Lithium-ion batteries are widely used in battery energy storage systems (BESS) because of their unique advantages, such as high energy density. State-of-health (SOH) estimation, as a critical function of a battery management system (BMS), is important to improve the safety and reliability of lithium-ion BESS. One objective of this dissertation is to develop fast and accurate SOH estimation methods to overcome shortcomings of conventional methods, such as slow estimation speed. Another main objective of this dissertation is to develop battery health-based control algorithms that utilize the output of SOH estimators. Chapter 1 presents an introduction to BESS and BMS and a literature review. It points out the challenges and importance of developing SOH estimation methods with improved performance such as speed and battery health-based SOC balancing control algorithms. Chapter 2 discusses the development of an in-house autonomous battery ageing platform. The developed platform can age a battery autonomously while obtaining and recording experimentally measured data of interest to support battery health diagnosis investigation and research. Chapter 3 analyzes the aggregated battery ageing data collected from the developed autonomous battery ageing platform. Several distinctive SOH indicators are identified to reflect the degradation level of battery to support the development of SOH estimators. Chapter 4 focuses on the development of power electronics based real-time online complex impedance spectrum measurement methods. These developed measurement methods support the development of online impedance based SOH estimators which provide fast SOH estimation for battery cells. In chapter 5, the correlations between the identified SOH indicators presented in chapter 3 and the SOH values of battery cells are utilized to develop deep neural network (DNN) based SOH estimators. It is observed that the diversity of SOH indicators used as the input of DNN can substantially improve estimation performance. Chapter 6 presents a battery health based SOC balancing control method. The presented method allows for drawing energy from battery cells intelligently based on the SOH differences among different battery cells, which helps to improve energy utilization efficiency and reliability of BESS. Chapter 7 concludes the research work presented in this dissertation and discusses potential future research.