Browse Topic: Software-in-the-loop (SIL)
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
Advancing HIL and SIL Validation for eVTOL from Tip to Battery to Tail by Bloomy Controls
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The paper discusses the workflow for model training and integration strategies for deep learning models within simulation tools like AMESIM and Simulink. With this approach significant time reduction is observed without affecting the accuracy when compared with the physical model. NN FMU also reduces efforts up to 40 % compared with traditional FMU conversion. CPU improvement from physical to NN FMU model achieved greater than 30 % reduction with the same accuracy. NN FMU maintains FMI compatibility and can be directly used in a wide range of XiL applications such as Model-in-the-Loop (MiL), Software-in-Loop (SiL), and Hardware-in-Loop (HiL) testing scenarios. This NN FMUs opens pathways for hybrid modelling approaches that combine data-driven and physics-based paradigms for automotive simulations.
Evaluating the impact of software changes on fuel consumption and emissions is a critical aspect of transmission development. To evaluate the trade-offs between performance improvements and potential negative effects on efficiency, a forward-looking Software-in-the-Loop (SiL) simulation has been developed. Unlike backward calculations that derive fuel consumption based solely on cycle speed and engine speed, this approach executes complete driving cycles as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC) within a detailed SiL environment. By considering all relevant influencing factors in a dynamic simulation, the method provides a more accurate assessment of fuel consumption and emission differences between two versions of the transmission software. The significant contribution of this work lies in the high-fidelity integration of a real virtual Transmission Control Unit (vTCU) software within a comprehensive, validated forward-looking SiL environment. This approach enables precise delta comparisons that capture transient dynamic interactions, facilitating early, reliable software testing and validation.
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on a 4.7 km Worldwide harmonized Light-Duty vehicles Test Cycle (WLTC) driving segment, the RL-optimized shift strategy reduced fuel consumption from a baseline of 0.46 L to 0.37 L. Furthermore, when starting from an already optimized shift map representative of a series production vehicle’s calibration, the artificial calibrator further enhanced fuel efficiency, achieving approximately a 0.6 % reduction in fuel consumption for the 4.7 km segment and nearly a 5 % reduction for the full WLTC. The artificial calibrator thus demonstrates a promising methodology to frontload calibration tasks in simulation, thereby offering the potential to reduce reliance on resource-intensive physical testing and to significantly accelerate the development of fuel-efficient powertrain control software.. The direct compatibility of parameter files with real vehicle Electronic Control Unit (ECUs) and the validated SiL behavior suggest high transferability of learned strategies, offering the potential for minimal fine-tuning on physical vehicles post-simulation.
Nowadays, Software-in-the-Loop (SIL) represents a crucial methodology in the development and validation of control systems, particularly in sectors such as automotive, marine, and aerospace. It involves creating a virtual representation of a real environment with varying levels of accuracy. Using SIL techniques, engineers can develop and test software in the early stages of the development cycle, reducing overall time-to-market and costs. Typically, to simulate complex control systems, a primary tool is used to manage and integrate an entire application-specific environment composed of application software, plants, sensors and actuators, and communication protocols. Although several commercial solutions are currently available on the market to support SIL activities, Dumarey Softronix wanted to explore the possibility of developing an in-house software tool to leverage the benefits of SIL. This paper provides a high-level overview of the main steps involved in developing a complete SIL framework in contexts compliant with Autosar and based on Functional Mock-up Interface (FMI) standards. In the first part of the paper, the main choices behind the development of a SIL tool will be presented, such as the selection of the GUI framework selection, the simulation engine and the co-simulation framework. In the second part, the authors will discuss the key features a SIL solution should offer, including C-code building, C-code debugging, calibration loader, XCP/ASAP2, and a bus analyzer. In the final part of the paper, the tool is assessed by selecting two projects and comparing the results of a HIL bench with SIL results. The comparison highlights the consistency and accuracy of the SIL tool, demonstrating its ability to replicate real-world scenarios in a virtual simulation environment.
