In this paper, a methodology is proposed that aims at selecting the most suitable energy storage system (ESS) for a targeted application. Specifically, the focus is on electrified military vehicles ...for the wide range of load requirements, driving missions and operating conditions call for such a cohesive framework. The method uses the Enhanced-Ragone plot (ERp) as a guiding tool to map the performance of different lithium-ion batteries, as a function of C-rate and temperature, and supercapacitors, on the specific power and specific energy log-log plane. A frequency-based segmentation strategy is employed to assign the requested power to the powertrain actuators. Both full-electric battery-powered and hybrid electric vehicle (including an internal combustion engine, battery and supercapacitors) configurations are considered. Using the ERp, ESSs that are able to match the C-rate corresponding to the power-to-energy ratio calculated from the load are selected. Moreover, weight, volume, number of cells and pack energy of the selected ESSs are also returned from the design framework. The algorithm is tested over three vehicle powertrains which strongly differ in load requirements - Tesla Model S, Tesla Semi truck and high-mobility multipurpose wheeled vehicle.
•Linking vehicle power-to-energy ratio and C-rate handled by the storage devices.•Matching load requirements with storage devices mapped on the Enhanced-Ragone plot.•Agnostic-based selector methodology for energy storage system.•Ragone plot used for scalable storage design, validated over vehicle applications.
This work focuses on developing a mobility control system for high-speed series-hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of this ...research includes modeling a series-hybrid powertrain specific to military tracked vehicles and conducting an in-depth analysis of its dynamic behavior. Subsequently, this study conducts a critical review of mobility control approaches sourced from the literature, identifying key techniques relevant to high-inertia vehicular applications. Building on foundational models, this study proposes a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system’s resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are conducted in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system’s capacity to improve the vehicular response and highlight its potential impact on future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.
Annotating automatic target recognition (ATR) is a highly challenging task, primarily due to the unavailability of labeled data in the target domain. Hence, it is essential to construct an optimal ...target domain classifier by utilizing the labeled information of the source domain images. The transductive transfer learning (TTL) method that incorporates a CycleGAN-based unpaired domain translation network has been previously proposed in the literature for effective ATR annotation. Although this method demonstrates great potential for ATR, it severely suffers from lower annotation performance, higher Fréchet inception distance (FID) score, and the presence of visual artifacts in the synthetic images. To address these issues, we propose a hybrid contrastive learning base unpaired domain translation (H-CUT) network that achieves a significantly lower FID score. It incorporates both attention and entropy to emphasize the domain-specific region, a noisy feature mixup module to generate high variational synthetic negative patches, and a modulated noise contrastive estimation (MoNCE) loss to reweight all negative patches using optimal transport for better performance. Our proposed contrastive learning and cycle-consistency-based TTL (C3TTL) framework consists of two H-CUT networks and two classifiers. It simultaneously optimizes cycle-consistency, MoNCE, and identity losses. In C3TTL, two H-CUT networks have been employed through a bijection mapping to feed the reconstructed source domain images into a pretrained classifier to guide the optimal target domain classifier. Extensive experimental analysis conducted on six ATR datasets demonstrates that the proposed C3TTL method is effective in annotating civilian and military vehicles, ships, planes, and human targets.
The paper describes issues related to reliability of military vehicles based on recorded operational events. Seeking the quantification of reliability for exploiting vehicles in military units, an ...extensive analysis of factors shaping the reliability level was made, taking into consideration all phases of military vehicles existence and a peculiar character of the exploitation process of military vehicles. The importance of reliability research in the decision process optimization was emphasized, controlling the efficiency and availability of the exploitation system.
One of the existing challenges toward the electrification of military vehicles is the selection of the most suitable energy storage device. Moreover, a single energy storage technology might not ...provide the most benefit out of powertrain electrification. In this paper, a generalized framework for the simultaneous selection of the optimal energy storage device, in the form of a standalone or hybrid solution, and online energy management is presented. This paper investigates the cooperation of energy-dense Li-ion batteries and power-dense supercapacitors to assist engine operation in a series hybrid electric military truck. Pontryagin's minimum principle is adopted as the energy management strategy in a forward-looking vehicle simulator, in which the optimal design and control parameters are found using particle swarm optimization. Simulation results show that adopting a hybrid energy storage system reduces fuel consumption by 13% compared to the case of battery-only hybridized powertrain.
