In recent years, lithium-ion batteries (LIBs) have gained immense popularity and widespread adoption in various applications. Due to the voltage and capacity requirements of these applications, a single LIB is often inadequate. To meet these demands, LIBs are interconnected in series and parallel configurations to achieve higher voltage and capacity levels. To improve performance and prolong the lifespan of battery packs, battery equalization methods are employed to ensure balanced state-of-charge (SOC) and voltage levels among individual battery cells. Passive battery equalization methods are commonly utilized in industrial applications due to their simplicity and cost-effectiveness. The implementation of complex techniques to enhance performance may not be suitable for passive equalization methods, as it would introduce complications and increase implementation costs. This paper introduces two innovative equalization methods: the rank-sum ratio (RSR) based method and the piecewise function based (PFSO) method. These methods leverage real-time internal resistance measurement devices to enhance the equalization process. To evaluate their effectiveness, the simulation model of battery module is developed based on the Thévenin-based equivalent circuit, utilizing data obtained from hybrid pulse power characteristic (HPPC) tests. Simulation results demonstrate that the proposed methods outperform other passive equalization methods, including the SOC-based and voltage-based methods. In a setting with a 16-cell battery module possessing a total capacity of 51.2V and 104Ah, the RSR and PFSO methods can equalize the battery module, adjusting the capacity from 98.97Ah to 102.56Ah and 101.12Ah, respectively, as opposed to the 100.90Ah and 100.09Ah achieved by the SOC-based and voltage-based methods.

Hong Kong faces critical construction challenges, including workforce aging, land shortages, and near-capacity waste disposal. Modular Integrated Construction (MiC) offers a promising solution. As Hong Kong has just recently adopted the MiC, quantitative studies that explore the actual performance differences between MiC projects and conventional on-site construction projects in Hong Kong are lacking. To fill this knowledge gap, this study utilizes an extended life cycle assessment–Life Cycle Performance Assessment to conduct on-site investigations and case studies on a MiC pilot residential project and a conventional on-site construction residential project in Hong Kong from multiple dimensions: cost, time, safety, and environment. The assessment indicators include five types of greenhouse gas emissions, cost performance, schedule performance, and safety-level index. This study found that the greenhouse gas emissions of the MiC project during the entire construction period were reduced by approximately 21.60% compared to traditional on-site construction projects. The most significant part of the greenhouse gas emissions of the two methods was the embodied emissions of construction materials, accounting for 83.11% and 87.17%. Compared with the conventional construction project, the factors that actively promote the reduction of greenhouse gas emissions in the MiC project are the embodied greenhouse gas emissions of building materials, the transportation of construction waste, and the resource consumption of equipment. In addition, there is no significant difference in the safety performance index of the two construction methods, but MiC projects have more efficient schedule performance management. Surprisingly, the cost control of MiC projects is not as good as that of conventional construction projects, which differs from existing research results in other regions.

This study investigates the impact of Industry 4.0 on carbon emissions within China’s logistics sector, focusing on its potential to support global carbon reduction goals. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, this research employed an Industry 4.0 index alongside IPCC guidelines to estimate logistics emissions. Through fixed effects and dynamic panel models, this study indicated an inverted U-shaped relationship among the development of Industry 4.0 and carbon emission management. Industries move to emitting more pollution to provide their required needs to all but once technologies advance, drastic decreases in combustion and emissions take place as the industries move more towards conservation and the integration of information continues to improve. This study investigates the non-linear impact of Industry 4.0 on carbon emissions in China’s logistics sector. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, the research employs a dynamic panel model to quantify the relationship between Industry 4.0 adoption and carbon emissions. The findings reveal an inverted U-shaped relationship, with emissions initially increasing by 10% at lower levels of technology adoption before decreasing by 15% as the Industry 4.0 index reached higher levels. This study provides empirical evidence supporting the strategic deployment of Industry 4.0 technologies to achieve sustainable logistics growth. Key policy implications and future research directions are discussed.