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A computational model for simulating the time-domain response of lithium batteries under arbitrary charging and discharging profiles is presented. The methodology is based on first formulating a mathematical model that describes the time-domain voltage–current characteristics of constant phase elements (CPEs), and then uses multiple series-connected CPEs, as well as charge transfer resistances, output resistances, and inductances, to form the computational model. The criteria for ensuring the stability of the computational model will be derived. The validity of the computational model is confirmed by comparing the simulation results of a model consisting of three series-connected CPEs with the measurement results of six SANYO 18650, 3300-mAh, 3.7-V rechargeable Li-ion batteries under controlled charging and discharging profiles. Finally, the accuracy of the computational model will be compared to the accuracy of the models using different orders of parallel resistor–capacitor networks.

A sophisticated discrete-time battery model with multiple constant phase elements (CPEs) for characterizing electrochemical impedance of batteries over wide frequency range will be presented. Its stability criteria and maximum allowable sampling period will be derived by using a high-order characteristic equation. The validity of the model will be confirmed by comparing the prediction results of a battery model consisting of three CPEs with the measurement results of a SANYO 18650, 3300mAh, 3.7V lithium-ion rechargeable battery under controlled charging and discharging conditions. A test setup with low contact impedance and high repeatability will also be outlined.

A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.