A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles. (1st August 2022) - Bo, Lin; Han, Lijin; Xiang, Changle; Liu, Hui; Ma, Tian
Abstract: In this paper, a Q-learning fuzzy inference system (QLFIS)-based online control architecture is proposed and applied for the optimal control of off-road hybrid electric vehicles (HEVs) to achieve better dynamic performance, fuel economy and real-time performance. A dynamic model, including a hybrid system, vehicle dynamics and road model, is established to obtain the state feedback according to the current driving environment under command. The optimal control strategy and objective function are both constructed by an adaptive network fuzzy inference system (ANFIS) due to its strong approaching ability. The fuzzy rules and parameters are trained online through the Q-learning algorithm and gradient descent method. This control framework provides a new control idea for the control of off-road vehicles. Without knowing the driving cycle in advance, it achieves a good control effect for different driving environments through online data collection and training. The QLFIS-based control strategy is compared to dynamic programming (DP)-based and rule-based strategies based on two different off-road driving cycles through simulation. The simulation results show that the vehicle dynamic performance and fuel economy are improved with respect to the rule-based strategy, while the calculation time is greatly reduced compared to that of the DP-based strategy. Highlights: Q-learning fuzzy inference system is adopted for real-time and optimum control. An optimization model balances electric power performance and energy management. A fuzzy controller adapts to off-road conditions with online iterative learning. The strategy improves power and economy of vehicle, reduces elapsed time by 98%.