Mario Barnard* and Mohamed Zohdy

Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA

*Corresponding Author: Mario Barnard, Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA.

Received: April 22, 2022; Published: May 25, 2022

Abstract

In this paper, a Model Predictive Estimator has been utilized to perform state estimation of a Plug-In Hybrid Electric Vehicle (PHEV). Moreover, in order to accomplish this task, a Kalman filter has to be implemented in order to improve the model predictive estimation. The prediction horizon and estimation horizon, also known as the moving average horizon, will be modified for this paper. The prediction horizon and estimation horizon will be the key concept of this paper, as the authors’ need to balance these two horizons. The cyber security of PHEVs will also be considered in this paper.

Keywords: Model Predictive Estimation (MPE); Linear Model Predictive Estimation (LMPE); Nonlinear Model Predictive Estimation (NLMPE); Plug-In Hybrid Vehicle (PHEV); Kalman Filter; State Estimate; Cyber Security; Controller Area Network (CAN)

References

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Citation

Citation: Mario Barnard and Mohamed Zohdy. “A Model Predictive Estimation (MPE) Approach to PHEVs". Acta Scientific Computer Sciences 4.6 (2022): 81-96.

Copyright

Copyright: © 2022 Mario Barnard and Mohamed Zohdy. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.