Browse Topic: Fleets
The persistent rate of accidents and fatalities involving legacy tactical military vehicles underscores a critical need for Enhanced Situational Awareness (ESA) technologies. However, the prohibitive cost and lengthy development cycles associated with full MIL-STD ruggedization often prevent these safety systems from reaching the in-service non-combat vehicles with limited driver visibility. This paper suggests a strategic shift in procurement policy: The adoption of relaxed ruggedization standards for vehicles operating in non-combat, administrative, and training roles. By deriving requirements from high-stress commercial sectors— such as heavy mining, steel production, and NASCAR racing—the military can utilize electronics designed for "extreme industrial" rather than "battlefield" environments. The principal objectives of this relaxation is cost reduction, lowering the barrier to entry and increasing the likelihood of ESA deployment across the legacy fleet. Furthermore, this approach aligns with Modular Open Systems Approach (MOSA) principles by enabling the integration of non-proprietary commercial devices. Utilizing these accessible technologies on legacy platforms creates a real-world testbed to evaluate technological advances rapidly. These insights can then inform and accelerate the development of future MIL-STD systems for combat vehicles, effectively shortening the traditional development life cycle while prioritizing the immediate enhanced protection of service member lives.
This position paper presents
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel ends. Traditional brake-force distribution strategies do not consider these pad-life differences, which can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO) in mixed-trailer operations [7, 9]. While modern electronically controlled braking systems (EBS) already incorporate pad wear based braking for the tractor itself [5], these capabilities do not extend across the entire vehicle combination because trailer-side communication is typically limited to standardized CAN protocols such as ISO 11992 and J1939 [1, 2, 3]. As braking systems become more software defined and rely heavily on distributed electronic communication, ensuring the authenticity and integrity of trailer originated brake information becomes essential for both functional safety and cybersecurity [6]. In the proposed architecture, trailers and dollies communicate brake related data to the tractor over the ISO 11992 Tractor-Trailer CAN (TT-CAN) network [1, 2], allowing the tractor Brake Control ECU to securely validate the source of the information and register each towed unit for health aware braking. Once authenticated pad life data is available, the tractor constructs a combination level brake health map covering every wheel end in the configuration. During normal braking, a supervisory allocator computes wheel end specific brake pressure targets that bias braking toward wheel ends with greater remaining pad life while ensuring full compliance with stopping distance regulations and stability requirements [4, 7]. By integrating authenticated pad wear information with tractor hosted supervisory control, the system improves braking consistency across mixed combinations, harmonizes pad utilization, enhances maintenance predictability, and reduces TCO while meeting the safety and cybersecurity expectations of modern commercial vehicle fleets.
State and federal legislation has helped double the consumption of renewable and biodiesel in North America over the past half decade. But is policy alone enough to make them ready for prime time? As crude oil prices continue to fluctuate globally, creating budgeting and logistical headaches for fleets of all sizes, renewable diesel (RD) fuels have quietly gained ground in North America. According to Chevron, total biomass consumption of renewable fuels in the U.S. has grown exponentially over the past five years, from 2.7 million gallons in 2020 to 5.1 million gallons in 2024. While that represents a fragment of annual diesel consumption in the U.S., the environmental benefits and viability of renewable fuels are growing greater everyday thanks to legislative support and technological investment from various fuel producers.
The trucking industry is dealing with a lot of uncertainty, and higher fuel and equipment costs are making it harder for companies to plan and buy new trucks. What can OEMs and fleet operators do to mitigate these challenges? Hybrid systems may be the answer. With internal combustion and electric power, hybrid systems reduce fuel consumption, improve transient performance, enable low-speed electric operation and create more flexible power management. Vehicles and equipment that could benefit from new or retrofit hybrid systems include refuse trucks, dump trucks, long-haul trucks, port equipment, excavators, terminal tractors and commercial delivery vehicles.
