Browse Topic: Engine control systems
Linear time-invariant (LTI) reduced-order models (ROMs) have been widely used in battery thermal management simulations due to their low hardware requirements, high computational efficiency, and good accuracy. However, the inherent assumption of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid. To address this limitation, a novel ROM is developed by decomposing the entire battery thermal system into two subsystems. All solid components are modeled as a traditional LTI ROM, while the coolant channel is represented using Newton’s cooling law. The two subsystems are then coupled through the exchange of heat transfer rate and temperature at the fluid–solid interface between the coolant and the cold plate. Model fidelity is further enhanced by introducing a spatially distributed heat flux during the generation of the LTI ROM for solid components. Validation is performed against CFD simulations at both module and pack levels, under constant and varying flow rates. The results demonstrate that the proposed ROM achieves high accuracy while requiring several orders of magnitude less computational time than the corresponding CFD models.
In recent years, computer-aided engineering (CAE) has become an essential practice in design and durability analysis of industrial components such as weldments. The current analytical trend for CAE-based fatigue life prediction of weldments includes procedures based on design guidelines, mesh-sensitive methods (e.g., local strain-life approach) and mesh insensitive methods (e.g., Volvo and Verity methods). As an inherent characteristic of weldments, the geometry of the weld is often simplified in failure analysis and important hotspots such as start/stop of the weld beads are not considered in the design process. However, such critical locations cannot be avoided in complex welded structures. Therefore, incorporating main geometrical details of the weld can improve the accuracy of critical regions identification and damage calculation using mesh-sensitive CAE-based methodologies. Herein, a framework for life prediction of welded components including the weld geometry is discussed and evaluated by its application to a coupled torsion beam axle. The weldment was simulated in finite element (FE) environment as a shell model with local mesh refinement and improved weld geometry. The FE model was validated by strain gage measurements of the actual component under single-channel constant amplitude load and critical locations in the component were accurately identified. Local stress-life and critical plane approaches were employed to predict fatigue life to failure resulting in reasonable accuracy within a factor of two. Despite the close results by the uniaxial and multiaxial fatigue damage criteria in this work, advanced life prediction approaches such as the critical plane concept are recommended due to their robustness for more complex and realistic loading conditions during service.
India’s commitment to carbon neutrality is significantly shaping the future architecture of commercial vehicle powertrains. While the use of CO₂-free technologies such as battery-electric drivetrains has already been successfully demonstrated across various applications, challenges related to limited range and the lack of high-power charging infrastructure continue to hinder widespread adoption, particularly for productivity-critical commercial vehicles. This has shifted the spotlight toward sustainable fuels, which offer the advantage of fast refueling times. Among these, hydrogen internal combustion engines (H₂ ICE) have gained increasing attention in recent years. In regions such as the European Union, the primary motivation for hydrogen is CO₂ reduction. In contrast, for markets like India, hydrogen also presents a strategic opportunity for reducing dependency on fossil fuel imports. Over the past four years, multiple performance and emission development projects across various H₂ ICE configurations have been carried out. A key enabler in these projects has been AVL’s Rapid Prototyping Engine Management System (RPEMS), featuring mature application software (ASW) designed to support port fuel injection (PFI, MPI), direct injection (DI), and high-pressure direct injection (HPDI) with diesel pilot. The system supports both steady-state and transient operation and includes functionality for exhaust aftertreatment control, such as single or dual-dosing SCR systems. For series production projects, the industry emphasizes compatibility with existing engine control strategies. Particularly for OEMs with in-house controls development, extending current functionalities to include H₂ ICE operation is more attractive than developing entirely new software from scratch. To validate this, AVL has adapted both its diesel-based (quality-controlled) and gas-based (quantity-controlled) software architectures to manage various hydrogen combustion strategies. Hybrid configurations are also possible, where standard EMS handles torque and air path control, while RPEMS manages hydrogen injection, ignition, and lambda control, enabling early-stage concept evaluation on engine testbeds or in vehicles. Additionally, robust detection and response to irregular combustion events such as knocking, misfire, and early or late pre-ignition, sometimes accompanied by backfire, are essential for ensuring engine protection and durability. This paper presents testbed results comparing diesel-based and gas-based control strategies applied to advanced H₂ ICE models. It also discusses approaches for irregular combustion diagnostics and the corresponding protective control measures.
The study emphasizes on development of Diesel Exhaust Fluid (DEF) dosing system specifically used in Selective Catalytic Reduction (SCR) of diesel engine for emission control, where a low pressure pumpless DEF dosing system is developed, utilizing compressed air for pressurizing the DEF tank and discharging DEF through air assisted DEF injection nozzle. SCR systems utilize Diesel Exhaust Fluid (DEF) to convert harmful NOx emissions from diesel engines into harmless nitrogen and water vapor. Factors such as improper storage, handling, or refilling practices can lead to DEF contamination which pose significant operational challenges for SCR systems. Traditional piston-type, diaphragm-type, or gear-type pumps in DEF dosing systems are prone to mechanical failures leading to frequent maintenance, repairs, and costly downtimes for vehicles. To overcome the existing challenges and to create a more reliable and simple DEF delivery mechanism the pumpless DEF Dosing system is developed. The system includes a completely sealed DEF tank, pressurized to a calibrated system pressure, by connecting the tank entry line to compressed air. Signal from the Engine Control Unit is provided to the metering valve positioned in the delivery line which controls the quantity to be dosed. Pressure reducing valve, quick relief valve and pressure sensor for feedback are integrated, with the tank and the metering valve to develop a completely reliable DEF dosing system. The system has been modelled in MATLAB and tested for different operating pressure, height and volume of the tank and by varying the signals provided to the metering unit. Overall, we can conclude that the pumpless DEF dosing system is a simplified version among the existing DEF dosing practices. When implemented it provides significant reduction in cost, complexity, repair and maintenance of the system eliminating the challenges and failures posed by the existing pump-based dosing systems.
In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicles and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to overfitting; and ii) SVM memory efficiency enables the design of a low-cost, compact size controller board. The first step of our research, described in this paper, is to test the algorithm proposed and verify its performance using the usual machine learning metrics. An open source dataset has been used for training and testing of our SVM-based algorithm and the promising results achieved are shown. As part of future work, this experimental control module will be installed on an H2-powered motor on test bench to assess its functionality and allow proper tuning.
Compressor durability is a critical factor for ensuring the long-term reliability of Mobile Air Conditioning (MAC) systems in passenger vehicles. This study presents a software based strategy for enhancing compressor life using Smart Fully Automatic Temperature Control (FATC), requiring no additional hardware. The proposed approach leverages existing inputs from the FATC and Engine Management System (EMS) to intelligently manage compressor operation, with a focus on addressing challenges related to prolonged non-usage. In extended inactivity scenarios such as during cold weather, vehicle exportation, storage, or breakdowns, lubrication oil tends to settle in the compressor sump, leaving internal parts dry. Sudden reactivation at high engine speeds under such conditions can cause increased friction, wear and even compressor seizure. To mitigate this, an intelligent reactivation protocol has been developed and integrated into the Climate Control Module (CCM). This protocol continuously monitors parameters such as ambient and evaporator temperatures, solar load and engine RPM to detect extended inactivity. Upon detection, it initiates a controlled compressor activation sequence involving short duration clutch engagement cycles, allowing gradual lubrication and preventing mechanical stress. The strategy includes a multivariable detection framework and dynamic threshold adaptation that tailors activation logic to real-time environmental and operational conditions. A Smart transition mechanism ensures smooth switching between safe and regular operation modes. Preliminary testing shows that this method effectively minimizes dry starts, reduces mechanical wear and supports long term compressor health. The proposed strategy offers a cost effective and robust solution for improving compressor durability, lowering maintenance costs and enhancing user satisfaction.
A hierarchical control architecture is commonly employed in hybrid torque control, where the supervisor CPU oversees system-level objectives, while the slave CPU manages lower-level control tasks. Frequently, control authority must be transferred between the two to achieve optimal coordination and synchronization. When a closed-loop component is utilized, accurately determining its actual contribution to the controlled system can be challenging. This is because closed-loop components are often designed to compensate for unknown dynamics, component variations, and actuation uncertainties. This paper presents a novel approach to closed-loop component factor transfer and coordination between two CPUs operating at different hierarchical levels within a complex system. The proposed framework enables seamless control authority transition between the supervisor and slave CPUs, ensuring optimal system performance and robustness. To mitigate disturbances and uncertainties during the transition, we introduce a model-based learning phase that reduces actuation mismatch. The effectiveness of the proposed approach is demonstrated through simulation results, focusing on the authority transfer of engine speed tracking between an engine control module and a hybrid powertrain supervisor. The results highlight the enhanced system performance and reliability achieved by the method described.
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