Recent progress on electrical failure and stability of perovskite solar cells under reverse bias

Halide perovskites have attracted widespread attention in the photovoltaic field due to their exception optoelectronic properties and remarkable defect tolerance. The power conversion efficiency of perovskite solar cells has rapidly increased, reaching 26.95%. However, the weak ionic bonding in perovskite materials make them highly sensitive to electric fields, leading to instability under reverse bias, which poses a significant challenge to their commercialization. During operation, partial shading of modules can cause the shaded perovskite sub-cells to become resistive. Consequently, under the influence of other sub-cells, these shaded sub-cells experience reverse bias, resulting in a substantial decline in device performance. Currently, there is no characterization technique available to directly investigate the failure mechanisms of perovskite solar cells under reverse bias. Furthermore, there is no consensus in existing research on the types of ion migration occurring within devices during reverse bias ageing. Since the failure mechanisms of perovskite solar cells under reverse bias remain unclear, effective stability strategies targeting these mechanisms have not been proposed. As a result, reverse bias instability continues to hinder the long-term operational stability of perovskite solar cells. Given these challenges, a comprehensive review of the electrical failure and degradation mechanisms of perovskite solar cells under reverse bias is imperative. This review summarizes the latest research progress on the reverse bias stability of perovskite solar cells, covering key aspects such as the maximum breakdown voltage, electrical evolution, ageing behavior, degradation mechanisms, stability enhancement strategies, and characterization techniques used in stability studies. Finally, this review highlights future research directions for investigating the ageing mechanisms of perovskite solar cells under reverse bias and proposes potential approaches, such as machine learning, to address the reverse bias stability issues of high-efficiency perovskite solar cells, in the hope of paving the way for further improving their reverse bias stability.