Critical review of main aging mechanisms and health prognostic methods for lithium-ion batteries. Comprehensive summary of challenges and prospects for future trends with potential solutions.

The paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.

Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This paper proposes a novel semi-supervised self-learning method for battery lifetime prediction. Firstly, three health indicators are extracted from the partial capacity-voltage curve. Secondly, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the health indicators are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This paper guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.