A Raspberry Pi based User Interactive and Assistive Fleet Management and Eco-Driving System

Transcription

1 A Raspberry Pi based User Interactive and Assistive Fleet Management and Eco-Driving System Thesis submitted for fulfilment of the requirements for the degree of Bachelor of Technology in Electronics and Communication Engineering by Bhagyeshwari Chauhan Y11UC075 Under Guidance of Mr Sandeep Saini Department of Electronics and Communication Engineering The LNM Institute of Information Technology, Jaipur May

2 Copyright BHAGYESHWARI CHAUHAN, 2015 All Rights Reserved 2

3 The LNM Institute of Information Technology Jaipur, India CERTIFICATE This is to certify that the project entitled A RASPBERRY PI BASED USER INTERACTIVE AND ASSISTIVE FLEET MANAGEMENT AND ECO- DRIVING SYSTEM submitted by BHAGYESHWARI CHAUHAN (Y11UC075) in partial fulfilment of the requirement of degree in Bachelor of Technology (B. Tech), is a bonafide record of work carried out by her at the Department of Electronics and Electrical communication Engineering, The LNM Institute of Information Technology, Jaipur, (Rajasthan) India, during the academic session under my supervision and guidance and the same has not been submitted elsewhere for award of any other degree. In my opinion, this thesis is of standard required for the award of the degree of Bachelor of Technology (B. Tech). Date Mr. Sandeep Saini Assistant Professor ECE Dept, LNMIIT 3

4 TO My Parents for helping me enlighten the candle of education in me and for their unconditional love and support throughout. 4

5 Acknowledgements To Mr. Sandeep Saini, for his constant guidance and support throughout my fourth year and without whom this document would not exist in its present form. 5

6 Abstract Today, in the era of Internet of Things, an enormous amount of research is going on in the direction of probing into vehicle s engine control units for collecting kinematic and diagnostic information, analysing and integrating them with software platforms and providing valuable services to drivers and passengers. This is largely facilitated by the car s several sensors forming an invehicle network which can communicate with cloud servers and acts as effective suppliers of massive data. With this objective of efficient data collection and analysis, in this thesis, a framework for a low cost integrated web-based platform is proposed for On Board Diagnostic services, vehicle tracking, theft detection and evaluation of a vehicle s performance to evaluate fuel efficient driving behaviour. A hardware and software prototype tool for communication with vehicle and analysis purpose has been presented in this report. Various steps of implementation as well as results have also been presented. 6

7 Contents Chapter Page 1. Introduction The Area of Work IOT in Automotive Space On-Board Diagnostics for Vehicle Data Analysis Smartphone and Automobiles Eco-Driving Problem Addressed Existing System Literature survey Electronic Control Unit On-Board Diagnostic (OBD) Signalling Protocols Parameter Conversion Methods Controller Area Network ELM On-Board Diagnostic (OBD) II UART Board RS232 Serial Port Raspberry Pi Global Positioning System Proposed Work Data Acquisition and Pre-Processing Module Data Analysis Module Eco-Driving Analysis Data Visualization Module Conclusions and Future Work Bibliography

8 List of Figures Figure Page 1 Internet of things- 50bn connected devices by Typical Vehicle Architecture 20 3 ELM327 Block Diagram 21 4 OBD II UART Board of Sparkfun 22 5 RS232 Serial Port Connector 23 6 Raspberry Pi 24 7 GPS Dongle 25 8 System Overview 26 9 Data Link Connector A Standard OBD Port OBD to UART Interpreter Diagnostic Data Obtained Parsed GPS Output Data measured for Eco-Driving Analysis Average Fuel Economy for various Shift points Web display of Vehicle Diagnostics, Real time monitoring and real time data Geo-Fencing for theft detection 34 8

9 Table List of Tables Page 1 Functions of the Mode of Operation in SAE Functions of the SAE 1979 PIDs 18 9

10 Chapter 1 Introduction 1.1 The Area of Work With each passing day, the number of gadgets that are connecting to the Internet is surpassing the number of people connecting to the Internet. The number of connected devices is going to increase to 4.9bn things in 2015 before growing fivefold to 25bn by 2020, according to projections from various technology research firms. This technology trend is pursued actively by the automotive industry, which is working on developing a 'Connected Cars'. Cars will be a major element of the expanding Internet of Things, with one in five vehicles having some sort of wireless network connection by 2020, accounting for more than a quarter of a billion cars on global roads. Connected vehicles are cars that access, consume, create, enrich, direct, and share digital information between businesses, people, organizations, infrastructures, and things. Those things include other vehicles, which is where the Internet of Things becomes the Internet of Cars. As these vehicles become increasingly connected, they become self-aware, contextual, and eventually, autonomous. Figure 1: Internet of things- 50bn connected devices by 2020 Source: Cisco 10

