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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Automation of Nanoparticle Synthesis Processes in a Plasma Environment Using LabVIEW

This work presents an automated control system for the synthesis of nanomaterials by plasma-enhanced chemical vapor deposition (PECVD), implemented using the LabVIEW software environment. The main objective of the study is to develop an integrated hardware-software platform that enables sequential control of the key stages of the PECVD process, including vacuum chamber preparation, pressure monitoring, working gas supply, plasma ignition, power matching, cyclic nanomaterial growth, and optical monitoring of nanoparticles in the plasma environment. The use of LabVIEW made it possible to integrate actuator control, experimental parameter acquisition, and realtime process visualization within a single automated system. The automated cycle begins with evacuation of the reaction chamber to a predefined base pressure. Transition to the next stage is permitted only after the specified pressure threshold has been reached, ensuring reproducible initial conditions for each experiment. The program then controls the supply of the working gas through mass flow controllers (MFCs). In this work, two gas-flow control modes were considered: analog control using a 0-5 V voltage signal and digital communication via RS-232 interface. It was shown that the analog approach requires accurate scaling of the control voltage, since applying 5 V corresponds to full-scale opening of the controller and results in the maximum gas flow. In contrast, the RS232 interface enables the gas flow rate to be specified directly in sccm, improving the accuracy, flexibility, and convenience of gas-environment control. After pressure stabilization, LabVIEW initiates RF plasma ignition and executes the RF matching algorithm aimed at minimizing reflected power and improving the stability of the plasma process. A separate software module implements the cyclic nanomaterial growth mode, in which the plasma-on time, plasma duration, and total number of synthesis cycles are predefined. This approach makes it possible to control material accumulation on the substrate and to correlate the process parameters with the morphological characteristics of the resulting nanostructures. The final module of the system is designed for optical monitoring of the nanoparticle cloud density in dusty plasma. For this purpose, the change in the intensity of laser radiation passing through the plasma region is recorded using a photodetector and a Keithley 2401 measuring unit connected to LabVIEW via RS-232 interface. The difference between the initial and modified optical signal intensity is used as a diagnostic parameter characterizing the formation and temporal evolution of nanoparticles. The developed system demonstrates that LabVIEW can be effectively applied not only for the automation of individual instruments, but also for the implementation of a complete digital control cycle for PECVD-based nanomaterial synthesis.

PECVD↗
DOE OSTI2025

Tracking and Positioning System for Floating Solar (CRADA Abstract)

The project goal is to develop a floating solar photovoltaics (FPV) tracking & position system that: (1) increases annual energy production of FPV projects by >10%, (2) lowers levelized cost of energy (LCOE) for FPV by >10%, and (3) leverages U.S. contract supply chain & manufacturing. The outcome of the project will be a certified tracking product that has undergone extensive field testing and is ready for commercial sales. The primary objectives for each budget period are: • BP1: Define product requirements, develop initial controls architecture and design other sub-components, complete small-scale pilot testing, install a larger-scale pilot, secure sites for commercial pilots, and develop the beta-version of a user portal. • BP2: complete control system and sub-component design, successful demonstration and testing at a commercial pilot, certification & bankability, finalize user portal, and complete various commercialization activities related to supply chain, customer acquisition, and sales. PNNL will provide support during both project phases for prototype development and testing of the controls architecture, software, and hardware components of the tracking and positioning system. PNNL will provide support during both project phases for prototype development and testing of the controls architecture, software, and hardware components of the tracking and positioning system. This effort represents PNNL’s first opportunity to support the floating solar photovoltaics (FPV) industry with capabilities, facilities, and personnel developed to contribute to the marine energy (e.g., wave and tidal energy) sector. This portfolio expansion leverages internal and DOE EERE investments and the growing visibility of PNNL-Sequim’s Marine and Coastal Research Laboratory (MCRL) and our marine research capabilities, in general. The development of effective and low-cost FPV platforms is a potential way to increase the nation’s set of tools for providing emission-free electricity without utilizing valuable terrestrial resources. Successful commercialization of such a project may lead to economic benefits through job creation, supply chain creation, and access to a cheaper source of electricity.

14 SOLAR ENERGY↗
DOE OSTI2025

Warm Front End Upgrade for the PIP-II Linac

The warm front end for the PIP-II Linac consists of an Ion-source, an RFQ, and four buncher cavities. The LLRF systems for these were the first ones developed more than a decade ago for use at the test stands. Some were VXI crate based, and others used early generation FPGA boards that are light in resources. These LLRF systems and the one for the first superconducting cryomodule, the HWR will be upgraded with the ARRIA 10 SOC-FPGA chassis with EPICS interfaces like the rest of the PIP-II Linac. The RFQ and the HWR have unique resonance control systems which are integrated into the LLRF controller hardware. The results of the initial testing with a cavity emulator will be presented.

