Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

Predicting system lifetime is important to ensure safe and reliable operation of products, which requires integrated modeling based on multi-level, multi-sensor information. However, lifetime characteristics of equipment in a system are different and failure mechanisms are inter-coupled, which leads to complex logical correlations and the lack of a uniform lifetime measure. Based on a Bayesian network (BN), a lifetime prediction method for systems that combine multi-level sensor information is proposed. The method considers the correlation between accidental failures and degradation failure mechanisms, and achieves system modeling and lifetime prediction under complex logic correlations. This method is applied in the lifetime prediction of a multi-level solar-powered unmanned system, and the predicted results can provide guidance for the improvement of system reliability and for the maintenance and protection of the system.

Lithium-ion batteries are widely used as basic power supplies and storage units for large-scale electric drive products such as electric vehicles. Their reliability is directly related to the life and safe operation of the electric drive products. In fact, the cells have a dependent relationship with the degradation process and they affect the degradation rate of the entire battery pack, thereby affecting its reliability. At present, most research focuses on the reliability of battery packs and assumes that their cells are independent of each other, which may cause the reliability of the evaluation to deviate greatly from the actual level. In order to accurately assess the reliability of lithium-ion batteries, it is necessary to build a reliability model considering the dependency among cells for the overall degradation of lithium-ion battery packs. Therefore, in this study, based on a lithium-ion battery degradation test, the Wiener process is used to analyze the reliability of four basic configurations of lithium-ion battery packs. According to the degradation data of the battery packs, the Copula function is used to quantitatively describe the dependent relationship in the degradation process of a single battery, and the quantitative dependent relationship is combined with the reliability model to form a new reliability model. Finally, taking the battery system of Tesla S as an example, a feasible optimization method for battery pack design is provided based on the model constructed in this work.

Brushless direct current (BLDC) motors are the source of flight power during the operation of rotary-wing unmanned aerial vehicles (UAVs), and their working state directly affects the safety of the whole system. To predict and avoid motor faults, it is necessary to accurately understand the health degradation process of the motor before any fault occurs. However, in actual working conditions, due to the aerodynamic environmental conditions of the aircraft flight, the background noise components of the vibration signals characterizing the running state of the motor are complex and severely coupled, making it difficult for the weak degradation characteristics to be clearly reflected. To address these problems, a weak degradation characteristic extraction method based on variational mode decomposition (VMD) and Laplacian Eigenmaps (LE) was proposed in this study to precisely identify the degradation information in system health data, avoid the loss of critical information and the interference of redundant information, and to optimize the description of a motor’s degradation process despite the presence of complex background noise. A validation experiment was conducted on a specific type of motor under operation with load, to obtain the degradation characteristics of multiple types of vibration signals, and to test the proposed method. The results proved that this method can improve the stability and accuracy of predicting motor health, thereby helping to predict the degradation state and to optimize the maintenance strategies.

Accompanied by the development of new energy resources, lithium-ion batteries have been used widely in various fields. Due to the significant influence of system performance, much attention has been paid to the accurate estimation and prediction about health status of lithium-ion batteries. In a battery pack, the structure connection causes sophisticated interaction between cells, or between the cells and the pack. Therefore, the degradation of any cell is the result of the deterioration of conjoint cells, and a rapid degradation speed for any individual cell can lead to the accelerated degradation of others beyond expectation, which is one of the primary reasons why the State of Health and life cannot be calculated precisely. To solve this problem, a novel method based on integrated state information from cells has been proposed to estimate status of packs, considering about the degradation effect that cells contribute to the corresponding pack. Using this method, the interactive relationship was described in the form of a neural network in order to mine the effect from the inter-degradation between cells. It was proven that the novel method had better performance than a method based only on the degradation indicators from battery packs.

Lithium-ion batteries are widely used in the energy field due to their high efficiency and clean characteristics. They provide more possibilities for electric vehicles, drones, and other applications, and they can provide the higher requirements necessary for the reliability of battery pack systems. However, it is easy for a battery pack to be unbalanced because of the dependence between the cells. The unbalanced state will make the degradation process more complex and cause abnormal discharge parameters, which brings challenges in the analysis of the state of health (SOH) of battery packs. In order to study the degradation process in the unbalanced condition, in this study, a degradation test of four different configurations of battery packs was designed and implemented, and the degradation process was primarily studied from the perspective of dependence. First, the degradation characteristics and dependency degree of different configurations of the unbalanced state were discussed. Second, a hypothesis test and a linear regression analysis were used to analyze the degradation process and the acceleration effect of a battery pack in the unbalanced state. Finally, partial least squares regression was used to establish the dependence model of battery packs in the unbalanced state. A high regression coefficient (R² > 0.9) and low p-value < 0.0001 indicated that the correlation of the degradation process was effectively quantified. The results provide a reference for optimizing a consistent design of battery packs and managing the SOH of battery packs.