In this paper, in order to improve the efficiency of dye-sensitized solar cells, we introduced zirconia [ZrO₂] nanofibers into a mesoporous titania [TiO₂] photoelectrode. The photoelectrode consists of a few weight percent of ZrO₂ nanofibers and a mesoporous TiO₂ powder. The mixed ZrO₂ nanofibers and the mesoporous TiO₂ powder possessed a larger surface area than the corresponding mesoporous TiO₂ powder. The optimum ratio of the ZrO₂ nanofiber was 5 wt.%. The 5 wt.% ZrO₂-mixed device could get a short-circuit photocurrent density of 15.9 mA/cm², an open-circuit photovoltage of 0.69 V, a fill factor of 0.60, and a light-to-electricity conversion efficiency of 6.5% under irradiation of AM 1.5 (100 mW/cm²).

This study establishes a standardized framework for thermal propagation test in nickel-7 lithium-ion battery systems through a high-frequency electromagnetic induction heating method. The non-intrusive triggering mechanism enables precise thermal runaway initiation within two seconds through localized eddy current heating (>1200 °C), validated through cell-level tests with 100% success rate across diverse trigger positions. System-level thermal propagation tests were conducted on two identical battery boxes. The parallel experiments revealed distinct propagation patterns influenced by system sealing quality. In the inadequately sealed system (Box 01), flame formation led to accelerated thermal propagation through enhanced convective and radiative heat transfer. In contrast, the well-sealed system (Box 02) maintained an oxygen-deficient environment, resulting in a controlled sequential propagation pattern. The testing methodology incorporating dummy modules proved efficient for validating thermal protection strategies while optimizing costs. This study contributes to a deeper understanding of thermal runaway propagation mechanisms and the development of standardized testing protocols for large-format battery systems.

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.