Asset Details
Do you wish to reserve the book?
A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle
Do you wish to request the book?
A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle
2022
Overview
To further improve the energy‐saving potential and robustness of the energy management strategy (EMS) for plug‐in hybrid electric vehicles (PHEVs) in a real‐time application, this paper proposes a modified model‐free‐adaptive‐control‐based (MFAC‐based) EMS to overcome the disadvantages in our previous MFAC‐based EMS. First, the influence of external disturbance on MFAC‐based EMS is discussed, and the results show that both the vehicle velocity and load change have a significant impact on its performance. Second, a modified MFAC‐based real‐time EMS is designed based on history driving data obtained from a repeated route in which a state‐of‐charge (SOC)‐constraint‐based reference SOC planning method is firstly proposed to simultaneously consider the vehicle velocity and changing load. Then, global SOC constraints are incorporated in Pontryagin's minimum principle (PMP) to enhance the adaptive capability of the proposed method. Finally, the optimal solution of PMP (i.e., optimal constant) is deployed as a benchmark, and the performance of the modified MFAC‐based EMS (namely MFAC‐II and MFAC‐III) is in contrast to the previous one (MFAC for short) under various real‐world driving cycles. The results demonstrate that the MFAC‐III has a remarkable improvement in both economic performance and robustness. Particularly, the energy‐saving effectiveness is close to the global optimum one in some driving conditions.
This paper proposes a modified model‐free‐adaptive‐control‐based (MFAC‐based) energy management strategy (EMS) to overcome the disadvantages of our previous MFAC‐based EMS. The modified EMS has a remarkable improvement in both economic performance and robustness for plug‐in hybrid electric vehicles.
Publisher
John Wiley & Sons, Inc
Subject