Bloomy: COTS HIL/SIL/COPPER Bird Testing Equipment
This study investigates the fault tolerance of a large-scale coaxial quadrotor Electric Vertical Takeoff and Landing (eVTOL) under motor failure through high-fidelity software-in-the-loop (SIL) simulations using PX4-Gazebo environment. The objective is to evaluate the vehicle's ability to maintain flight stability and complete critical missions under various propulsion failure scenarios, without the control system being explicitly aware of which motors have failed. Four motor failure cases-single, two adjacent, two diagonally opposite, and three distributed motor failures-were introduced during takeoff, hover, cruise, and hover under crosswind missions. Results show that the eVTOL maintained controllability and mission completion under all scenarios, with increasing levels of performance degradation under more severe failures. Notably, considerable yaw instabilities of about 10 degrees occurred under two diagonally opposite motor failures. The highest thrust demands after motor failures were observed during cruise mission, with some motors demanding about 80% to 90% of their maximum throttle. Hover under crosswind revealed compounded challenges in attitude control during descent under severe failure cases compared to calm weather. These findings underscore the robustness of the integrated control system and vehicle configuration in managing motor failure scenarios.
Bloomy: HIL/SIL Verification and Validation Testing
Validation plays a crucial role in any Electronic Development process. This is true in the development of any automotive Electronic Control Unit (ECU) that utilizes the Automotive V process. From Research and Development (R&D) to End of Line (EOL), every automotive module goes through a plethora of Hardware (HW) and Software (SW) testing. This testing is tedious, time consuming, and inefficient. The purpose of this paper is to show a way to streamline validation in any part of the automotive V process using Python as a driving force to automate and control Hardware-in-the-loop (HIL) / Model-in-the-loop (MIL) / Software-in-the-loop (SIL) validation. The paper will propose and outline a framework to control test equipment, such as power supplies and oscilloscopes, load boxes, and external HW. The framework includes the ability to control CAN communication signals and messages. A visual Graphical User Interface (GUI) has also been created to provide simplified operation to the user without knowledge of the backend. This provides a user-friendly display to synchronize multiple SW and HW toolsets together. The proposed methodology creates an efficient automation suite that can be tailored to any type of validation. The GUI provides portability, diversity, and simplicity, which allows for a streamline and efficient process for validation. The paper will refer to user cases to demonstrate the process and setup to perform functionality followed by the conclusion.
The evolution of automotive Electronic Control Unit (ECU) technology brings the additional safety, comfort, and control to the vehicle. With an exponential increase in the complexity involved in modern-day ECU, it is very important to verify and validate robustness, functionality, and reliability of ECU software [1]. As of now, Hardware in loop [HIL] and Vehicle in Loop validations are well known software functional validation methods. However, these methods require physical setup, which can incur more cost and time during the development phase. In recent years, ECU virtualization gained attention for development and validation of automotive ECUs [2]. The goal is to minimize the effort on software testing. This paper focuses on virtualization of Electric Vehicle (EV) powertrain system using SIL approach. The objective is to provide an adaptable EV-virtualization environment for virtual-ECU (vECU) verification and validation. This paper focuses on standardization of SIL simulation setup by using modularized plant models. The term virtualization refers to a methodology of simulating automotive ECUs in a virtual environment (IT equipment) using SIL approach. In this virtual validation approach, ECU software stack is compiled and converted into an executable which can run on general purpose IT equipment with Windows or Linux environment. This executable is called a vECU. As there are no hardware dependencies, virtual ECUs provide the advantage of testing various scenarios in a simulated environment [3]. The demonstrated EV virtualized SIL solution could ultimately result in faster and more cost-effective software development cycle.
The Virtual Autonomous Navigation Environment (VANE) is a set of tools that have been developed over a decade to assist autonomy developers in building autonomous systems. VANE has high-fidelity, physics-based sensors and vehicle models that interact with virtual environments built by utilizing decades of experience in characterizing environmental conditions. These models and environments are used in software-in-the-loop simulations to assist in the development and evaluation of autonomous vehicles in a cost-effective and time-sensitive manner. The software-in-the-loop simulations have been verified with data from concurrent physical testing and are used by autonomy developers to improve the safety, scalability, and cost effectiveness of testing autonomous vehicles.
