Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

Dual-layered Multi-Objective Genetic Algorithms (D-MOGA): A Robust Solution for Modern Engine Development and Calibrations

Heavy-duty (HD) diesel engines are the primary propulsion systems used within the freight transportation sector and are subjected to stringent emissions regulations. The primary objective of this study is to develop a robust calibration technique for HD engine optimization in order to meet current and future regulated emissions standards during certification cycles and off-cycle vocation activities. Recently, California - Air Resources Board (C-ARB) has also shown interests in controlling off-cycle emissions from vehicles operating in California by funding projects such as the Ultra-Low NOx study by Sharp et. al [1]. Moreover, there is a major push for the complex real-world driving emissions testing protocol as the confirmatory and certification testing procedure in Europe and Asia through the United Nations - Economic Commission for Europe (UN-ECE) and International Organization for Standardization (ISO). This calls for more advanced and innovative approaches to optimize engine operation to meet the regulated certification levels.;A robust engine calibration technique was developed using dual-layered multi-objective genetic algorithms (D-MOGA) to determine necessary engine control parameter settings. The study focused on reducing fuel consumption and lowering oxides of nitrogen (NOx) emissions, while simultaneously increasing exhaust temperatures for thermal management of exhaust after-treatment system. The study also focused on using D-MOGA to develop a calibration routine that simultaneously calibrates engine control parameters for transient certification cycles and vocational drayage operation. Several objective functions and alternate selection techniques for D-MOGA were analyzed to improve the optimality of the D-MOGA results.;The Low-NOx calibration for the Federal Test Procedure (FTP) which was obtained using the simple desirability approach was validated in the engine dynamometer test cell over the FTP and near-dock test cycles. In addition, the 2010 emissions compliant calibration was baselined for performance and emissions over the FTP and custom developed low-load Near-Dock engine dynamometer test cycles. Performance and emissions of the baseline calibrations showed a 63% increase in engine-out brake-specific NOx emissions and a proportionate 77% decrease in engine-out soot emissions over the Near-Dock cycle as compared to the FTP cycle. Engine dynamometer validation results of the Low-NOx FTP cycle calibration developed using D-MOGA, showed a 17% increase brake-specific NOx emissions over the FTP cycle, compared to the baseline calibrations. However, a 50% decrease in engine-out soot emissions and substantial increase in exhaust temperature were observed with no penalties on fuel consumption.;The tools developed in this study can play a role in meeting current and future regulations as well as bridging the gap between emissions during certification and real-world engine operations and eventually could play a vital role in meeting the National Ambient Air Quality Standards (NAAQS) in areas such as the port of Los Angeles, California in the South Coast Air Basin

A New Generation of Hydrogen-Fueled Hybrid Propulsion Systems for the Urban Mobility of the Future

The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO2 and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of a more suitable fuel injection system and the control of the combustion process are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated, allowing the identification of a “sweet spot” in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40%, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMSs) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allows targeting H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and aftertreatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains

Self-Learning Neural controller for Hybrid Power Management using Neuro-Dynamic Programming

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the cost-to-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89874/1/draft_01.pd

Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

The Application of Neural Networks for Prediction of Concentration of Harmful Components in the Exhaust Gases of Diesel Engines

Means of transport (vehicles) are a part of everyday life. The main purpose of the vehicles is to transport the goods and the passengers. The road traffic has the largest share in transport. Majority of the transport vehicles use the IC engine as a power unit. The application of engines driven by fossil fuels has caused the road traffic to exhibit its poor side in terms of harmful effect on environment and human health. The constant tightening of the legal regulations has led the engine manufacturers to continuously improve the IC engines. Determination of the exhaust emission parameters usually requires an experiment. The possibility of neural network application for pollutants concentration prediction in the exhaust gases from diesel engines based on experimental data is considered in this paper. Some inputs, not previously considered in the reviewed literature, are introduced into the developed model of the neural network. By comparison with experimental data, it was established that the developed neural network model can successfully predict concentration of pollutants and that it can be used for future research

Development of a virtual methodology based on physical and data-driven models to optimize engine calibration

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems