### Literature Review  
There are recent works that have analyzed the scientific literature related to EMSs for hybrid vehicles [8; 9; 10; 11]. The common approach of these reviews is to classify the EMSs depending on the nature of the algorithm that the EMS is based on. Then, the advantages and disadvantages of each kind of EMS are discussed and also the evolution of the EMSs and the future trends. [PERSON] et al. published in 2017 a work about EMS for plug-in HEVs (PHEVs) [11]. In this work, it was stated that EMSs are usually divided into two principle groups: rule-based and optimization-based strategies. The study presented a section focusing on optimization-based strategies and their strengths and limitations. It concluded that \"the EMS cannot be really optimized unless detailed information about the future route is available. Since strong uncertainties surrounding driving experience hinder accurate predictions, augmented vehicular connectivity and evolution toward increasing levels of autonomy will mark a watershed for fuel consumption reduction and strategy optimization\". Apart from this, in the overview of EMSs, the authors stated that the popularity of RB strategies in the HEV industry is due to their ease of implementation and understanding and their performance for low levels of hybridization. Although their drawbacks have been evidenced through simulations when they are compared to optimization-based EMSs, the authors remarked that fair comparisons are only applicable if a certain level of drive cycle information is available, which is generally not the case in real life. The same classification was carried out in the case of the work of [PERSON] et al. [8], where the EMSs were firstly categorized as rule-based strategies and optimization-based strategies. On the one hand, two strategies are categorized as rule-based strategies: deterministic rule-based (RB) and fuzzy logic-based (FLB). On the other hand, the optimization-based strategies presented are the equivalent consumption minimization strategy (ECMS), model predictive control (MPC), deterministic dynamic programming (DDP), Pontryagin's minimum principle (PMP), stochastic dynamic programming (SDP), neural network-dynamic programming (NN-DP), reinforcement learning (RL) and adaptive dynamic programming (ADP). From them, ECMS and MPC are classified as instantaneous optimization; PMP, DDP and SDP as global information-driven optimization; and RL, NN-DP and ADP as data-driven strategies. The performance of the strategies considered is qualitatively analyzed based on the following parameters: fuel economy, realization degree, computational burden, computational time and real-time performance. The results of this analysis are summarized in a radar chart where the EMSs are rated for each parameter using five levels. For example, in the case of real-time performance:  
\"The rule-based strategy can be regarded as the first level, due to its convenience in adjusting parameters, its simplicity, and its practicality, while the DP-based strategy can be regarded as the last level, due to its being time consuming and requiring a tremendous amount of memory. ECMS and the MPC-based strategy, as instantaneous strategies, can be considered as the second level. Meanwhile, the ADP-based strategy (containing the RL-based strategy and the NDP-based strategy) can be implemented based on updating data, and can therefore be regarded as the second level. The SDP-based strategy can be regarded as the third level, while the PMP-based strategy can be considered as the fourth level due to the difficulty it has in solving the Hamiltonian function.\"  
In 2019, another article was also published [9]. The EMSs considered in this work are classified into 15 categories: deterministic RB, FLB, deterministic DP, quadratic programming (QP), game theory (GT), genetic algorithm (GA), convex optimization, particle swarm optimization (PSO), NN-based, analytical optimization, PMP, different versions of ECMS, SDP, different versions of MPC and RL-based.