SUSTAINABLE POWER CONVERSION LAB
Laboratory
Laboratory
Battery Energy Storage and On-Board Chargers
Battery energy storage and advanced charging technologies are essential to the electrification of transportation and the development of sustainable energy systems. Our research focuses on cutting-edge battery technologies and power electronic converters, covering advanced modelling of lithium-ion and sodium-ion batteries, battery modules and packs, hybrid and all-solid-state battery systems, and intelligent battery management systems (BMSs). We also develop high-efficiency, high-power-density on-board chargers (OBCs) for electric vehicles, exploring innovative converter topologies, advanced semiconductor devices, and intelligent control strategies. By integrating physics-based modelling, AI, and hardware-software co-optimization, we aim to enhance the efficiency, safety, reliability, and cost-effectiveness of battery energy storage and charging systems, accelerating their practical deployment in next-generation electrified transportation and industrial applications.
Single-Stage OBC
Version-1
3 kW~ 6.6 kW
1/3 phase compatible
220Vac/200-400Vdc
Efficiency≈94%
Version-2
3 kW~ 6.6 kW
1/3 phase compatible
220Vac/200-400Vdc
Version-3
1 kW
1 phase
220Vac/200-400Vdc
2.14 kW/L
New Charging Scheme for Li-ion Battery Cell Based on An AI-Enhanced Electro-Thermal-Degradation Model
We propose a hybrid modeling and control framework that combines physical interpretability with data-driven intelligence. The Random Forest–enhanced electro-thermal-degradation model accurately captures nonlinear electro-thermal behaviors, while a constraint-aware Bayesian Optimization scheme optimizes charging speed and degradation suppression under safety limits. Implemented through a dual-loop structure with temperature and current regulation, the strategy enables secure, efficient, and real-time charging on embedded microcontrollers with minimal computational cost. Experimental results show substantial improvements in charging time, thermal safety, and cycle life over conventional CC-CV and temperature-regulated methods. This work has been published in the IEEE Transactions on Power Electronics under the title “A Secure-Sustainable-Fast Charging Strategy for Lithium-ion Batteries based on a Random Forest-Enhanced Electro-Thermal-Degradation Model”. A concise conference version of this work was presented here.
New Control Scheme for Li-ion Battery Modules (Multiple Cells) Based on A Physics-Informed Neural Network
We propose a hybrid modeling and predictive control framework designed to mitigate cell-to-cell temperature nonuniformity in Li-ion battery modules. A two-dimensional Thermal Network Model (2D-TNM) is developed for fast and accurate multi-cell temperature prediction, augmented by a Physics-Informed Neural Network (PINN) that captures the nonlinear internal resistance behavior under physical constraints. To efficiently identify model parameters, Bayesian Optimization (BO) is employed. Building on this model, a Model Predictive Temperature Balancing Control (MPTBC) strategy is implemented to dynamically regulate individual cell currents through a Single-Input Multi-Output Switched-Capacitor (SIMO-SC) converter, achieving real-time thermal balancing with low computational complexity. Experimental validation demonstrates that the proposed method reduces cell-to-cell temperature differences to below 0.5°C, enhances thermal uniformity, and mitigates degradation compared to conventional equal-current charging. This work has been published in the IEEE Transactions on Industrial Electronics under the title “Physics-Informed Neural Network-Enhanced Model Predictive Temperature Balancing Control for Li-ion Battery Modules”. A concise conference version of this work was presented here.
New Equivalent Circuit-Based Mechanical Force Estimation Method for Hybrid Solid-State Batteries
We propose a compact equivalent circuit-based coupled mechanical model (CMM) that enables hybrid solid-state batteries to estimate internal mechanical force using only standard BMS signals, eliminating the need for dedicated force or pressure sensors. By combining an SOC-dependent steady-state force map with thermal and current-induced viscoelastic dynamics, the model captures key mechanical behavior while remaining physically interpretable and computationally lightweight. This provides a practical way to introduce mechanical-state awareness into existing BMS platforms, supporting future force-aware diagnostics, safer charging, and mechanically constrained control without significant hardware or computational overhead. Experimental validation shows normalized force-estimation RMSEs below 2% across different C-rates at 25 °C and below 5% across the tested temperature range. This work has been accepted for publication in IET Renewable Power Generation under the title “Equivalent Circuit-Based Coupled Mechanical Modeling of Hybrid Solid-State Batteries.”
