## 3 Energy Management Strategies for HEVs
### Optimization-Based Control Strategy
#### 3.2.2 Real-Time Optimization  
By optimizing the energy flow for the vehicle's transient operating conditions, the transient optimization-based energy management system enables flexible control without requiring the prediction of future driving information or limiting it to a certain duty cycle. This method is relatively small in computation and easy to accomplish in practical applications. However, although the instantaneous optimization method can achieve the optimal effect at a specific moment, it is not equivalent to global optimization, and thus cannot guarantee the optimal performance during the whole driving process. To overcome this limitation, global optimization techniques have been introduced, but due to their causal nature, these methods are not appropriate for analyses done in real time. By introducing a cost function dependent solely on the system's current state, the global optimization criterion is effectively reduced to instantaneous optimization, resulting in an EMS that is both flexible and efficient in practical applications. Common optimization methodsinclude energy management techniques based on minimum equivalent fuel consumption and MPC. (a) Equivalent Consumption Minimization Strategy. The ECMS calculates the current system state and the online measurable quantity of fuel equivalents in real time to convert the motor energy consumption to the engine fuel consumption. Without assuming that the driving mode has been known beforehand, the ECMS finds and implements the best solution instantly. The proposed methodology develops a comprehensive fuel cost function, rooted in the calculation of equivalent fuel consumption. It further enhances the emission optimization process by incorporating weighting factors, ultimately resulting in a multi-objective function solution. Consequently, ECMS not only facilitates real-time energy management, but also achieves a balanced optimization of vehicle dynamics, fuel efficiency, and emission performance. Figure 9 shows a simplified flowchart used to recognize the input and output states of the ECMS used. [PERSON] et al. (2020) [159] investigated energy efficiency improvement problems of HEVs with automated manual transmissions from both design and control aspects, utilizing an optimal control software package validated with DP and ECMS. [PERSON] et al. (2020) [160] introduced a new optimal control problem for parallel HEVs that considers both fuel consumption and battery aging in the cost function. [PERSON] et al. (2020) [161] introduced a recurrent neural network-driven adaptive ECMS framework tailored for PHEVs, thereby guaranteeing a nearly global optimal performance in terms of energy management control. [PERSON] et al. (2021) [162] formulated a self-adaptive ECMS specifically for HEVs, employing dynamic adjustments of the equivalent factor to achieve near-optimal fuel efficiency. [PERSON] et al. (2020) [163] proposed an adaptive energy management approach for automated parallel HEVs based on ECMS to optimize gearshift commands and torque distribution. [PERSON] et al. (2021) [164] employed a Breadth-First Search (BFS) algorithm to dynamically adjust the equivalent factor in real-time, introducing a BFS-based adaptive ECMS specifically tailored for HEVs. Moreover, [PERSON] et al. (2022) [165] combined the Adaptive Neuro-Fuzzy Inference System and ECMS to minimize hydrogen consumption in HEVs, achieving an 8.7% reduction compared to other control strategies. [PERSON] et al. (2022) [103] tackled challenges encountered in contemporary Reinforcement Learning frameworks for EMS tasks. They introduced an adaptive hierarchical EMS approach that seamlessly integrates heuristic ECMS expertise with a deep deterministic policy gradient, thus enhancing  
Figure 9: Simplified flowchart for realizing ECMS input and output states [158]. the system's performance and adaptability. [PERSON] et al. (2024) [166] introduced a real-time multi-criteria optimization technique utilizing an adaptive ECMS.