Research Interests
1. Energy storage systems
2. Autonomous vehicles and delivery systems
3. Machine learning based power management of PHEV
4. Multiscale/multiphysics modeling and optimal design of Lithium ion battery
5. Machine learning based battery internal status estimation, failure prognostics and charging/discharging management
Multiphysics modeling, optimal design and health conscious fast charging
1. Multiphysics modeling based on experimental degradation analysis
Motivation: How do Li-ion batteries degrade over long-term cycling (degradation analysis)
SEM examination of cathode fracture
2. Battery sensing
Motivations: Knowing the internal states is extremely important
3 electrode cell for electrode potential monitoring
Internal thermal monitoring
2. Battery Life Optimization
Motivations: How should we design Li-ion batteries to achieve longer cycle life (optimal design)
Method: Physics based modeling, optimal design, and advanced control
Results: Battery capacity fade is reduced significantly from 60% to 20% after design optimization; optimal design parameters are derived.
3. Health conscious charging of battery packs
Motivations: How should we control Li-ion batteries to achieve longer cycle life (optimal control)
Method: Physics based modeling, optimal design, and advanced control
Health-conscious fast charging and balancing
Machine learning based battery internal status estimation, failure prognostics and charging/discharging management
Motivations : advanced battery management systems must take into account the internal status of lithium ion batteries in order to ensure safe operation and to prolong the battery useful life.
Method: Neural network based internal status estimation, such as, temperature, stress, lithium plating, SOC, etc… And also prediction (prognostics) of thermal runaway, or severe side reactions during operation.
Results: high prediction accuracy of internal status based on machine learning method. High accurate prediction of severe side reactions.
Autonomous vehicles and delivery systems
The future of mobility: Autonomous, Connected, Electric, Shared.
Sensing, localization, path planning, sensor fusion, AI-based auto driving
Electric in-hub all wheel drive system
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Machine learning based power management of PHEV
Motivations : the existing power management systems for PHEVs are developed base don pre-sampled driving cycles, while the real-world driving cycles can vary greatly depending on the traffic conditions, and therefore be much different comparing to the pre-sampled ones.
Method: To reduce the energy consumption in real-world driving cycles, the real-time traffic data is fed into an machine learning algorithm to extract the optimal control policy given the real-time traffic conditions.
Results: a 7.12% reduction in fuel consumption is achieved