11 1.1.1 IOT in the Automotive Space There have been exciting recent developments around IOT in the automotive industry. Automakers, telecommunication service providers and leading technology companies are coming together to build the Connected Car. The year of 2014 started with Google announcing the formation of an Open Automotive Alliance with top car manufacturers (GM, Honda, Audi, Hyundai) and chipmaker NVIDIA, to bring Android OS into car dashboards. Apple soon followed with the release of CarPlay in early March with Volvo, Ferrari, Mercedes-Benz and later with other automakers such as BMW, Ford, Jaguar, Honda, Nissan, etc. Prior to this, QNX (now owned by BlackBerry) has been commonly providing an enhanced, embedded platform with connectivity for infotainment in high-end automobiles. These big players in mobile platforms are setting their eye on creating a common platform to allow a connected experience across the smartphone and the vehicles. Telecom Service Providers (TSPs) are actively contributing in this space with dedicated Machine-to- Machine communication services through embedded SIM chips. Vodafone is working with BMW and Volkswagen to bring connectivity to their vehicles. AT&T provides a modular platform Drive with 4G LTE network connectivity for automakers to offer customized features such as diagnostics, voice recognition, entertainment and automotive app store, for an enhanced in-vehicle experience for users On Board Diagnostics for Vehicle Data Analytics The On-Board Diagnostics (OBD/OBD-II) port is commonly used in automobile service and maintenance. While much of the information from this such as faults, vehicle and engine speed, engine temperature, fluid levels, gear shifts, battery status, etc. is accessed regularly at vehicle repair shops, till now it was largely used only when some problem arises. However, with smartphones pairing with such vehicles, this information can be readily made available to the vehicle owners, giving them a better picture of the car performance. Monitoring these parameters actively and with some level of on-device analytics, drivers can get proactive service alerts on their smartphones and potential faults can be identified for early diagnosis and care. For predictive analytics to be effective, the vehicular data from a large number of vehicles needs to be aggregated for detailed study and analysis. While that can help to detect and derive patterns, a subset of the rules can be made available for users through smartphone apps. 11

12 1.1.3 Smartphones and Automobiles Commercial smartphones commonly have sensors such as Accelerometer, Gyroscope or Orientation sensor and GPS. By docking the smartphone to the vehicle, data from these sensors can be used to detect driving patterns such as sharp turns, sudden acceleration, hard braking, drifting and speeding. This can be used to profile the driver as safe or aggressive, to rate and compare different drivers and share such data with insurance providers for customized premiums. Thus, leveraging the smartphones to the fullest without depending on vehicles having connectivity embedded within can potentially bring out smart cars such as driver monitoring, crash detection and vehicle diagnostics. These applications demonstrate the profiling a driver for a trip based on the occurrences of pre-defined significant events, detecting a crash and triggering alerts to pre-configured contact numbers as well as on-device analytics with complex event processing to provide continuous feedback to the driver. The concept applications have been developed using on-board sensors and suitable micro-controllers Eco-Driving The concept of eco-driving has acquired a great importance and relevance in recent years due to the increase in the automotive fleet size and its influence on climate change. Vehicles are important energy consumers and major emitters of pollutant gases. The vehicle pollution produce causes more deaths than road traffic accidents Reducing energy consumption is therefore a priority for governments, vehicle manufacturers and users. Energy savings in vehicles depends mainly on two factors: the vehicle and the driver. Regarding the vehicle factor, manufacturers have taken a series of measures to reduce energy consumption: 1. Aerodynamic design with less air resistance 2. Reduction in the fuel consumption of the engine and in CO2 emissions 3. Use of hybrid engines (petrol / electric) 4. Vehicle weight reductions that requires less power to move In order to improve the driver factor, eco-driving assistants are designed to provide appropriate advices that stimulate the application of eco-driving principles and facilities their learning. Eco-driving assistant uses the information obtained through the diagnostic port with the smartphone s information (Sensors, GPS and Internet connection) to accurately model the driver s driving style from the point of view of energy consumption. The objective of the eco-driving is to reduce energy consumption by applying a set of rules based on physics that seek to reduce the demand for power. 12

