for vanadium redox flow battery
Model Predictive Control for Lithium-Ion Battery Optimal Charging
Charging pattern optimization for lithium-ion batteries with an electrothermal-aging model
Electrochemical estimation and control for lithium-ion battery health-aware fast charging
A fast estimation algorithm for lithium-ion battery state of health
Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs
Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery
A framework for simplification of PDE-based lithium-ion battery models
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Showing 14 research projects
User behaviour informed learning and intelligent control for charging of vehicle battery packs
Electrochemical model parameter identification for health-aware battery management
AI-cloud-based Vehicle Management Strategies for Electrified Vehicles
Lithium-ion battery control for faster charging and longer life
Data driven battery aging prediction
Energy optimisation and control of autonomous electric vehicles
Battery control via adaptive modeling and predictive control
BattVolt - Battery control with dynamic reconfiguration and controllable voltage
Dynamic reconfiguration of vehicle battery systems
Optimal usage of vehicle battery by multi-scale modelling
More efficient and health conscious usage of lithium ion batteries by adaptive modeling