Research Areas

Focus Area 1: Liquid Electrolyte Development via Machine Learning

Importance: Battery electrolyte typically has one or more organic solvents, salts, and additives. They significantly impact battery performance such as capacity retention, rate performance, and safety. Electrolyte development still relies on expert knowledge and expertise through a trial-and-error approach. This approach is effective but time-consuming, resulting in a slow electrolyte development process in the past three decades. We are applying machine learning and artificial intelligence approaches to facilitate the search for the optimal electrolyte formula.

Applications: Li batteries, Na batteries, Zinc batteries

Focus Area 2: Solid State Electrolyte Development

Importance: All-solid-state batteries (ASSBs) are becoming a promising energy storage technology as they bring the safety of state-of-the-art batteries to the next level by replacing flammable organic liquid electrolytes with nonflammable solid electrolytes (SEs). The good mechanical properties of SEs further allow the usage of metal anodes to achieve very high energy density. Thus, developing SEs with desired properties is crucial to the commercialization of ASSBs. However, none SEs can meet all the requirements. We are focusing on halide-based SEs and developing strategies to improve their ionic conductivity and high-voltage stability.

Applications: Li batteries, Na batteries

Focus Area 3: Evaluate and Predict the Status of Batteries at Cell Levels

Importance: The status of a battery is crucial for its management, including state-of-charge (SOC) and state-of-health (SOH). Accurately evaluating and predicting these statuses onboard can help avoid safety issues during practical applications. Thus, battery management systems (BMS) have been widely used with different levels of complexity. Correlating the status of a battery to its material properties has not been extensively explored. We are focusing on developing new models and algorithms to improve the prediction accuracy of SOC and SOH with the help of understanding materials' properties. 

Topics: 

Applications: LIBs, secondary used batteries, solid-state batteries