State of charge (SOC) estimation is crucial for battery management systems (BMSs), particularly in EV applications. Various estimation methods [1,2,3] have emerged, ranging from simple direct measurements to sophisticated algorithmic approaches. Direct measurement monitors voltage or current, offering simplicity but limited accuracy due to non-linear voltage−SOC relationships and environmental influences. Coulomb counting integrates current overtime for real-time estimation but requires accurate initial SOC to prevent drift. The open-circuit voltage (OCV) method correlates resting voltage with SOC, providing good accuracy but requiring extended rest periods and showing sensitivity to temperature and aging effects.

Advanced estimation techniques have evolved to address these limitations. Kalman filtering approaches [4], including Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), combine measurement data with battery models for dynamic estimation, offering noise resilience but requiring precise modeling and substantial computation. Observer-based methods, including Luenberger and sliding mode approaches, provide robust performance under dynamic conditions while depending on accurate system models. Electrochemical modeling delivers unmatched accuracy through fundamental equation solving but involves complex implementation and heavy computational load.

Recent advances in machine learning have demonstrated significant potential for improving SOC estimation in LIBs without explicit modeling, though these approaches demand substantial computational resources despite their high accuracy with sufficient training data. The complexity and variability of real-world LIBs and EV time-series data present significant challenges for both comprehensive dataset collection and model reliability. Sarda et al. [5] highlighted critical challenges in their comprehensive review, particularly emphasizing non-linear battery behavior and evolving battery management system (BMS) requirements, while Obuli et al. [6] identified Gaussian Process Regression as particularly effective despite challenges with variable driving conditions. Khan et al. [7] advanced the field through innovations in deep neural networks, addressing battery behavior in dynamic environments and proposing novel approaches to architectural design and hyperparameter optimization. Selvaraj et al. [8] further enhanced this progress by implementing Bayesian methods for hyperparameter tuning, demonstrating robust SOC predictions across diverse operational conditions, including varying vehicle velocities and environmental factors.

To address real-world data limitations stemming from the high cost and time demands of data collection, researchers have increasingly turned to data augmentation techniques as a cost-effective alternative to extensive physical testing. Iglesias et al. [9] demonstrated the effectiveness of various augmentation methods, including adversarial and automatic approaches, in generating synthetic data that better represent time-series patterns, building upon Wen et al.’s [10] foundational taxonomy of time-domain, frequency-domain, and advanced decomposition-based methods. Lee et al. [11] introduced Temporal Adversarial Data Augmentation (TADA), leveraging time warping to better simulate real-world variations, while Victor et al. [12] provided a comprehensive framework for data augmentation across multiple data types, demonstrating its effectiveness in various machine learning tasks through detailed case studies. Domain-Adaptive Designable Data Augmentation (DADDA) [13] integrates inverse generation with domain adaptation to facilitate rapid design solutions for advanced EV systems. However, accurately modeling dynamic responses across diverse driving conditions remains a significant challenge.

Generative Adversarial Networks (GANs) have revolutionized data synthesis, enabling the creation of realistic images, audio, videos, and more [14]. These models are widely applied in fields such as cybersecurity for intrusion and anomaly detection [15], medical imaging for data augmentation and disease diagnosis [16], and healthcare for synthesizing patient time-series data [17]. GANs excel at generating synthetic data that closely resemble real-world distributions, providing scalable solutions when collecting real data is costly, hazardous, or ethically challenging [18,19]. The framework consists of two neural networks: a generator that produces synthetic data by mapping random noise to the target data distribution and a discriminator that evaluates whether the input data are real or synthetic. These networks compete in a minimax game where the generator aims to fool the discriminator, and the discriminator seeks to distinguish real data from synthetic. This adversarial training allows GANs to model complex data distributions without explicit density estimation. Time-series GANs have demonstrated significant utility in improving energy storage predictions by integrating physical and digital systems [20]. Advanced architectures such as Progressive GANs and StyleGANs have enabled high-resolution image generation and fine-grained control over synthetic outputs [21]. Despite challenges like training instability and mode collapse [22], GANs continue to evolve, incorporating techniques like reinforcement learning and convolutional neural networks to enhance their capabilities [23]. Their ability to generate high-quality synthetic data has made GANs a cornerstone of unsupervised learning, driving innovations across various domains.

Researchers have made significant strides in generating battery time-series data to improve SOC estimation for LIBs. Wong et al. [24] combined a generative GAN (gGAN) with an SOC estimator, generating realistic battery features like voltage, current, and temperature essential for accurate SOC calculations. However, ensuring the synthetic data accurately reflects real-world conditions remains challenging, as discrepancies can impact model performance. The TS-DCGAN model [25] generates synthetic battery data to enhance SOC estimator training. While this approach addresses the scarcity of high-quality datasets, it requires significant computational resources and expertise for integration into existing frameworks. Similarly, TimeGAN [26] generates synthetic time-series data while maintaining realistic temporal dependencies and feature correlations, though generating long sequences with complex temporal relationships remains difficult.

Recent developments in GAN architecture have focused on addressing specific challenges in battery system applications. Wasserstein GAN (WGAN) [27] adapts to the sequential nature of SOC data, preserving long-term dependencies and improving the accuracy of SOC estimation models. However, synthetic data may still struggle to replicate rare or edge cases, limiting its effectiveness under variable operating conditions. Soo et al. [28] introduced a modified TimeGAN that generates virtual battery datasets to enhance model training with limited real data, though such synthetic data may not fully represent real-world scenarios. ITF-GAN [29] combines a deep autoencoder with GANs to generate synthetic data, while Zhang et al. [30] developed the TimesNet model, a deep learning framework enhanced with Gaussian noise-based data augmentation to simulate diverse operating conditions.

GANs have shown promise in generating synthetic data across various domains, including image synthesis and time-series generation. Building upon the Pix2Pix architecture [31], which demonstrated successful image-to-image translation, subsequent works like HR-Pix2Pix [32] and Ambient-Pix2PixGAN [33] extended its capabilities to high-resolution images and medical imaging with noisy inputs, respectively. However, challenges related to training stability and generalization to real-world data persisted [34]. These studies highlight the potential of GANs for data augmentation but also emphasize the importance of careful model design and evaluation to ensure the quality and utility of the generated data. To address the specific challenges of time-series data in EV SOC estimation, TS-p2pGAN, a novel GAN-based framework, was developed for augmenting multivariate time-series data.

The remainder of this paper is organized as follows: Section 2 introduces the dataset and provides details of the TS-p2pGAN architecture and training methodology. Section 3 covers the experimental setup, evaluation metrics, and both quantitative and qualitative results. Finally, Section 4 concludes with a summary of the findings and potential directions for future research.