Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler–Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.