Eng

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems.

This study numerically investigates the hydrothermal behaviour of a Jeffrey nanofluid with relevance to maritime thermal systems. The coupled nonlinear governing equations for momentum, heat, and mass transport are solved using a shooting technique that accounts for magnetohydrodynamic effects, Darcy porous-media resistance, viscous dissipation, and spatially varying internal heat generation. Variable thermophysical properties, including temperature-dependent viscosity and density, are also considered. The results reveal that porous resistance, fluid elasticity, and thermophysical variations significantly influence velocity, temperature, and concentration fields. The combined effects of porous drag and variable properties markedly alter the characteristics of heat and mass transfer. These findings provide insights into thermal and mass-transport performance, including skin friction, heat transfer, and concentration distributions, which are critical metrics for porous heat exchangers and nanofluid-based maritime coatings. Here, maritime relevance is represented via a generalised porous nanofluid model rather than a specific material. Among the key findings, increasing the slip velocity factor can reduce the surface skin-friction coefficient by approximately 48.7%, while the heat-transfer rate increases by nearly 27.1%, accompanied by a decrease of about 18.9% in the Sherwood number. Conversely, raising the density factor enhances the skin friction coefficient by roughly 103.8% and also augments the heat and mass transfer rates by about 61.3% and 106.1%, respectively. Likewise, at zero relaxation–retardation ratio, the flow reduces to the Newtonian case. Increasing this factor reduces the local Nusselt number by about 1.45%, indicating a slight weakening of heat transfer due to elastic effects. Furthermore, the reliability of the current numerical framework is established through a dual-validation approach, including an analytical assessment of limiting cases and a rigorous comparison with established data from the literature.

Traditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. To address the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimensions using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, which enable the integration and analytical assessment of multivariate geoscientific data. In comparison with existing 3D-UGEE systems, the proposed 3D-UGEE system integrates a broader range of functional modules, conducts in-depth integration and mining of multi-source geological data, boasts robust 3D graphical display and interactive capabilities, and achieves more efficient operational performance. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Qingdao demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE practices.