Sliding mode control for overturning prevention and hardware-in-loop experiment of heavy-duty vehicles based on dynamical load transfer ratio prediction

Aiming at the rollover risk of heavy-duty vehicles, an adaptive rollover prediction and control algorithm based on identification of multiple road adhesion coefficients is proposed, and the control effect has been verified by hardware-in-the-loop experiments. Based on the establishment of a 3 DOFs (Degree of freedom) vehicle dynamic model, the roll angle of the vehicle dynamic model is estimated in real time by using Kalman filter algorithm. In order to ensure the real-time operation of anti-rollover control strategy for multi-body dynamic heavy vehicle model, a sliding mode variable structure controller for anti-rollover of vehicles is designed to determine the optimal yaw moment. Specially, the recognition algorithm of road surface type is integrated into the control rollover algorithm. When the control system with road recognition algorithm recognizes whether the vehicle is in danger of rollover, it can not only adjust the state of the vehicle, but also shorten the time to reach the stable area of the vehicle's lateral load transfer rate by about 2 s. In order to further improve its adaptability and control accuracy, a Hardware-in-loop test platform for three-axis heavy-duty vehicles is built to verify the proposed anti-rollover control strategy. The results prove that the proposed control strategy can accurately predict the rollover risk and control the rollover in time.

3 kW Thermoelectric Generator for Natural Gas-Powered Heavy-Duty Vehicles—Holistic Development, Optimization and Validation

Emissions from heavy-duty vehicles need to be reduced to decrease their impact on the climate and to meet future regulatory requirements. The use of a cost-optimized thermoelectric generator based on total cost of ownership is proposed for this vehicle class with natural gas engines. A holistic model environment is presented that includes all vehicle interactions. Simultaneous optimization of the heat exchanger and thermoelectric modules is required to enable high system efficiency. A generator design combining high electrical power (peak power of about 3000 W) with low negative effects was selected as a result. Numerical CFD and segmented high-temperature thermoelectric modules are used. For the first time, the possibility of an economical use of the system in the amortization period of significantly less than 2 years is available, with a fuel reduction in a conventional vehicle topology of already up to 2.8%. A significant improvement in technology maturity was achieved, and the power density of the system was significantly improved to 298 W/kg and 568 W/dm3 compared to the state of the art. A functional model successfully validated the simulation results with an average deviation of less than 6%. An electrical output power of up to 2700 W was measured.

Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error.

Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.

Accelerating to zero: speeding up the decarbonization of heavy-duty vehicles in the EU

The objective of this report is to analyse the current status and outlook for decarbonization of the heavy-duty vehicle sector in the EU. The authors focus on developments over the coming 10 years, and how much the sector’s emissions could be reduced through energy efficiency improvements, electrification, and increased biofuel deployment.