With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs.

Hierarchical titanium dioxide (TiO₂) has emerged as a promising photocatalytic material owing to its excellent chemical stability, environmental benignity, low cost, and high density of surface-active sites. Nevertheless, its practical application is constrained by rapid photogenerated charge-carrier recombination and poor visible-light utilization resulting from its wide bandgap. In the present study, a hierarchical TiO₂/CdS quantum dot (QD) nanocomposite was engineered through a facile and cost-effective pseudo-successive ionic layer adsorption and reaction (p-SILAR) technique with controlled CdS QD deposition. The structural, morphological, optical, and electrochemical properties of the synthesized photocatalysts were systematically investigated using SEM, TEM, XRD, XPS, UV-Vis spectroscopy, photoluminescence (PL), and electrochemical impedance spectroscopy (EIS). The results confirmed the successful deposition of highly dispersed CdS QDs onto the hierarchical TiO₂ framework without altering its morphology or crystal structure. The formation of the heterojunction significantly enhanced visible-light absorption and reduced the optical bandgap from 3.19 eV for pristine TiO₂ to 2.37 eV for the TiO₂/CdS QD nanocomposite. Furthermore, PL and EIS analyses demonstrated suppressed electron–hole recombination and improved interfacial charge-transfer characteristics, respectively. Owing to these synergistic effects, the optimized TiO₂/CdS QD photocatalyst achieved 85.3% degradation of methylene blue under visible-light irradiation within 120 min, exhibiting substantially superior performance to pristine hierarchical TiO₂. Radical scavenging experiments revealed that superoxide radicals ^●O₂⁻ and photogenerated holes (h⁺) were the dominant reactive species governing the degradation process. The enhanced photocatalytic activity is attributed to the combined effects of efficient visible-light harvesting, accelerated charge separation, and effective interfacial charge migration across the TiO₂/CdS QD nanocomposite. These findings highlight the potential of hierarchically structured TiO₂/CdS QD nanocomposites as efficient and economically viable photocatalysts for environmental remediation and wastewater treatment applications.