Lithium-ion batteries (LBs) have become increasingly popular for use in electric vehicles, aircraft, and portable electronic devices due to their high-energy storage capacity and extended lifespan. As a result, the demand for Li-ion batteries has risen significantly compared to other rechargeable batteries. During normal working conditions, any fault in the battery may lead to severe damage to equipment or human. As a preventative measure, developing a Prognostic and Health Management (PHM) system that can detect faults early on is essential. PHM systems can provide early warning of faults and improve reliability and safety. A Prognostic and Health Management (PHM) system for batteries comprises three components: determining the State of Charge (SOC), the State of Health (SOH), and the Remaining Useful Life (RUL). This paper will explore deep learning (DL) techniques to predict the SOC, SOH, and RUL of batteries. Generally, DL based method for PHM has four main stages, data collection, extraction of features, training, and testing. DL-based techniques for PHM of LB will be discussed in detail and also make comparisons to understand the effectiveness.