The traditional Battery Management System (BMS) faces certain limitations in fully utilizing battery capacity and performance during the long cycle life operation of Electric Vehicles (EVs). These constraints include limited real-time data collection, low processing speed, lack of predictive maintenance, and very limited accuracy in predicting health and degradation chemistry. A Battery Digital Twin (BDT) can effectively address these limitations of the BMS. Battery Digital Twins (BDT) can be viewed as a cyber-physical system comprising four key elements: virtual representation, bidirectional connection, simulation, and connection across the life cycle phases of an EV battery. The performance of a Li-ion battery largely depends on the cathode chemistry, component design, and operating conditions. The battery should be manufactured in a manner (such as cylindrical or prismatic cell) that prevents explosion, leakage, and gas generation inside the battery. To enhance the performance and safety of the battery, sensor data, including current, voltage, and temperature, can be continuously monitored through external measurement devices to generate the State of Charge (SoC) of the battery. Individual battery parameters such as state of charge, power, energy, health, and safety can continuously send data to a real-time monitoring system. An advanced BDT can contribute to enhanced computational capacity, real-time data collection and analysis, visualization, predictive maintenance, life cycle management, failure prediction based on degradation chemistry and ML algorithms for various OEMs. Recent developments in key technologies have facilitated the development of smart features within the Digital Twins (DT) system, such as Big Data for real-time fast and accurate analysis, AI/ML for model training and decision-making, the Internet of Things for live communication, Cloud for storage and fast computation, and Blockchain for LCM/Battery Passport. In this review, we have systematically integrated the early adoption of BDT models and their systematic advancement with continuously evolving physical and AI/ML-based models.