Automated driving, electrification, cloud computing and the push toward software-defined vehicles are forcing automotive and commercial-vehicle developers to revamp design strategies. Tools suppliers are moving to help engineers develop and verify solutions that address the complete vehicle environment, a task that requires a growing number of design tools. During the recent dSPACE World Conference in Munich, Germany, several vehicle manufacturers described their strategies for coping with these trends. dSPACE, which supplies hardware/software-in-the-loop (HIL/SIL) tools, announced plans to see if tool makers can find a way to help developers by making it easier to integrate data created using different development software.
The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels. The camera SiL performs its functions (e.g., object detection, active safety functions) and provides output (e.g., braking request) to the active safety simulation tool. The vehicle plant model updates the vehicle state (e.g., applies braking). The vehicle’s position is updated in the context of the test scenario, and a new VDS frame is generated. Preliminary results are presented and discussed along with potential uses of this simulation setup in vehicle active safety development.
Autonomous vehicles (AVs) are self-contained vehicles equipped with control systems to execute various tasks. The Lane Departure Warning (LDW) system is widely employed to prevent the most common cause of vehicle collisions. An autonomous lane-departure system will aid and reduce such collisions. When the vehicle is at risk of drifting or departing its lane, the LDW system monitors its relative position to the lane edge and sends an alarming warning signal to the driver. This work uses an ML-based technique to detect lane markers in an Indian context using a high-resolution camera mounted on the car. Considering that, the LDW system requires three primary operations. The camera geometry information is used to divide the acquired image into two parts: a road part and a non-road component. Then, to circumvent the obstacles caused by the perspective effect, inverse perspective mapping is applied. Then, using a sliding window technique, lane markers are filtered, and Canny edge detection is performed. In addition, the Hough transform method is employed to detect lane markers. If the system detects the vehicle is too close to the right, or left lane markings, a warning light, vibration, or sound will be activated, according to the euro NCAP standard for a lateral offset of 0.76 m. TiHAN IITH test track evaluates ADAS functionality for the Indian market as per euro NCAP standard. In addition, a TiHAN IITH test track was built and validated using real-time testing with ADAS capabilities using the SIL-based software environment. Many sensors in the vehicle dynamics of the mid-size M1 automobile have been adapted to communicate with the SIL environment. For experimental validation, SIL provides iterations of repeated actions for test scenarios that pass or fail based on the NCAP test score.
Simulation of real time situations is a time tested software validation methodology in the automotive industry and array of simulation technologies have been in use for decades and is widely accepted and been part & parcel of software development cycle. While software that is being developed needs detailed plan, architecture and detailed design, it also matters during its development that, it is built in the right way from the very beginning and is fine tuned constantly. Especially for Software-In-Loop simulation (SIL), plenty of practices/tools/techniques/data are being used for simulation of system/software behavior. When it comes to choosing the right simulation technique and tools to be adopted, often there are discussions revolve around cost, feasibility, effectiveness, man-power, scalability, reusability etc. As automotive software validation is data driven, we deal with myriad of ground truth data for simulations, ranging from vehicle dynamics to vehicle models to environment factors (road, test track, weather). While infusing the ground truth data (physical data) in a simulation environment is still possible, availability of the same is often sparse, owing to test track constraints, availability of target objects, high risk maneuvers (involving higher vehicle speeds, narrow road curvatures, weather factors, accident reconstruction etc.) To tide over these constraints, in recent times, organization are inclined towards adopting virtual software simulation techniques, as it offers precision, scalability, cost and time effectiveness, and helps developers with early feedback to fine tune the software. CarMaker (Third Party Simulation tool) covers all the benefits stated above when it is integrated with the software (to be tested) in a closed loop. Vehicle data, Sensor data and real time scenarios (use cases) can be readily modelled for customer requirements for a product/SW and simulated and tested readily to provide to the developers on system/SW performance, ranging from ego dynamics, trajectory, camera/radar calibration, component/subsystem performance, accident severity, accident implications dealing with costs & damages, impact velocity, collision mitigation etc. Closed loop simulations are widely in use during development and testing of safety/cruising applications like ACC (Adaptive Cruise Control), EBA (Emergency Braking Assist), LCF (Lateral Control Functions) and also assists in functional areas of NCAP (New Car Assessment Programme) [1] and GIDAS (German In-Depth Accident Study) topics.
