Sustainable Electric Micromobility Through Integrated Power Electronic Systems and Control Strategies

Abstract

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.

1. Introduction

In the dynamic urban transportation landscape, micromobility has emerged as a crucial element, comprising compact, shared, and often electric transportation modes. However, its understanding is nuanced, requiring a comprehensive grasp of its plural reality. EMM encompasses diverse interpretations, reflecting contemporary urban mobility’s complexity. Curtis et al. [1] define EMM as modes with electric propulsion, shared accessibility via mobile apps, crucial for first/last mile travel. Their framework outlines functional, transport-related, passenger-specific, usage-related, and attractiveness-related criteria for nuanced exploration. Various EMM solutions are depicted in Figure 1.

Despite its growing significance, the field of EMM suffers from a notable lack of comprehensive information and detailed resources. While studies on electric vehicles provide valuable insights, they often overlook the unique technical and practical challenges specific to micromobility. The fragmented nature of the existing literature further complicates the understanding of EMM, as many works focus primarily on policy, sustainability, or conceptual frameworks rather than technical details. This gap has necessitated a significant effort to consolidate and analyze the scattered information, aiming to provide a unified and detailed exploration of EMM in this work.

The paper [2] defines EMM as transformative in urban transportation, focusing on short-distance trips. It includes compact vehicles under 350 kg, with a max speed of 45 km/h, like bicycles, E-scooters, skateboards, offering flexible, eco-friendly urban travel solutions. EMM aims to ease congestion and enhance urban life. Ref. [3] compares Parisian transportation modes’ carbon footprints in 2020, showing active modes like walking/cycling have low emissions (2 g CO₂eq/pkt), while motorized modes like taxis have high emissions (up to 300 g CO₂eq/pkt). EMM modes, like shared bikes and e-scooters, fall between, offering environmentally friendlier alternatives. Ref. [4] discusses personal vehicles like bike sharing, e-scooters, Segways, offering practical, flexible solutions for short trips, easing first/last mile challenges. The popularity of scooters and micro-vehicles as urban transport modes is rising [5,6], but their environmental impact, especially those using fossil fuels, is concerning [7,8]. Gasoline/diesel combustion produces CO₂, contributing to climate change [6]. Cleaner, sustainable energy solutions for scooters and micro-vehicles are in demand [9]. This includes the adoption of electric technologies, renewable energy sources, and improving energy efficiency [7,10]. Electric scooters have rapidly gained prominence in the urban landscape, with sales projected to exceed one million in France [11], as illustrated in Figure 2.

Beyond environmental and urban mobility advantages, EMM presents significant economic and social value. The deployment of micromobility systems has contributed to job creation across multiple domains including vehicle manufacturing, fleet maintenance, infrastructure management, and digital services such as navigation and platform development, especially in cities transitioning to shared mobility models [12]. EMM also contributes to the reduction in household transportation expenses, offering more affordable alternatives to car ownership, which is particularly impactful for low-income groups [13]. On a macroeconomic level, the micromobility industry is showing strong growth: the e-bike market alone is projected to exceed USD 70 billion by 2027, and the global e-scooter market is expected to reach USD 42 billion by 2030 [12]. These indicators underline EMM’s increasing relevance not only as a green transport solution but also as a driver of inclusive economic development and urban employment revitalization [14].

Table 1. Economic and Market Growth Indicators of Micromobility.

Indicator	Value/Trend	Source
E-bike market value projection (2027)	USD 70 billion	[12]
E-scooter market projection (2030)	USD 41.98 billion	[12]
Employment impact	Jobs in maintenance, logistics, manufacturing, digital mobility services	[14]
Individual cost reduction	Micromobility is significantly cheaper than car ownership	[13]
Traffic replacement potential	Up to 31% of daily car round trips replaceable by micromobility	[22]
Health-related cost benefits	EUR 0.16/km saved by cycling vs EUR 0.15/km cost by car use	[22]

provides an overview of key economic and market growth indicators associated with the expansion of micromobility solutions. EMM offers compact, eco-friendly alternatives, reducing congestion and reliance on pollutant-emitting vehicles [15,16]. Its reduced environmental impact, mainly from electric sources, decreases emissions and pollutants [7,10]. Many countries actively promote EMM through policies, infrastructure improvements, and awareness campaigns [9,17]. The Netherlands and Germany lead in promoting EMM, investing in cycling infrastructure and offering incentives [8,18]. In Asia, China and Japan expand bike-sharing services and improve cycling infrastructure [6,19]. France, Sweden, and Norway also support EMM with initiatives and infrastructure improvements, recognizing its benefits for congestion, pollution, and public health [20,21]. Globally, EMM is seen as a solution to urban mobility challenges, with the market projected to reach 216.3 billion dollars by 2032, led by the Asia-Pacific region [6,10].

Figure 3 highlights the key components of the EMM system. the micro electric vehicle is composed of:

• In-wheel motors for propulsion;
• Power converter for managing motor operations (traction, regeneration, etc.);
• Battery, charger, and controller for energy supply and system management.

Previous reviews have extensively examined EMM conceptually, but technical aspects remain unaddressed. For instance, Ref. [6] explores shared mobility, sociodemographic characteristics, and travel habits. Ref. [17] analyzes urban sustainability, emission reduction, and policy. Ref. [23] emphasizes microvehicle market growth, road safety concerns, and research frontiers. This work aims to fill the gap in the literature on EMM by focusing on technical dimensions. The paper is structured as follows: Section 2 discusses electric motors, their applications, and advancements. Section 3 covers batteries, their role in sustainability, and developments. Section 4 examines power converters. Section 5 explores control techniques’ impact on EMM performance and safety. The final section presents conclusions on EMM’s technological aspects.

2. Commonly Used Electric Motors

EMM emphasizes sustainable propulsion technology for urban mobility [5]. Ref. [24] analyzes electric bicycle motors, favoring central motors for balanced weight and torque distribution. The study notes a shift from BLDC to PMSM motors due to their efficiency, compactness, and adaptability. PMSM motors provide better power regulation, faster response, and greater energy efficiency, improving electric bike autonomy and user experience.

E-bikes, gaining popularity for their eco-friendliness, rely on efficient propulsion systems. The lightweight BLDC motor, commonly used in the rear hub, ensures extended battery life with minimal maintenance. However, brushed DC motors offer higher torque. AC induction motors, powerful yet complex and costly, are less common [25].

Table 2. Examples of EMM Motors on the Market with Motor Categories.

Product Description	Year	Motor Type
PMSM Motor Less Charging Time Comfortable Designed Modern Electric Scooters [26]	2022	PMSM
Electric trike passenger tricycle tourist etrike [27]	2019	Induction Motor
RFPM Motors, Napino [28]	2014	SRM
AC motor DA-90S [29]	2019	BLDC Motor

outlines electric motor categories in electro-micromobility applications.

Table 3. Motor Applications in Micromobility.

