Due to the high power and energy density and also relative safety, lithium ion batteries are receiving increasing acceptability in industrial applications especially in transportation systems with electric traction such as electric vehicles and hybrid electric vehicles. In this regard, to ensure performance reliability, accurate modeling of calendar life of such batteries is a necessity. In fact, potential failure of Li-ion battery packs remains a barrier to commercialization. Battery pack life is a critical feature to warranty and maintenance planning for hybrid vehicles, and will require adaptive control systems to account for the loss in vehicle range, and loss in battery charge and discharge efficiency. Failure not only results in large replacement costs, but also potential safety concerns such as overheating or short circuiting which may lead to fires. That's why health monitoring, fault detection and end of life prediction capability in battery-equipped systems are of great importance. This paper reviews recent research and achievements in the field of Li-ion battery health monitoring and prognostics. The different models, algorithms and techniques being applied to estimate state of charge (SoC) and capacity, and prediction of the remaining useful life (RUL), are presented along with an analysis of the pros and cons of each model or method. It is hoped that these review and discussions prepare a wider perspective on progresses and challenges of Li-ion battery health monitoring and prognostics.