13 1.2 Problem Addressed While, vehicles are widely viewed as simple transportation medium, there lies a huge scope of probing into the vehicle s control systems and create intelligent applications by connecting the vehicle to the internet. This possibility is not being harnessed extensively in India due to high prices of such systems along with less awareness about their use cases. Kinematic data obtained from vehicles can be further useful in providing timely diagnostic information in case of a sub system failure, to prevent further damage, as well as intelligent analysis of this data can evaluate a driver s driving style and suggest improvements. This reduces the overall fuel consumption, thus benefitting the user of costs and saving fuel. Therefore, the above possibilities are exploited and presented in this thesis, while providing a low cost hardware-software platform. 1.3 Existing System For the driver to learn the efficient driving techniques, we can use an ecodriving assistant. There are several studies like [Boriboonsomsin et al., 2009] that value the suitability of eco-driving assistants to make the user acquire a more efficient driving style. The use of efficient driving techniques has a positive impact on fuel saving as has been demonstrated in many studies such as [Mierlo et al,. 2004]. There are also several proposals to analyse which variables affect fuel consumption. [Kuhler et al., 1978] introduced a set of ten variables. These variables are used in laboratories for consumption and emissions of vehicle fuel. Other authors such as [André, 1996; Fomunung et al., 1999] increased the number of parameters or replaced some of them to improve results. The drawback of these proposals is that they do not take into account the environment where the vehicle circulates that often has a significant influence on energy consumption. Otherwise, there are in the market several commercial solutions that attempt that the driver acquires habits of efficient driving. Nissan has designed a system [Eco-Pedal, 2008] that suited the acceleration depending on the circumstances. If the driver exerts on a gas pressure exceeding the recommended one, he is alerted by a warning light that was accelerating incorrectly. Garmin has developed a program called [EcoRoute Garmin, 2011] for their GPS devices to get the most economical route in terms of fuel economy. This software also has a scoring system that rewards good driving style from the point of view of energy saving. In addition to these proposals, [Audi EcoTraining, 2011; Fiat Eco-Drive, 2011; HondaEcoAssist, 2011] also have eco-driving assistants. These solutions use data from vehicle sensors to assess the driving style from the energy efficiency point of view and then deliver efficient eco-driving tips. The problem is that they are dependent 13

14 on vehicle model and are usually offered as an extra. Another type of solution to minimize fuel consumption is the proposal of [Ford and Google Cloud Prediction, 2011]. They propose to predict the behaviour of the driver to minimize energy consumption. Vehicle will act as a data acquisition system and it will send the captured data to Google. Google using historical driving data and the Google Prediction API can predict certain aspects during the displacement of the vehicle allowing the optimization of the powertrain. This solution, based on cloud computing, presents a serious problem since the vehicle s data are very sensitive and could be used by an attacker for bad purposes. On the other hand, this solution is being developed for Ford vehicles and it is not valid for other vehicles. 14

15 Chapter 2 LITERATURE SURVEY In this chapter, the related works or research done before and the devices used in this project will be reviewed respectively Electronic Control Unit (ECU) In order to ensure the safety of driver and passengers, the car owners normally send their cars to service centre for servicing purposes periodically. The mechanics in service centre usually connect the computer in the service centre to the car diagnostic system to check the statistic data like temperature and pressure in certain parts of the car engine. The embedded system that controls one or more of the electrical system or subsystems in a motor vehicle is called a Electronic Control Unit. Types of ECU include Electronic/engine Control Module (ECM), Powertrain Control Module (PCM), Transmission Control Module (TCM), Brake Control Module (BCM or EBCM), Central Control Module (CCM), Central Timing Module (CTM), General Electronic Module (GEM), Body Control Module (BCM), Suspension Control Module (SCM), control unit, or control module. Taken together, these systems are sometimes referred to as the car's computer. An Engine control unit (ECU/ECM) is a type of electronic control unit that controls a series of actuators on an internal combustion engine to ensure optimal engine performance. It does this by reading values from a multitude of sensors within the engine bay, interpreting the data using multidimensional performance maps (called lookup tables), and adjusting the engine actuators accordingly. 2.2 On Board Diagnostics (OBD) On-Board Diagnostic (OBD) II system is a vehicle s self-diagnostic and reporting system which can help the vehicle owner and mechanic to know the vehicle s current condition. The OBD II system can provide vehicle s live information like vehicle s speed, engine RPM and engine coolant temperature to the user. Besides that, the user can also repair the vehicle in a more effective way by referring to the Diagnostic Trouble Code (DTC) of the OBD II system. Most of the cars nowadays normally have the OBD II system and it will be very convenient for the user to check the condition of their vehicle. 15