In this article, a formation flying technique designed for a multiple unmanned aerial vehicles (multi-UAV) system to provide low-cost and efficient solution for civilian and military applications is presented. First, a modular leader-follower formation algorithm was developed to accomplish the formation flying with off-the-shelf low-cost components and sensors. Second, a proportional-integral-derivative (PID) controller was utilized for velocity control of the UAVs to maintain the tight formation. Third, a particle swarm optimization-optimized reciprocal velocity obstacles (PSO-RVO) algorithm was utilized for obstacles avoidance and collision avoidance between the UAVs while navigating, with the aid of sonar ranging sensors onboard. The formation flying algorithm developed was tested through both simulation and experiment using two quadcopters with global positioning system (GPS) signals. For the simulation, the algorithm developed was tested on a virtual quadcopter using an open-source software-in-the-loop (SITL) simulator. With the aid of the experimental test, the effectiveness of the proposed formation flying algorithm is evaluated. With a separation distance of 5 m between the UAVs, the proposed system is able to achieve an average separation error of 0.3872 m and percentage of root mean square error (RMSE) of 9.7%. Therefore, it is shown that the proposed formation flying system is very effective.
Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed. A shallow neural network model is set up in Matlab for modelling the combustion phase and the knock intensity. The training of the network is performed with a Design of Experiment (DoE) approach, where different numbers of neurons are tested using training algorithms and activation functions. The output of the simulation is then compared to the experimental values. The results are evaluated using the Root Mean Square Error and R-square indexes, and the combination of the number of neurons, training algorithm, and activation function, which yield the lowest error and the highest R-square, are selected. In the second part, the ANN models are then coupled to analytical sub-models previously developed and individually validated, to build a complete engine simulator, and they are implemented in Simulink. The performance of this simulator is then evaluated by comparing simulated results and experimental data, both under steady-state and transient conditions.
This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping. This paper begins by outlining the controller development progression from a previously published engine-in-the-loop study to the in-vehicle driver-in-the-loop testing highlighted in this work. The purpose of this field testing is to quantify the level of agreement between field data and prior results, particularly noting the influence of noise, disturbances and unmodeled dynamics. Also detailed are the steps to effectively implement the predictive horizons in experimentation with human rather than automated driver inputs by replacing the conventional speedometer with an augmented driver display. The driver display seeks to maintain driver awareness and route preview simultaneously. Additional features of the instrumented truck testing protocol include detailed route maps on speed limits, intersections, traffic and road grade; highly accurate fuel flowrate measurements calibrated via gravimetric methods; and closed-loop control between the predictive controller, driver, and vehicle controller area network (CAN). These combined capabilities allow for model validation, investigation of the effects of human-in-the-loop on controller performance, and identification of unmodeled dynamics and disturbances. The results show that the driver advisory implementation matches fuel consumption predictions from software-in-the-loop simulations and engine-in-the-loop studies to within 2-9%. Following the study of in-vehicle performance and variability, these results also validate, using real road tests, the up to 21% fuel savings achieved by using the hierarchical control scheme with a combination of multiple chassis and powertrain optimization technologies.
