# Combining a Universal OBD-II Module with Deep Learning to Develop an Eco-Driving Analysis System
###### Abstract  
Vehicle technology development drives economic development but also causes severe mobile pollution sources. Eco-driving is an effective driving strategy for solving air pollution and achieving driving safety. The on-board diagnostics II (OBD-II) module is a common monitoring tool used to acquire sensing data from in-vehicle electronic control units. However, different vehicle models use different controller area network (CAN) standards, resulting in communication difficulties; however, relevant literature has not discussed compatibility problems. The present study researched and developed the universal OBD-II module, adopted deep learning methods to evaluate fuel consumption, and proposed an intuitive driving graphic user interface design. In addition to using the universal module to obtain data on different CAN standards, this study used deep learning methods to analyze the fuel consumption of three vehicles of different brands on various road conditions. The accuracy was over 96%, thus validating the practicability of the developed system. This system will greatly benefit future applications that employ OBD-II to collect various types of driving data from different car models. For example, it can be implemented for achieving eco-driving in bus driver training. The developed system outperforms those proposed by previous research regarding its completeness and universality.  
eco-driving; universal on-board diagnostics II (OBD-II); controller area network (CAN); deep learning; fuel consumption +
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to install. Since 2008, all vehicles equipped with OBD-II communication interfaces have employed a controller area network (CAN bus) as the main architecture for the internal communication network [11; 12]. The CAN bus is mainly used as the communication protocol in heavy-duty commercial vehicles and sedans, which respectively use CAN2.0A (an 11-bit identifier) and CAN2.0B (a 29-bit identifier). Vehicles of different types may have a baud rate of either 500 or 250 Kbp, along with a communication protocol of either ISO-15031 [13], ISO-27145 [14], or SAE-J1939 [15]. Devices with different CAN standards cannot communicate. To date, studies have not discussed the compatibility between CAN busses. Therefore, a universal OBD-II module will greatly increase the practice of eco-driving. The present study proposes an eco-driving assistant system to improve driving behaviors and encourage eco-driving. This system contains a universal OBD-II module capable of obtaining information from different CAN standards and employs deep learning to predict fuel consumption, thereby increasing the applicability and adoption of the eco-driving assistant system. The contributions of the study are:  
* A universal OBD-II module is developed to collect driving information from various car models.
* Three deep learning methods are combined with the universal OBD-II module to predict fuel consumption.
* Three different car models are used to evaluate fuel consumption.
* An intuitive driving graphic user interface is designed for the eco-driving assistant system.  
The rest of the paper is organized as follows. The related research is discussed in Section 2. The proposed eco-driving assistant system is described in Section 3, including the universal OBD-II module, graphical user interface, deep learning algorithms, and the test of three cars. The results and a related discussion are presented in Sections 4 and 5, respectively. The conclusions are presented in Section 6.