Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network

The lifespan, power density, and transient response make supercapacitors a component of choice for the electric vehicle and renewable energy industry. Supercapacitors' long lifecycle often makes it difficult for designers to assess the system's reliability over the complete product cycle. In the existing literature, the remaining useful life (RUL) estimations utilize up to 50% state of health (SOH) degradation data to successfully predict the RUL of the supercapacitors with reasonable accuracy, making them impractical in terms of time and resources required to collect the data. The time to acquire data imposes restrictions on developing a data-driven RUL prediction model for the supercapacitors. The objective of this study is to reliably predict the SOH degradation curve of the supercapacitors with the availability of less than 10% degradation data to avoid time and cost consuming lifecycle testing. This study presents a novel combination of deep learning algorithm-Deep Belief Network (DBN) with Bayesian Optimization and HyperBand (BOHB) to predict the RUL of the supercapacitors in the early phases of degradation. The proposed method successfully predicts the degradation curve using the data of the initial 15 thousand cycles (less than 6% data for training in most of the cases), which is very promising since the supercapacitor has yet to show much degradation at this stage, thus reducing up to 54% time for the development of the RUL prediction model. The proposed model shows good accuracy with percent error and root mean squared error (RMSE) ranging from 0.05% to 2.2% and 0.8851 to 1.6326, respectively. The robustness of the model is also tested by injecting noise in the training data during training.

Automated summary

This study introduces a novel method to predict the remaining useful life (RUL) of supercapacitors in the early stages of degradation, by combining deep learning algorithm with Bayesian optimization. The proposed method successfully reduces the time and resources required for developing the RUL prediction model while maintaining good accuracy of the model.