With the enhancements in vehicle electrification and autonomous vehicles, Traffic systems are also being improved at an accelerated rate to aid the development of improving fuel economy standards. For this to be possible, it is essential that traffic can be accurately modeled and predicted. The existing toolsets are proprietary and expensive and traffic modeling is not a trivial task due to its dependence on various factors such as place, time, and weather. To address these issues, an entirely open-source Software-In-Loop (SIL) fleet-focused traffic modeling toolset has been developed with the ability to take environmental factors with powertrain-in-the-loop into account leveraging Simulation of Urban Mobility (SUMO) and python. The proposed SIL toolset encompasses the creation of a microscopic traffic distribution which accounts for the usual traffic trends of a typical day. Parameters such as the number of vehicles entering the network and the speed of all the vehicles at a time of a day can be controlled by tunable weather conditions, which is obtained using weather APIs or datafiles. Given a network, origin and destination roads can be defined to create the routes based on shortest distance using default algorithms (like DUAROUTER) in SUMO on a pre-defined real network from Open Street Map or similar mapping standard. In this current study, the ego vehicle has been called into the traffic system after every hour, and information like battery charge, vehicle speed, and time of its trip are logged after every second at different traffic conditions. This developed framework can be used to test electric vehicles or fleets in a typical traffic scenario (which is determined by the weather conditions) and the effects of the traffic on the ego vehicle’s properties like the state of charge of the battery can be studied and modeled. This work can be extended to CAVs by using communication tools like Veins and OMNET++.
Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified. These core competencies are then considered both in isolation and in conjunction with other potential confounding factors (e.g. other traffic or atmospheric conditions); with “edge cases” being represented as compounded or unique sets of confounding factors. Using this approach, a candidate scenario set is presented, along with a discussion of nuances and necessary considerations in scenario selection. These approaches are combined in a simulated environment to demonstrate their use. Finally, a strategy is proposed to automate the overall scenario testing process to make the execution less cumbersome. This process of test scenario creation strictly follows the ISO 26262 concept phase to verify the safety goals and functional safety requirements.
Multispeed eDrive or eAxles arguably benefit the overall performance and efficiency of an electric vehicle. The majority of the benefits can be derived from rightsizing and optimal control over the gear shift sequence under various driving scenarios. This paper focuses on developing an optimal shift schedule and precise shift controls for a special utility three-wheeled electric vehicle using a Model-Based Design approach. The supervisory control logic is implemented using Stateflow. Further, the entire shift mechanism with the controller, stepper motor and driver is modeled in the Matlab-Simulink-Simscape environment. A novel solution of integrating the SolidWorks CAD model of the gearbox provided by the manufacturer with the shift mechanism using Simulink Multibody is presented. Finally, the controller model and C- code test methods are presented to validate the behavior and functional requirements using MIL, SIL and PIL on a prototype microcontroller chip.
Model-based system simulations play a critical role in the development process of the automotive industry. They are highly instrumental in developing embedded control systems during conception, design, validation, and deployment stages. Whether for model-in-the-loop (MiL), software-in-the-loop (SiL) or hardware-in-the-loop (HiL) scenarios, high-fidelity plant models are particularly valuable for generating realistic simulation results that can parallel or substitute for costly and time-consuming vehicle field tests. In this paper, the development of a powertrain plant model and its correlation performance are presented. The focus is on the following modules of the propulsion systems: transmission, driveline, and vehicle. The physics and modeling approach of the modules is discussed, and the implementation is illustrated in Amesim software. The developed model shows good correlation performance against test data in dynamic events such as launch, tip-in, tip-out, and gearshifts. To quantify the correlation accuracy, metrics are defined to provide a numerical assessment of plant model behavior and accuracy. In this study the engine is treated as a torque source, to concentrate on transmission, driveline, and vehicle dynamics. If necessary, a full-fledged engine model based on GT-Power can be connected through co-simulation interface. Co-simulation of software from different vendors greatly expands the plant modeling capability and the flexibility of coupling with controllers. Co-simulated models for both SiL and HiL purposes are discussed, with the focus on the interface options, cosim running configurations, model reduction, and bench validation. Although the vehicle modeled here has a conventional powertrain with an automatic transmission, the modeling architecture and correlation methodology can be readily applied to alternative propulsion systems.
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