Reyes, Lennon [Fermilab] (ORCID:0009000891155652)↗
DOE OSTI2024

Resilient Consensus State Observer for Nonlinear Systems and Against Attacks

Herein, we investigate the design of a resilient consensus observer that yields correct and continuous state estimates of a nonlinear control system whose state observations are subject to attacks. The observations are assumed to be obtained from multiple sensors and/or communication channels employed to provide redundancy. It is shown that, in an attack-free setting, individual globally convergent observers can be designed for each sensor/channel using the corresponding state-dependent Jacobian system. To counter attacks, a resilient consensus observer is then designed by merging the observers estimates to produce a smooth consensus estimate. Resilient convergence of the consensus estimate to the true state is ensured under the condition that the sparsity of the attacks being less than a half of the number of redundant channels. As an illustrative example, the proposed scheme is successfully applied to the chaotic circuit synchronization problem in which their synchronization has been attacked.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Redis-Based Streaming Architecture for Accelerator Beam Instrumentation DAQ Systems

The Fermilab Acceleraor Division, Beam Instrumentation Department, is always adopting modern and current software methodologies for complex DAQ architectures. This paper highlights the Redis Adapter (RA) as the key software component enabling high performance, modular communication between digitizers and distributed control systems by leveraging Redis and containerization. The RA provides a unified, efficient interface between Redis based data streams and consumer systems. In the legacy architecture, digitized data flowed through the custom, UDP based Distributed Data Communication Protocol in the middle layer. In the current system, DDCP remains the ingestion path, while the RA serves as the decoupling layer. The proposed system replaces old VME digitizers with a SOM-based digitizer that communicates with Redis using the RA. The RA acts as both a performance-critical bridge and a protocol-agnostic adapter, ensuring compatibility with legacy control frameworks while enabling future scalability and modularity. This restructuring of the middle layer also helps the system achieve high throughput, reduce latency, and simplify the data path. Finally, we will demonstrate how RA is utilized in our two core products to deliver both legacy compatibility and future flexibility.

Joshi, S. [Fermilab]↗
DOE OSTI2024

Static actuator-sharing algorithm for concurrent control of multiple plasma properties

Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and fusion pilot plant may require the integration of different plasma control algorithms. Such integration requires the conversion of individual controller commands into physical actuator requests while accounting for the coupling between different plasma properties. This work proposes a tokamak and scenario-agnostic actuator-sharing algorithm (ASA) to perform the above-mentioned command-request conversion and, hence, integrate multiple plasma controllers. The proposed algorithm implicitly solves a quadratic programming (QP) problem formulated to account for the saturation limits and the relation between the controller commands and physical actuator requests. Since the constraints arising in the QP program are linear, the proposed ASA is highly computationally efficient and can be implemented in the tokamak plasma control system in real time. Furthermore, the proposed algorithm is designed to handle real-time changes in the control objectives and actuators’ availability. Nonlinear simulations carried out using the Control Oriented Transport SIMulator illustrate the effectiveness of the proposed algorithm in achieving multiple control objectives simultaneously.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

How Much Reserve Fuel: Quantifying the Maximal Energy Cost of System Disturbances

Motivated by the design question of additional fuel needed to complete a task in an uncertain environment, this paper introduces metrics to quantify the maximal additional energy used by a control system in the presence of bounded disturbances, compared to a nominal, disturbance-free system. In particular, we consider the task of finite-time stabilization for a linear, time-invariant system. We compare the nominal energy required to achieve this task in the disturbance-free system to the worst-case energy over all feasible disturbances. Solving for the worst-case energy over all disturbances first leads to an optimal control problem with a least-squares solution, and then an infinite-dimensional optimization problem where we derive an upper bound on the solution. The comparison of energies is accomplished using additive and multiplicative metrics, for which we derive bounds. Simulation examples on an ADMIRE fighter jet model demonstrate the practicability of these metrics, and their variation with the distance of the initial condition from the origin and the task completion time.

koopman operator, resilience↗
DOE OSTI2026

Modeling, Validation, and Control of the IEA‐15 MW Reference Wind Turbine and VolturnUS‐S Platform

This paper presents the acausal modeling, validation, and control of floating offshore wind turbines (FOWTs). The model simulates the IEA‐15 MW reference turbine and the semi‐submersible VolturnUS‐S platform utilizing a Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS), which integrates the key coupled aero‐hydro‐elasto‐servo dynamics and is being developed by authors at the University of Central Florida. Verification and validation are conducted using numerical data from the industry‐standard simulation platform OpenFAST and experimental data from the Floating Offshore‐wind and Controls Advanced Laboratory (FOCAL) project, in which the authors were involved. Numerical results demonstrate the model's ability to qualitatively capture loads and responses across various load cases, highlighting the impact of the control system under different wind and wave conditions and opening new opportunities for optimizing FOWT designs. This paper provides wind turbine researchers with valuable insights into system characteristics, system frequencies, damping effects, and internal reaction forces, serving as a reference for future studies in FOWT modeling and control.

