Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Design of Computer Multimedia Intelligent Platform Using Big Data Analysis

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

Research on Hydrogen Consumption and Driving Range of Hydrogen Fuel Cell Vehicle under the CLTC-P Condition

Hydrogen consumption and mileage are important economic indicators of fuel cell vehicles. Hydrogen consumption is the fundamental reason that restricts mileage. Since there are few quantitative studies on hydrogen consumption during actual vehicle operation, the high cost of hydrogen consumption in outdoor testing makes it impossible to guarantee the accuracy of the test. Therefore, this study puts forward a test method based on the hydrogen consumption of fuel cell vehicles under CLTC-P operating conditions to test the hydrogen consumption of fuel cell vehicles per 100 km. Finally, the experiment shows that the mileage calculated by hydrogen consumption has a higher consistency with the actual mileage. Based on this hydrogen consumption test method, the hydrogen consumption can be accurately measured, and the test time and cost can be effectively reduced.

The Basics of Developing an Alternative Concept for Commercial and Military Vehicle Operation (Random Strategy)

The efficiency of commercial automobiles and wheeled military vehicles mainly depends on the choice of maintenance (M) and current repair (CR) concept. In the paper the difficulties of adapting the (M) and (CR) planning strategies to the structural characteristics of modern transport facilities are pointed out. The advantages of using the (M) and (CR) random strategy for transport facilities based on the stochastic nature of failures and malfunctions are substantiated. Considering the failures and malfunctions as random values and identifying the patterns of their distribution based on γ percentage resources, it is proposed to develop a list of regulated maintenance and repair work, periodicity and labor intensity based on a random strategy, which will increase the efficiency of preserving the technical resource of the rolling stock throughout the entire life cycle of the vehicle.

Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data

Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.

Influence of Different Allocation Methods for Recycling and Dynamic Inventory on CO2 Savings and Payback Times of Light-Weighted Vehicles Computed under Product- and Fleet-Based Analyses: A Case of Internal Combustion Engine Vehicles

Light weighting by material substitution is a key to reducing GHG emissions during vehicle operation. The GHG benefits are a salient factor in selecting lightweight materials for vehicles. Although the literature has performed lightweight material selections using GHG benefits under product- and fleet-based life-cycle inventory (LCI) analyses, recycling effects have therein been accounted for by arbitrarily selecting allocation methods for recycling, as the consensus on their selection is absent. Furthermore, studies have mistreated the temporal variations of the LCI parameters (the dynamic inventory (DI)), though that could be an important factor affecting the overall LCI results when allocation methods for recycling are in place. Therefore, to investigate their influence on greenhouse gas (GHG) benefit evaluations, an LCI case study was conducted, centered on aluminum- and magnesium-substituted internal combustion engine vehicles (ICEVs) at the product- and fleet- levels. “CO2 savings” and the “CO2 payback time”, as well as four allocation methods for recycling, were considered to represent the GHG benefits and address the recycling effects, respectively. The dynamic inventory was based on the world average electricity grid mix change. The results indicate that changing the conditions of the DI and the allocation methods for recycling could alter the better performing material under fleet-based analyses. Therefore, we ascertained that the choice of the allocation method for recycling and conducting fleet-scale dynamic LCI analyses in the presence of the DI is pivotal for material selections.

Determining the Electrical Capacity of Lithium BEV and PHEV Battery in Situ

The work deals with the quality of batteries in electric vehicles manufactured today’s and the determination of warranty conditions and guarantees for built-in lithium batteries. However, the design, testing, prototyping and creation of new vehicles is not without errors, as the experience of some manufacturers, including the more inexperienced Mercedes, shows now. On a tested sample of the Mercedes E350e PHEV, a capacity loss of more than 30% from the original nominal value was measured after 18 months of vehicle operation, well above the manufacturer's expectations and declarations.

Modeling the Effects of Implementation of Alternative Ways of Vehicle Powering

The trend to replace traditional fossil fuel vehicles is becoming increasingly apparent. The replacement concerns the use of pure biofuels or in blends with traditional fuels, the use of hydrogen as an alternative fuel and, above all, the introduction of electric propulsion. The introduction of new types of vehicle propulsion affects the demand for specific fuels, the needs for new infrastructure, or the nature of the emissions to the environment generated by fuel production and vehicle operation. The article presents a mathematical model using the difference of two logistic functions, the first of which describes the development of the production of a specific type of vehicle, and the second, the withdrawal of this type of vehicle from traffic after its use. The model makes it possible to forecast both the number of vehicles of each generation as a function of time, as well as changes in energy demand from various sources and changes in exhaust emissions. The results of the numerical simulation show replacing classic vehicles with alternative vehicles increases the total energy demand if the generation of the next generation occurs earlier than the decay of the previous generation of vehicles and may decrease in the case of overlapping or delays in the creation of new vehicles compared to the course of the decay function of the previous generation. For electric vehicles, carbon dioxide emissions are largely dependent on the emissions from electricity generation. The proposed model can be used to forecast technology development variants, as well as analyze the current situation based on the approximation of real data from Vehicle Registration Offices.

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

This work develops and implements an NMPC control system to facilitate fuel-optimal platooning of Class 8 vehicles over challenging terrain. Prior research has shown that Cooperative Adaptive Cruise Control (CACC), which allows multiple Class 8 vehicles to follow in close succession, can save between 3 and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g. rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of Nonlinear Model Predictive Control (NMPC) with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. This comparison yields the comparative fuel savings and energy efficiency benefit of NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.