16 On-Board Diagnostic (OBD) system is a useful system in diagnosing the vehicle s live condition and reporting the related car s live condition. The first on-board computer system on a vehicle with scanning capability was introduced by Volkswagen in 1969 in their fuel-injected type 3 models. After that, other car manufacturers continued to improve the on-board diagnostic system in their own type of car models. In 1994, the OBD II system was first introduced and the OBD II specification was made mandatory for all cars sold in the United States of America in From that time, the OBD II system is widely used by the car manufacturers and this kind of diagnostic system is quite common for the cars nowadays. The OBD II system is given a different name in each country. For instance, in Europe country the OBD II system is called European On-Board Diagnostics (EOBD) and in Japan they called OBD II system of vehicle as Japan On-Board Diagnostics (JOBD) Signalling Protocols Basically, the OBD II system can be divided into 5 main types of OBD II signal protocols which are: 1. SAE J1850 PWM, 2. SAE J1850 VPW, 3. ISO , 4. ISO KWP2000 and 5. ISO CAN. In common, the vehicle can only apply one of the protocols at the interface of OBD II. We can determine the type of OBD II protocol on a vehicle easily by checking the pins on J1962 connector. On top of that, the OBD II system helps to get various types of data from the electronic control unit (ECU) of the car when the mechanics or car owners want to know the condition of their car. Different types of diagnostic data or parameters can be extracted from car by knowing the parameter identification numbers (PIDs) of the OBD II system. There are several modes can be used to communicate with the OBD II system and they are called communication protocols. There are ten modes of operation described in the latest OBD-II standard SAE J1979. They are as follows: Mode (hex) 01 Show Current Data Description 02 Show Freeze frame data 03 Show Stored Diagnostic trouble codes 16

17 04 Clear Diagnostic trouble codes and stored values 05 Test results, Oxygen Sensor monitoring (non CAN only) 06 Test Results, other components/systems monitoring 07 Show pending diagnostic trouble code 08 Control Operation of on board component/system 09 Request Vehicle Information 0A Permanent DTC (Cleared DTC) Table 1: Functions of the Mode of Operation in SAE 1979 Besides the mode of operation, parameter identification numbers (PIDs) of are plays an important part in extracting the live data from vehicle. The car manufacturers will normally set the SAE standard J1979 PIDs to vehicles but some manufacturers may add in some additional specific PIDs to their vehicle to show more parameters of the engine. Table 2 shows part of the SAE J1979 PIDs in mode 01 and their respective functions. Mode (Hex) PID (Hex) Function PIDs supported [01-20] Monitor status since DTCs is cleared DTC is freeze Status of fuel system Engine load value is calculated Engine coolant temperature (ECT) Short term fuel % trim Bank Long term fuel % trim Bank Short term fuel % trim Bank Long term fuel % trim Bank 2. 17

18 01 0A Fuel pressure. 01 0B Intake manifold absolute pressure 01 0C Engine RPM. 01 0D Speed of vehicle 01 0E Timing advance. 01 0F Intake air temperature Table 2: Functions of the SAE 1979 PIDs Parameter Conversion Methods 1. Engine RPM 010c is the OBD command used to get the engine s RPM. As mentioned above, the response would contain 4 bytes of data. The first two bytes are identification bytes to the mode used. The next two bytes are to be taken and are to be used in the following formula. Rpm value= ((A*256) + B)/4 A and B represent the decimal equivalent of first and second bytes of data. The range of the RPM value obtained would be from a minimum of 0 to a maximum value of 16, revolutions per minute. 2. Vehicle Speed To obtain the value of speed, the PID would be 010d. Unlike Engine Rpm, the data bytes returned are only one and not two. To obtain the value of speed, just converting this hexadecimal value to decimal value would yield the value of speed. There are also units for the speed parameter and here we are getting speed in km/h. The maximum value of this would be Engine Load 0104 would be the PID code to obtain the value of Engine load. Here too, the returned number of bytes would be just one. The value obtained would be in percentage (%) of load. Assuming the returned decimal equivalent value is A, the formula would be Engine Load = (A*100/255) % 18

