Reinforcement learning energy management control strategy of electric tractor based on condition identification - Liqiao Li; Jiangchun Chen; Jing Nie; Zongyu Gao (International Journal of Electrical Power & Energy Systems)
lithium-titanate battery(LTB) and supercapacitor(SC) hybrid power drive electric tractors (ET) are one of the necessary means to achieve truly green agriculture. However, low traction efficiency, short battery life, and high energy consumption are the main reasons hindering the industrialization of ET. This paper proposed a Q-network reinforcement learning(RL) energy management control strategy(EMCS) of ET based on driving condition identification(CI). Firstly, the power demand of ET during driving is regarded as a Markov process. The historical driving data are used to construct the driving conditions of ET and obtain the Markov power state transfer probability matrix(MPSTPM) under different CI; Second, to minimize the energy consumption of lithium-titanate battery and supercapacitor hybrid power system(HPS), the power allocation strategy for ET under different CI is obtained by a Q-network RL algorithm; Finally, an learning vector quantization neural network(LVQNN) is used to identify the current ET driving CI through online and real-time, and the control system makes real-time power output decision through the current driving CI. Using the actual ET driving data, the simulation results show that the Q-network RL of based on CI optimized EMCS can reduce the energy loss of HPS. Compared with the state machine control strategy and dynamic programming (DP) control strategy through simulation platform where it is shown to Q-network RL control strategy based on CI provide better performance. Compared with the state machine control strategy, the Q-network RL reduction the totle energy consumption 13.28 % and achieved 94.3 % performance compared with DP. Finally, the 90 kW experiments platform was used for experimental verification, and the test results verified the feasibility of this Q-network RL based on CI energy management control strategy in practical engineering applications.