Research Interests
1. Autonomous vehicles and delivery systems
2. Energy storage systems
3. Machine learning based state estimation, diagnostics and prognostics
4. Machine learning based power management of PHEV
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 state estimation, diagnostics and prognostics
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.
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 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
