Driving cycle assessment is one of the common methods to evaluate a vehicle’s real-world condition also monitor fuel consumption and emissions. The basic challenge in the extraction of the Driving cycle is data analysis to develop and define the suitable behavior of the device. Clustering, classification, and recognition of Driving patterns are important steps in the extraction of a suitable Driving cycle. Generally, the accuracy of modeling and recognition of AI-based methods is indicated by more than 90% and other outputs comply with big data. Thus, in this research, we endeavored to evaluate the effect of using artificial intelligence on the Driving cycle of off-road vehicles. The major part of off-road vehicles are agricultural vehicles such as tractors which are divided into three categories based on agriculture operations; light, heavy, and extra heavy. In addition, the procedure of agricultural operation is effective on fuel consumption, loading, and exhaust emissions. The results of this research showed that the use of conventional machine learning methods for clustering and classification can be used for any volume of features. However, with an increase in features, the complexity of region segmentation and the effect of farm management factors cause overtraining conditions in the learning algorithm and reduce the accuracy of the extracted Driving cycle and prediction of Driving behavior. Therefore, it is necessary to use advanced algorithms with deep learning capabilities. Therefore, extracting the intelligent Driving cycle for agricultural tractors based on the type of agricultural operation with the help of artificial intelligence methods can reduce fuel consumption, pollution, and optimal farm management.

In the present study, the aim is to optimize full and half-toroidal continuously variable transmission (CVT) in order to minimize the vehicle fuel consumption (FC) in ECE Driving cycle. First, a model for both toroidal CVT efficiency is presented. A simulation model of the considered power train is described. The control strategy of CVT speed ratio based on minimizing the vehicle FC is introduced, and the algorithm of calculating the vehicle FC is shown. Afterwards, both types of CVT are optimized using Particle Swarm Optimization method (PSO) with the aim of minimizing the vehicle FC in ECE Driving cycle, and the optimized geometries are achieved. It is found that a remarkable fuel economy can be achieved through optimization of each type. The effects of the vehicle weight on the optimized geometries are examined. It will be shown that, the optimized geometry of full-toroidal type is not strongly influenced by the vehicle weight, while the optimized geometry of half-toroidal one varies through variation of the vehicle weight.

Driving cycle is used to assess fuel consumption, pollutant emissions and performance of the vehicle. The aim of this paper is to extract the Driving cycle for refuse collection truck and estimate its braking energy. For this purpose, after selecting the target truck and geographic area, the equipment needed to measure the required variables were prepared and mounted on the truck. Then, the actual data were collected from the performance of the target Truck while performing its mission. Since the amount of braking energy depends on the speed, truck mass and road grade, the speed of the vehicle is measured simultaneously with the truck mass and road grade. The collected data are then processed and subdivided into micro-trips. The micro-trips are clustered according to the number of state spaces using the K-Means algorithm. Next, the representative micro trips are selected from within the clusters and the final Driving cycle is generated. The representative Driving cycle shows that the truck speed is zero at 47% of the working time. Finally, the amount of braking power and accumulative braking energy in the Driving cycle is calculated.

Recently, environmental concern and demand for a catalyst’ s high performance have increased and many research activities focused on the operation of a three-way catalyst (TWC) at the end of its lifetime. Catalyst aging is the loss of catalytic activity over time that has crucial importance in emission of three-way catalyst. The aim of this paper is, investigating experimentally the performance of a fresh and aged three-way catalytic converter (TWC) in legislative Driving cycle. For this, vehicle emission test with fresh and aged catalyst was carried out. In this study a commercial catalyst was used, and was aged in a motor-rig, with SBC cycle (Standard Bench cycle). The total conversions of HC, CO and NOx were decreased over the lean-rich cycles in transient conditions. In the real Driving condition, almost in the all-time, the engine has transient condition and in this condition, the non-aged catalyst, achieves higher conversion for times with lambda variations.

Design of a suitable Control Strategy for operating a hybrid propulsion system in different types of roads and Driving cycles is one of the most challenging subjects in hybrid vehicle research areas. Intelligent Control Strategies have been designed to meet the above requirement. The control signals in an intelligent control strategy of the hybrid vehicles are generated according to the Driving cycle type. This is done by using a Driving cycle identification unit. In this paper, design of a fuzzy based Driving cycle identifier has been presented. The main idea in this unit is that any arbitrary Driving cycle is similar to a group of standard Driving cycles according to some degrees of similarity. As a result, the control strategy of the hybrid powertrain in the arbitrary Driving cycle is affected by the optimized control strategy of the standard diving cycle based on the degree of similarity. Here, the subset of sufficient features is determined by using the floating search method as a useful feature selection algorithm. Also, the fuzzy clustering method is used to generate the values of similarity degrees to each standard Driving cycle. Finally, the performance of the fuzzy Driving cycle identification unit is assessed.

