Effect of Real World Driving and Different Drive Modes on Vehicle
                    Emissions and Fuel Consumption

Lairenlakpam, Robindro; Jain, A. K.; Gupta, Poonam; Kamei, Wittison; Badola, R.; Singh, Yaduvir

doi:10.4271/2018-01-5017

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…In recent years, many research papers on cold start emissions have been published (i.a. [13,17,19,24,30]). The need to take into account emissions from the cold start phase has been noticed not only in Europe but also worldwide.…”

Section: Introductionmentioning

confidence: 99%

Cold start emissions from a gasoline engine in RDE tests at different ambient temperatures

Kurtyka¹,

Pielecha²

2020

Combustion Engines

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The implementation of the 3rd package of the RDE test procedure has extended the test method by considering emissions from a cold start period into the total exhaust emissions from a vehicle. The article presents the research results of exhaust emissions of a vehicle equipped with a gasoline engine. The tests were carried out at two different ambient temperatures, in line with the requirements of the RDE test procedure for passenger cars, meeting the Euro 6d-Temp emissions standard. The obtained results were analyzed, i.e. there were compared the engine and vehicle operating parameters and the values of road exhaust emissions during the cold start at two different ambient temperatures. The summary presents the shares of the cold start phase for each exhaust emission compound in the urban part of the test and the entire RDE test, depending on the ambient temperature (8ºC and 25ºC).

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Section: Introductionmentioning

confidence: 99%

Cold start emissions from a gasoline engine in RDE tests at different ambient temperatures

Kurtyka¹,

Pielecha²

2020

Combustion Engines

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“…The experimental purpose and futuristic insight of the ADAS for an electric vehicle are completed based on the various user-defined physical inputs using software such as Simulink in MATLAB [ 22 ]. The proposed methodology is based on the various inputs in the cloud infrastructure from which the behavior of electric vehicles for different drive cycles [ 24 ] in series, parallel and hybrid configurations [ 14 ] are analyzed continuously. The need for such prototyping is to optimize the powertrain requirements for different modes such as route-based drive, online drive, and integrated drive with path planning [ 11 ] and for driving mode selection [ 13 ] with the aid of ML algorithms for an economic drive [ 12 ].…”

Section: Proposed Designmentioning

confidence: 99%

“…After acquiring all the mentioned critical parameters through sensor clusters, the work uses e V2V and V2I communications to transmit the data between vehicles and also to the infrastructure where analytics is performed. Once the custom modes are designed from the OEM side, the model is incorporated with the process in the existing vehicle without any need for additional hardware through OTA (over-the-air) update [ 24 , 25 ] which would be the most promising feature of the proposed system and the unique analysis completed in the proposed model.…”

Section: Proposed Designmentioning

confidence: 99%

“…Utilizing these kinds of software-centric systems offers efficient ADAS even for low-cost vehicles in addition to the sophisticated luxury class of the vehicles which improves safety and reaches the industry’s goal for zero vehicle-related accidents, fatalities, and obsolete comfort for drivers. The advanced driver assistance system (ADAS) in an automotive is designed to serve the purpose of cruise control only during initial times, which was then aided with cognitive computing [ 4 ] and sensor clusters used in the emergency brake systems [ 18 ], drive beam, blind spot monitoring, auto parking, lane departure warning and assist to support the driving through increased safety and guidance [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. The modern systems utilize intercommunication between vehicles, where the pedestrian detection and collision avoidance feature data were shared among vehicles to improve the error-prone systems in critical situations using different architectures such as V2V and V2I integrated with Internet of Things (IoT) structure [ 3 ] taking into consideration the different level security during communication.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability

Suganthi

Kumar

Harish

et al. 2023

Sensors

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In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.

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“…The touring mode is activated under normal driving conditions, which can function with All-wheel drive and fuel economy modes to optimize the vehicle performance (Lairenlakpam et al, 2018) Continued Data-Centric Engineering e14-3 speeds [AVS], allowable vehicle cabin air temperatures [AVC]), whose details were discussed in Section 6.…”

Section: Terrain/crawlmentioning

confidence: 99%

Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

Kolachalama

Malik

2022

DCE

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In this article, a novel drive mode, “intelligent vehicle drive mode” (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLV in the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM).

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Effect of Real World Driving and Different Drive Modes on Vehicle Emissions and Fuel Consumption

Cited by 11 publications

References 11 publications

Cold start emissions from a gasoline engine in RDE tests at different ambient temperatures

Cold start emissions from a gasoline engine in RDE tests at different ambient temperatures

Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability

Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

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