A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Abstract

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

“…However, the actual vehicle operation data are constantly changing, which may lead to a large deviation between the predicted value and the actual value [91]. Deep neural networks (DNN) are composed of neurons at multiple levels [92]. The automatic learning of data and automatic feature extraction can be realized through multiple feedback training, which is suitable for establishing the dynamic variation process between feature data and fuel consumption [93].…”

Design of Nature tourism transportation in Kulon Progo from Yogyakarta International Airport to Borobudur

“…therefore, the proposed intelligent medium-sized car should meet smart transportation. for example, this vehicle must be equipped with cctVs or video cameras (in the front, rear and inside the car), global positioning system (GpS) sensors on vehicles to monitor vehicle position and fuel consumption [40], motion sensors, GpS on mobile phones, rfID sensors and other types of Iot sensors. also, on the road equipped with any Iot [41].…”

“…the design of the medium-size vehicle with the proposed intelligent system is also similar to the Smart application for Every car (SaEc) [42]. thus, we proposed an intelligent medium-sized bus with 18 seats consisting of 16 passenger seats, one driver seat and one tour guide seat that be equipped with cctVs or video cameras (in the front, rear and inside the vehicle), GpS sensors on cars to monitor vehicle position and fuel consumption [40] motion sensors, GpS on mobile phones, rfID sensors and other types of Iot sensors.…”