Model Predictive Energy Control Including Mechanical Fatigue Life of a Two-Motor Multi-Speed Electric Vehicle

Tao, Yikai; Schoeneberger, D.; Rinderknecht, Stephan; Morhard, B.; Schweigert, Daniel; Gerlach, Martin; Obernolte, U.

doi:10.1016/j.ifacol.2020.12.1009

Cited by 3 publications

(1 citation statement)

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“…It simply has to do with the current situation and activity. It has no relationship with the historical stat [11,12]. The standard NEDC operating state is used in this paper, and the speed and necessary power under that situation are presented in Figures 2 and 3.…”

Section: Energy Management Model Of Hybrid Electric Vehiclementioning

confidence: 99%

An Energy Management Strategy of Power-Split Hybrid Electric Vehicles Using Reinforcement Learning

Zhou

Zhao

Wang³

2022

Mobile Information Systems

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With the rapid development of science and technology, the automobile industry is also gradually expanding due to which energy security and ecological security are seriously threatened. This paper was aimed at studying the energy organization strategy of power-split hybrid electric vehicles based on a reinforcement learning algorithm. A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by using a planetary gear set to split and combine the power generated by electric machines and a combustion engine. This improves the fuel economy to some extent. However, to increase the fuel economy to a greater extent. This study primarily introduces the hybrid electric vehicle’s structure and presents a reinforcement learning-based management of energy approach for hybrid electric vehicles. It constructs the vehicle power model of HEV and Markov probability transfer model, then designs the energy control strategy based on reinforcement learning, and finally compares it with the energy control strategy based on PID (Proportional-Integral-Derivative). Using MATLAB/Simulink, the cycle conditions of NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure) are selected to carry out simulation experiments. The energy management technique suggested in this study, which is based on reinforcement learning, may efficiently enhance the usage rate of automotive gasoline. The replication results show that the fuel consumption per 100 km (kilometers) based on reinforcement learning management strategy is 4.6% and 2.7% lower than the PID management strategy under two working conditions. The economy of fuel of the hybrid electric vehicle is also effectively improved.

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Section: Energy Management Model Of Hybrid Electric Vehiclementioning

confidence: 99%