One important component in a vehicle is the braking system. The main function of the braking system is to provide deceleration to stop the vehicle speed. One important part of the braking component is the brake lining. But to know the physical condition of the brake lining, it is necessary to dismantle the wheel and brake components. So that one of the initial stages of development is to make an application that can predict the remaining life of the brake lining without having to do the demolition. Therefore, this study will design the calculation with the physical condition monitoring approach of the brake lining itself. In addition, in this study, the author will try to find a relationship between driving behaviour and its effect on the wear rate of the brake lining. After testing the road with 3 different driving behaviours, it was found that the driver with eco behaviour would consume 0.42%, normal behaviour as much as 1.65% and sport behaviour as much as 44.96% of the thickness of the original brake lining. There was a significant result in sports driving behaviour because in this behaviour the braking pressure and temperature will be very high when compared to eco and normal. Furthermore, the application error which obtained from the application was higher than the other two behaviour.

Engine lubricant is a liquid chemical that is given between two moving objects to reduce the frictional force that occurs. Engine lubricant needs to be replaced periodically to maintain the durability of four-wheeled vehicles. Manufacturers of four-wheeled vehicle manufacturers have given the lubricant replacement period to consumers, but this time period is only a reference. The purpose of this research is to study the calculation method to find the right time to replace the lubricant along with its parameters, study the relationship and characteristics between engine lubricant temperature with coolant temperature, and study the relationship of driving behaviour to decrease engine lubricant conditions. This research was carried out by taking data on engine lubricant temperature and coolant using ANCEL and then analysed it to get the formula for determining engine oil temperature and found the mean absolute error of 0 to 3.60. This research is done by testing the driving behaviour of eco, normal, and sport as far as 300 km for each driving behaviour. In this study, engine speed and coolant temperature data are taken through OBD II and then processed using Raspberry Pi into RPS and engine lubricant temperature then further processed by the backend then the data is sent to Android. On the Android application, the output of the data processing results is displayed as a percentage of engine lubricant conditions, engine lubricant remaining distance, and engine lubricant remaining time. The test results show that in sports driving behaviour, the condition of engine lubricants decreased the most at 3.9% followed by normal at 3.18% and the lowest decreased was eco with 2.39%.