Motor Type	Applications and Common Usage	References
PMSM	Electric Scooter (High), Electric Bike (Moderate),	[30,31,32]
	Hoverboard (Low), Electric Moped (High)	[33,34]
IM	Electric Scooter (High), Electric Bike (Moderate),	[24,35,36]
	Hoverboard (Low), Electric Moped (High)	[37,38]
SRM	Electric Scooter (High), Electric Bike (Moderate),	[39,40,41]
	Hoverboard (Low), Electric Moped (High)	[42,43,44]
BLDC	Electric Scooter (High), Electric Bike (High),	[45,46,47]
	Hoverboard (Moderate), Electric Moped (High)	[48,49,50]
AFPM	Electric Scooter (Moderate), Electric Bike (Moderate),	[51]
	In-Wheel integration (High), Compact EVs (High)	[52]
IWHM	Electric Scooter (High), Electric Bike (High),	[53,54]
	Light EVs (High), Robotic Platforms (Moderate)	[55]

highlights the diverse applications of various motor types in micromobility, showcasing their unique strengths and suitability for different devices. PMSMs, known for their efficiency and precise control, are commonly used in electric scooters and mopeds, while BLDC motors, valued for their compact design and high power density, dominate in bicycles and scooters. Induction motors (IMs) offer durability and cost-effectiveness, making them reliable for urban settings, whereas SRMs stand out for their fault tolerance and ease of maintenance in light vehicles. The Table 3 emphasizes how each motor type aligns with the specific operational needs and design priorities of modern micromobility solutions.

2.1. Induction Motors (IM)

Induction motors are ubiquitous in the realm of EMM. They serve as pivotal components in electric scooters, hoverboards, and similar micromobility devices, profoundly influencing their operational efficiency and performance.

Induction motors (IMs) offer several advantages for electrical micromobility. Firstly, they are renowned for their reliability, ensuring consistent performance and minimal downtime [56]. Additionally, IMs boast high efficiency, contributing to the overall energy efficiency of micromobility systems [56]. Moreover, IMs play a crucial role in promoting efficient and sustainable urban mobility solutions, aligning with the growing emphasis on environmentally friendly transportation options [16]. However, it is worth noting some limitations. IMs may exhibit limited torque at low speeds, which could impact acceleration, posing a challenge in certain urban environments [57]. Furthermore, the implementation of control systems for IMs can be complex, requiring sophisticated technology and expertise, which may increase the overall system complexity and cost [57].

The author in [58] deals with the design and control of the induction motor used for electric vehicle (EV) propulsion. The emphasis is on finding optimal power for the induction motor to meet desired performance criteria such as minimum weight, volume, and cost, while considering driving cycles such as European urban and suburban cycles. The main parts of the induction motor are presented in Figure 4:

Authors in [16] delineate the constituent elements of IMs, encompassing a footboard, handlebar with column, brakes, wheels, battery, motor, control unit, and lights [57]. Likewise, Article [56] delves into the intricate architecture of IMs, spotlighting core components such as stator windings, rotor, and bearings, underscoring their intricacy and operational efficiency. Finally, the study in [16] offers insights into the technical intricacies of IM, underscoring their instrumental role in powering micromobility devices tailored for short-distance commuting.

The electrical characteristics of an induction motor can be succinctly represented through a fourth-order state space model, as depicted in [60] Equation (1):

$$\left[\begin{array}{c}{v}_{qs}\\ v_{ds}\\ v_{qr}\\ v_{dr}\end{array}\right]=\left[\begin{array}{cccc}{R}_s+s{L}_s& -{\omega }_e{L}_s& s{L}_m& -{\omega }_b{L}_m\\ -{\omega }_e{L}_s& R_s+s{L}_s& -{\omega }_e{L}_m& p{L}_m\\ s{L}_m& ({\omega }_e-{\omega }_r)L_m& R_r+s{L}_r& ({\omega }_e-{\omega }_r)L_r\\ -({\omega }_e-{\omega }_r)L_m& s{L}_m& -({\omega }_e-{\omega }_r)L_r& R_r+s{L}_r\end{array}\right]\left[\begin{array}{c}{i}_{qs}\\ i_{ds}\\ i_{qr}\\ i_{dr}\end{array}\right]$$

(1)

The representation of electromagnetic torque in relation to inductances is provided in [60] Equation (2):

$$T_{em}={\displaystyle \frac{3}{2}}·\left({\displaystyle \frac{P}{2}}\right)·L_m·(i_{sq}·i_{rd}-i_{sd}·i_{rq})$$

(2)

IM’s eco-energetic features help cut greenhouse gas emissions, favoring greener transportation modes [61,62]. These qualities, along with simplicity and durability, boost micromobility device performance and advocate for sustainable, eco-friendly transportation [35].

2.2. Permanent Magnet Synchronous Motors (PMSM)

PMSMs are constructed with a permanent magnet rotor and a stator with coils arranged in a synchronous manner [63]. The rotor contains permanent magnets, typically made of materials such as Neodymium Iron Boron (NdFeB), which generate a magnetic field. Meanwhile, the stator comprises coils that produce a rotating magnetic field when supplied with electric current. This interaction between the magnetic fields of the rotor and stator enables the motor to produce rotational motion.

PMSMs offer significant advantages over other types of electric motors. Firstly, they boast high efficiency, effectively reducing energy consumption and enhancing overall performance [64]. Additionally, PMSMs feature high torque density, providing ample power in a compact and lightweight design, which is particularly advantageous for micromobility applications [63]. Moreover, their brushless design requires minimal maintenance, resulting in reduced downtime and operating costs [64]. Lastly, PMSMs offer smooth operation with precise speed and torque control, ensuring a comfortable and responsive driving experience [63].

The paper [65] explores the complexities related to the accurate representation of the rotor position to control a PMSM. It introduces a new algorithm based on a first-order Taylor series approximation. The emphasis is on solving issues such as current in micro-mobility applications like scooters and electric motorcycles.

The powertrain model presented in [66] provides a comprehensive approach to evaluating the longterm performance of an electric scooter, considering both engine dynamics and the effects of battery aging. While the precise PMSM model with an inverter provides detailed information, the simplified model without an inverter allows for faster and more efficient long-term simulations. The main parts of the induction motor are presented in Figure 5.

