Fuzzy logic based equivalent consumption optimization of a hybrid electric propulsion system for unmanned aerial vehicles

Abstract

This paper presents an energy management strategy for a hybrid electric propulsion system designed for unmanned aerial vehicles. The proposed method combines the Equivalent Consumption Minimization Strategy (ECMS) and fuzzy logic control, thereby being named Fuzzy based ECMS (F-ECMS). F-ECMS can solve the issue that the conventional ECMS cannot sustain the battery state-of-charge for on-line applications. Furthermore, F-ECMS considers the aircraft safety and guarantees the aircraft landing using the remaining electrical energy if the engine fails. The main contribution of the paper is to solve the deficiencies of ECMS and take into consideration the aircraft safely landing, by implementing F-ECMS. Compared with the combustion propulsion system, the hybrid propulsion system with F-ECMS at least reduces 11% fuel consumption for designed flight missions. The advantages of F-ECMS are further investigated by comparison with the conventional ECMS, dynamic programming and adaptive ECMS. In contrast with ECMS and dynamic programming, F-ECMS can accomplish a balance between sustaining the battery state-of-charge and electric energy consumption. F-ECMS is also superior to the adaptive ECMS because there are less fuel consumption and lower computational cost.

Trip-Oriented Model Predictive Energy Management Strategy for Plug-in Hybrid Electric Vehicles

“…In this study, a five-order polynomial equation is employed to simulate the relationship between the fuel rate efficiency and engine A five-order polynomial equation is employed to simulate the relationship between the motor efficiency and motor power in the driving mode, as: (20) where the fitting coefficients k 1 to k 5 , solved by the least square method, equal −1.967e −06 , 1.21e −04 , −0.002862, 0.02551 and 0.7049, respectively. The Gaussian function is employed to illustrate the relationship between the motor efficiency and motor power in the regeneration mode, as: (21) in which the fitting coefficients a 1 , a 2 , b 1 , b 2 , c 1 and c 2 , equal 2.342, 9.688, 19.76, -3154, 12.52 and 2191, respectively.…”

“…13. According to the current power and state of the motor, the efficiency of the motor can be calculated by (20) and (21), and then the battery power can be found by (23). As such, the calculation of the fuel rate and efficiency of motor can be simplified by the corresponding equations, and thus the computation effort be dramatically reduced.…”

“…In [20], the PMP is exploited to optimize the speed curve and power distribution in real-time to achieve lowest fuel consumption according to the future traffic flow. In [21], the optimal reference SOC trajectory is found by the PMP under different driving conditions, which provides a solid theoretical basis for designing rule-based strategies. In [22], PMP is leveraged to obtain the optimal fuel consumption with knowing the driving cycle a priori, and the solutions are considered as the foundation of the real time controller.…”

“…Ref. [21] introduces a fusion algorithm based on the fuzzy logic control and ECMS, and simulation results reveal the feasibility of reducing the fuel consumption. In [28], the DP is leveraged to find the optimal working region of engine in a PHEV, and rules are excavated from offline optimal results.…”

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

“…On the contrary, the instantaneous optimization method conducts real‐time power distribution according to optimization objectives. As one of the representative real‐time optimization EMSs, equivalent consumption minimum strategy (ECMS) is widely used due to its near‐optimal performance and it does not depend on prior knowledge of driving cycles 18 . The key to employing ECMS is to convert the electricity consumption from the energy storage device into hydrogen consumption via an equivalent factor (EF) and the acquisition of EF is recently a research hotspot in EMS.…”