A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

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

Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction

The energy management of a Hybrid Electric Vehicle (HEV) is a global optimization problem, and its optimal solution inevitably entails knowing the entire mission profile. The exploitation of Vehicle-to-Everything (V2X) connectivity can pave the way for reliable short-term vehicle speed predictions. As a result, the capabilities of conventional energy management strategies can be enhanced by integrating the predicted vehicle speed into the powertrain control strategy. Therefore, in this paper, an innovative Adaptation algorithm uses the predicted speed profile for an Equivalent Consumption Minimization Strategy (A-V2X-ECMS). Driving pattern identification is employed to adapt the equivalence factor of the ECMS when a change in the driving patterns occurs, or when the State of Charge (SoC) experiences a high deviation from the target value. A Principal Component Analysis (PCA) was performed on several energetic indices to select the ones that predominate in characterizing the different driving patterns. Long Short-Term Memory (LSTM) deep neural networks were trained to choose the optimal value of the equivalence factor for a specific sequence of data (i.e., speed, acceleration, power, and initial SoC). The potentialities of the innovative A-V2X-ECMS were assessed, through numerical simulation, on a diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. A virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against a wide database of experimental data. The simulations proved that the proposed approach achieves results much closer to optimal than the conventional energy management strategies taken as a reference

Distributed Control and Learning of Connected and Autonomous Vehicles Approaching and Departing Signalized Intersections

This thesis outlines methods for achieving energy-optimal control policies for autonomous vehicles approaching and departing a signalized traffic intersection. Connected and autonomous vehicle technology has gained wide interest from both research institutions and government agencies because it offers immense promise in advancing efficient energy usage and abating hazards that beset the current transportation system. Energy minimization is itself crucial in reducing the greenhouse emissions from fossil-fuel-powered vehicles and extending the battery life of electric vehicles which are presently the major alternative to fossil-fuel-powered vehicles. Two major forms of fuel minimization are studied. First, the eco-driving problem is solved for a vehicle approaching a traffic signal intersection using the deep reinforcement learning approach. The task is to find the optimal control input to the vehicle approaching a signalized intersection given the traffic signal pattern. It is assumed that the vehicle is made aware of the traffic signal through vehicle-to-vehicle and vehicle-to-infrastructure communication. A microscopic fuel-consumption model is considered. The system model, system constraints, and fuel consumption model are translated to the reinforcement learning framework. The model is then trained and simulations are presented. Practical deployment considerations are also discussed. Next, the multi-agent vehicle platooning control is considered. Vehicle platooning exploits the aerodynamics of vehicles that follow each other closely in a line to reduce the total energy consumption of the vehicle fleet. Graph-theoretic methods that characterize the interaction of multi-agents are studied using matrix-weighted graphs. Particularly, the roles of the matrix weight elements in matrix-weighted consensus are examined and the results are demonstrated on a network of three agents. The results are applied in vehicle platoon splitting and merging for a vehicle approaching a traffic stop

Building information modeling (BIM) and green building index (GBI) assessment framework for non-residential new construction building (NRNC)

The global construction industry endorsed Building Information Modeling (BIM) and its many advantages. However, despite this endorsement, BIM still failed to attract Malaysian companies to use BIM in green building assessment, especially for the assessment of Green Building Index (GBI), and maintain GBI certification during building occupancy using BIM features. The main issue of utilizing BIM as a GBI assessment tool is the applicability of BIM Tools to digitalize GBI credit by design team, which results in the digitization of GBI criteria into BIM Model. This study aims to identify common components related to the capability of BIM to digitalize and assess GBI criteria. These components include BIM uses and tools and GBI criteria and processes. This study applied quantitative and qualitative approaches to collect data. The quantitative approach used questionnaires, which were distributed to 900 GBI members, i.e. GBI certifiers and facilitators. The survey generated a response rate of 32% during eight months of data collection. The results were analyzed using SPSS and SmartPLS. Four model categories were identified, namely, BIM uses, BIM tools, GBI criteria and GBI certification process. These categories were used to assess the BIM–GBI framework. The results obtained from the questionnaire showed that only 16 BIM uses must be included in the BIM execution plan of the GBI project for assessment purposes. The results also showed that the BIM tools present different levels of effect on the GBI criteria. The capability of BIM to assess GBI could be stronger in the design assessment (DA) than in the operation assessment, which supports the suggested BIM–GBI assessment framework. The second data collection was conducted through a focus group interview with BIM and GBI experts. Two interview sessions were conducted. Results show that the assessment method has a significant correlation in the BIM– GBI framework. The following categories were identified for the BIM assessment framework: BIM uses, BIM tools, and control, which were based on the GBI criteria for scoring and certification. Findings from the BIM and GBI assessment method framework show that GBI credits can be digitalized using different BIM uses directly and indirectly assessed by BIM tools for each GBI credit in both GBI assessment process. Based on the qualitative result of this research showed that BIM can help the design team to achieve 55% point in design assessment (DA) only and this helps the building to achieve GBI certification in level 4 of certified rating. On the other hand, 45% points of GBI credits can be digitals in completion and verification assessment (CVA). The framework provides a guide for the design team and facility management in digitalizing and assessing GBI criteria using BIM application during design assessment (DA) and completion and verification assessment (CVA) for new nonresidential constructions. The framework also offers and provides insights that will enable designers to understand the relationship between BIM and GBI criteria, which will contribute to BIM integration in Stage 3 and automate GBI assessment for the Malaysian construction industry

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Characterizing driving behavior and link to fuel consumption for university campus shuttle minibuses

Abstract: This paper focuses on the effect of aggressive driving behavior on fuel consumption of a vehicle. Different from the traditional statistical analysis method, this paper adopts the frequency domain analysis method to analyze driving aggressiveness and apply a quantitative driving aggressiveness evaluation metric. At the same time, the fuel consumption impact caused by the driving aggressiveness under different driving situations is analyzed. The results are demonstrated for two university shuttle bus. Fuel consumption rate of each vehicle is determined by using available on-board diagnostics (OBD) data including intake air mass flow rate of engine and air/fuel equivalence ratio. The experimental results show that the degree of influence of driving aggressiveness on fuel consumption is not the same in different driving situations. The higher the speed of the driving situation, the greater the difference in fuel consumption caused by driving aggressiveness.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023