Research on fault diagnosis system of electric vehicle power battery based on OBD technology

Wang, Liye; Wang, Lifang; Liu, Weilong; Zhang, Yu-wang

doi:10.1109/iccds.2017.8120458

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…However, in more recent researches, the potential of the OBD-II to extract data from vehicles and generate useful information that can be used in various vehicular segments is beginning to be more broadly explored. Examples of this tendency are discussed in [ 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Related Workmentioning

confidence: 99%

“…[ 45 ] stands out, since worldwide researches on the battery failure diagnostic system have not achieved remarkable accomplishments [ 46 ]. Thus, [ 45 ], conducted an experiment to examine the battery diagnosis in electric vehicles with the use of OBD-II. They found that, with the battery diagnosis system, it is possible to predict faults as well as to ensure the reliable operation of electric vehicles.…”

Section: Related Workmentioning

confidence: 99%

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A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Silva

Vieira

Signoretti

et al. 2018

Sensors

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In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Silva

Vieira

Signoretti

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, the sensing system and on-board diagnostic (OBD) system are vital to the driving safety and performance maintenance of EVs. For example, the user can be quickly identified through the OBD data [ 53 ]. However, the OBD technology applied in EVs is imperfect and should be improved in the future [ 54 ].…”

Section: Empirical Analysismentioning

confidence: 99%

Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain

et al. 2022

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Introduction Identifying the technologies that will drive technological changes over the coming years is important for the optimal allocation of firms’ R&D resources and the deployment of innovation strategies. The citation frequency of a patent is widely recognized as representative of the patent’s value. Thus, identifying potential highly cited patents is an important goal. A number of studies have attempted to distinguish highly cited patents from others based on statistical models, but a more effective and applicable method needs to be further developed. Methods This paper treats the prediction of later patent citations as a classification problem and proposes a novel framework based on machine learning methods. First, a indices system to identify highly cited patents is constructed using multiple factors that are believed to influence citation frequency. Second, various machine learning models are utilized to identify highly cited patents. The optimized model with the best generalization capability is selected to predict the future impacts of newly applied patents, which may be representative of emerging significant technologies. Finally, we select the electric vehicle (EV) domain as a case study to empirically test the validity of this framework. Results The optimized support vector machine (SVM) model performs well in identifying highly cited EV patents. Technological frontiers in the EV domain are identified, which are related to the topics of information systems, batteries, stability control, wireless charging, and vehicle operation. Discussion The good performance in prediction accuracy and generalization capability of the method proposed in this paper verifies its effectiveness and feasibility.

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“…Currently, most self-diagnostics for vehicles use non-OEM or OEM-based OBD methods [20][21][22]. However, they do not use any neural network model.…”

Section: Fault Diagnosis Of a Vehiclementioning

confidence: 99%

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

et al. 2018

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This paper proposes "An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning" that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle's total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.

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Research on fault diagnosis system of electric vehicle power battery based on OBD technology

Cited by 13 publications

References 12 publications

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

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