The objective of this project is to create a physics-based probabilistic prognostics platform for lithium-ion (Li-ion) batteries. Li-ion battery technology is playing an increasingly important role in realizing wide-scale adoption of hybrid and electric vehicles, and renewable energy sources. Successful development of the proposed platform will produce major advancements in extending battery life while ensuring battery safety. Advances in energy storage management could reduce the costs and promote the wide-scale adoption of hybrid and electric vehicles and renewable energy sources, which in turn will reduce the dependence of our nation on foreign sources of energy. The research findings will be disseminated to the battery industry, main stakeholders, and decision makers through collaboration with an industry leader in battery safety. An inclusive education and outreach plan will help (1) train a globally competitive workforce in battery reliability modeling by incorporating research findings into classroom teaching, (2) provide research experiences to undergraduate and K-12 students with an emphasis on increasing the participation of women and underrepresented minorities, and (3) raise public awareness about battery reliability and safety by giving talks to the local communities.
The novelty of the proposed prognostics platform is its ability to integrate mechanistic degradation analysis into remaining useful life prediction using probabilistic models. More specifically, model-based smoothing and learning are adopted, in conjunction with bias-corrected half-cell models, to infer the degrees of degradation from noisy voltage and current measurements. This is followed by physics-based prognostics, where model-based tracking is used to predict future degradation trajectories and remaining useful life. The resulting prognostics approach leverages a combination of physical knowledge and sensor data to achieve gains in prediction accuracy and robustness. The proposed platform will advance the field of battery health management by furthering understanding on: (1) how to validate mechanistic half-cell models using relatively few expensive experiments; (2) how to predict the long-term degradation using only early-life data; (3) the role of physics in designing prognostics approaches; and (4) the coupling effects of degradation modes on capacity fade and failure.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
