Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems

Abstract

Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.