# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Rule-Based Energy Management Strategies
#### 3.1.2 Fuzzy Rule-Based Energy Management Strategies  
Fuzzy rule-based control establishes the choice of variables for domain partitioning in advance and builds a library of fuzzy rules [67]. The inputs to the controller are transformed into control outputs for each part of the vehicle's energy source through two processes of fuzzy inference and defuzzification [68]. The relative simplicity of fuzzy rule controllers enables tweaking and adaptation when required, increasing the degree of control. Because of its non-linear structure, it is even more beneficial in complicated systems including modern powertrains. Fuzzy rule-based control currently comes in a variety of forms, including conventional fuzzy control strategies, adaptive fuzzy control strategies, and predictive fuzzy control strategies [69; 70]. Conventional fuzzy control is frequently employed to enhance fuzzy efficiency and, as a result, make the engine operate efficiently. This method is assisted by an electric motor, which forces the ICE to work in a high-efficiency area at all times while remaining within the battery SOC. In [71], a fuzzy controller was created for a HEV with a parallel structure. A set of principles were created in a fuzzy controller to efficiently determine the divide between the two powerplants, i.e., the electric motor and ICE, which referred to the driving instruction, the SOC of the energy store, and the motor speed. Today, the use of adaptive fuzzy control strategies is increasingly widespread as they have the potential to enhance both fuel economy and emissions. Individual targets can be controlled by adaptive fuzzy controllers by altering the value of the weights given to them. Reference [72] presented an adaptive fuzzy logic-based energy management strategy (AFEMS) to estimate the power delivery between the ultracapacitor charge and the battery pack. Due to the intricate real-time control problem, a fuzzy logic system was used, which is shown in Figure 6. The fundamental goals of this adaptive fuzzy logic controller were to increase system effectiveness, reduce battery current variance, and maintain SOC. In [73], [PERSON] et al. proposed a new adaptive control strategy for driving styles by using particle swarm optimization (PSO) combined with fuzzy expert algorithms. The aim of this strategy was to explore the influence of driving style on the fuel economy of a PHEV. For energy management, predictive fuzzy controllers utilize information provided by a global positioning system from scheduled transportation trips. Frequently, vehicle speed, vehicle condition, and route location are used as inputs to the predictive controller. The predictive fuzzy controller calculates the optimal torque distribution on the basis of the existing vehicle movement history, as well as speculation on the possible future movements of the vehicle. In [74], a series HEV (SHEV) with batteries capable of supporting the maximum traction power needs was taken into consideration. Fuzzy logic was used to  
Figure 6: Block diagram of the proposed AFEMS controller. predictably lower the amount of SHEV initiates, strengthening the system performance and component lifespan. An online predictive control strategy named dual-loop online intelligent programming (DOIP) has been proposed for velocity prediction and energy-flow control [75]. In this approach, a deep fuzzy predictor was developed to predict driver-oriented velocity using fuzzy granulation technology. The vehicle performances with different control strategies are summarized in Table 2.  
The main benefits of rule-based control methods for energy management include their simplicity and usability, which make it simple to comprehend and accomplish the primary control goals in actual automobiles [76]. Rule-based control strategies are now commonly used in the production vehicle market because of their low computational effort, adaptability, and good reliability [77]. Despite its widespread use, there are still several major issues that need to be addressed in the rules-based approach to HEV control.