## 1 Introduction  
Hybrid electric vehicles (HEVs) combine the advantages of traditional fuel vehicles with battery electric vehicles (BEVs). However, unlike BEVs, HEVs have a more complex structure. According to the structure and energy flow of the powertrain, HEVs are mainly divided into series HEVs (SHEVs), parallel HEVs (PHEVs), and series-parallel HEVs (SPHEVs). Meanwhile, HEVs are also divided into plug-in HEVs and non-plug-in HEVs, depending on whether the battery can be charged externally. Apparently, higher requirements are imposed on the energy management strategy (EMS) of the vehicle [1; 2]. The EMSs of HEVs are responsible for distributing the energy flow between the engine, the motor, and the battery under the premise of satisfying the vehicle's power demand, so as to achieve the optimal overall fuel consumption and emission performance [3; 4]. At present, the EMSs applied to HEVs are mainly rule-based, including deterministic rule-based EMSs and fuzzy rule-based EMSs (F-EMSs) [5; 6]. Among them, the deterministic rule-based EMSs are widely used in HEVs because of their simplicity, small computation, and strong practicability, but their disadvantages are also obvious. The logical threshold control strategy must be determined in advance, and the adaptability to different driving cycles is poor. Although F-EMSs cannot achieve global optimal energy management, fuzzy control itself has strong robustness and good real-time performance, and it can describe control rules that are difficult to quantify. Therefore, the application prospect is very good [6; 7]. Up to now, many research results were achieved through the use of fuzzy control in the energy management of electric vehicles. The F-EMSs of parallel hybrid electric vehicles (PHEVs) were studied in References [8; 9; 10; 11], which achieved good control performance. Aiming at the problem whereby fuzzy control cannot realize self-learning and poor self-adaptability, the particle swarm optimization (PSO) algorithm was used to optimize the parameters of fuzzy control rules and membership function, and the state of charge (SOC) changes of the battery were well controlled in Reference [6]. In Reference [12], pointing to the complexity of the quantization factor tuning process, a PSO algorithm with the compression factor was introduced to optimize and improve the robustness and accuracy of the fuzzy controller. The above studies improved the adaptability of F-EMSs to some extent by optimizing fuzzy control rules and membership functions, but they did not consider the impact of driving cycles. Recent studies showed that driving cycles have a significant impact on the fuel consumption and emission performance of HEVs. Therefore, many researchers carried out studies on driving cycle recognition (DCR) technology and incorporated it into EMSs [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. [PERSON] et al. [14] summarized the application of traffic information and driving data in the field of automobile energy conservation and environmental protection. [PERSON] et al. [15] studied the self-learning method of urban road driving cycles for electric vehicles, and, in Reference [16], the F-EMS for HEVs based on DCR was optimized. Through the analysis and recognition of driving cycles of HEVs, a hybrid control strategy based on real-time changes of driving cycles was proposed in Reference [17]. A novel EMS for a plug-in HEV was proposed based on the driving cycle model and a dynamic programming algorithm [18], while an F-EMS based on DCR was proposed in Reference [19] to improve the fuel economy of a PHEV, which consisted of DCR and a fuzzy torque distribution controller. Researchers such as [PERSON] et al. used intelligent control methods such as clustering to study the identification of traffic conditions and driving segments, and they studied the most effective energy-saving control methods of HEV through driving pattern recognition [20; 21; 22]. [PERSON] et al.