New technologies, advanced materials, evolving mission profiles and fast-changing requirements are forcing the aerospace and defense (A&D) industry to dramatically increase the speed of engineering. Companies must design, validate and bring more complex products to market faster than ever, even as software, electronics and autonomy continue to reshape what aircraft, spacecraft and defense systems can do. At the same time, a growing production challenge is emerging. Workforce shortages, supply chain disruption and pressure to reduce cost and cycle time are converging with new demands for greater volume and flexibility. Defense programs are seeing increasing need for larger quantities of lower-cost systems such as drones, while commercial aerospace companies continue to work through backlogs and reinforce their fleets. To keep pace, the industry must accelerate innovation while also scaling production with greater speed, resilience and adaptability.
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
OEMs, integrators and suppliers must continuously process, assess and identify platform vulnerabilities to prioritize and implement updates that protect systems from cyberattacks and data breaches. Security researchers have demonstrated that the control systems in vehicles and machines are open to attack. In 2010, researchers from the University of Washington and the University of California, San Diego demonstrated that by gaining physical access to a vehicle, they could manipulate critical systems like brakes and engines. Just a few years later, security researchers Charlie Miller and Chris Valasek remotely compromised a vehicle over the internet, controlling steering, braking and acceleration, leading to a 1.4 million vehicle recall. In 2024, researchers at Colorado State University successfully demonstrated a wireless drive-by hack by exploiting vulnerabilities in common electronic logging devices (ELDs). In their proof-of-concept test, they achieved remote control over a truck by reflashing the ELD with malicious firmware, which allowed them to slow down a moving truck and show a design for a truck-to-truck worm virus that could theoretically spread through a fleet.
Though the U.S. EPA has rolled back many emissions regulations surrounding the mobility industry, its HD rules remain intact, meaning manufacturers must hit the world's most stringent NOx requirement. It was clear at a panel of industry experts that the new rule was still causing confusion among operators and fleet owners. The EPA's new limits are set at 0.035 grams per horsepower-hour during normal operation, 0.050 grams at low load and 10.0 grams at idle. A panel immediately following revealed how companies have hit the tough target, which goes into effect in January of 2027.
These days, no one blinks an eye at two-day, one-day, or even same-day package deliveries. Despite the plethora of localized fulfillment and distribution centers cropping up to meet demand, delivery trucks still need to make considerably long drives, often on highways. To reduce fuel and maintenance costs as well as carbon emissions, companies are investing in electric trucks for their delivery fleets. While turning to electric trucks is a promising solution, the battery packs incorporated into these automotive designs often fall short in the lifetime needed to make consistent long-distance highway travel feasible.
Developing a comprehensive autonomy solution for the Army's current and future aircraft fleet requires a robust computational and perception capability for decision-making across the entire flight envelope without a pilot. This also requires a flight control system and infrastructure capable of executing autonomous decisions in complex mission environments. Ongoing development of automation and autonomy, utilizing a wide range of perception sensors, has been conducted on platforms such as Sikorsky's S-70 and the Army's UH-60Mx aircraft. This work builds upon previous efforts and leverages ongoing collaborations with industry, the Department of War (DoW), and the Defense Advanced Research Projects Agency (DARPA) to advance autonomous capabilities for both optionally piloted and uncrewed aircraft.
The Sikorsky S-92® helicopter fleet, representing more than 300 aircraft and 2.6 million flight hours, is relied upon to support a large range of important missions across the globe. In previous efforts, a high-fidelity CFD-CSD based full-aircraft simulation methodology, co-simulated with production FCS, was developed and applied to model both coaxial aircraft and single main/tail rotor configurations (Refs. 1-5). The CFD solver is based on the CREATE™-AV HELIOS toolset (Ref. 6) and the CSD solver is based on Rotorcraft Comprehensive Analysis System (RCAS) (Ref. 7). The current paper further correlated the CoSim methodology (Ref. 1) with the S-92® helicopter flight-test database at both hover, cruise and edge-of-envelope maneuver flight conditions. The consistent correlations for flight dynamics, static and fatigue component loads at conditions across the flight envelope demonstrate the reliable predictive capability of the high-fidelity CoSim methodology to be-used as a virtual digital flight test and to support advanced design at early stage.