The stator currents of the surface PMSM along the d-q axis can be represented in [68] Equation (3):

$$\left[\begin{array}{c}{i}_d\\ i_q\end{array}\right]=\left[\begin{array}{cc}-{\displaystyle \frac{R}{L_d}}& {\omega }_s\left({\displaystyle \frac{L_q}{L_d}}\right)\\ -{\omega }_s\left({\displaystyle \frac{L_d}{L_q}}\right)& -{\displaystyle \frac{R}{L_q}}\end{array}\right]\left[\begin{array}{c}{i}_d\\ i_q\end{array}\right]+\left[\begin{array}{cc}{\displaystyle \frac{1}{L_d}}& 0\\ 0& {\displaystyle \frac{1}{L_q}}\end{array}\right]\left[\begin{array}{c}{v}_d\\ v_q\end{array}\right]+\left[\begin{array}{c}0\\ -{\omega }_s{\displaystyle \frac{{\lambda }_{af}}{L_q}}\end{array}\right]$$

(3)

Authors in [68] the mechanical Equation (4) of the motor involves parameters such as $v_d$ and $v_q$, which represent the stator voltages along the d-q axis, R standing for the stator resistance, $L_d$ and $L_q$ indicating the stator inductances in the d-q axes, $i_d$ and $i_q$ representing the d-q axis stator currents, ${\omega }_s$ denoting the electrical rotor speed, and ${\lambda }_{af}$ representing the rotor permanent magnetic flux.

$$T_e=J{\displaystyle \frac{d{\omega }_r}{dt}}+B{\omega }_r+T_L$$

(4)

The expression for the electromagnetic torque of the PMSM is given as [68] Equation (5):

$$T_e=\left({\displaystyle \frac{3}{2}}\right)P\left({\lambda }_{af}{i}_q+(L_d-L_q)i_d{i}_q\right)$$

(5)

In this equation, $T_e$ represents the electromagnetic torque, P is the number of pole pairs, ${\lambda }_{af}$ is the rotor permanent magnetic flux, $i_d$ and $i_q$ are the d-q axis stator currents, and $L_d$ and $L_q$ are the stator inductances in the d-q axes.

The low maintenance needs of PMSMs, thanks to no brushes or commutators, reduce downtime and costs. With precise speed and torque control, they offer a smooth, reliable driving experience [69]. Continuous advancements in PMSM tech highlight their crucial role in enhancing EMM solutions, signaling efficiency, performance, and reliability gains in electric vehicles.

2.3. Switched Reluctance Motors (SRM)

Switched Reluctance Motors (SRMs) are valued for their simple design and suitability in electric vehicle applications [70]. Unlike other motors, SRMs lack windings or permanent magnets in the rotor, relying on magnetic reluctance for motion [63].

SRMs offer advantages like high efficiency, fault tolerance, and simplified maintenance, making them cost-effective in Electric Mobility Machines (EMMs) [63].

In a study on electric scooters [39], a new axial flux SRM was developed to address the need for efficient, affordable drive systems. The authors discuss the advantages of scooters in urban areas and the global demand for electric scooters.

Another investigation [71] examines SRM design for electric scooters, showing simulation results and performance characteristics. The study emphasizes the importance of torque-speed and output power for real-world performance assessments.The main parts of the SRM are presented in Figure 6:

The SRM model is given as [73] by Equation (6):

$$\left[\begin{array}{c}{i}_d\\ i_q\\ i_0\end{array}\right]={\displaystyle \frac{2}{3}}\left[\begin{array}{ccc}cos\left({\theta }_e\right)& cos\left({\theta }_e-{\displaystyle \frac{2\pi }{3}}\right)& cos\left({\theta }_e+{\displaystyle \frac{2\pi }{3}}\right)\\ -sin\left({\theta }_e\right)& -sin\left({\theta }_e-{\displaystyle \frac{2\pi }{3}}\right)& -sin\left({\theta }_e+{\displaystyle \frac{2\pi }{3}}\right)\\ {\displaystyle \frac{1}{2}}& {\displaystyle \frac{1}{2}}& {\displaystyle \frac{1}{2}}\end{array}\right]\left[\begin{array}{c}{i}_a\\ i_b\\ i_c\end{array}\right]$$

(6)

In this equation, $i_d$, $i_q$, and $i_0$ represent the d-q-0 components of current, ${\theta }_e$ is the electrical angle, and $i_a$, $i_b$, and $i_c$ represent the components of current in the stationary reference frame.

The voltage equation is described as [73] by Equation (7):

$$\begin{array}{cc}\hfill \left[\begin{array}{c}{v}_d\\ v_q\\ v_0\end{array}\right]=R\left[\begin{array}{c}{i}_d\\ i_q\\ i_0\end{array}\right]& +\left[\begin{array}{ccc}{L}_{dc}+{\displaystyle \frac{L_{ac}}{2}}cos\left(3{\theta }_e\right)& -{\displaystyle \frac{L_{ac}}{2}}sin\left(3{\theta }_e\right)& L_{ac}\\ -{\displaystyle \frac{L_{ac}}{2}}sin\left(3{\theta }_e\right)& L_{dc}-{\displaystyle \frac{L_{ac}}{2}}cos\left(3{\theta }_e\right)& 0\\ {\displaystyle \frac{L_{ac}}{2}}& 0& L_{dc}\end{array}\right]\left[\begin{array}{c}{\dot{i}}_d\\ {\dot{i}}_q\\ {\dot{i}}_0\end{array}\right]\hfill \\ \hfill & +{\omega }_e\left[\begin{array}{ccc}-L_{ac}sin\left(3{\theta }_e\right)& -L_{dc}-L_{ac}cos\left(3{\theta }_e\right)& 0\\ L_{dc}-L_{ac}cos\left(3{\theta }_e\right)& L_{ac}sin\left(3{\theta }_e\right)& L_{ac}\\ 0& 0& 0\end{array}\right]\left[\begin{array}{c}{i}_d\\ i_q\\ i_0\end{array}\right]\hfill \end{array}$$

(7)

In this equation, $v_d$, $v_q$, and $v_0$ represent the d-q-0 components of voltage, R is the resistance matrix, $L_{dc}$ is the direct-axis inductance, $L_{ac}$ is the quadrature-axis inductance, ${\theta }_e$ is the electrical angle, ${\dot{i}}_d$, ${\dot{i}}_q$, and ${\dot{i}}_0$ are the derivatives of the d-q-0 current components, and ${\omega }_e$ is the electrical rotor speed.

The torque equation in the synchronous d-q frame can be formulated as [73] by Equation (8):

$$T_e={\displaystyle \frac{3}{2}}P{L}_{ac}\left(i_0{i}_q-{\displaystyle \frac{1}{4}}(i_d^2+i_q^2)sin(3{\theta }_e+2\beta )\right)$$

(8)

In Equation (8), $T_e$ represents the electromagnetic torque, P denotes the number of pole pairs, $L_{ac}$ is the quadrature-axis inductance, $i_d$, $i_q$, and $i_0$ are the d-q-0 components of current, ${\theta }_e$ is the electrical angle, and $\beta$ is a phase shift angle.

The SRM’s simple design, lower potential cost, and efficiency suit light vehicles and short trips well [62,74]. Research, like that in [39], focuses on optimizing SRMs for electric scooters to meet demand for affordable, efficient drive systems. SRMs offer promise for electric mobility, including scooters, balancing simplicity, cost, and efficiency. Further research aims to enhance SRM designs and control strategies for better performance in electric vehicles.

2.4. Axial Flux Permanent Magnet Machines (AFPM)

Axial Flux Permanent Magnet Machines (AFPMs) are gaining momentum in electric micromobility systems due to their compact disc-shaped configuration, which allows higher torque density and improved spatial efficiency compared to traditional radial flux machines. This makes them particularly advantageous for e-scooters, e-bikes, and in-wheel motors where space is limited and performance is essential [51,75].

