This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. More specifically, the proposed approach improves the representation of vehicle dynamic behavior by introducing a unified vehicle–tire modeling framework. To facilitate computational tractability and algorithmic implementation, the model is systematically linearized and discretized. Furthermore, the Tube-based Robust Model Predictive Control strategy is developed to improve adaptability to uncertainty in the road surface adhesion coefficient. The Tube-based Robust Model Predictive controller ensures robustness by establishing a robust invariant tube around the nominal trajectory, effectively mitigating road surface variations and enhancing stability. Finally, a co-simulation platform integrating CarSim and Simulink is employed to validate the proposed method’s effectiveness. The experimental results demonstrate that Tube-RMPC improves the path-tracking performance, reducing the maximum tracking error by up to 9.17% on an S-curve and 2.25% in a double lane change, while significantly lowering RMSE and enhancing yaw stability compared to MPC and PID.

Lithium-ion batteries have garnered significant attention owing to their exceptional energy density, extended lifespan, rapid charging capabilities, eco-friendly characteristics, and extensive application potential. These remarkable features establish them as a critical focus for advancing next-generation battery technologies. However, the commonly used organic liquid electrolytes in batteries are explosive, volatile, and possess specific toxic properties, resulting in persistent safety concerns that remain to be addressed. Composite polymer electrolytes (CPEs) exhibit enhanced safety and stable electrochemical performance, emerging as one of the most promising alternatives. However, single polymers often need to meet the multifaceted performance requirements of batteries. In this study, a composite polymer electrolyte was prepared using solution casting, consisting of a blend of polyurethane (TPU) and polyacrylonitrile (PAN), along with the ceramic filler Li_1.3Al_0.3Ti_1.7(PO₄)₃ (LATP) and lithium perchlorate (LiClO₄). The optimal formulation, which included 40 wt% TPU, 60 wt% PAN, and 10 wt% LATP, exhibited a commendable ionic conductivity of 2.1 × 10⁻⁴ S cm⁻¹, a lithium-ion transference number (t_Li⁺) of 0.60, and notable electrochemical stability at 30 °C. The LiFePO₄/Li battery assembled with this CPE demonstrated excellent cycling stability and rate capability at room temperature. It delivered a discharge specific capacity of 130 mAh g⁻¹ at 1C. Under a charge–discharge rate of 0.2C, the battery achieved a discharge specific capacity of 168 mAh g⁻¹, retaining 98% of its capacity after 100 cycles at 25 °C. Additionally, the CPE exhibited robust safety performance. Consequently, this composite polymer electrolyte holds significant promise for application in lithium-ion batteries.

Solid-state electrolytes (SSEs) have emerged as a promising solution for next-generation lithium-ion batteries due to their excellent safety and high energy density. However, their practical application is still hindered by critical challenges such as their low ionic conductivity and high interfacial resistance at room temperature. The innovative application of 3D printing in the field of electrochemistry, particularly in solid-state electrolytes, endows energy storage devices with attractive characteristics. In this study, ceramic-in-gel polymer electrolytes (GPEs) based on PVDF-HFP/PAN@LLZTO were fabricated using a direct ink writing (DIW) 3D printing technique. Under the optimal printing conditions (printing speed of 40 mm/s and fill density of 70%), the printed electrolyte exhibited a uniform and dense sponge-like porous structure, achieving a high ionic conductivity of 5.77 × 10⁻⁴ S·cm⁻¹, which effectively facilitated lithium-ion transport. A structural analysis indicated that the LLZTO fillers were uniformly dispersed within the polymer matrix, significantly enhancing the electrochemical stability of the electrolyte. When applied in a LiFePO₄|GPEs|Li cell configuration, the electrolyte delivered excellent electrochemical performance, with high initial discharge capacities of 168 mAh·g⁻¹ at 0.1 C and 166 mAh·g⁻¹ at 0.2 C, and retained 92.8% of its capacity after 100 cycles at 0.2 C. This work demonstrates the great potential of 3D printing technology in fabricating high-performance GPEs. It provides a novel strategy for the structural design and industrial scalability of lithium-ion batteries.

High safety gel polymer electrolyte (GPE) is used in lithium metal solid state batteries, which has the advantages of high energy density, wide temperature range, high safety, and is considered as a subversive new generation battery technology. However, solid-state lithium batteries with multiple layers and large capacity currently have poor cycle life and a large gap between the actual output cycle capacity retention rate and the theoretical level. In this paper, polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP)/polyacrylonitrile (PAN)—lithium perchlorate (LiClO₄)—lithium lanthanum zirconium tantalate (LLZTO) gel polymer electrolytes was prepared by UV curing process using a UV curing machine at a speed of 0.01 m/min for 10 s, with the temperature controlled at 30 °C and wavelength 365 nm. In order to study the performance and failure mechanism of multilayer solid state batteries, single and three layers of solid state batteries with ceramic/polymer composite gel electrolyte were assembled. The results show that the rate and cycle performance of single-layer solid state battery with gel electrolyte are better than those of three-layer solid state battery. As the number of cycles increases, the interface impedance of both single-layer and three-layer electrolyte membrane solid-state batteries shows an increasing trend. Specifically, the three-layer battery impedance increased from 17 Ω to 42 Ω after 100 cycles, while the single-layer battery showed a smaller increase, from 2.2 Ω to 4.8 Ω, indicating better interfacial stability. After 100 cycles, the interface impedance of multi-layer solid-state batteries increases by 9.61 times that of single-layer batteries. After 100 cycles, the corresponding capacity retention rates were 48.9% and 15.6%, respectively. This work provides a new strategy for large capacity solid state batteries with gel electrolyte design.

This article presents a systematic evaluation of the mechanisms, recent process advances, and practical applications of gas nitriding for improving the corrosion and wear resistance of steels. It first revisits the thermodynamic and kinetic foundations of conventional gas nitriding, with particular emphasis on ammonia dissociation, nitrogen potential regulation, and the coupled processes of surface adsorption, dissolution, and diffusion. Subsequently, the microstructural characteristics of the compound layer and diffusion layer are summarized, and their respective roles in controlling frictional behavior and electrochemical performance are discussed. Emerging catalytic nitriding techniques, such as surface nanocrystallization (e.g., UNSM, SMAT, and SP), pre-oxidation-assisted gas nitriding, rare-earth or alloy-element catalysis, laser-assisted gas nitriding, and low-temperature nitriding, are reviewed. A comparative analysis highlights their effectiveness in improving hardness, wear resistance, and corrosion resistance. In addition, the synergistic mechanisms by which nitrided layers suppress micro-electrochemical heterogeneity, facilitate the formation of dense passivation films, and improve resistance to abrasive and adhesive wear through the dispersion of fine nitrides are analyzed. Finally, key scientific and engineering challenges are identified, particularly the absence of quantitative models describing corrosion-wear coupling at the microscale. In response to these gaps, prospective research directions are proposed, including multiscale mechanistic studies, high-fidelity simulations, scalable industrial routes for efficient low-temperature nitriding, and data-driven process-structure-property prediction frameworks enabled by machine learning. Collectively, this work establishes a theoretical foundation and research roadmap for optimizing nitriding processes and accelerating their industrial application in high-reliability engineering components.