Model Predictive Energy Control Including Mechanical Fatigue Life of a Two-Motor Multi-Speed Electric Vehicle. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Tao, Y.; Schoeneberger, D.; Rinderknecht, S.; Morhard, B.; Schweigert, D.; Gerlach, M.; Obernolte, U.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 14109
• Page End: 14116
• Publication Date: 2020
• Subjects: Automotive Control -- Battery Electric Vehicle -- Powertrain -- Multi-Objective Energy Management Strategy -- Optimal Control -- Fatigue Life -- Service Life -- Model Predictive Control -- Real-Time Control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1009
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Over the last three decades, energy management strategies considering minimum energy consumption have been extensively studied in the field of automotive engineering. On the contrary, the fatigue life of mechanical parts in powertrains is rarely considered. This paper addresses a Real-time-oriented Multi-Objective Energy Management Strategy aimed at both the energy consumption and the fatigue life of mechanical parts in the powertrain of a Two-Motor Multi-Speed Battery Electric Vehicle (BEV). This strategy is based on Model Predictive Control (MPC), while Dynamic Programming (DP) is embedded to solve the non-linear optimal control problem in the prediction horizon. The online simulation results show that this MPC-based strategy prolongs the service life of the powertrain with a minor sacrifice in energy consumption, and that this strategy achieves a sub-optimal result close to the offline optimal result from DP. Moreover, the result from MPC-based strategy approaches the optimal result with prolonging prediction horizon.