This paper reports the development and implementation of an adaptive lithium-ion battery monitoring system. The monitoring algorithm is based on the nonlinear Dual Extended Kalman Filter (DEKF), which allows for simultaneous states and parameters estimation. The hardware platform consists of an ARM cortex-M0 processor with six embedded analogue-to-digital converters (ADCs) for data acquisition. Two definitions for online state-of-health (SOH) characterisation are presented; one energy-based and one power-based. Moreover, a method for online estimation of battery's capacity, which is used in SOH characterisation is proposed. Two definitions for state-of-power (SOP) are adopted. Despite the presence of large sensor noise and incorrect filter initialisation, the DEKF algorithm poses excellent SOC and SOP tracking capabilities during a dynamic discharge test. The SOH prediction results are also in good agreement with actual measurements.