The surging demand driven by the rapid expansion of the electric vehicle market has placed high-performance and cost-effective Li-ion batteries (LIBs) in the spotlight. Li-rich Mn-based layered oxides (LRMs) have attracted increasing attention because of their high specific capacity (>250 mAh g(- 1)) and low cost. However, the commercialization of LRMs remains challenging due to severe capacity loss and voltage decay. Recent studies have shown that Co plays a critical role in the structure and performance degradation of LRMs by inducing irreversible oxygen release. Therefore, reducing or eliminating Co content in LRMs is highly desirable. Herein, we first provide a systematic mechanistic summary of the detrimental effects of Co on the electrochemical performances of LRMs to highlight the necessity of Co-free design for LRMs. Thereafter, we review in detail the popular modification strategies and mechanisms for boosting the electrochemical performances of Co-free Li-rich Mn-based layered oxides (CFLRMs). Finally, conclusions and prospects for driving the practical application of CFLRMs are presented. This review underscores the indispensable potential of Co-free design in enhancing the structural stability and electrochemical performances of CFLRMs as qualified candidates for next-generation high-energy LIBs. A mechanistic understanding of advanced design strategies has become urgent for the largescale application of CFLRMs. We propose that synergistic modification strategies combining high-entropy doping and surface reconstruction offer a highly effective approach to enhance the structural stability and interfacial compatibility of CFLRMs. More efforts will be devoted to promote the Co-free design of LRMs for the large-scale application of next-generation LIBs featuring high energy and low cost.
Municipal solid waste incineration fly ash (MSWIFA), a hazardous waste due to leachable heavy metals, poses a critical disposal challenge. Alkali-activated MSWIFA solidified bodies were modified with graphene oxide (GO) and nano-SiO2/GO composite (GOS) for MSWIFA’s harmless treatment and resource utilization, with their effects on mechanical properties, heavy metal solidification and stabilization (S/S) and microstructure investigated. The results showed that the compressive strength of solidified bodies increased then decreased with rising nanomaterial dosage, peaking at 0.07
For the problems of insufficient measured signal samples throughout the full life cycle of bearings and the challenges in obtaining real-time data of local defect sizes, a remaining useful life prediction method for rolling bearing inner rings under natural spalling conditions was proposed, based on data/physics combination driven model. Firstly, considering that the difference of load distribution characteristics between inner ring and outer ring in bearing, the fatigue spalling evolution model was constructed to simulate the fatigue crack initiation and propagation in bearing inner ring under cyclic loading based on the damage initiation and evolution behavior. Secondly, the physics-based dynamic model was developed to characterize the evolution of fatigue spalling, which can generate degradation information without relying on extensive measured data. Then, the recursive FMD was employed to extract defect-related features, which can realize global update by bring feature into highfidelity model. Furthermore, CWGAN-GP was introduced to realize local updates, sample increment and optimization for the high-fidelity model. Finally, based on the constructed full-life-cycle signals, GRU was used to map the degradation state to the corresponding health index, which can perform RUL prediction.
This article investigates the optimization problem of fuel economy for heavy-duty commercial vehicles. A Dynamic Programming–Based Fuel-Saving Predictive Cruise Control (DP-FSPCC) method is proposed, which is based on the Bellman optimality principle and uses the cost function to evaluate the optimal feedback control gain, thereby improving the fuel economy of heavy-duty commercial vehicles on complex roads with varying slopes. To address the issues of low accuracy in road feature representation and poor adaptability to different driving conditions in existing slope reconstruction algorithms, the road ahead is dynamically segmented for high-precision processing by integrating ADASIS (Advanced Driver Assistance Systems Interface Specifications) map information with significant turning point detection and dynamic sensitivity analysis. An engine fuel consumption mapping model based on local gradient information is established to provide an accurate cost function for dynamic programming. Furthermore, a feedforward optimization mechanism based on slope classification is proposed. This mechanism adopts a differentiated cost function weight design strategy for different road conditions, making the control strategy more in line with actual driving experience, effectively reducing the computational complexity of dynamic programming and improving the real-time performance and optimization efficiency of the algorithm. Finally, through numerical simulations and real-vehicle tests on highways, the effectiveness and superiority of the proposed method are verified.
Inertia relief analysis is essential for simulating unconstrained structures subjected to external loads, such as aerospace and automotive systems, where traditional displacement boundary conditions are infeasible. This study systematically integrates the inertia relief technology into the framework of Smooth Finite Element Method (S-FEM). Key contributions include a stabilized S-FEM formulation for inertia relief, and a systematic comparison of computational performance against standard FEM. By smoothing strain fields at element boundaries, S-FEM enhances solution accuracy and mesh adaptability. Approaches for handling inertia relief in cases with and without fictitious constraints are presented, effectively eliminating the rigid-body modes of free-floating systems. Using the mass center of the structure as the reference point, the global acceleration vector is constructed via the rigid-body mode matrix. The consistent mass matrix is adopted instead of the lumped mass matrix to ensure the accuracy of inertial force calculations. The high-performance Pardiso solver is employed to efficiently resolve the resulting symmetric indefinite linear systems, which provides stable and efficient support for solving complex models. Five benchmark cases are utilized to demonstrate the efficiency of the proposed method. Results underscore the framework’s potential for industrial applications requiring rapid analysis of mobile structures without artificial constraints.
