## 4 Energy Management Systems
### Optimization-Based Energy Management Systems
#### 4.2.2 Equivalent Consumption Minimization Strategy  
The fundamental concept behind the equivalent consumption minimization strategy (ECMS) revolves around reducing the overall fuel consumption, which encompasses both the real hydrogen consumption and the electric motor's equivalent fuel consumption. A pivotal variable in this process is the equivalent factor (EF), which plays a critical role in determining management performance [99]. Typically, researchers employ an offline optimization approach, as depicted in Figure 20a, to derive the EF. [PERSON] et al. [100] utilized a genetic algorithm, while [PERSON] et al. [101] applied DP to optimize EF, especially for the new European driving cycle (NEDC). This optimization of EF, compared to unoptimized ECMS, led to a substantial enhancement in vehicle fuel efficiency. However, the offline EF values may not always ensure optimal fuel economy under various operational conditions [102; 103].  
Consequently, certain academics have suggested adaptable EF regulation approaches that consider the various operational state, including methodologies like working type identification [104], SOC feedback [105; 106], and prediction of demand power [107; 108]. As illustrated in Figure 20b, [PERSON] et al. [104] engaged in the offline optimization of EFs for four distinct working conditions and dynamically fine-tuned EFs based on the prevailing operational state. This method enhanced fuel economy and adaptability. However, this regulatory approach excels primarily when dealing with specific operational conditions.  
Consequently, an innovative method centered on SOC feedback to regulate the EF, incorporating a reference SOC to adjust the EF [106]. This method effectively enhanced the adaptability of the control strategy to a wide spectrum of driving scenarios. Meanwhile, [PERSON] et al. [105] devised a dynamic term that factored in the battery's SOC for real-time EF adjustments. The results demonstrated the potential of this method to improve the adaptability of the ECMS and effectively curb fuel consumption. But this method necessitates the calibration of initial EF values and proportional coefficients through iterative and time-intensive experiments [107]. Furthermore, this regulatory technique presents challenges in maintaining control variables at reference levels, potentially leading to growing  
Figure 20: (a) Offline optimization flow chart. (b) Operating condition identification schematic diagram.  
steady-state errors and pushing the system towards instability, which could impede the battery's ability to respond to sudden power demands [109; 110; 111].  
The problem highlighted in the preceding method was addressed by [PERSON] et al. [107], who introduced an adaptive ECMS grounded in power prediction. This approach leveraged predicted power data for localized optimization and implemented periodic EF updates as part of the optimization process. The study's outcomes illustrated that this approach maintained the SOC consistently close to the reference level with minimal fluctuations, ultimately resulting in a marked enhancement in fuel economy.