One of the key advantages of AFPMs is their ability to provide high torque at low rotational speeds without requiring additional mechanical transmission systems. The absence of mechanical couplings increases energy transmission efficiency, reliability, and reduces weight and volume, particularly in low-speed, high-torque applications [51].

The power delivered by an axial flux motor can be modeled as a function of the stator dimensions as shown in Equation (9):

$$P_d=K_{pd}·{\displaystyle \frac{D_2+D_1}{2}}·{\displaystyle \frac{D_2-D_1}{2}}·{\displaystyle \frac{D_1+D_2+D_3+D_4}{4}}$$

(9)

where $D_1$ through $D_4$ are diameters related to coil and magnet placement, and $K_{pd}$ is a performance factor based on flux density, efficiency, and design parameters [51].

The torque-speed control in AFPMs can be achieved through digital switching of coil connections. The rotational speed $SRPM$ for a PM machine of order n is expressed as:

$$SRPM={\displaystyle \frac{30·f_E}{2^{n-2}}}$$

(10)

where $f_E$ is the electrical frequency and n is the motor order [51].

AFPMs also benefit from optimized U-type coil topologies (UC coils), which allow modular stator designs. These coils are connected in such a way that the magnetic flux path is short and symmetric, improving performance. FEM simulations confirm the uniform magnetic field distribution and validate the analytical models [52].

As illustrated in Figure 7, the axial geometry and layout of the magnets and coils lead to compact, efficient machines ideal for micromobility platforms.

AFPMs combine high torque density, efficient packaging, and improved energy efficiency. Their modular design and elimination of mechanical couplings make them ideal candidates for next-generation electric micromobility applications.

2.5. In-Wheel Hub Motors (IWHM)

In-Wheel Hub Motors (IWHMs) represent a transformative concept in electric micromobility, where the propulsion system is integrated directly into the wheel, eliminating traditional drivetrain elements such as gearboxes, shafts, and differentials. This architecture enables greater vehicle design flexibility, weight savings, and improved energy efficiency, making it especially suitable for compact urban vehicles such as e-scooters and e-bikes [53,55].

IWHMs, particularly those based on radial and axial flux topologies, allow distributed drive configurations, providing each wheel with independent torque control. This capability enhances traction, steering response, and regenerative braking, ultimately increasing vehicle range and dynamic safety [54]. Figure 8 illustrates a common IWHM integration architecture.

Mathematically, the torque output T of an IWHM can be estimated using:

$$T={\displaystyle \frac{P}{\omega }}={\displaystyle \frac{60P}{2\pi N}}$$

(11)

where P is the power output in watts, $\omega$ is the angular velocity in rad/s, and N is the rotational speed in RPM. This simplified relation underscores the critical need for high torque at low speeds, a fundamental design requirement for direct-drive IWHMs [55].

While IWHMs offer clear advantages—such as better packaging, simplified transmission, enhanced regenerative braking, and space for larger batteries—the technology also poses significant challenges. These include increased unsprung mass affecting ride comfort, thermal management complexity, and stringent sealing requirements due to their proximity to harsh road conditions [53,55].

To address these issues, recent research has focused on advanced materials (e.g., soft magnetic composites), integrated motor-inverter topologies, and innovative cooling systems including spray and oil-based approaches [54]. Manufacturers like Elaphe and Protean have developed commercially viable IWHMs with torque densities exceeding 30 Nm/kg and integrated control electronics, highlighting the technology’s readiness for high-volume applications [55].

IWHMs offer a promising pathway for next-generation micromobility platforms. Their modularity, control precision, and drivetrain simplification make them ideal for lightweight electric vehicles, provided that issues like thermal performance, sealing, and mechanical robustness are adequately addressed in future developments.

2.6. Brushless Direct Current Motors (BLDC)

Brushless DC (BLDC) motors are widely used in Electric Mobility Machines (EMMs) for their efficiency and reliability. They feature a stationary stator and a rotating rotor with permanent magnets, offering precise motion control for electric vehicles like bicycles and micro-scooters [63,76].

BLDC motors are intricately designed with specific stator winding patterns to interact with rotor magnets, eliminating the need for physical commutators and improving reliability and maintenance [76]. They also incorporate sensor systems for accurate phase commutation and efficient operation [63]. Compared to brushed DC motors, BLDC motors deliver higher efficiency, require less maintenance, and offer improved reliability. Their brushless design enhances power density and thermal management, making them suitable for demanding electric mobility applications [63,76].

In a study on electric scooters [77], a BLDC motor was developed for the wheel, addressing sustainability concerns and demonstrating successful performance in a lightweight electric vehicle. Another article [78] discusses the design of a brushless DC motor with axial field and permanent magnet drive for electric bicycles, driven by the increasing demand and environmental concerns in regions like China and Southeast Asia. The main parts of the brushless DC Motor are presented in Figure 9:

The state space model of BLDC is given as [80] by:

$${\displaystyle \frac{d{i}_a}{dt}}={\displaystyle \frac{1}{L-M}}\left[v_{as}-R_s{i}_a-k_p{\omega }_m{e}_{as}\left({\theta }_r\right)\right]$$

(12)

$${\displaystyle \frac{d{i}_b}{dt}}={\displaystyle \frac{1}{L-M}}\left[v_{bs}-R_s{i}_b-k_p{\omega }_m{e}_{bs}\left({\theta }_r\right)\right]$$

(13)

$${\displaystyle \frac{d{i}_c}{dt}}={\displaystyle \frac{1}{L-M}}\left[v_{cs}-R_s{i}_c-k_p{\omega }_m{e}_{cs}\left({\theta }_r\right)\right]$$

(14)

$${\displaystyle \frac{d{\omega }_m}{dt}}=\left(-{\displaystyle \frac{B}{J}}\right){\omega }_m-\left({\displaystyle \frac{1}{J}}\right)\left(T_e-T_l\right)$$

(15)

$${\displaystyle \frac{d{\theta }_r}{dt}}={\displaystyle \frac{P}{2}}{\omega }_m$$

(16)

Equations (12)–(14) define the rates of change of currents $i_a$, $i_b$, and $i_c$, respectively, along with phase voltages and other motor parameters such as resistance, gain, rotor speed, back EMFs, and rotor angle.

The dynamic equations illustrate the behavior of BLDC motors in electric scooter design, addressing sustainability and promoting electric vehicle adoption [81,82]. BLDC motors play a vital role in advancing electric mobility due to their performance and reliability.

Urban micromobility’s development hinges on selecting appropriate electric motors for light vehicles like scooters and bicycles. A comparison

Table 4. Advantages and Disadvantages of Different Motor Types for Micromobility Applications—Part 1.

