Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

Abstract

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.

Machine Learning and IoT Trends for Intelligent Prediction of Aircraft Wing Anti-Icing System Temperature

“…ANNs are considered a subset of ML [25][26][27][28][29][30][31][32][33][34][35][36][37]. ANNs are considered one of the most important elements of deep learning, as they lie at the heart of the algorithms underlying this learning.…”

“…Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is higher than the specified value of the threshold, that node is exited and activated, and data are sent to the next network layer [25][26][27][28][29][30][31][32][33][34][35][36][37]. Otherwise, no data will be sent to the next network layer.…”

“…For example, an image or speech recognition task can be completed in minutes using ANNs compared to hours using manual recognition by human experts. Therefore, neural networks have been applied in many applications, such as the Google search algorithm-the most famous example-weather prediction, and the classification and control of aircraft malfunctions [25][26][27][28][29][30][31][32][33][34][35][36][37]. The structure of the proposed data transfer in the wing anti-icing system is given in Figure 7.…”

“…Through the previous discussion, we can see that ANNs have been used efficiently in improving the performance of CFD-based approaches for several different case studies. In addition, ANNs have been used in other applications, for example, to improve the performance of controllers in aircraft and hybrid electric vehicles [36,37]. As a result of the emergence of Industry 4.0 and its close connection with the IoT, researchers are interested in exploiting the IoT and applying it in many applications, such as BMS, economics, management, aviation, and many other fields [38][39][40][41][42][43][44][45][46].…”

Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

“…By leveraging HIL, this study gains enhanced testing reliability and efficiency, making it possible to conduct comprehensive examinations of system components under various simulated conditions. Notably, it was utilized for the construction of a cost-efficient, intelligent neural predictive control for hybrid electric vehicles [30] .In a separate study, HIL was implemented for optimizing linear controllers via genetic algorithms, specifically for a brushless DC motor in electric scooters under fluctuating operational conditions [31]. The HIL validation can be implemented using two dSPACE card boards .…”