17 WIND ENERGY↗
DOE OSTI2025

Autonomous Infrared and Small (Wide) Angle X-Ray Scattering (IR-S(W)AXS) Capability

Thin water films are 2-D, nanoconfined layers that form on solid surfaces exposed to humid atmospheres—environments ubiquitous across catalysis, corrosion science, soil science, and subsurface geochemistry. At relative humidity (RH) values below saturation, these films are Å–nm thick and exhibit properties that differ sharply from bulk water, including disrupted H-bonding and impeded mass transport. Owing to their high surface-to-volume ratio, dissolution of the solid can rapidly drive strong supersaturation with respect to secondary phases. Reactivity in thin water films is highly sensitive to film thickness, and critically, thickness evolves during reaction because the hygroscopicity of the interfacial system changes as ions accumulate or diminish in the film and as reaction products transform. To accurately probe and control these dynamics, a capability is needed that can measure and automatically maintain a constant water-film thickness while simultaneously monitoring solid dissolution, nucleation, and growth. This project developed an autonomous Infrared/Small Angle X-ray Scattering-Wide Angle X-ray Scattering (IR/(W)SAXS) for investigating reactivity in thin water films on solid surfaces exposed to humidified gases. The capability consists of an IR spectrometer, a (W)SAXS instrument, and a mass flow controller system for generating variably humidified gas flows to a custom reaction cell. Progress on each of the major components of the capability are detailed below.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Evaluation of a 5kW Solid Oxide Electrolysis Cell Stack

Solid oxide electrolysis cells (SOECs) are a developing technology for hydrogen production. They are promising due to their utilization of thermal energy to reduce their electricity consumption. The Department of Energy (DOE) has set goals to reduce the price per kilogram of hydrogen, and Idaho National Laboratory (INL) is conducting research to develop and demonstrate advanced methods and technologies to achieve making hydrogen a more affordable, efficient, and sustainable energy source. In this project, INL is collaborating with a vendor to evaluate and validate the design of a 5kW SOEC stack. The test aims to demonstrate a safe startup, operation, and shutdown on INL’s 5kW test stand, providing third-party validation in a different environment. A successful 50-hour test with stable hydrogen production will pave the way for a subsequent 1000-hour test. This project emphasized learning about electrolysis and the various support systems, referred to as balance of plant (BOP) equipment. These systems required modification while swapping to the vendor 5kW SOEC stack, most prominently the furnace door design and the controls system. It also included gaining skills in creating computer-aided design (CAD) drawings for the new door while using available materials and ensuring it met high-temperature requirements. Upon completion of modifications to the test stand, the SOEC will be test-fitted with respective wiring and piping. Instrumentation and piping will be checked for leaks and quality. This must be completed before the vendor’s engineers arrive to observe the test plan, startup, and a collection of 50 hours of data. The vendor’s engineers will ensure their stack performed sufficiently in the 50-hour test to enable the progression to a 1000-hour test.

08 - HYDROGEN↗
DOE OSTI2024

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

42 ENGINEERING↗
DOE OSTI2026

Preliminary proof-of-concept of real-time divertor heat flux control from infrared cameras with nitrogen injection in the DIII-D tokamak

In future tokamak reactors like ITER and the Fusion Pilot Plant (FPP), real-time feedback control of heat flux to the plasma-facing components (PFC) will be critical for steady-state operation. This work presents the first experimental demonstration of real-time divertor heat flux estimation with infrared thermography and feedback control with impurity seeding on the DIII-D tokamak. The flexible infrastructure of the Plasma Control System (PCS) on DIII-D makes this new capability possible. The PCS software runs on a gateway computer system, and five real-time compute nodes. An array of low latency streaming digitizers from D-TACQ Solutions connects to these real-time computers to collect and process data, and send commands to actuators during plasma discharges. This system handles the signal IO from the tokamak and allows the PCS to utilize the diagnostic data necessary to perform control in real-time. Feedback control on heat flux was accomplished by feeding infrared camera data from the “Infrared TV” (IRTV) camera to a custom-developed User Datagram Protocol (UDP) server. This server transmits infrared data to a newly developed PCS algorithm that estimates the heat flux to PFC. Here, a proportional integral derivative (PID) controller minimizes the error between a heat flux reference and the real-time estimate by injecting nitrogen gas into the divertor.

Algorithm development↗
DOE OSTI2024

National Security Programs - Cyber: MMAREJBLIGE – Modular Multi Agent Grid Emulation for Joined Breakdowns in Linked Generative Emulations - 23-0644

Modular Multi Agent Grid Emulations for Joined Breakdowns in Linked Generative Emulations (MMAREJBLIGE) introduces an agent-based modeling framework into real-time cyber-physical emulation to achieve a context-aware environment that introduces operator/attacker/external-condition variability to improve emulation fidelity and testing rigor. We detail our agent framework design, internal communication via message passing, and time synchronization, as well as the individual components of the system. We include a brief analysis of several scenarios run on a real-time, hardware-in-the-loop, Industrial Control Systems (ICS) test-bed which include normal operation, physical disruption, disruption with mitigation, and disruption with mitigation during a cyber denial-of-service (DOS) attack.

42 ENGINEERING↗
DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