Proactive Power Management Scheme for Hybrid Electric Storage System in EVs: An MPC Method

Yuying Hu, Cailian Chen, Tian He, Jianping He, Xinping Guan, Bo Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Hybrid electric storage system (HESS) is a promising power supply for electric vehicles (EVs) to prolong battery cycling life. Battery longevity is affected by the magnitude and fluctuation of the charging/discharging power profiles. While vehicle driving, unexpected high power demand can cause battery degradation and should be supplied by the supercapacitor (SC) in the HESS. However, the limited capacity of SCs restricts the HESS benefit. Thus, one of the crucial while challenging issues for an HESS is how to effectively manage the power splitting between batteries and SCs to satisfy the vehicle's driving demand as well as reducing battery degradation rate. In this paper, a proactive power management scheme is proposed to extend the EVs battery life with the HESS. First, we exploit a time-series forecasting method to predict the short-term vehicle velocity and calculate the future power demand based on prediction results. Next, due to the nonlinear dynamics of the HESS, the T-S fuzzy modeling method is adopted to approximate system nonlinearity and develop an empirical model. Finally, a model predictive control (MPC) based power management problem is formulated. Prediction errors are considered in MPC formulation to improve system robustness. Based on driving profile tests, simulation results demonstrate that the magnitude and fluctuation of battery current are both reduced and the battery life is prolonged by 17.81% compared with the existing methods.

Original languageEnglish (US)
Article number8903478
Pages (from-to)5246-5257
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume21
Issue number12
DOIs
StatePublished - Dec 2020

Bibliographical note

Funding Information:
Manuscript received June 21, 2018; revised January 26, 2019 and June 26, 2019; accepted November 6, 2019. Date of publication November 18, 2019; date of current version November 30, 2020. This work was supported by the National Natural Science Foundation of China under Grant 61622307, Grant 61933009, Grant 61731012, and Grant 6182800052. The Associate Editor for this article was S. Darbha. (Corresponding author: Cailian Chen.) Y. Hu, C. Chen, J. He, X. Guan, and B. Yang are with the Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China, and also with the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: hyy0022@sjtu.edu.cn; cailianchen@sjtu.edu.cn; jphe@sjtu.edu.cn; xpguan@sjtu.edu.cn; bo.yang@sjtu.edu.cn).

Keywords

  • Power management
  • battery lifetime
  • hybrid electric storage system (HESS)
  • model predictive control
  • velocity prediction

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