Vehicle connectivity provides excellent opportunities for collecting valuable information on driving, road, traffic, and environmental conditions, which can then be utilized to improve a wide range of performance characteristics. In this study, we show how such historical information on driving conditions of connected hybrid mining trucks can be leveraged to develop a new adaptive equivalent fuel consumption minimum strategy (A-ECMS) for reducing their fuel consumption. We choose a power-split hybrid system as our baseline powertrain which includes a diesel engine, two motor-generators, three planetary gear sets working as a power-split device, and a battery. We employ the vehicle's traveling information along with the tuned equivalent fuel consumption minimum strategy (ECMS), developed by using the truck powertrain model, to train a long short-term memory (LSTM) network. The trained LSTM is then employed to solve the reference value of the state of charge (SOC) in real-time according to the current state of the vehicle. In the LSTM training process, a random disturbance is added to improve the robustness to the vehicle's speed variations. Adjustments by a fine-tuned proportional-integral (PI) controller eliminates the difference between the current SOC and the reference SOC to achieve an optimal adaptive equivalent coefficient. We apply the genetic algorithm (GA) to fine-tune the gains of the PI controller for different SOC values and road segments. Simulation results in the MATLAB/Simulink environment show that the resulting A-ECMS strategy reduces the vehicle fuel consumption up to 6.5% as compared with a rule-based (RB) control strategy.
LSTM-based adaptive energy management of connected hybrid mining trucks for improving fuel efficiency
2022-10-08
840198 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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