Driving cycles are mostly developed in order to investigate emissions, fuel consumptions and/or legislations defined for a particular vehicle. On the other hand, automotive powertrains are designed or selected according to power demand analysis and simulations which are closely related to the vehicle Driving pattern. When a car is utilized for a non-purposed application, its Driving cycle is different so the design targets are not met. It results in lower efficiency and waste of resources, due to over or under engineering and lack of satisfaction. In this study, Driving cycle of a typical passenger car is investigated in order to evaluate its design fitness. The Driving cycle of the car is a combination of long modal trips which are assembled by large sets of transient micro-trips. It is constructed using actual data collected by a GPS and data logger. After filtering noises by LOWESS technique, statistical distribution of the dataset is verified so that Monte Carlo method can be implemented. Based on the acceleration results of the simulation, power demands of the vehicle and duty cycles can be derived and compared to the engine characteristics. The discrepancies indicate that the design of the vehicle powertrain does not match its application especially as a fix-route taxi.

A novel liquid cooling system for pouch-type lithium-ion batteries (LIBs) is proposed by focousing on uniform temperatue disturbution and effective heat dissipation. The system utilizes a michrochannel cold plate with an innovative coolant disturbution design. This study proposes a novel microchannel disturbution path design with each microchannel dimensioning 1 mm2 and embeded in the battery's ciritical region to enhance the thermal contact among the LIB and the microchannels. This study aims to simulate and evaluate the performance of cooling system under varius Iranian environmental conditions (Tehran, Shiraz, Isfahan, and Bandar Abbas) and operational parametrs (channel pattern, flow rate) to achieve optimal battery temperature and reduce energy consumption.

The importance of the effect of geographic climate on the performance parameters of cars requires comprehensive studies and research in this field, especially in Iran, where the diversity of climatic conditions is clearly evident. The factor scores of each of the influential components in determining the climate divide Iran into four climatic categories, which are: (1) dry, (2) humid with heavy rainfall, (3) semi-humid, semi-dry and (4) humid with little rainfall. Therefore, in this research, the effect of geographic climate on the characteristics of statistical data and Driving cycle has been investigated. For this purpose, four cities of Arak (semi-arid to semi-humid), Tehran (dry), Ahvaz (humid with low rainfall) and Rasht (humid with high rainfall) were selected as representatives of their climate. Then, by reducing the dimensions of the data from 12 to 2 statistical data characteristics, using PCA analysis, and then by clustering the data using the chemical mean method, and extracting the Driving cycles of each city under different climatic conditions, the effect of the geographic climate on the characteristics of the statistical data and the Driving cycles of the case was investigated, and also the data of the city of Rasht on rainy and non-rainy days were separated and analyzed, and in the results of the investigations, it was observed that climatic conditions can have significant effects on the average Driving speed (about 21 percent), travel time ( about 18 percent) as well as average travel speed (about 22 percent) and car stopping percentage (about 84.4 percent). As a result, it can be said that geographical climate is one of the most influential factors on Driving cycles.

Internal Combustion Engine Vehicles or ICEVs play a major role in air pollution for big cities like Tehran. Catalyst converters manage to contain much of the generated pollution from combustion and are considered as a mandatory part of any vehicle. Manufacturer guidelines clarify a certain mileage for catalyst to be regarded as completely degraded and thus unable to contain harmful gasses from combustion. In this paper we test the theory that catalyst degradation is greatly affected by Driving condition by using kinematic simulation of vehicle in different Driving cycles. Catalyst degradation parameters are derived for the reference Driving cycle (NEDC) and then developed accordingly for other Driving conditions. The vehicle model with degradation sub-model is then utilized for investigating different scenarios of replacing old catalyst with a new one. Results clearly support the idea that Driving cycles greatly affect the degradation age of 3-way catalysts and vehicle emissions are increased up to 5 times by using catalyst after its age limit. For Tehran Driving cycle, 75K kilometers appears to be the aging limit. Authors conclude that local investigation of Driving cycles is necessary for finding degradation limits and careful designation of parts replacement plans can help to massively increase air quality in large cities.

Each Driving cycle designed to monitor fuel consumption, vehicle emissions, and the specific Driving patterns of a region is created to facilitate better planning and enhancement of local standards. In this study, the Real Driving Emissions (RDE) method, regarded as one of the most precise techniques for determining Driving cycles, was employed. To gather the necessary data, GPS from mobile phones was utilized while traveling on city buses through the streets of Kermanshah. This data collection included recording speed, acceleration, duration of constant speed, deceleration, altitude, number of braking events, and the duration of stops along the route from origin to destination. The research employed the clustering of real city data and the K-means method to derive the Driving cycle for Kermanshah’s city buses. Analyzing the collected data reveals that bus drivers in Kermanshah tend to avoid rapid acceleration and high constant speeds, leading to reduced fuel consumption in the city bus cycle, indicating that this behavior is intentional and aimed at this goal.