Authors: Mojtaba Khakpour Komarsofla | Harpreet Singh | Amirkianoosh Kiani
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Approaches for Predicting Supercapacitor Degradation Behaviour Due to the effective development of advanced energy storage technologies, supercapacitors are considered one of the promising solutions that can realize high-power density and long cycle life [1]. This research is performed to develop a machine learning model that will be able to predict the capacity retention of commercial supercapacitors after being subjected to extensive cyclic testing. In this respect, five different commercial supercapacitors were tested with a Neware battery cycler device under three different case studies to gather a robust data set for modeling. The first case study tested supercapacitors at 5A of current and a voltage of 2.6V for 30,000 cycles as seen in Fig. 1-a. The second case scenario uses the same current but upped the voltage to 3.5V, conducting tests for 20,000 cycles as seen in Fig. 1-b. The third case study tested the supercapacitors with a reduced current of 3A and a voltage of 3.5V, also for over 20,000 cycles as seen in Fig. 1-c. This comprehensive dataset was inclusive of critical parameters like capacity retention, discharge time, charge time, energy efficiency, etc. that provided useful insight into the degradation behavior of supercapacitors operating under variant conditions. The collected data were used for the training of a machine learning model on a neural network architecture-MLP. The primary target variable to be predicted will be the capacity retention of supercapacitors. During training, the MLP model achieved a high R-squared value of 0.98, demonstrating a strong correlation between the input parameters and the capacity retention. The model had a prediction error of about 5% which are presented in Figs. 1(d-f), and thus is highly accurate in forecasting the capacity retention of supercapacitors included in this dataset. A separate supercapacitor, not included in the dataset previously used for training, has been cyclically tested under similar conditions to validate how well the trained ML model generalizes and is reliable on instances other than those in the dataset. The actual performance of this supercapacitor has been compared with the predictions made by the MLP model. The model predicted that with an error margin of roughly 7% which is shown in Table 1, highlighting its potential for predicting capacity retention outside the training dataset. This research underlines the feasibility of using machine learning in predicting the degradation behavior of supercapacitors as a new way to address challenges in managing their lifecycle [2-3]. The prediction capability of the developed model will contribute significantly to industries dependent on energy storage systems, as an accurate forecast of capacity retention can assist in optimizing maintenance schedules and improving system reliability while reducing operational costs [4]. Therefore, the outcomes of this paper will contribute to the development of supercapacitor architecture and operational management in various real applications. This study successfully demonstrates the capability of machine learning in predicting the capacity retention of commercial supercapacitors subjected to cyclic testing. Based on a large amount of experimental data and state-of-the-art ML techniques, the developed model achieves high accuracy and reliability. The insight derived from this study adds not only to the knowledge base in supercapacitor degradation but also to practical applications that could be useful in industries seeking effective and efficient ways of managing energy storage at lower costs. With increasing energy demands across the globe, innovations such as the one presented in this study are necessary in the pursuit of sustainability and resilience in modern energy systems. References: [1]. Mahala, Siddharth, Kavian Khosravinia, and Amirkianoosh Kiani. "Unwanted degradation in pseudocapacitors: Challenges and opportunities." Journal of Energy Storage 67 (2023): 107558. [2]. Khosravinia, Kavian, and Amirkianoosh Kiani. "Unlocking pseudocapacitors prolonged electrode fabrication via ultra-short laser pulses and machine learning." Iscience 26, no. 4 (2023). [3]. Zhu, Shan, Jiajun Li, Liying Ma, Chunnian He, Enzuo Liu, Fang He, Chunsheng Shi, and Naiqin Zhao. "Artificial neural network enabled capacitance prediction for carbon-based supercapacitors." Materials Letters 233 (2018): 294-297. [4]. Ren, Jiahao, Xirong Lin, Jinyun Liu, Tianli Han, Zhilong Wang, Haikuo Zhang, and Jinjin Li. "Engineering early prediction of supercapacitors’ cycle life using neural networks." Materials Today Energy 18 (2020): 100537. Figure 1 Degradation (telecommunications) ; Supercapacitor ; Computer science ; Chemistry ; Telecommunications ; Capacitance Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies