Search Results (5)

The automotive industry faces unprecedented pressure to address stringent emissions regulations, evolving consumer expectations, and the urgent need for sustainable mobility solutions. As the global fleet transitions toward lower environmental impact, there is an increasing demand for engineering innovations that can rapidly and cost-effectively modernize existing vehicles. This paper presents a quantitative control-variable analysis, attributing ECU remapping, hardware upgrades (capability envelopes), and water–methanol injection contributions in a production compression-ignited retrofit, achieving 211% power scaling alongside −18% NO_x/−30% opacity compared to baseline values. The study specifically investigates the implementation of ECU tuning, hardware modifications, and auxiliary systems such as WMI, demonstrating their vital role in enhancing vehicle performance, reducing emissions, and extending the operational lifespan of current fleets. By providing actionable engineering solutions, this work supports the industry’s urgent transition to more sustainable and efficient mobility, positioning retrofitting as a cornerstone of future automotive development and environmental compliance. Full article

The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system. Full article

The cooling system is an important subsystem of an internal combustion engine, which plays a vital role in the engine’s dynamical characteristic, the fuel economy, and emission output performance at each speed and load. This paper proposes an economical and precise model for an electric cooling system, including the modeling of engine heat rejection, water jacket temperature, and other parts of the cooling system. This model ensures that the engine operates precisely at the designated temperature and the total power consumption of the cooling system takes the minimum value at some power proportion of fan and pump. Speed maps for the cooling fan and pump at different speeds and loads of engine are predicted, which can be stored in the electronic control unit (ECU). This model was validated on a single-cylinder diesel engine, called the DK32. Furthermore, it was used to tune the temperature of the water jacket precisely. The results show that in the common use case, the electric cooling system can save the power of 255 W in contrast with the mechanical cooling system, which is about 1.9% of the engine’s power output. In addition, the validation results of the DK32 engine meet the non-road mobile machinery China-IV emission standards. Full article

In the current study, a 0D/1D engine model built in the commercial code GT-Suite was coupled with the Electronic Control Unit (ECU) model created in the Simulink environment, aiming to more accurately predict the interaction of the engine and aftertreatment system (ATS) operating parameters, both during steady-state and transient maneuvers. After a detailed validation based on extensive experimental data from a heavy-duty commercial diesel Internal Combustion Engine (ICE), the engine model was fine-tuned and the 0D predictive diesel combustion model, DIPulse, was calibrated to best predict the combustion process, including engine-out NOx emissions. For correct prediction of the engine’s behavior in transient operations, the complete control strategy of the air path, including boost, exhaust gas recirculation (EGR), main and pilot Start of Injection (SOI), injection pressure, and exhaust flap, was implemented in the Simulink environment. To demonstrate the predictive capability of the model, a hot World Harmonized Transient Cycle (WHTC) was simulated, obtaining good agreement with the experimental data both in terms of emissions and performance parameters, confirming the reliability of the proposed approach. Finally, a case study on possible fuel consumption improvement through thermal insulation of the exhaust manifold, exhaust ports, and turbocharger was carried out. Full article

This paper presents a simulator model of a diesel engine with a turbocharger for hardware-in-the-loop (HIL) applications, which is used to obtain engine performance data to study the engine performance under faulty conditions, to assist engineers in diagnosis and estimation, and to assist engineers in model-based calibration (MBC). The whole diesel engine system is divided into several functional blocks: air block, injection block, cylinder block, crankshaft block, cooling block, lubrication block, and accessory block. The diesel engine model is based on physical level, semi-physical level and mathematical level concepts, and developed by Matlab/Simulink. All the model parameters are estimated using weighted least-squares optimization and the tuning process details are presented. Since the sub-model coupling may cause errors, the validation process is then given to make the model more accurate. The results show that the tuning process is important for the functional blocks and the validation process is useful for the accuracy of the whole engine model. Subsequently, this program could be used as a plant model for MBC, to develop and test engine control units (ECUs) on HIL equipment for the purpose of improving ECU performance. Full article