19 4. Throttle Position The PID code for the throttle position would be 0111 and it is similar to engine load. The value is in percentage, data bytes obtained are one and the same formula is used to obtain the percentage of throttle. Throttle Position = (A*100/255) % 2.3 Controller Area Network (CAN) The increase in atmospheric pollution partly due to vehicle emissions has caused governments to establish strict laws to sway the automotive industry to build greener automobiles. Vehicle manufacturers are able to satisfy these norms by continuously pushing the level of sophistication in their products. Highly sophisticated vehicles require more complex control strategies, thus more electronic control units (ECU) interacting with the physical world through actuators and sensors. 50 to 100 ECUs can now be found in a single vehicle. Therefore, the information sharing between numerous controllers has become essential. This work focuses on the communication technology connecting those controllers to form a robust network. This efficient and robust field bus is termed controller area network (CAN). Some of the CAN features are its multimaster capability; its built-in error detection and correction capability; as well as its unique fault confinement. CAN bus is an acronym for Controller Area Network which, is a vehicle bus standard. It allows communication between microcontrollers and devices in a vehicle without a host computer. It is a multi-master broadcast serial bus standard for connecting Electronic Control Units (ECU). As shown in the figure below, the CAN bus is connected to all the modules in the car. Hence, it plays a major role as a main medium of communication between different modules. CAN messages can be transmitted periodically, on request, or on a state change at a uniform and fixed bit rate up to 1 Mb/s, within a CAN bus or CAN network. The diagnostic information can be obtained by the use of Parameter Identification numbers or PID s as they are called. With the help of a diagnostic tool, a user can send the PID s to obtain the respective parameters. 19

20 Figure 2: Typical Vehicle Architecture 2.4 ELM327 Almost all of the automobiles produced today are required, by law, to provide an interface for the connection of diagnostic test equipment. The data transfer on these interfaces follow several standards but none of them are directly usable by PCs or smart devices. The ELM327 is designed to act as a bridge between On-Board Diagnostics (OBD) ports and a standard RS232 serial interface. In addition to being able to automatically detect nine OBD protocols, the ELM327 also provides support for high speed communications, a low power sleep mode, and the J1939 truck and bus standard. 20

21 Figure 3: ELM327 Block Diagram Fig. 3 represents the block diagram of an ELM327 device. As you can see the RS232 interface can either be serial USB or Bluetooth interface. The status LED in the device start glowing when- it is plugged in and is ready to receive commands. It has a small memory which saves data like the protocol being used, query times and so on. The ELM327device has its own set of AT commands specific to it. The commands are enlisted below 1. AT Z: This command is basically used to reset the ELM327device. All the settings are returned to their default values and the device would be in the idle state, waiting for values. This is the first command that is sent so that, the device is reset and then it is ready. 2. ATSP 0: This is the command that follows AT Z. Its general form is AT SP h. h denotes the type of protocol to be used. Based on the value of h this command sets the ELM327device to use the protocol selected, saves this selection as the new default protocol. There are 12 different protocols that the ELM327 device can support. h can be replaced by any values from 0-9 and A, B, C each, denoting a different protocol. Here we have chosen value 0 which denotes automatic. Using this, the ELM327device itself scans and tries all the protocols if necessary. Once it finds the right one, it enables the memory function and the protocol is remembered thus, becoming the new default protocol. 3. AT SP a2: This is the third and the final AT command that we are sending to the ELM327 device. It is actually of the form AT SP ah where, h and a can be interchanged. So, AT SP ha is the same as AT SP ah. This command enables us to choose a starting protocol, at the same time retains the ability to automatically search for a valid protocol when, there is a connection failure. Even with the memory option off, it saves the 21

22 protocol information. So, once the current protocol fails it automatically starts to search for a new one. Once these commands are sent by a PC/Handheld device, it starts sending the OBD specific commands. The OBD commands do not start with AT. This is how the ELM327device recognizes that they are the OBD commands. Each of these commands has two parts. The first part is called the mode. There are ten different modes, each representing a different function. The mode 0x01 which has a function of showing the current data is used here. The second part of the PID represents the parameters PID. For instance, RPM s PID is 0c. Therefore, to request the current RPM value the code would be 010c. The response from the OBD is in the form of; ID of the module responded and the data. The responses are based on the type of data requested by the ELM device. For instance, let s consider the same RPM parameter. The response for the PID we sent would be a total of 4 bytes. The first two bytes represent the response as identification to mode 1.The next two bytes contain the encoded RPM values in Hexadecimal format. So, to get the actual value, these hexadecimal values are to be converted to decimal format. 2.5 On Board Diagnostic (OBD) II UART Board OBD II UART board is a device which helps to interface with the OBD II bus of the vehicle. The OBD II UART board actually acts as a serial interface to allow the microcontroller to communicate with the Electronic Control Unit (ECU) of the vehicle by using ELM 327 AT command. Besides that, OBD II UART board also supports many major OBD-II standards like CAN and BUS. The baud rates of UART interface is from 38bps to 10Mbps and OBD II UART board also has a secure boot loader for easy firmware updates. Figure 4: OBD II UART board of Sparkfun 22

