### Literature Review
In this work, Section 3 presented a valuable analysis of the fundamentals of each kind of EMS together with the evolution of control strategies, i.e., how a particular strategy evolved from its predecessor. At the end of this section, a new way of classification is presented that goes further than previous traditional classifications that proposed two categories: offline and online strategies. The reason is that \"although online EMS can execute in CPU-time within simulation, they may not be apposite for real-time application. Hence, the full-fledged simulation-based online EMSs must go through a simplification process before being encoded on the hardware of ECU\". Thus, they proposed a new classification for these simplified versions of EMSs as: premeditated, casual and blended. Premeditated EMSs comprise a precalculated control policy which is not altered until the EMS is reprogrammed via human intervention. Since the control policy is premeditated, the EMS makes the control decisions almost instantly based on the values of inputs. This class of EMSs performs only a few computations to make each control decision. The casual EMSs are neither dependent on precalculated control policies nor on future knowledge of the real-world drive cycle. Unlike premeditated EMSs, casual EMSs have to execute major calculations at each time-step to yield a control decision. Finally, blended EMSs combine the benefits of both premeditated and casual EMSs. Blended EMSs alleviate the drawbacks of both premeditated and casual EMSs in terms of their real-time implementation. The novel classification strategy will eventually motivate future researchers to articulate EMS not just for the sake of simulation-based performance but also for their real-time and real-world implementation. Furthermore, the work, focused on this new classification that only considered online strategies (called \"utilitarian EMSs\"), provided an in-depth section developing the implementation of these EMSs from theoretical foundation to hardware implementation. More recently, in 2020, [PERSON] et al. [10] presented a state-of-the-art with two parts: (1) an overview of HEV topologies and (2) a review of the EMSs based on historical perspectives and the main concepts of each control strategy. According to the authors, \"although several alternative classifications can be found in the literature, the generally accepted arrangements agree with the existing EMSs, which include three major types: rule-based (RB), optimization-based (OB) and learning-based (LB). The RB-EMSs can be sub-classified into deterministic and fuzzy-logic EMSs working based on a set of predefined rules without prior knowledge of the trip. By contrast, OB-EMSs can be classified into offline and online optimisation based on the information level of driving conditions employed\". After this classification, a comprehensive comparison was provided, highlighting their controllability contributions, fundamental principles and advantages and disadvantages. As a result, the research gaps were identified and corresponding future research directions were provided. Apart from this, the idea of a versatile EMS that could include a mixture of different techniques (RB, OB and LB) forming an integrated EMS (iEMS) is proposed in this work. Having reviewed these works, it is clear that the existing EMSs are classified into categories whose fundamentals are widely studied. The evolution of these EMSs and the future trends towards a global optimal in real time are also provided. However, although the quantity of works dealing with EMSs is numerous and, consequently, the results provided by them in terms of vehicle performance are large, the comparisons provided by the works do not go beyond advantages/disadvantages, challenges and implementation issues, i.e., there are only qualitative comparisons. It would be interesting to collect the results to quantify the improvement of trending EMSs with respect to RB strategies that are the common EMSs for HEVs in industry.