Researchers introduced pioneering artificial intelligence techniques to improve the estimation of lithium-ion battery state of health (SOH) and remaining useful life (RUL). Employing deep learning models such as DSwin transformer architectures and discrete wavelet transform (DWT) inputs, these methods enhance prediction accuracy critical for electric vehicle and energy storage management. These advances promise safer, more reliable battery usage, optimizing performance throughout battery lifecycles.