## 3 Energy Management Strategies for HEVs
### Rule-Based Control Strategies
#### 3.1.2 Fuzzy Rule-Based Control Strategy  
FL control theory incorporates fuzzy set theory, which extends traditional TRUE and FALSE set theory, and FL, which extends traditional logic of how a system determines its output. Within FL, the veracity of a statement is expressed not as an absolute, but as a matter of varying degrees, rendering it remarkably resilient and responsive in real-time scenarios. Considering the HEV's energy management system, which comprises numerous subsystems exhibiting nonlinear and time-varying properties, the implementation of FL rules for management control emerges as a viable and effective alternative. FL can directly translate the designer's experience into control rules, providing a powerful tool for intelligent control. With FL, rule-based behavior is recognized and expert knowledge can be encoded into a rule base, which in turn can be used in the decision-making process [105]. The primary advantage of FL is its adaptability and flexibility, which significantly enhances control freedom. The nonlinear structure of FL is particularly suitable for managing complex systems, such as energy management systems for HEVs. Consequently, implementing FL for the control of these energy management systems not only enhances the overall performance of the vehicle but also serves as a practical solution. Figure 6 illustrates a schematic diagram of a typical FL control strategy. [PERSON] et al. (2020) [106] proposed an intelligent regenerative braking strategy for power-split HEVs, utilizing FL and rule-based control to design braking torque controllers. Similarly, [PERSON] et al. (2020) [107] introduced a fuzzy approach grounded in optimization techniques for a hybrid construction excavator. This strategy seamlessly integrates FL control and a rule-based algorithm, enabling an efficient allocation of power across diverse energy sources. [PERSON] et al. (2020) [108] introduced a nonlinear model predictive control (MPC) for heavy-duty HEVs, which outperformed rule-based control strategies and showed similar results to offline global optimization strategies. This suggests that nonlinear MPC may offer advantages over traditional rule-based strategies in certain applications. [PERSON] et al. (2021) [109] conducted a comprehensive evaluation comparing hybrid vehicle EMSs, revealing a significant 25% enhancement in fuel economy when employing an ECMS compared to traditional rule-based methods. This indicates the potential for optimization-based strategies to enhance vehicle performance and efficiency. [PERSON] et al. (2021) [110] proposed a fuzzy control strategy for parallel HEVs, combining fuzzy control with rule-based strategies to optimize engine and motor output torque. [PERSON] et al. (2022) [111] presented a master-slave electro-hydraulic HEV with a rule-based control strategy for managing energy distribution and switching operating modes. This hybrid approach may offer improved energy control and efficiency in hybrid vehicles. [PERSON] et al. (2023) [112] explored the potential of hybrid electric propulsion systems (HEPS) in reducing energy consumption and emissions in unmanned aerial vehicles (UAVs). Given the limitations of conventional EMS in maintaining SOC during engine failure, they proposed the FL-ECMS method. This approach achieved a noteworthy reduction of at least 18.6% in fuel consumption and CO\({}_{2}\) emissions for HEPS-equipped UAVs. Furthermore, it effectively addressed the challenge of conventional EMS in sustaining the battery SOC within the required level in the event of engine malfunction. In conclusion, the above literature shows that rule-based FL control strategies can play an important role in optimizing energy management and improving the performance of HEVs, especially when combined with other optimization techniques such as nonlinear MPC or FL optimization. The rule-based FL, known for its strong robustness and reasonability, is particularly suitable for controlling complex hybrid nonlinear systems due to its independence from the accuracy of the system model.