Criteria	IM	PMSM	SRM
Efficiency	Relatively lower efficiency, especially at low speeds [83].	High efficiency, even at low speeds [83].	High efficiency, especially at low speeds [84].
Power Density	Lower power density, can be bulky [85].	High power density, compact design [85].	Moderate power density [85].
Noise and Vibration	More noise due to rotor/stator interactions [85].	Low noise and vibration levels [83].	Generally low levels [84].
Durability	Requires maintenance due to brush wear [85].	Long lifespan, minimal maintenance [85].	Depends on design [84].
Control Precision	Limited speed control precision [61].	Precise control [61].	Precise torque and speed control [84].
Weight and Size	Bulky and heavy [85].	Compact and lightweight [85].	Moderate weight and size [85].
Regenerative Braking	Limited capability [83].	Excellent energy recovery [83].	Effective regeneration [84].
Environmental Friendliness	Brushes require replacement [85].	Eco-friendly, minimal wear [85].	Fewer components [84].

and

Table 5. Advantages and Disadvantages of Different Motor Types for Micromobility Applications—Part 2.

Criteria	BLDC	AFPM	In-Wheel
Efficiency	High efficiency [61].	Very high due to minimized losses [51].	Gearless, independent control [53].
Power Density	High power density [85].	Ultra-compact, high torque [75].	Depends on wheel size and cooling [54].
Noise and Vibration	Low noise and vibration [83].	Smooth with UC coils [51].	Road-induced vibrations [55].
Durability	Long lifespan, low maintenance [85].	Low wear, high reliability [52].	High mechanical demands [54].
Control Precision	Precise torque control [61].	FEM-informed control [75].	Precise via individual control [53].
Weight and Size	Compact and lightweight [85].	Pancake form, very compact [51].	Increases unsprung mass [53].
Regenerative Braking	Excellent energy recovery [83].	Tunable torque regeneration [51].	Effective with torque vectoring [55].
Environmental Friendliness	Minimal wear [85].	Recyclable designs [52].	No gear trains, eco-friendly [54].

assesses the performance of various motor types in critical areas.

These motors have been evaluated based on various criteria important for micromobility applications:

2.7. Discussion

PMSMs are emerging as highly promising candidates for micromobility applications, where considerations such as pollution, fuel consumption, and power-to-volume ratio are crucial, PMSM and Brushless DC (BLDC) motors appear more advantageous. These motors exhibit lower pollutant emissions, lower fuel consumption, and a higher power-to-volume ratio. These factors contribute to their appeal in the micromobility applications of electric vehicles, aligning with the growing emphasis on environmental sustainability and energy efficiency in urban transportation. Our perspective regarding the presented work is a comparative simulation to the performance of those electric motors in MATLAB/Simulink 2022b tool based on a real dataset trajectory.

3. Commonly Used Electric Batteries

One of the key elements of EMM is the battery technology used in these vehicles, moving towards a trend of more sustainable and environmentally friendly urban mobility.

Table 6. Electric Batteries Used in Electric Scooters.

Product	Year	Types of Battery
Electric Scooter MV Racer Lithium [86]	2023	Lithium-ion
Yugbike Electric Scooter with Steel Guard [87]	2022	Lithium Iron Phosphate
Niu [88]	2023	Sodium-Sulfur

describes the different types of electric batteries used in various electro-micromobility applications.

Among the different types of electric batteries used in different EMM applications, several chemistries stand out for their unique advantages.

3.1. Lithium Iron Phosphate

Lithium Iron Phosphate (LFP) batteries have gained considerable attention in the electric micromobility domain due to their favorable balance of performance, safety, cost, and sustainability. Unlike other lithium-ion chemistries, LFP batteries do not rely on critical materials such as cobalt or nickel, making them not only more cost-effective but also less geopolitically sensitive and environmentally burdensome [89,90]. These attributes render them particularly well suited for compact and lightweight urban mobility platforms like e-bikes and e-scooters.

Structurally, LFP batteries consist of a LiFePO₄ cathode and a graphite anode. They operate at a nominal voltage range of 3.2–3.3 V and utilize a LiPF₆ electrolyte system, which ensures a stable and reversible lithium intercalation process within an olivine-structured lattice [89,90].

Table 7. Characteristics of LFP Battery [89,90].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
LFP	LiFePO4	LiPF6 in organic solvents; Li+ intercalation in stable olivine lattice	Graphite	3.2–3.3

summarizes the core electrochemical properties of LFP cells:

One of the most distinguishing characteristics of LFP batteries is their inherent safety. They exhibit strong thermal and chemical stability, with superior resistance to thermal runaway compared to other chemistries like lithium cobalt oxide (LCO) and nickel manganese cobalt (NMC). In particular, fire emission tests demonstrated that LFP cells did not ignite at low states of charge (0% and 30% SOC), a stark contrast to LCO cells [91]. This makes LFP batteries ideal for high-density urban environments where safety is paramount [89,91]. Additionally, their durability—with their life cycle often exceeding 3000 cycles—makes them attractive for shared micromobility services and delivery fleets [92]. Figure 10 presents a LFP battery:

From an environmental standpoint, LFP technology offers further advantages. Life Cycle Assessment (LCA) studies show that while the battery cell—particularly the cathode—remains the largest contributor to the overall carbon footprint, LFP batteries rank lower in environmental burdens such as human toxicity, acidification, and resource depletion compared to cobalt- and nickel-based alternatives [94]. Their reliance on more abundant and less toxic materials plays a significant role in minimizing their ecological impact.

Furthermore, LFP batteries are highly compatible with circular economy strategies. Even after their first life in micromobility applications, they often retain 70–80% of their original capacity (SOH), making them well-suited for second-life applications like residential or grid-scale energy storage [95,96]. Their relatively stable structure and lower risk of thermal events also simplify handling, disassembly, and refurbishment [95].

Nonetheless, one of the main limitations of LFP batteries remains their relatively lower gravimetric and volumetric energy density (typically 90–140 Wh/kg), which makes them less suitable for long-range or high-performance electric vehicles [89]. To address this, ongoing research—especially from leading manufacturers such as BYD and Tesla—is focused on improving the energy density while preserving the safety and affordability that characterize LFP systems [89].

In summary, LFP batteries represent a highly robust, sustainable, and economically viable solution for urban electric micromobility. Their excellent safety profile, long operational lifespan, and second-life potential make them a cornerstone for building cleaner and smarter urban transportation systems.

3.2. Nickel Manganese Cobalt (NMC)

Nickel Manganese Cobalt (NMC) batteries have become one of the most prevalent lithium-ion chemistries in electric micromobility, due to their high energy density, moderate cost, and long cycle life. Initially developed for electric vehicles and power tools, NMC cells are now widely deployed in e-scooters, e-bikes, and other light electric vehicles (LEVs) [92,97]. Their versatility stems from their tunable cathode composition: nickel contributes to energy density, cobalt improves structural stability, and manganese enhances safety and reduces resistance [89,98].

Structurally, NMC batteries use a layered oxide cathode, commonly represented as LiNi_xMn_yCo_zO₂, where the ratios evolve from NMC111 to more nickel-rich compositions such as NMC622 and NMC811 [92]. These variations optimize specific performance metrics like energy density and material cost.

