Changfu Zou
Changfu Zou is an Associate Professor in the Automatic Control research unit. His research focuses on modeling and automatic 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, Polestar, Scania, and CEVT AB. As the project PI, he has received funding from the Swedish Research Council (incl. the Starting Grant and the Project Grant), European Commission (e.g., for projects BatCon, MoreSafe, SmartHEM, ULICBat, TEAMING), Swedish Energy Agency (incl. 3 projects within the Vehicle Strategic Research and Innovation Program), Swedish Foundation for International Cooperation in Research and Higher Education, Swedish Electromobility Center, etc. Specifically, he has hosted four researchers to achieve the Marie Skłodowska-Curie Fellowships, which are among Europe's most competitive and prestigious research and innovation fellowships. He 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 PhD degree in Automation and Control Engineering from the University of Melbourne, Australia.
He currently serves as an associate editor/editorial board member for journals, such as IEEE Transactions on Vehicular Technology, IEEE Transactions on Transportation Electrification, and Cell Press journal iScience. He has been a recipient of awards, such as the IEEE Vehicular Technology Society Best Vehicular Electronics Paper Award, the Scholarship of National Information and Communication Technology, Australia (NICTA), the Melbourne Research Scholarship, etc.
Showing 61 publications
Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging
Detecting abnormality of battery lifetime from first-cycle data using few-shot learning
Early prediction of battery life by learning from both time-series and histogram data
Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management
Vehicle-to-grid optimization considering battery aging
Analysis of Potential Lifetime Extension Through Dynamic Battery Reconfiguration
Practical battery state of health estimation using data-driven multi-model fusion
Optimization-free fast charging for lithium-ion batteries using model inversion techniques
MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
Combining offline and online machine learning to estimate state of health of lithium-ion batteries
State of Power Prediction for Battery Systems with Parallel-Connected Units
Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation
Analysis and Estimation of the Maximum Switch Current during Battery System Reconfiguration
Sensitivity Analysis of the Battery System State of Power
Fast Charging Control of Lithium-Ion Batteries: Effects of Input, Model, and Parameter Uncertainties
Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits
Model Order Reduction Techniques for Physics-Based Lithium-ion Battery Management: A Survey
A PDE Model Simplification Framework for All-Solid-State Batteries
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
Novel Mesoscale Electrothermal Modeling for Lithium-Ion Batteries
Next-Generation Battery Management Systems: Dynamic Reconfiguration
Cell Balancing Control for Lithium-Ion Battery Packs: A Hierarchical Optimal Approach
Dynamic modeling and coordinate control for an engine-generator set
Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method
Near-Fastest Battery Balancing by Cell/Module Reconfiguration
Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter
Load-responsive model switching estimation for state of charge of lithium-ion batteries
Model-based state of charge estimation algorithms under various current patterns
Model Predictive Control for Lithium-Ion Battery Optimal Charging
Electrochemical estimation and control for lithium-ion battery health-aware fast charging
Random forest regression for online capacity estimation of lithium-ion batteries
Real-time monitoring of capacity loss for vanadium redox flow battery
Charging pattern optimization for lithium-ion batteries with an electrothermal-aging model
A fast estimation algorithm for lithium-ion battery state of health
Technological developments in batteries: A survey of principal roles, types, and management needs
A framework for simplification of PDE-based lithium-ion battery models
Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery
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Showing 20 research projects
Multiphysics modelling and monitoring of lithium-ion cells for next-generation management
E-powertrain predictive maintenance using physics informed learning
Data-driven lifetime extension and performance optimization for vehicle battery systems
User behaviour informed optimal control for vehicle-home-grid integration
User behaviour informed learning and intelligent control for charging of vehicle battery packs
Modelling plating morphology in lithium-ion batteries for enhanced safety
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