1. Tracking the maximum power point of hysteretic perovskite solar cells using a predictive algorithm
- Author
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Changlei Wang, Yue Yu, Dewei Zhao, Yanfa Yan, Lei Guan, Wei-Qiang Liao, Alexander J. Cimaroli, and Corey R. Grice
- Subjects
Materials science ,Maximum power principle ,02 engineering and technology ,General Chemistry ,010402 general chemistry ,021001 nanoscience & nanotechnology ,Tracking (particle physics) ,01 natural sciences ,Maximum power point tracking ,0104 chemical sciences ,Hysteresis ,Dwell time ,Materials Chemistry ,Point (geometry) ,0210 nano-technology ,Current density ,Algorithm ,Voltage - Abstract
Among the variety of approaches, such as current density–voltage (J–V) measurements with different voltage scan directions and rates, steady-state efficiency measurements, and maximum power point tracking (MPPT), MPPT is the most reliable method for characterizing the efficiency of organic–inorganic lead halide perovskite solar cells with strong hysteretic behavior. However, MPPT based on the commonly used simple perturb-and-observe (P&O) algorithm can still significantly underestimate the true maximum power point if the hysteresis is severe and a relatively small dwell time at each bias step is used. Here, we present a predictive P&O algorithm to model the current data which allows the prediction of the steady-state current density for each bias set point. As a result, our predictive MPPT speeds up the tracking process and measures the true maximum power point, regardless of the degree of hysteresis, suggesting a useful approach for characterizing the performance of hysteretic solar cells.
- Published
- 2017