A comprehensive roadmap for advancing Electric Micromobility (EMM) systems addressing the fragmented and scarce information available in the field is defined as a transformative solution for urban transportation, targeting short-distance trips with compact, lightweight vehicles under 350 kg and maximum speeds of 45 km/h, such as bicycles, e-scooters, and skateboards, which offer flexible, eco-friendly alternatives to traditional transportation, easing congestion and promoting sustainable urban mobility ecosystems. This review aims to guide researchers by consolidating key technical insights and offering a foundation for future exploration in this domain. It examines critical components of EMM systems, including electric motors, batteries, power converters, and control strategies. Likewise, a comparative analysis of electric motors, such as PMSM, BLDC, SRM, and IM, highlights their unique advantages for micromobility applications. Battery technologies, including Lithium Iron Phosphate, Nickel Manganese Cobalt, Nickel-Cadmium, Sodium-Sulfur, Lithium-Ion and Sodium-Ion, are evaluated with a focus on energy density, efficiency, and environmental impact. The study delves deeply into power converters, emphasizing their critical role in optimizing energy flow and improving system performance. Furthermore, control techniques like PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed to enhance safety, efficiency, and adaptability in diverse EMM scenarios by using cutting-edge semiconductor devices like Silicon Carbide (SiC) and Gallium Nitride (GaN) in well-known configurations, such as buck, boost, buck–boost, and bidirectional converters to ensure great efficiency, reduce energy losses, and ensure compact and reliable designs. Ultimately, this review not only addresses existing gaps in the literature but also provides a guide for researchers, outlining future research directions to foster innovation and contribute to the development of sustainable, efficient, and environmentally friendly urban transportation systems.

Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are seldom reported. This paper addresses both questions on an EMM-class test bench built around a 36 V, 250 W in-wheel BLDC motor. A proportional-integral (PI) regulator and a first-order Takagi-Sugeno (TS) fuzzy regulator are specified in Simulink, auto-coded to ANSI-C by Embedded Coder, and deployed unchanged on an STM32F446RE target driving a custom three-phase inverter through six-step Hall commutation. Over a six-step, 180 s duty cycle reaching 21.1 km/h, the two regulators are shown to occupy opposite ends of the speed-versus-damping trade-off. On the 30 to 100 RPM ascending step under load, the PI reaches the set-point in $0.4\pm 0.1$ s with $21.6\%$ overshoot and the TS in $2.7\pm 0.1$ s with $1.5\%$ overshoot, both quoted at the resolution of the 10 Hz acquisition, and over the complete duty cycle, a window that also contains segments on which neither regulator has control authority, the TS lowers the tracking RMSE by 9.4%. A structural analysis of the deployed firmware excludes the realisation form as the cause. The positional and incremental forms are algebraically equivalent while the command is unsaturated, which is the regime of the step above. Under saturation, the incremental accumulator of the TS is not clamped and winds up exactly as the positional PI integrator does. The two loops are also shown to share the same unfiltered speed feedback and the same command saturation limits. The difference is traced instead to the effective gains realised by the seven consequents. Far from the set-point, the TS applies an integral gain three to twelve times weaker than the PI for a comparable proportional gain. A fixed-gain PI in that range is predicted to reproduce the response for one eighth of the Flash. The embedded cost of both regulators is then quantified on the target from the linker map, the fuzzy controller occupying 2325 Bytes of Flash against 266 Bytes for the PI, a factor of 8.7, and 200 Bytes of stack against 32 Bytes, a factor of 6.3, rising to 248 Bytes against 32 Bytes when the complete call tree is counted, for 0.45% of the available Flash. The complete platform, comprising the inverter, the Hall front end, the auto-generated firmware, and a Python supervisory interface, is described together with its deployed timing, PWM, and saturation parameters.