Research Area

Research Area

• Development of 12V lithium battery state-of-health (SOH) estimation algorithm 

  • Development and validation of deep learning-based SOH estimation logic/model

  • Development of simplified SOH estimation model and validation on 32-bit MCU-based BMS 

• Development of Battery State and Fault Diagnosis Technology

  • A deep learning model is developed to generate normal battery data, and the predicted data from the model is compared with the actual measured data

  • If the deviation exceeds the allowable range, a fault is diagnosed 

• Research on AI-based Dynamic fast charger 

  • Developing algorithms for optimal charging profiles and dynamic current control

  • Estimating SOC and SOH to enhance battery performance and longevity

• Development of a High-Efficiency Modular 25-kW DC Fast-Charging Power Module for Electric Vehicles

  • Three-Level Vienna Rectifier + PSFB Converter with Series/Parallel Mode Switching 

  • Development of Output Voltage and Power Factor Control for the Vienna Rectifier, and CC/CV Charging Control Algorithm for the DC-DC Converter 

  • Configuration of three parallel modules delivering up to 75 kW output with a peak efficiency of 97.12%, achieving a 2.7% efficiency improvement compared to existing products 

• Development of a 2-MHz, 35-W GaN FET–Based Ultra-Compact and High-Efficiency Automotive LED Driver

  • Study on PCB Design Method for Reliable Operation of GaN Switches at 2MHz 

  • High-speed switching achieved through sub-nanosecond rise and fall times

  • Verification of stable operation through PCB optimization

• MATLAB & Simulink-Based Research on EV Digital Twin 

  • Implementation of an EV Digital Twin with an Edge + Cloud Architecture using MATLAB & Simulink 

  • Development of Simulation Model Applied to the EV Digital Twin