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.

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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

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

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