Table 8. Characteristics of NMC Battery [89,92].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
NMC	LiNiMnCoO2	LiPF6 in organic carbonate solvents; layered intercalation structure	Graphite	3.6–3.7

summarizes the electrochemical characteristics of NMC batteries:

NMC cells provide gravimetric energy densities typically ranging between 140–200 Wh/kg, and up to 270 Wh/kg in high-nickel configurations like NMC811 [89]. They also support high discharge rates (often >10C) and fast charging, enabling efficient power delivery in dynamic urban driving conditions. Their cycle life ranges from 1000 to over 2000 cycles, depending on use conditions and temperature management [90,92].

In terms of safety, NMC batteries are more sensitive to overcharging and thermal abuse than LFP chemistries, particularly at high nickel contents. Thermal runaway and flammability remain key concerns, especially in shared micromobility fleets exposed to variable charging patterns and user behaviors [98]. Therefore, robust Battery Management Systems (BMS) are essential to maintain safe operation, including overvoltage and overtemperature protection.

Environmentally, NMC cells contain critical and geopolitically sensitive materials, including cobalt and nickel. Recent developments have aimed to reduce cobalt content and improve sourcing transparency. Additionally, as NMC packs reach end-of-life, they present significant potential for second-life applications such as stationary energy storage, power banks, or backup systems [92,97]. Recovery processes through hydrometallurgy allow partial extraction of cobalt and nickel, although challenges in dismantling glued cell structures persist [97].

Micromobility manufacturers like Xiaomi and Segway-Ninebot frequently use NMC-based packs in their 36V and 48V systems due to the chemistry’s compact size and energy-per-mass advantage [92]. Figure 11 illustrates a typical modular NMC ACEDIS 24 V 108 Ah C20/LNMC24-108.

While the evolution of battery technologies is steering toward LFP and solid-state chemistries for enhanced safety, NMC remains a strong candidate where energy density and performance are prioritized. Its adaptability to different use cases and integration into circular business models ensure its continued relevance in the transition toward sustainable urban transport [89,92].

3.3. Nickel-Cadmium Batteries

The use of nickel in battery manufacturing highlights a commitment to quality and durability, crucial for gaining consumer trust and market competitiveness. Nickel batteries are key components in India’s electric scooter industry, as noted in [100]. Research detailed in [101] focuses on evaluating Saft STM 5.140 Nickel Cadmium batteries, with a developed electrical model based on comprehensive test data. Despite environmental concerns outlined in [102], understanding the comparative positioning of nickel-cadmium batteries among other technologies is essential for shaping decisions in the electric vehicle sector. The

Table 9. Characteristics of a Nickel-Cadmium Battery [103].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
Ni-Cd	NiOOH	KOH aqueous solution	Cd	1.2
		Cd + 2OH− ⇌ Cd(OH)2 + 2e− (anode)
		2NiOOH + 2H2O + 2e−
		⇌2Ni(OH)2 + 2OH− (cathode)
		2NiOOH + Cd + 2H2O
		⇌Ni(OH)2 + Cd(OH)2 (total reaction)

shows the Characteristics of a Nickel-Cadmium battery:

The paper [104] highlights the phenomenon of thermal overheating that can occur when using nickel-cadmium batteries, leading to the explosive release of hydrogen. This presents potential risks, especially in critical applications such as aircraft. The significant accumulation of hydrogen in the electrodes over time is also emphasized, with specific values being high compared to other reversible hydrides. Figure 12 shows a Nickel-Cadmium E Bike Lithium Battery 72 V 40 Ah.

Nickel batteries, particularly nickel-cadmium (NiCd) variants, offer competitive energy density for electric scooter autonomy and feature a mature technology with a well-understood electrical model [100,101]. NiCd batteries also exhibit appreciable energy efficiency of 72.5 percent, effectively utilizing stored energy in electric vehicles [102]. However, they are susceptible to drawbacks including memory effect, environmental concerns due to cadmium content, and higher weight, impacting scooter performance and energy efficiency [100,102].

Ni-Cd batteries are used in niche applications like backup power and energy storage due to their resilience against overcharging and discharge [106]. However, they have a notable self-discharge rate and suffer from the “memory effect”, reducing capacity if not fully discharged before recharge [107]. Despite higher costs compared to Li-ion batteries, Ni-Cd batteries persist in specific applications for their durability and performance in challenging conditions [108].

3.4. Sodium-Sulfur Batteries

Sodium-sulfur batteries are emerging as promising candidates for energy storage, offering high power and energy density, thermal stability, reduced cost, and increased safety [109]. Research detailed in [110] underscores the reliability of Sodium/Sulfur batteries, examining their lifespan through Weibull characteristics. Reliability is crucial due to potential unpredictable failures, including solid electrolyte cracking or sealing issues. The

Table 10. Characteristics of Sodium-Sulfur Battery [111,112].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
Sodium-Sulfur (NaS)	S	Na + nS → Na2Sn ($5\ge n\ge 2$)	Na	1.8
		→ Na2Sn ($4>n\ge 2$) (Discharge process)
		Na2Sn ($4>n\ge 2$) → 2Na + nS
		+ Na2S2 ($5>n\ge 3$) (Charge process)

shows the Characteristics of Sodium-Sulfur battery:

This type of battery represents a significant advance in the field of batteries for electric vehicles, as described in [113]. These batteries, developed by Chloride Silent Power, Ltd. (CSPL) in collaboration with the Chloride Group, offer interesting prospects in terms of cost, performance, and reliability. Figure 13 shows a Sodium sulphur battery installation in Japan.

The advantages of supercapacitors include high power and energy density, competitive cost compared to other technologies, and diverse applications in electric vehicles and energy storage [109,110,113]. However, they face challenges such as high operating temperature requiring complex thermal management and reliability issues related to unpredictable failures [109,110].

NaS (Sodium-Sulfur) batteries, designed for high-temperature operation around 300 °C, excel in long-term energy storage with stability and extended retention [115,116]. Despite requiring precise voltage management and high-temperature operation, they are valued for rapid response and handling substantial energy peaks, especially in industrial and renewable energy settings [106,117].

3.5. Lithium-Ion Batteries

In [118], the electric scooter uses a lithium-ion battery, impacting its ecological footprint. This battery is central to the proposed EV charging system in [119], highlighting its importance. Lithium-ion batteries are preferred in EVs for their light weight and long lifespan, offering an efficient energy solution. The

Table 11. Characteristics of Lithium-Ion Battery [103].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
Lithium-Ion	CoO2	Organic electrolyte + Li salt	C + Li	3.7
		Li(C) ⇌ Li+ + e− (anode)
		Li+ + e− + CoO2 ⇌ LiCoO2 (cathode)
		Li(C) + CoO2 ⇌ LiCoO2 (total reaction)

shows the Characteristics of Lithium-Ion battery:

The exploration into [120] highlights the dilemma between the technological advantages of Li-ion batteries and the critical economic factor in the choice of Chinese consumers. The transition to Li-ion batteries in the e-bike market is conceivable but will depend on the delicate balance between performance and cost in the minds of users. Figure 14 shows a picture of a M5 Electric Scooter Lithium-ion battery (36 V 7.5 Ah).