23 2.6 RS-232 SERIAL PORT RS-232 serial port is the serial communication interface which allows the transferring or receiving of the data at the rate of one bit at a time. It is widely used in the serial communication of devices like computer, modem and printer. Until nowadays, the serial port is still applicable to some of the systems like industrial automation system and scientific instrumentation system. The connector plug for RS-232 serial port is the 9pin D plug as shown in the Figure 5. RS-232 serial port can be divided into male and female pins. Normally, the pins on the RS 232 serial port of computer is male pins, so we need a RS 232 serial port with female pins to connect any external peripheral devices with computer. Figure 5: RS232 serial port connector 2.7 Raspberry Pi The Raspberry Pi here is used as an effective computing platform and thus can be called the heart of the system. Raspberry Pi is a low cost mini-computer, with low power consumption suitable for high speed processing. A budget friendly module, it can control processes or be deployed as a low cost development platform for both software developers and network engineers. The computer, which costs $35 ( 23) - or $25 ( 16.50) without Ethernet - is a 23

24 complete 700 MHz ARM CPU with a GPU and 256MB of RAM, using an SD card as a hard drive. It has two USB ports, Ethernet and audio as well HDMI at 1080p. Figure 6: The Raspberry Pi The process of data collection and processing along with wireless transfer to a remote server is done through a Raspberry Pi module receiving sensor inputs from the car as well as connected to various peripheral devices like GPS and 3G dongle. 2.8 Global Positioning System (GPS) Over the years, the technology involved in manufacturing an automobile has become more advanced, as automakers shift their focus from basic transportation to the design of features that make a vehicle safer, more comfortable, and more easily operated. One such feature is the Global Positioning System (GPS). Originally conceived as a navigation aid for the military, the Global Positioning System, or GPS, has since grown from relatively humble beginnings as different supporting technologies have been developed, some of which are within reach of consumer budgets. All that GPS does is provide a set of coordinates which represent the location of the GPS unit with respect to its latitude, longitude and elevation on planet Earth. It also provides time, which is as accurate as that given by an atomic clock. The actual application of the GPS technology is what leads to such things as navigation systems, GPS tracking devices, GPS surveying and GPS mapping. GPS in itself does not provide any functionality beyond being able to receive satellite signals and calculate position information. But it does that very well. GPS is utilized for a wide variety of applications in personal cars. The first and 24

25 the most obvious is navigation. It is not only being exploited in navigation but also during emergency situations like theft of the car, automated driving system etc. Once we know our location, we can find out where we are on a map and GPS mapping and navigation is perhaps the most well-known of all the applications of GPS. Using the GPS coordinates, appropriate software can perform all manner of tasks, from locating the unit, to finding a route from A to B, or dynamically selecting the best route in real time. This acts as an efficient way to create more efficient routes and thus saving fuel consumption and fuel costs. Figure 7: GPS Dongle The Raspberry Pi is interfaced with a GPS Dongle shown above, which acts as a GPS receiver. A GPS receiver picks up the transmissions of at least four satellites and combines the information in those transmissions with information in an electronic almanac, all in order to figure out the receiver's position on Earth. GPS receiver communication is defined with the NMEA-0183 specification, the standard protocol for data transmission between GPS talkers and listeners which uses a simple ASCII, serial communication protocol that defines how data are transmitted in a "sentence" from one "talker" to multiple "listeners" at a time. Most computer programs that provide real time position information understand and expect data to be in NMEA format. This data includes the complete PVT (position, velocity, time) solution computed by GPS receiver. 25

26 Chapter 3 PROPOSED WORK AND RESULTS The overall framework of a user interactive diagnostic system is illustrated in Figure 8. The system comprises of a set of dedicated hardware interfaced with a car s OBD computer which obtains various Electronic Control Unit (ECU) sensor readings via CAN bus. The readings obtained from the sensors are transmitted to a remote server, where they can be processed and analysed to: 1. Predict vehicle performance and evaluate driving behaviour. 2. Detect faults encountered in the vehicle. 3. Facilitate vehicle tracking and theft alarm via Geo-Fencing using an integrated GPS. Figure 8: System Overview The system has two major modules which are discussed in next sections. 26

