Vibration-based predictive maintenance is an essential element of reliability engineering for modern automotive powertrains including internal combustion engines, hybrids, and battery-electric platforms. This review synthesizes advances in sensing, signal processing, and artificial intelligence that convert raw vibration into diagnostics and prognostics. It characterizes vibration signatures unique to engines, transmissions, e-axles, and power electronics, emphasizing order analysis, demodulation, and time–frequency methods that extract weak, non-stationary fault content under real driving conditions. It surveys data acquisition, piezoelectric and MEMS accelerometry, edge-resident preprocessing, and fleet telemetry, and details feature engineering pipelines with classical machine learning and deep architectures for fault detection and remaining useful life prediction. In contrast to earlier reviews focused mainly on stationary industrial systems, this review unifies vibration analysis across combustion, hybrid, and electric vehicles and connects physics-based preprocessing to scalable edge and cloud implementations. Case studies show that this integrated perspective enables practical deployment, where physics-guided preprocessing with lightweight models supports robust on-vehicle inference, while cloud-based learning provides cross-fleet generalization and model governance. Open challenges include disentangling overlapping sources in compact e-axles, coping with domain and concept drift from duty cycles, software updates, and aging, addressing data scarcity through augmentation, transfer, and few-shot learning, integrating digital twins and multimodal fusion of vibration, current, thermal, and acoustic data, and deploying scalable cloud and edge AI with transparent governance. By emphasizing inverter-aware analysis, drift management, and benchmark standardization, this review uniquely positions vibration-based predictive maintenance as a foundation for next-generation vehicle reliability.

Adaptive vehicle control systems are crucial for enhancing safety, performance, and efficiency in modern transportation, particularly as vehicles become increasingly automated and responsive to dynamic environments. This review explores the advancements in bio-inspired actuators and their potential applications in adaptive vehicle control systems. Bio-inspired actuators, which mimic natural mechanisms such as muscle movement and plant tropism, offer unique advantages, including flexibility, adaptability, and energy efficiency. This paper categorizes these actuators based on their mechanisms, focusing on shape memory alloys, dielectric elastomers, ionic polymer–metal composites, polyvinylidene fluoride-based electrostrictive actuators, and soft pneumatic actuators. The review highlights the properties, operating principles, and potential applications for each mechanism in automotive systems. Additionally, it investigates the current uses of these actuators in adaptive suspension, active steering, braking systems, and human–machine interfaces for autonomous vehicles. The review further outlines the advantages of bio-inspired actuators, including their energy efficiency and adaptability to road conditions, while addressing key challenges like material limitations, response times, and integration with existing automotive control systems. Finally, this paper discusses future directions, including the integration of bio-inspired actuators with machine learning and advancements in material science, to enable more efficient and responsive adaptive vehicle control systems.

Electric and hybrid vehicle (EV/HEV) motor bearings demand greases that remain sufficiently mobile at sub-zero temperatures. In this study, a series of polyurea-thickened grease formulations was prepared with three different low-viscosity synthetic base oils: polyalphaolefin (PAO), polyalkylene glycol (PAG), and polyol ester (POE), all with International ISO VG 32. Flow pressure was measured from 0 °C to –40 °C using a tester per DIN 51805-2, where the measured pressure indicates grease yield strength and relates to start-up torque and churning resistance in sealed EV bearings. A modified protocol introduced 4- and 8-hour relaxation periods at –20 °C and –30 °C to evaluate time-dependent stiffening. Under standard conditions, all greases showed acceptable flowability (well below 1400 mbar at –30 °C), with PAO exhibiting the lowest pressures. At –40 °C, PAO and PAG remained extrudable, while POE exceeded the 3225 mbar instrument limit due to crystallization. Extended cold soak caused only modest pressure increases for PAO and PAG but a rapid rise for POE (>3000 mbar after 4 h at –30 °C). These results demonstrate that base oil composition profoundly influences low-temperature grease mobility. In particular, the ester base oil’s tendency to crystallize under prolonged cold exposure led to markedly worse flow behavior, highlighting the need for appropriate base oil selection and modified test protocols for EV applications. This test provides a screening measure of cold-flow mobility, not a direct in-bearing torque evaluation.

Mesoporous silica nanoparticles (MSNs) have gained significant attention due to their unique properties, including high surface area, tunable pore size, and excellent thermal and chemical stability. These properties arise from well-established synthesis routes, precise structural control, and versatile surface functionalization strategies, which make MSNs highly suitable for applications in oil and gas engineering as well as biomedical engineering/biomedicine. In the oil and gas sector, MSNs have been utilized for enhanced oil recovery, gas separation, and corrosion inhibition, offering improved efficiency and environmental benefits. In biomedical engineering, MSNs are extensively researched for drug delivery, bioimaging, and tissue engineering, providing controlled and targeted therapeutic applications. Their ability to be functionalized with a variety of chemical groups for direct use in polymer nanocomposites enhances their versatility across industries. However, challenges remain in large-scale production, safety assessments, and regulatory compliance, necessitating further research and development. This paper explores the diverse applications of MSNs polymer nanocomposite in the aforementioned fields, supported by well-researched examples, and discusses future directions to maximize their potential impact. Focus is given on the MSNs structure, synthesis techniques and their postproduction surface functionalization that render them appropriate for use in the discussed applications.

Grease compatibility is an important consideration for both grease manufacturers and end-users. When switching from one lubricating grease to another, the concern is whether the two greases can safely coexist or if mixing them will degrade performance and potentially harm equipment. Almost 60% of the global grease demand is based on lithium soap thickener chemistry, so finding viable alternatives and understanding their compatibility is vital. In this study, the compatibility limits of different greases were experimentally examined by preparing binary blends of a conventional lithium complex grease (LiX) with three alternative thickener types: aluminum complex (AlX), polyurea (PU), and calcium sulfonate complex (CaSX) greases. Three mixing ratios (10:90, 25:75, 50:50) were tested for each combination to simulate partial or nearly complete grease replacement. A total of nine blended samples, along with the four neat (unmixed) greases, were evaluated according to the ASTM D6185 protocol for grease compatibility. Primary test criteria included dropping point, shear stability, and high-temperature storage stability, while secondary evaluations covered oxidation stability and anti-wear performance. The results revealed that the lithium complex–polyurea combination achieved the highest compatibility score overall, while the lithium complex–calcium sulfonate complex blends were the least compatible by ASTM criteria. The study demonstrates the importance of both standardized tests and practical performance limits in judging grease pair compatibility.