## 2 Rule-Based Strategies
### Fuzzy Logic-Based Strategy  
With the in-depth study of fuzzy theory and based on fixed threshold control, the fuzzy logic-based strategy was developed to optimize the predefined control rules by fuzzification, rule base, fuzzy reasoning and defuzzification, offering strong adaptability and robustness. Figure 3: Depiction of the comparison between the CD–CS strategy and blended strategy. Simple fuzzy rule-based strategies consider battery SOC as the primary input and combine this with other vehicle parameters, such as torque demand, vehicle speed, vehicle acceleration, motor speed and engine speed. For instance, the fuzzy controller takes vehicle speed, battery SOC and torque demand as inputs and outputs the torque request of internal combustion engine (ICE), resulting in a more than 10% increase in fuel economy compared with the standard system [40]. In addition, the performance of this proposed strategy was discussed with respect to fuel mileage, battery usage and driver performance. Furthermore, a fuzzy logic-based EMS was proposed for a through-the-road HEV, which applied fuzzy logic with a pair of membership functions to determine the appropriate power distribution in real time [41]. Simple fuzzy rule-based strategies have low computation requirements, but require engineering experience and have non-optimal results. In order to have good performance, there are three main methods of optimization. One method is to intelligently select fuzzy rules by looking up the table of off-line computed DP results. In [42], the mode selection between all-electric and hybrid is based on the fuzzy logic controller, which uses a set of rules extracted from DP results. Meanwhile, the controller can be adaptive to different driving conditions by using driving condition information. To further improve the fuel efficiency of EMS for HEVs, [43] proposes an energy management control strategy combining the conventional rule-based strategy and the global optimization strategy. Meanwhile, driving pattern cycle recognition is used to classify current driving conditions into one of the driving patterns, and the DP method is applied to design fuzzy-logic control strategy of each driving pattern. Similar to threshold optimization for the deterministic rule-based strategy, the fuzzy logic-based strategy can also be optimized by intelligent algorithms, such as GA, PSO, QP, SA, and so on [44; 45]. Another method is the adaptive controller based on fuzzy logic, which can identify driver behavior and optimize itself for these situations. This method does not need predetermined rules and can be optimized for various drive cycles. For instance, a fuzzy rule-based EMS can automatically identify the driver's style, intentions and preferences in order to inform driver of the optimal operating mode to minimize fuel consumption [46]. The driver's style can be roughly divided into aggressive, conservative, and multi-variant styles. Based on the collected data of vehicle speed, acceleration, throttle opening, slope, or slip rate, the classification of driving style can be acquired by K-means clustering or neural network. The driving style, as one of the influencing factors, is beneficial to having a better performance in terms of fuel economy, which is mainly reflected in the impact on shift frequency, or acceleration/deceleration. Simple fuzzy logic controllers can effectively reduce computational burden; however, they are still based on predetermined rules and lack of optimality in fuel economy for HEVs. Although some adaptive fuzzy logic-based controllers can solve the above problems, they cannot be used easily in real-time implementations due to their computational burden. Moreover, fuzzy logic controllers need more than one set of rules to optimize a system if control variables/objectives are multiple, which will increase the strategy's complexity. To achieve practical application while ensuring a good performance of control results, the rule-based strategy can be improved by combining intelligent transportation systems.