| Paper ID | ASPS-7.4 |
| Paper Title |
Improving Stability of Adversarial Li-ion Cell Usage Data Generation using Generative Latent Space Modelling |
| Authors |
Subhankar Chattoraj, Universite Jean Monnet, Saint-Etienne, Univ. Lyon, India; Sawon Pratiher, Indian Institute of Technology, Kharagpur, India; Souvik Pratiher, Mu Sigma Business Solutions Private Limited, Bangalore, India; Hubert Konik, Universite Jean Monnet, Saint-Etienne, Univ. Lyon, France |
| Session | ASPS-7: Data Science & Machine Learning |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing over IoT [OTH-IoT] |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The quality and quantity of cell usage data (CUD) availability are crucial for reliable lithium-ion (Li-ion) battery modeling. Further, the model needs to encompass the non-linear and complex system dynamics, such as diverse aging mechanisms and dynamic operating characteristics. In general, the CUD acquisition from the electrochemical energy storage systems is a time-dependent, tedious, and lengthy, expensive process, which is often noise-corrupted with spurious outliers. Outliers’ robust, realistic synthetic CUD generation is essential for accelerating domain-specific technological developments. Time-series generative adversarial networks (TimeGAN) have been the state-of-the-art for latent space sequential data modeling by optimizing both the adversarial and supervised objectives while preserving the multivariate sequences’ temporal correlation dynamics [1]. The original TimeGAN formulation adopts the binary cross-entropy loss function, leading to vanishing gradient stability problems during the training process [2], [3]. Least-squares based formulation overcome such an issue without considering outliers influence [4]. In this treatise, some robust loss-functions for the TimeGAN architecture are explored for generating realistic Li-ion CUD. Extensive experimental validation on publicly avail-able datasets illustrates the amended TimeGAN framework’s improved stability w.r.t generator and discriminator scores. |
