**7. Discussion**

This paper reviewed the current technologies, control methods, optimization methods, and design methods for EREV vehicles, including the architecture, key components and their interactions with each other, the sizing of components, and methods to find the optimal system-level design. Although at first glance, there seem many different configurations, the most commonly used have an electric motor in the central position. However, the use of an in-wheel motor is typical if the vehicle has high performance. The central aspect to consider with the technologies used to recover energy and increase autonomy is the cost of implementing them. Some technologies are more economical and easier to control. Additionally, the fuel or resource cost for the range extender to work is a limiting factor for integration and profitability; it helps the electric vehicle concerning autonomy. All the technologies presented in this work aid electric vehicles. Depending on the budget that each researcher or research center has, it can implement and carry out tests to verify and optimize the implementation of the selected technologies. Currently, the combustion engine is the most common technology used to increase autonomy. That is why most researchers seek to reduce ICEs' fuel consumption to increase energy efficiency and recover energy. By analyzing the literature, we can conclude that the use of optimization methods will depend on the scope of the research, but usually involves finding the most efficient configuration of all components, thereby solving different optimization layers for design. These could be further used in more extended coordination methods to include the selection of topologies and technologies. For instance, these extended coordination methods might include: (i) simultaneous topology and sizing design, alternating with controller design; (ii) controller design nested for simultaneous topology and sizing, (iii) topology alternating with sizing or control; or (iv) simultaneous topology, sizing, and control design. We have presented a guide for determining critical components and the interactions between them in order to design a new topology and optimize all levels depending on the technologies used.

To substantially reduce the computational burden, the introduction of approximations of the original problem should shorten the driving cycle used for design, or one should use parallel computing. Driving cycles used as input for the control (energy managemen<sup>t</sup> strategy) or any simulation should be short, realistic, and representative of realistic driving types. In some cases, it is necessary to create a personalized driving cycle to analyze the behavior of the extended-range electric vehicle concerning energy consumption, range, and emissions. A problem that remains open is how to address multiple topologies with a large variety in terms of component types and quantities in a more intuitive way. In addition, optimization problems and automatic construction of topologies spur on the development of control algorithms that automatically handle various topologies. Optimization objectives can be defined to include, in addition to fuel, also cost, emissions, and performance aspects to solve the design problem at the system level, to find a competitive EREV configuration for the market. User-friendly methodologies are needed to help developers so that the industry at large achieves the best designs early in HEV development.

**Author Contributions:** Conceptualization, D.S.P.-B. and J.d.D.C.-N.; writing—original draft preparation, D.S.P.-B.; writing—review and editing, J.I.-R. and R.A.R.-M.; supervision, J.d.D.C.-N. and R.A.R.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Tecnologico de Monterrey, gran<sup>t</sup> number a01366354, and by the National Council for Science and Technology (CONACYT), gran<sup>t</sup> number 862836.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank the CIMA lab, Toluca Campus, for the valuable collaboration.

**Conflicts of Interest:** The authors declare no conflict of interest.
