Optimal energy management strategy for dual-power coupling tractor based on the adaptive control technology

Abstract

Hybrid tractors (HT) are regarded as the efficient agricultural machine due to their energy conservation performance and faster torque response to deal with load fluctuations. However, the strategy to allocate the battery and fuel energy for demand power should be discussed. In this paper, an on-line management strategy of the HT is proposed to optimize the energy consumption of engine and motor and to reduce torque ripple for power units. A new architecture for replacing power shift and continuously variable transmission technology is proposed. Then, the modified equivalent consumption minimization strategy (ECMS) is used to optimize the torque distribution in which the equivalent factor is further calculated for the real-time process. Besides, the modification of ECMS in variable working conditions can effectively analyse the torque distribution between the motor and engine. The numerical test is implemented that the effectiveness of the proposed energy strategy is validated in plowing conditions. The consequences indicated that the proposed power distribution strategy can adaptively allocate the torque demand according to the fluctuation load. Comparing with the traditional rule-based strategy, the proposed strategy can reduce 6.2% of the energy, and decrease torque ripple with the proposed tractor architecture.

Research on Energy Management Strategy for Series Hybrid Tractor under Typical Operating Conditions Based on Dynamic Programming

“…Zhao et al [25] proposed an energy management strategy based on the principle of maximizing external energy efficiency, which significantly reduces equivalent hydrogen consumption and improves the overall efficiency of the hybrid tractor. Dou et al [26] proposed an energy management strategy based on an equivalent fuel consumption minimization algorithm. This strategy can adaptively distribute the required torque based on the load condition, resulting in better fuel consumption compared to rule-based energy management strategies.…”