In this paper, two main subjects are addressed. The first subject is the description of the considered hybrid electric propulsion system together with the power-oriented modeling of the employed gearbox. The gearbox modeling is performed by differentiating the cases of gearshift taking and not taking place, and the resulting model can be directly implemented in the Simulink environment using standard libraries. The second subject is the development of a new algorithm for determining the vehicle gearshift strategy in order to optimize the efficiency of the electric machine driving the transmission. The algorithm, which is causal and real-time implementable, is derived from an off-line benchmark optimal solution computed using dynamic programming, which, although being optimal, is a-causal and not real-time implementable. On the selected case study driving scenario the algorithm shows good performance, achieving an electric machine average efficiency that is only 2.1% lower than the optimal off-line dynamic programming solution.
Power-Oriented Gearbox Modeling and Gearshift Strategy Optimization Using Dynamic Programming / Tebaldi, Davide; Villani, Manfredi; Rizzoni, Giorgio. - (2023). (Intervento presentato al convegno 2022 IEEE 61st Conference on Decision and Control (CDC) tenutosi a Cancun, Mexico nel 06-09 December 2022) [10.1109/CDC51059.2022.9993309].
Power-Oriented Gearbox Modeling and Gearshift Strategy Optimization Using Dynamic Programming
Davide Tebaldi
;
2023-01-01
Abstract
In this paper, two main subjects are addressed. The first subject is the description of the considered hybrid electric propulsion system together with the power-oriented modeling of the employed gearbox. The gearbox modeling is performed by differentiating the cases of gearshift taking and not taking place, and the resulting model can be directly implemented in the Simulink environment using standard libraries. The second subject is the development of a new algorithm for determining the vehicle gearshift strategy in order to optimize the efficiency of the electric machine driving the transmission. The algorithm, which is causal and real-time implementable, is derived from an off-line benchmark optimal solution computed using dynamic programming, which, although being optimal, is a-causal and not real-time implementable. On the selected case study driving scenario the algorithm shows good performance, achieving an electric machine average efficiency that is only 2.1% lower than the optimal off-line dynamic programming solution.File | Dimensione | Formato | |
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