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Dual-motor battery electric vehicles (DM-BEVs) are a trending technology in the electric vehicle market. They have the potential to achieve higher energy savings and dynamic performances compared to single-speed, single-motor BEVs. However, a more complex and robust energy management system (EMS) is needed to achieve these benefits. Hence, this work reviews the design process and real-time implementation of EMSs tailored for DM-BEVs, starting from the fundamental concepts of two-motor coupling. The advantages and disadvantages of the most popular dual-motor architectures and their influence on the EMS design complexity are presented, followed by a revision of the reported energy management controllers. Besides the most prominent methods, classified as rule-based or optimization-based techniques, reinforcement learning-based EMSs are discussed in detail, given their near-optimal, real-time implementation and adaptability to newer, unforeseen drive cycles. Finally, the standard procedures and equipment required to assess the EMS' performance with hardware-in-the-loop tests are presented. Conclusions and open challenges for the energy management controllers of DM-BEVs are discussed at the end of this work. [Display omitted] • The energy management design for dual-motor battery electric vehicles is explained. • Dual-motor architectures' influence on energy management complexity is discussed. • Existing and trending control approaches are presented. • The controller's migration for HIL and in-vehicle tests are explained. • Challenges and opportunities for dual-motor battery electric vehicles are listed. [ABSTRACT FROM AUTHOR]