27 3.1 Data Acquisition and Pre-Processing Module The process of data collection is done through a Raspberry Pi module receiving sensor inputs from the car. The Raspberry Pi is a Broadcom BCM2835 System on Chip (SoC) microcomputer platform which includes an ARM 900 MHz processor, also found in modern smartphones, which is capable of high speed data processing and transfer. The Raspberry Pi is used here for providing a low cost solution to integrate various peripheral devices like a UART board for communication with the OBD port, GPS for vehicle tracking and a 3G module for wireless transfer of data to a dedicated remote server. Kinematics related data is supplied by the vehicle CAN bus through OBD-II port. The OBD port is a 16 pin male Data Link Connector (DLC) which requests data from a vehicle using OBD-II Parameter IDs (PIDs) associated with a vehicle s functional units and communicates with it via the CAN signalling protocol. Figure 9: Data Link Connector Figure 10: A standard OBD port 27

28 Each vehicle uses one of the signalling protocols to communicate within its various ECUs. The test vehicle used for this system implementation uses the ISO CAN protocol as its signalling protocol. The CAN bus forms an interconnected network of all sensors and actuators contained in various ECUs, with bit rates up to 1 Mbit/s. It also allows Raspberry Pi and peripheral devices to communicate with each other within a vehicle without a host computer. Figure 11: OBD to UART Interpreter Figure 11 shows a UART-OBD interpreter device which is used to establish a connection between the Raspberry Pi and the CAN bus, thus acting as a bridge between the OBD port and a standard RS232 interface. This enable us to translate CAN messages into standard UART messages thus facilitating connectivity with a PC/Handheld device for further data collection and analysis. The combination of Raspberry Pi, Data Link Connector and the OBD-UART interpreter forms a scan tool for this vehicle. This scan tool is triggered by the microcomputer sends a PID to the vehicle s bus. The control unit responsible for that PID responds with the corresponding value to the bus which in turn interacts with the microcomputer. Figure 12: Diagnostic Data obtained 28

29 Figure. 12 shows diagnostic data obtained by calling PIDs for Vehicle Speed, RPM, Engine Coolant temperature, Throttle sensor, battery life etc. via a python code. This is data obtained and stored in a txt file, by running the code for one iteration. Further, the python code is made to run for infinite iterations during trips made by the test car to obtain and store this real time data in an SQL database. Apart from recording real time diagnostic data during a particular trip, location data is also recorded in parallel to further use for navigation based services. Figure.13 depicts a typical GPS output being displayed. This data is obtained over multiple iterations over a trip and is saved in an SQL database via a 3G Dongle. Figure 13: Parsed GPS output Mapping the coordinates to Google Earth would require the data to be converted into GPX format. This is generated by GPSBabel. It enables owners of many different brands of GPS units to view their GPS data in several popular consumer map programs such as Google Earth. The above parsed data is hence converted to KML format which can then be directly imported to Google Earth for location based tracking. 29

30 3.2 Data Analysis In this module, analysis and visualization of collected data is explained. Collected data is analysed for eco-driving techniques and with the help of a user friendly GUI, the results are displayed to the user Eco Driving Analysis A very important criterion for evaluating a driver s performance is through analysis of his/her driving behaviour with respect to fuel saving practices. The concept of Eco-driving has gained huge importance in recent years, intelligent driving techniques allow saving fuel, independent of the vehicle s embedded technology. A more efficient driving style would require an eco-driving assistant to evaluate the factors that lead to increased fuel consumption and generate feedback along with a driving advice targeted to a particular driving style that may not comply with fuel friendly driving styles. Typically, increased fuel consumption occurs due to the following driving styles: 1. Hard accelerations and decelerations 2. Hard Braking 3. Frequent Idling 4. Wrong gear practices To evaluate the user s driving style, real time OBD data is recorded in a database during the course of various trips carried out by the user. Generic data obtained from the OBD via PIDs which are of interest for eco-driving analysis are Vehicle Speed (km/hr), RPM, Throttle (%) and Mass airflow (MAF) (gm/sec). This data is further used to calculate fuel consumption and hence economy of the trip. Figure 14: Data measured for Eco Driving Analysis 30

