## 6 Conclusions  
FCHEVs have gained significant attention in recent years due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. The integration of fuel cells with other power sources, such as batteries or ultracapacitors, can enhance the overall efficiency and performance of FCHEVs. However, an effective EMS is crucial for optimal performance, and a poor EMS can result in reduced efficiency and the accelerated degradation of the FC system and battery units. This review aims to address these issues by categorizing and discussing the various topologies and connections of FCHEVs and comparing several EMSs.  
Nevertheless, upon conducting a thorough quantitative and qualitative examination of the existing literature concerning EMSs for FCHEVs, it is evident that several formidable issues remain to be addressed, which are expounded upon below:  
1. Attaining optimality in rule-based EMSs poses a significant challenge. While it is possible to enhance management outcomes by integrating certain offline optimization techniques into the rules, the efficacy of these algorithms heavily relies on the availability of specific driving cycle information. Furthermore, parameters optimized for particular driving conditions may not be applicable to different scenarios.
2. The application of global optimization-based EMSs, such as DP, directly in real-time scenarios is not feasible. The optimization of local optimization-based EMSs like ECMS and MPC presents challenges as it amplifies computational loads and memory constraints, thereby impeding real-time applicability. Moreover, contemporary driving prediction methodologies predominantly revolve around recognizing historical driving patterns and have not demonstrated effectiveness in optimizing for diverse and unpredictable driving cycles. Furthermore, the majority of research in MPC prediction is confined to computer-based simulations.
3. The effectiveness of neural network training in EMS is intrinsically tied to the quality of the database, making it ill-suited for direct deployment in energy management systems.
4. In the realm of multi-objective optimization, the challenge lies in selecting suitable evaluation criteria for each performance indicator, which includes aspects like power performance, fuel economy, power system durability, and energy source size structure. Achieving a harmonious balance among these multiple objectives is a key goal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference No.** & **Year** & **Type of Topology** & **Type of EMS** \\ \hline
[94] & 2014 & FCS + Battery + UC & DP \\
[98] & 2018 & FCS + Battery & DP \\
[115] & 2018 & FCS + Battery & TCS, NN \\
[81] & 2018 & FCS + Battery + UC & FLC \\
[116] & 2019 & FCS + Battery & NN \\
[108] & 2021 & FCS + Battery & Local Optimization ECMS \\
[117] & 2021 & FCS + Battery & MPC, NN \\
[118] & 2021 & FCS + Battery & NN, Q-learning \\
[126] & 2022 & FCS + Battery + UC & FLC, Q-learning \\
[127] & 2022 & FCS + Battery + UC & FLC, MPC \\ \hline \hline \end{tabular}
\end{table}
Table 5: FCHEV topology and EMS research in the past decade.  
**Author Contributions:** Conceptualization, A.R., M.I.M. and M.M.; methodology, A.R. and M.M.; software, A.R.; validation, M.I.M. and M.M.; formal analysis, A.R. and M.M.; investigation, A.R., M.I.M. and M.M.; resources, M.I.M. and M.M.; data curation, A.R. and M.M.; writing--original draft preparation, A.R.; writing--review and editing, M.I.M. and M.M.; visualization, A.R. and M.M.; supervision, M.I.M. and M.M.; project administration, M.I.M. and M.M.; funding acquisition, M.I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.