Safety monitoring of lithium-ion batteries is critical due to their high energy density and associated risks, such as thermal runaway, overcharging, and deep discharging. Two key metrics monitored are the State of Charge (SOC) and State of Health (SOH). SOC provides a real-time measure of the remaining charge in the battery, ensuring efficient energy usage while preventing conditions that could lead to overheating or damage [120,122]. SOH assesses the battery’s overall condition, including capacity degradation, internal resistance, and temperature behavior, offering insights into the long-term reliability and safety of the system [123,124].

Battery Management Systems (BMS) are indispensable in lithium-ion applications. They continuously monitor SOC and SOH, regulate cell balancing, control charging and discharging rates, and mitigate safety risks by identifying and preventing potential hazards like overvoltage, overcurrent, or excessive temperature. Advanced BMS implementations also enable predictive analytics, using data trends to anticipate and address issues before they compromise safety or performance [122,125].

Lithium batteries offer numerous advantages, including high energy density, reasonable lifespan, lightweight design, fast charging capability, and low energy loss [118,119,120]. However, they come with disadvantages such as high cost due to expensive materials, dependence on critical materials like lithium, cobalt, and nickel, as well as the risk of fire or explosion under certain conditions [118,119,120].

Lithium-ion batteries play a crucial role in the automotive industry’s shift towards electric vehicles, offering high energy density for decent range without excessive weight [117]. They are also integral to energy storage systems, integrating renewable sources like solar and wind into power grids for cleaner energy [107]. Their versatility extends from portable electronics to electric mobility and energy management [108], but optimal performance requires careful temperature control and maintenance.

3.6. Sodium-Ion (Na-Ion) Batteries

Sodium-ion (Na-ion) batteries have emerged as a promising alternative to lithium-ion chemistries in electric micromobility applications. Their advantages lie in the abundance of sodium, compatibility with existing lithium-ion production infrastructure, improved sustainability, and reduced material criticality [126,127]. These features make them a viable choice for cost-sensitive and resource-efficient urban transportation systems such as e-bikes and e-scooters.

Technically, Na-ion batteries typically use a Prussian Blue analogue, polyanionic materials, or layered oxide cathodes combined with hard carbon anodes [127,128]. A typical Na-ion system features a nominal voltage around 2.5–3.0 V, gravimetric energy densities of 100–150 Wh/kg, and cycle lives exceeding 2000 cycles in some commercial prototypes [89,129].

Table 12. Characteristics of Sodium-Ion Battery [89,127].

Battery System	Cathode	Electrolyte/Reaction	Anode	Nominal Voltage (V)
Na-ion	Prussian Blue/NaMO2	NaPF6 in organic solvents; Na+ intercalation	Hard Carbon	2.5–3.0

outlines the electrochemical configuration:

From a safety perspective, Na-ion batteries are considered more stable thermally than high-nickel lithium-ion chemistries, and offer lower flammability risk due to their chemistry and lower operating voltage [128]. Their compatibility with aluminum current collectors (instead of copper) for both electrodes adds further safety and cost reduction [89].

In micromobility, their role is rapidly growing. Demonstrations by companies like HiNa, Faradion, and Tiamat have proven Na-ion battery use in e-bikes, e-scooters, and mini electric vehicles [89]. Several prototypes have shown strong performance with over 2000 cycles and power retention at high charge/discharge rates (e.g., 10 C) [127]. These properties are particularly suited for urban shared-mobility where frequent cycling and fast charging are essential.

Environmentally, Na-ion batteries present a reduced reliance on scarce or geopolitically sensitive materials like cobalt, nickel, and lithium [126]. Life Cycle Assessment (LCA) models also indicate potentially lower impacts in terms of resource depletion and toxicity, particularly when Fe- and Mn-rich cathodes are used [129].

In terms of economic and scalability outlook, over 240 GWh of Na-ion production capacity is projected to come online globally by 2030 [126]. Figure 15 illustrates a typical Na-ion battery pack designed for micromobility.

While current Na-ion energy densities (typically 100–150 Wh/kg) trail behind those of NMC and LFP, advancements in cathode structures (such as layered oxides with high Na content) are anticipated to close this gap by the early 2030s [127]. These improvements aim to reach 180–200 Wh/kg with lower production costs, making Na-ion a key enabler of affordable and scalable urban electric transport solutions.

To assess and wisely choose the most suitable battery for a specific application, it is essential to understand the key characteristics of each battery type. The

Table 13. Comparison of Battery Technologies.

Attribute	NMC (LiNiMnCo)	Ni-Cd	Li-Ion	Na-Ion	LFP	Na-S (Sodium-Sulfur)
Nominal Voltage	3.6–3.7 V [89,98]	1.2 V [116]	3.7 V [117]	2.5–3.0 V [126,127]	3.2–3.3 V [89,90]	1.8 V [111,112]
Energy Density (Wh/kg)	140–270 [89,97]	100–250 [117]	100–250 [117]	100–160 [89,127]	90–140 [89]	150–240 [109,113]
Specific Power (W/kg)	200–1000 [92,98]	800–2000 [117]	800–2000 [117]	150–600 [89]	120–160 [89]	Moderate [109]
Cost (€/kWh)	120–200 [97]	255–388 [117]	150–200 [117]	70–150 [126]	Moderate [89]	Moderate [113]
Cycle Life (cycles)	1000–2000+ [92,98]	1000–2000+ [117]	500–1000+ [117]	1500–3000 [127,129]	1000–3000 [90,95]	2500+ [110]
Deep Discharge Sensitivity	Yes [98]	No [115]	No [115]	No [89]	No [89]	Yes [109]
Self-Discharge	Moderate [92]	Low [115]	Very low [115]	Low [89]	Very low [91]	Low [109]
Primary Applications	E-bikes, e-scooters, EVs, ESS [92]	Tools, backup [116]	EVs, storage [131]	Micromobility, ESS [127,129]	E-scooters, storage [90,95]	ESS, renewable buffering, industrial use [109,115,117]

provides a comparison of different battery types based on several essential parameters, offering a comprehensive overview of the performance and features of each battery type:

Table 14. Battery Technologies in Micromobility Applications.