This paper introduces a robust supervised machine learning framework for estimating helicopter gross weight during the takeoff phase. The methodology leverages high-fidelity datasets from Airbus's global in-service fleet to ensure a reliable training foundation. At the core of the approach is a long short-term memory recurrent neural network, supported by a patented data-curation pipeline designed to maintain high data integrity. To align with rigorous aviation safety standards, the study outlines a learning assurance process compliant with EASA guidelines, specifically addressing safety assessment objectives for machine learning. A central innovation is the characterization and monitoring of the model's operational design domain through multidimensional functional principal component analysis. By projecting high-dimensional, non-linear sensor data into a manageable tabular subspace, this approach enables the definition of safety envelopes using explainable and efficient classical methods. Validated against diverse real-world flight profiles, the framework demonstrates high predictive accuracy, marking a significant milestone toward deploying the model on airborne targets for safety-critical functions such as condition-based maintenance.
Helicopter air tours operate in one of the most challenging and least-controlled environments of commercial aviation, yet the safety outcomes of these operations remain inconsistent across regulatory frameworks. This study examined 55 helicopter air tour accidents in the United States from 2014 to 2024 using data from the NTSB Case Analysis and Reporting Online database. Defining event narratives, contributing factors narratives, and probable cause were coded to identify causal relationships between accidents and identify safety trends between 14 CFR Part 91 operations and Part 135 operations. CFR Part 91 operations exhibited accident rates approximately three times higher than Part CFR 135, averaging 3.94 per 100,000 flight hours compared to 1.23. Maintenance/mechanical was the most common initiating cause for accidents under CFR Part 91, accounting for 52% of cases compared to 37% under Part 135. Pilot/related cases were more prevalent under CFR Part 135, accounting for 53% of accidents. The two regulatory frameworks operated substantially different fleets, with CFR Part 91 relying on reciprocating-engine helicopters (76%) and Part 135 on turbine-powered aircraft (81%). Engine and powerplant/related events accounted for 27% of all defining events, and nearly half of all events involved a technical or mechanical initiator.
Within the next years, it is expected that the capabilities that are demanded to the rotorcraft fleet would be enhanced with respect to the current ones. Very long range, speed above typical rotorcraft performance, hot and high HOGE capability and high payload capacity are foreseen, together with limitation on aircraft take-off weight (TOW): among these sizing cardinal requirements, speed characteristics and long-range operations drive the sizing towards innovative solution, to overcome the physical limitation of a conventional rotorcraft. The work starts with a performance-based comparison of different fast rotorcraft architectures, comparing it with respect to the conventional helicopter, used as benchmark. Once first investigation loop is completed with a preliminary sizing analysis, a detailed one is focused on tiltrotor architecture, showing the impact of hover and high-speed capability on lifting and powerplant systems, as well as the impact of sizing criteria on the overall performance. In such second step, a matrix scenario is proposed, where both requirements and sizing criteria are evaluated to show the peculiarity on tiltrotor solution. In conclusion, considerations on balanced criteria for tiltrotor sizing are reported, with focus on sizing trade-off.
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The testing aims to verify the system’s effectiveness in improving energy efficiency, clarify the cost of autonomous vehicles electrification, and identify the most cost-effective low-carbon fleet composition under different scenarios. The results demonstrate that ride-pooling system outperforms both ride-hailing and private vehicles. Ride-pooling achieves 15–25% lower carbon intensity and 18–25% energy savings compared to private vehicles. It is also found that EVs present, on average, an 8–12% higher trip rejection rate than ICE fleets, demonstrating that electrifying Robotaxis comes at the cost of reduced service levels or increased costs. The study ultimately finds that electrifying Robotaxis at a moderate level (40–60%) can achieve a good trade-off between environmental benefits, service quality, and cost.