Today, batteries are widely used in mobile devices, vehicles and military applications. Batteries meet the energy needs of the equipment on the vehicle in military vehicles which is one of the ...application areas. During the military vehicle is stationary, the vehicle engine must not work for camouflage. Therefore, it needs a battery charge circuit. Controlled charging/discharging of the batteries is very important for the cycle life and safety of the batteries. Many conventional battery chargers units charge it without considering the battery temperature. In this study, adaptive battery charging method design and circuit is taken into consideration, considering the battery temperature. The adaptive battery charging algorithm has been applied to charge the batteries of military vehicles and in accordance with military standards (MIL-STD-1275-E voltage surges and MIL-HDBK-454B electronic equipment standard). In this study, experiments with different charge rates have been made and conventional constant current and constant voltage method and proposed adaptive battery charging method result are compared. Experimental results show that the battery temperature decreases 15°C with the proposed charging method. This adaptive battery charging method has been shown to prevent overheating of the battery.
Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A ...typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles.
MIL-STD-1553 has been used for the past four decades by the military as a standardized, reliable, and fault-tolerant communication bus to provide connectivity between different embedded components in ...mission-critical military vehicles. The bus was designed with a great focus on reliability, responsiveness, and fault tolerance. However, its security aspects were an afterthought. Indeed, in the early 1970s, the notion of cyberattacks was not ubiquitous as it is today. Attacking computerized systems located at very high altitudes was an inconceivable scenario for many people, including security engineers. With current developments in cybersecurity and telecommunication networks, the security analysis of the MIL-STD-1553 bus reveals that the system is not immune from cyberattacks. The bus is vulnerable to many attacks that could seriously damage the entire system. Rebuilding the security of MIL-STD-1553 from scratch is cost prohibitive and a very complex, not scalable, and inflexible approach. A common alternative to embedding security to the existing system is the development of an intrusion detection system that can be added to the MIL-STD-1553 bus with minimal cost. In this article, we review and discuss some possible attack vectors on the MIL-STD-1553 bus. Then, we analyze the risk and consequences of each attack vector on a fighter jet. This review and analysis will provide security engineers with a holistic overview of possible attacks and their related risk on MIL-STD-1553 to better design an effective intrusion detection system.
Abstract
Background
To fully understand the injury mechanisms during an underbody blast (UBB) event with military vehicles and develop new testing standards specific to military vehicles, one must ...understand the injuries sustained by the occupants.
Methods
Injury data from Service Members (SM) involved in UBB theater events that occurred from 2010 to 2014 were analyzed. Analysis included the investigation of prominent skeletal and visceral torso injuries. Results were categorized by killed-in-action (n = 132 SM) and wounded-in-action (n = 1,887 SM).
Results
Over 90% (553/606 SM) of casualties in UBB events with Abbreviated Injury Scale (AIS) 2+ injury sustained at least one skeletal fracture, when excluding concussion. The most frequent skeletal injuries from UBB were foot fractures (13% of injuries) for wounded-in-action and tibia/fibula fractures (10% of injuries) for killed-in-action. Only 1% (11/1037 SM) of all casualties with AIS 2+ injuries had visceral torso injuries without also sustaining skeletal fractures. In these few casualties, the coded injuries were likely due to trauma from a loading path other than direct UBB loading.
Conclusion
Skeletal fractures are the most frequent AIS 2+ injury resulting from UBB events. Visceral torso injuries are infrequent in individuals that survive and they generally occur in conjunction with skeletal injuries.
A driver of a military vehicle is exposed to whole-body vibration when driving in field terrain conditions, and the driver’s muscles become tired. Muscle fatigue occurs most strongly near both ...shoulders, which must be used to steer the vehicle in the target direction. The degree of a muscle fatigue is predicted to correlate with the exposure amount of the human body to vibration. In this study, the vibration driving a military vehicle on a field terrain test road was simulated using a 6-degree-of-freedom (DOF) exciter, and the muscle fatigue was analyzed by measuring electromyography (EMG) signals from subjects before and after the vibration exposure. Surface EMG (sEMG) measurements were taken at the deltoid and trapezius muscles of subjects for the muscle fatigue analysis. Before and after the vibration exposure, the Maximal Voluntary Contraction (MVC) state was determined. The change of the median frequency of the sEMG signal was measured in this state and analyzed. The output values of the sEMG signal at MVC decreased after the vibration exposure, suggesting reduced muscle activation. The change of the median frequency value after the vibration exposure was sharply reduced, which means that the muscles are fatigued more rapidly with the same load.
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