Database Design

The previous measurements or available data related to vehicle dynamics and road con- ditions have a significant role in the study in order to propose the adaptivity method for the integration of velocity and adaptive semi-active suspension control. These mea- surements or available data must be stored in a database to use in dedicated time and method. For this reason, the database has been designed to use the adaptivity method for this thesis. In this study, the detection or estimation of road model and irregularity is not focused on; thus, the position information of road irregularities is stored in the database.

Figure 2.17: Data collection.

These collected data are gathered and stored in a database. Two systems are pro- posed in this section:

• GPS-based Database: This database is GPS-based and used in multiple papers during research.

• Cloud-aided system: The database has been integrated into the cloud, while com- puting and database access has been performed in the cloud.

2.3.2.1 GPS-based Database

Several measurements of the vehicle that is equipped with passive suspension have been gathered and stored in the designed database. The reason for the usage of passive suspension is its availability on the market. The majority of the vehicles on the market still use passive suspension and it is easy to access these measurement data. The data collection architecture is shown in Figure2.17.

The road model consists of different road irregularities, such as random roughness, 5-7cm several bumps and potholes, sinusoidal irregularity, 3-5-7-10-12cm bumps, and 3-5-7cm potholes. These road irregularities are shown in Figure 2.18. Here, elevations of bumps, potholes, and several bumps/potholes vary according to their height, i.e., for the 7 cm bumps road irregularity, the height of these bumps is 7 cm.

The data is collected by the on-board unit of the vehicle with multiple sensors (GPS, acceleration, tire deformation, velocity). The collected data is listed below, while these collected data are gathered and stored in a database:

• Vertical acceleration

• Tire deformation

• Velocity

• Position

Several sensors are used for measurement: acceleration sensor, tire deformation sen- sor, velocity sensor, and GPS. The tire deformation sensor is not common and accessible easily, while there are several methods and studies to design the sensor that measures

(a)

(b)

(c)

(d)

38

the tire deformation (Xiong and Yang [2018]; Xiong and Tuononen [2014]; Sergio et al.

[2006]).

The GPS-based database comprises previous measurement data and position infor- mation of the road irregularities. Some of the measurements are shown in Table 2.3.

Note that the calculation ofζ is introduced in Chapter3.

2.3.2.2 Cloud-aid System

The capabilities of the cloud, database and computing technologies are used in order to improve the comfort and safety of such equipped vehicles in this thesis. The cloud system communicates with the vehicle and gathers, processes, stores, and distributes the data.

The general-purpose V2C2V architecture is illustrated in Figure 2.19. Vehicles are connected to the internet with wireless networks. The cloud system provides access to the database as needed for cloud computing and designing the scheduling variable. The cloud system can provide huge computing power; its use for vehicle control is limited by the availability of the network. Sensors provide required data from vehicles, raw and processed data can be transmitted to the cloud system for computing and reaching the database. Combining on-board collected data with data from the cloud and the ability to perform control calculations in the optimal location (either on-board or cloud) maximizes situational awareness both across distributed and on-board systems. Real- time updates to databases keep environment information, such as weather and road conditions, up-to-date (Li et al.[2015,2014]).

The cloud part of the system is implemented on a private infrastructure cloud. It is also possible to implement the introduced architecture on the other clouds with minor modifications. The selected database engine is MongoDB due to its handling geospatial information feature. The cloud application uses a Flash framework for providing a RESTful API to the cloud functionality and is written in Python.

It is necessary to explain the cloud system with a pilot study. Database and decision computing are integrated into the cloud system in this pilot study. The process of decision-making for the scheduling parameter in the cloud system is the following:

• numerous measurement data are collected by the on-board unit from the passive suspension system, and this measurement data is uploaded to the cloud system.

• the database, which consists of road information, is built by gathering information from vehicles through the developed cloud application.

• the performance index is defined in order to find the importance between driving comfort and road holding in the cloud application.

• according to road irregularity, vehicle velocity, performance results, and perfor- mance index, the corresponding scheduling variable is calculated in the related location by the decision algorithm in the cloud.

Table 2.3: Cloud database.

Road Irregularity Vel. R^va ζ^va R^td ζ^td

Roughness

20 0,0302 2,28 0,0005 4,55 40 0,0564 4,25 0,0014 12,73 60 0,0804 6,06 0,0019 17,27 80 0,1093 8,24 0,0031 28,18 3 cm bump

20 0,0352 2,65 0,0005 4,55 40 0,0706 5,32 0,0013 11,82 60 0,134 10,11 0,0022 20,00 80 0,19 14,33 0,0032 29,09 3 cm pothole

20 0,0351 2,65 0,0005 4,55 40 0,071 5,35 0,0013 11,82 60 0,134 10,11 0,0022 20,00 80 0,188 14,18 0,0032 29,09 5 cm bump

20 0,058 4,39 0,0005 4,54 40 0,115 8,71 0,0012 10,90 60 0,217 16,41 0,0022 20,00 80 0,293 22,15 0,0033 30,00 5 cm pothole

20 0,0581 4,38 0,0005 4,54 40 0,1165 8,78 0,0013 11,82 60 0,2178 16,42 0,0022 20,00 80 0,29 21,87 0,0032 29,00 7 cm bump

20 0,081 6,11 0,0005 4,54 40 0,16 12,06 0,0013 11,80 60 0,291 21,94 0,0022 20,00 80 0,401 30,24 0,0033 30,00 7 cm pothole

20 0,081 6,10 0,0005 4,54 40 0,1625 12,25 0,0013 11,80 60 0,3091 21,31 0,0022 20,00 80 0,4784 36,07 0,0033 30,00 10 cm bump

20 0,115 8,71 0,0013 11,80 40 0,217 16,40 0,0022 20,00 60 0,293 22,10 0,0033 30,00 80 0,537 40,50 0,0036 35,40 12 cm bump

20 0,1392 10,50 0,0006 5,45 40 0,245 18,48 0,0014 12,73 60 0,5509 41,55 0,003 27,27 80 0,7468 56,32 0,0056 50,91 Multiple bumps

& potholes (5cm)

20 0,37 27,90 0,011 100 40 0,431 32,50 0,0017 15,40 60 0,466 35,20 0,0029 26,30 80 0,444 33,50 0,0041 37,20 Multiple bumps

& potholes (7cm)

20 0,3769 28,42 0,0012 10,91 40 0,4902 36,97 0,0018 16,36 60 0,6506 49,06 0,0034 30,91 80 0,6644 50,1140 0,0047 42,73

Figure 2.19: V2C2V architecture.

• the calculated scheduling variable is used in the LPV controller to modify the behavior of the LPV controller.

• the corresponding damper force is forwarded to the damper of the vehicle.