As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of fuel systems, it is necessary to apply a cradle-to-grave approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by linking real vehicle movements with emissions data and energy consumption. By using real vehicle movements, environmental conditions (ambient temperature, etc.) and traffic situations (traffic jams, etc.) can be integrated into the emissions assessment. To capture the influencing factors more realistically, the assessment is performed at hourly rather than annual time intervals, unlike most previous studies. This new approach provides therefore a more detailed and realistic cradle-to-grave analysis of the Austrian passenger car fleet, making it possible to test individual measures in future scenarios and to define a coordinated strategy for minimizing the fleet’s future global greenhouse gas emissions.
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of anomaly severity. A fully automated pipeline ingests daily telemetry, performs session segmentation, executes predictive modeling, and records anomaly outcomes in backend databases for continuous monitoring and engineering review. The proposed framework enables continuous, fleet-wide predictive maintenance of DU oil pumps. It improves early detection of degradation, reduces vehicle downtime, enhances safety, and increases customer satisfaction. More broadly, it highlights the potential of large-scale data analytics and machine learning to advance predictive maintenance and reliability in electric vehicle (EV) systems.
Off-highway equipment operates in an environment defined by extremes - extreme loads, extreme duty cycles, extreme temperatures and extreme expectations. OEMs and fleet operators face mounting pressure to deliver more power, more uptime and more precision from platforms that are becoming increasingly compact, intelligent and complex. Whether the task is hauling, lifting, dumping, clearing or moving materials, the equipment must deliver consistent, reliable performance without compromise. This pressure is reshaping the mobile-hydraulic ecosystem. The industry is steadily shifting away from piecemeal systems and toward integrated, intelligent power architectures that maximize efficiency across the entire vehicle. Leaders in this space, Eaton among them, demonstrate how a system-level approach to PTOs, hydraulic pumps and control valves is enabling a new generation of off-highway innovation.
The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.
For any fleet or logistics manager, the specter of a downed Class 8 truck is a constant concern. The costs aren't just in parts and labor; they're in lost productivity, missed deadlines and potential damage to your reputation. While many factors can sideline a heavy-duty vehicle, one of the most persistent and costly culprits is hydraulic system failure. These failures often trace back to a single, preventable issue: contamination.
Vertical Take-Off and Landing (VTOL) aircraft introduce complex monitoring challenges due to distributed propulsion, lightweight structures, and variable operating conditions. This paper presents advanced Frequency and Orders domain techniques that repurpose existing flight control, propulsion, and structural sensor data to enhance observability without additional instrumentation. By transforming vibration, acoustic, and electrical signals into frequency and order domains, the approach enables detection of harmonics, resonance, and fault signatures tied to rotor dynamics, supporting adaptive control and predictive maintenance. Beyond rotor systems, these techniques are equally effective for monitoring electric motor health, gearbox wear, bearing degradation, and structural coupling effects in composite airframes. They also provide insight into power electronics and thermal management systems by identifying spectral anomalies linked to electrical imbalance or cooling inefficiencies. Aggregated fleet data strengthens prognostic capabilities, enabling early detection of systemic issues and trend analysis. Applications include mitigating ground resonance and modal instabilities, as well as improving reliability of propulsion and structural subsystems. Integration into avionics emphasizes computational efficiency, scalability, and compliance with standards such as DO-160 [1], DO-178 [2], ARP4761 [3] and ARP4764 [4]. Simulation and bench testing confirm feasibility, demonstrating potential to enhance safety, reliability, and lifecycle cost for next-generation urban air mobility platforms.
This paper discusses uncrewed aerial vehicles (UAVs) that can have additional applications beyond their respective civilian, industry, or military applications. The increasing popular electric UAVs in advanced air mobility (AAM) and urban air mobility (UAM) networks can be utilized to increase the efficiency and impact of emergency response in both urban and remote settings. The paper will explore the design considerations and requirements for these dual-use vehicles for specific public good missions, while presenting a survey of additional public good missions that could significantly benefit from additional ready-to-go drones. Additionally, this paper aims to explore the logistics required to implement a system for incorporating civilian, industrial, and military drones into a reserve fleet for emergency and disaster relief efforts.
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