Composite materials are widely used in the automobile industry, compared with traditional mechanical connection, in terms of efficiency and manufacture cost structure, adhesively-bonded joints are usually the material most appropriate connection technology. Adhesively-bonded joints have good performance under fatigue load and the stress concentration is smaller. Lack of reliable design methods, long-term aging behavior, and fatigue life assessment of the adhesive joint, which are limits the bonded joints technology popularization and application. In order to describe the crack propagation process more accurately, an improved generalized crack propagation model was proposed to predict the fatigue life of composite bonded parts. The crack propagation process is divided into three stages, and the influence of machining technology, residual stress, and actual temperature in the process of machining are considered. The model provides an effective theoretical basis for accurately predicting the residual life of composite bonded structures. Experimental and simulation results show that the proposed model expands the prediction range of the existing theoretical model, and the prediction accuracy is significantly improved.

To more accurately predict the fatigue life of components under the action of random loads, it is necessary to explore the influence of the interaction between the load sequence and the load on the life prediction. Based on the Manson-Halford method and Corten-Dolan model, this paper establishes a fatigue cumulative damage model that takes into account both the load order and the interaction between loads, and also takes into account the loads near the fatigue limit. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. Comparing the calculation results of the proposed model with the results of Palmgren Miner, Manson-Halford method, and Corten-Dolan model, it is found that the fatigue damage model established can reasonably predict the fatigue life of parts. Comparison and verification of examples further prove the accuracy and reliability of the proposed model.

State of Health (SOH) estimation for lithium-ion batteries is a complex task, often plagued by issues such as slow estimation speed and high practical application difficulty in existing methods. In this paper, capacity is established as the criterion for defining State of Health. We conducted an analysis of Incremental Capacity (IC) curves, examining the characteristic features of the incremental capacity curves throughout the entire lifecycle of lithium-ion batteries at different temperatures and charge-discharge rates. The investigation revealed that the peaks in the incremental capacity curves correspond to nearly constant State of Charge (SOC) values. Building upon this discovery, a rapid SOH estimation method based on IC curves is proposed. The method was validated using data from the complete lifecycle of 18,650 cells, yielding a maximum SOH estimation error of 3%. Furthermore, the proposed method was applied to 240 cells in an energy storage battery pack at a specific station, resulting in a maximum SOH estimation error of 3.9%. This demonstrates the feasibility of the method under practical conditions, highlighting its high precision in SOH estimation. The proposed approach holds significant promise for real-time and rapid SOH estimation and detection, offering valuable insights for battery health monitoring.

How to use efficient and accurate methods to estimate the capacity of lithium batteries has always been an important research topic. Traditional capacity estimation methods are time-consuming and require strict experimental conditions, making them unsuitable for real-time applications. This article introduces the concept of the inflection point of the charge/discharge curve in the SOC-V curve and proposes a fast estimation method for battery capacity by combining the advantages of the IC curve peak and SOC inflection point methods. By analyzing the charge and discharge data of grouped batteries, it was found that there is a certain correspondence between the inflection point of the SOC-V curve and the peak point of the IC curve. This relationship remains stable during battery aging and can provide a reliable basis for battery SOH evaluation, further improving the estimation accuracy of SOH. This method significantly reduces experimental time, is more suitable for practical applications, and provided an efficient and practical technical means for battery performance evaluation.