Fuzzy predictive control strategy for plug-in hybrid electric vehicles over multiple driving cycles

Najjaran, Samieh; Rafiee, Zahra; Hassanzadeh, Mojtaba

doi:10.1007/s40435-021-00862-9

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 5 , the decision matrix for the energy consumption algorithms is presented. According to the literature review, fuzzy logic approaches [ 71 ], support vector machines [ 62 ], neural networks [ 41 ], deep learning techniques [ 43 ] and phenomenological models [ 39 ] have been proposed to determine vehicle energy consumption. These approaches usually use GPS signals (from smartphones) and vehicle measurements such as vehicle speed, acceleration, RPM, throttle, and mass/air flow.…”

Section: Resultsmentioning

confidence: 99%

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

show abstract