31 The following analysis was carried out: 1. Detection of hard braking and acceleration: This is recorded by looking for rapid changes in vehicle speed and rapid throttle movement. 2. Excessive Idling: When the amount of fuel used in a vehicle during standby mode exceeds the amount of fuel required to start the engine immediately after shutdown, the vehicle is said to undergo idling, which leads to increased fuel consumption. Amount of fuel wasted due to excessive idling of vehicle is recorded. Since fuel related parameters were not directly available via PIDs in the test car, fuel consumption was calculated using MAF based algorithms. 3. Optimal RPM range for shifting gear: A vehicle s performance is largely affected by wrong gear practices, which, if not corrected, lowers the fuel economy of a car. Hence it is imperative to follow correct gear shifting practices to obtain maximum economy from a vehicle. Moreover, a vehicle s fuel consumption depends on the engine speed calculated in Revolutions per Minute (RPM). To observe the effect of shifting gears on the amount of fuel consumed during a particular trip, while keeping the RPM during the shift constant, a series of test drives were conducted to predict the optimal RPM range in which gear should be shifted in manual transmission cars for improving fuel efficiency. A 2014 Maruti Suzuki SX4 (Manual Transmission Indian variant) was used in the test drives. In each trip, the gear was shifted consistently when the engine speed lied within a certain RPM range. This shift range was , and RPM, and corresponding to these, fuel economy was calculated. To analyse fuel economy for a complete trip, the average economy was computed for each trip. Results obtained from the above experiment are shown in Fig. As evident from the figure, the highest average fuel economy of approximately km per litre was obtained when the gear was consistently shifted in the RPM range of , while the RPM range produced the lowest which was 8.69 km per litre. 31

32 Figure 15: Average fuel economy for various shift points Generic OBD data along with calculated fuel parameters on the basis of above analysis techniques, predicts wrong driving behaviour and advises the user to follow a specific driving style based on previous driving behaviour analysis. Some useful driving styles that can typically be followed are: 1. Shift up as soon as possible. 2. At steady speeds use the highest gear possible and shift in the RPM range of Try to maintain a steady speed by anticipating traffic flow 4. Shut down the engine for longer stops to prevent excessive idling. 5. Do not drive faster than 120 km/h. The above advices along with detailed trip analysis is shown to the user via a personalized web portal which is illustrated in the next section 32

33 3.3. Data Visualization Information obtained from the scan tool together with location information fetched from the GPS module are sent to the remote server where the data is stored in form of MySQL Database. A web application requests data from the server, processes it and displays it through a Graphical User Interface (GUI) on user portal. The web application designed accomplishes the following functions: 1. Display of real time vehicle data, Diagnostic Trouble codes and tracking information. 2. Display of vehicle performance in terms of Eco Driving behaviour. 3. Provision of Geo-Fencing the vehicle for theft detection. Fig. 16 illustrates a user-friendly personalized web application designed to provide an overview of current vehicle status by displaying real time vehicle data like speed, RPM, Geolocation of the vehicle, fault detection in terms of Diagnostic Trouble Code (in the testing carried out, it was DTC-P0123 corresponding to malfunction of Throttle/Pedal Position Sensor). Moreover, statistics of a past trip can be viewed which include the distance covered during the trip, the average speed of the vehicle over the trip, number of hard brakes and hard accelerations undergone by the vehicle during the trip and the fuel economy of the vehicle over the trip. Figure 16: Web display of Vehicle Diagnostics, Real time tracking and Eco- Driving related parameters 33

34 The functionality of Geo-fencing has been showed in Fig. 17. The user initially sets a geo-fence around his/her vehicle after which the application constantly monitors the location of the vehicle and notifies the user as soon as the vehicle crosses the geo-fenced boundary. On receiving a notification, the user can then use real time geo-tracking to retrieve his/her vehicle. In such a situation of theft, the user can still monitor the car along with informing authorities to track it down. Figure 17: Geo Fencing for theft detection 34

35 Chapter 4 Conclusions and Future Work This paper presents an integrated hardware/software framework to show vital information about a vehicle to its user by providing a useful platform for monitoring performance related statistics as well as provide location based services through a simple, personalized web portal. An attempt to assess basic driving behaviour through generic OBD data has been made, but to model a driver s driving style to assess if he/she is performing optimized driving through the data obtained from Vehicle Diagnostics port is not sufficient. To obtain a more precise consumption model and develop a real time Eco-Driving assistant, other external information like real time traffic updates along with location positioning can be useful. A lot of scope still lies by using data from the Diagnostics port along with various online parameters to create a real time assistant as well. For future work, the development of a real time personal assistant shall be worked on, enabling ease of access to the user while presenting real time advises based on such analysis. 35

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