Battery Type	Applications and Common Usage	References
NMC (LiNiMnCo)	Electric Scooter (High), Electric Bike (High), Hoverboard (High), Electric Moped (High); known for energy density, used in modern LEVs and explored in second-life storage	[89,92,97,98]
NiCd	Electric Scooter (Moderate), Electric Bike (Low), Hoverboard (Low), Electric Moped (Moderate)	[132,133,134,135,136,137]
Na-S	Electric Scooter (High), Electric Bike (Moderate), Hoverboard (Low), Electric Moped (High)	[133,138,139,140,141]
Li-ion	Electric Scooter (High), Electric Bike (High), Hoverboard (Moderate), Electric Moped (High)	[120,122,123,124,125,133]
Na-ion	Electric Scooter (Moderate), Electric Bike (Moderate), Hoverboard (Moderate), Electric Moped (Moderate); valued for safety, lower cost, and resource availability in future markets	[89,126,127,129]
LFP (LiFePO4)	Electric Scooter (High), Electric Bike (High), Hoverboard (Moderate), Electric Moped (High); known for safety, recyclability, and second-life potential	[89,90,91,95]

summarizes the common usage of these battery types across various micromobility applications, highlighting their advantages and frequency of use.

Renewable energy storage plays a crucial role in enhancing the viability of sustainable energy systems, particularly in addressing the intermittent nature of renewable sources like solar and wind. Energy storage systems (ESS) enable the capture and retention of excess energy during peak production periods, ensuring a reliable energy supply even when generation is low. This capability is especially vital for mitigating gaps in availability, such as during cloudy days or at night for solar power, or during low-wind periods for wind energy.

For microvehicles, energy storage systems are pivotal, not only for ensuring consistent performance but also for reducing reliance on fossil fuels. Efficient ESS solutions, such as lithium-ion batteries, facilitate the widespread adoption of EMM, directly contributing to climate change mitigation by reducing greenhouse gas emissions [120,142]. However, limitations persist, including the high cost of advanced batteries, resource-intensive manufacturing processes, and challenges related to recycling and end-of-life disposal [143].

3.7. Discussion

This study emphasizes the importance of continuous innovation in battery management to optimize performance, increase durability, and promote environmental commitment, with lithium-ion batteries playing a key role in advancing greener and more efficient micromobility. Implementing a robust battery management system (BMS), such as our proposal using Matlab/SIMULINK, proactively monitors and adjusts key parameters of lithium-ion batteries. Intelligent algorithms within the BMS aim to maximize performance, extend battery life, enhance safety, and optimize energy usage, contributing to a more environmentally friendly micromobility landscape.

Acknowledging climate urgency requires investment in research and development to advance energy density, battery sustainability, and safety. Investing in advanced battery technologies and collaboration shapes a greener, smarter urban landscape for future generations. Micromobility emerges as a promising path toward sustainable cities and lifestyles, transcending trend to become an essential driver of progress toward a sustainable urban future.

4. Power Converter

The increasing global energy demand, driven by rapid industrialization, population growth, and the transition to renewable energy sources, necessitates efficient power conversion systems. Power electronics, particularly AC-DC and DC-DC converters, are critical in addressing this need by ensuring reliable energy conversion for various applications such as electric vehicles (EVs), renewable energy systems, industrial automation, and consumer electronics [144,145]. AC-DC converters play a pivotal role in converting alternating current from grids or renewable sources into direct current required by most modern electronic systems, while DC-DC converters are essential for adapting voltage levels to meet specific load requirements [146,147].

In renewable energy applications, AC-DC converters are integral to photovoltaic (PV) and wind energy systems, ensuring efficient integration with electrical grids and energy storage devices [148,149]. Similarly, in EVs, AC-DC converters enable battery charging from the AC power grid, while DC-DC converters regulate power distribution between the battery and the vehicle’s motor, enhancing overall efficiency and reliability [145,150]. These converters also support bidirectional energy flow, enabling energy recovery during regenerative braking in EVs [144,147].

Advances in semiconductor materials, such as Silicon Carbide (SiC) and Gallium Nitride (GaN), have significantly improved the performance of both AC-DC and DC-DC converters. These innovations have led to reduced switching losses, increased power densities, and compact designs, making them ideal for high efficiency applications in next-generation power systems [148,151]. However, challenges such as thermal management, electromagnetic interference (EMI), and system complexity remain critical areas for ongoing research and development [149,150].

4.1. DC-DC Converter

DC-DC converters are pivotal components in modern power electronic systems, facilitating the conversion of DC voltages between different levels with high efficiency and reliability. Their applications span renewable energy systems, electric vehicles, telecommunications, and portable electronic devices, making them indispensable in diverse domains. These converters are broadly classified into non-isolated and isolated types, each with unique topologies, operational principles, and control strategies. The recent advancements in power electronics have significantly enhanced the performance, efficiency, and applicability of these converters, positioning them as critical tools in sustainable and advanced technological systems.

The various types of DC-DC converters, along with their features and applications, are summarized in

Table 15. Types of DC-DC Converters.

Type	Description	Applications	References
Buck Converter	Steps down the input voltage to a lower output voltage.	Power regulation in electronics.	[152,153]
Boost Converter	Steps up the input voltage to a higher output voltage.	Renewable energy systems and EVs.	[153,154]
Buck–Boost Converter	Combines buck and boost functionality to provide higher or lower output voltage.	Battery management systems.	[154,155]
SEPIC Converter	Allows step-up and step-down voltage regulation with continuous input and output current.	Solar inverters and portable devices.	[155,156]
Cuk Converter	Provides both step-up and step-down with low ripple on input and output currents.	Power conditioning for industrial systems.	[155,156]
Luo Converter	Offers high voltage gain and reduced component stress.	High-voltage power systems.	[155]
Half-Bridge Converter	Isolated converter with transformer for moderate power applications.	Telecommunication and auxiliary power systems.	[154,157]
Full-Bridge Converter	Isolated converter for high-power applications with enhanced efficiency.	EV chargers and renewable energy systems.	[154,156]
Push-Pull Converter	Compact isolated converter ideal for low to medium power levels.	Battery chargers and small power systems.	[154,156]
Resonant Converters	Achieve soft switching to reduce losses and increase efficiency.	High-frequency applications.	[154,156]
ZVS/ZCS Converters	Use Zero Voltage or Zero Current Switching to minimize switching losses and EMI.	High-performance power supplies.	[156,157]

:

DC-DC converters are indispensable in modern power electronic systems, offering efficient voltage conversion and regulation across a variety of applications. In renewable energy systems, particularly photovoltaic (PV) applications, they significantly enhance energy efficiency by employing Maximum Power Point Tracking (MPPT) algorithms. These algorithms ensure optimal utilization of solar energy, even under fluctuating environmental conditions, making DC-DC converters vital for sustainable energy solutions [152,155,156].

In electric vehicles (EVs), DC-DC converters are integral for managing battery systems, providing efficient power delivery for propulsion and auxiliary systems. Their ability to operate under varying loads while maintaining stable performance ensures their critical role in EV powertrain systems [154,156,158].

Across these applications, DC-DC converters prove their importance by enabling precise voltage regulation, high energy efficiency, and adaptability, underscoring their critical role in advancing technology across multiple industries.

Illuminating Features:

-

Exceptional Efficiency: Modern designs achieve efficiencies exceeding 95% by utilizing advanced materials and control techniques [156,157].

-

Compact and Lightweight Structures: Their modular designs enhance portability, crucial for wearables and IoT devices [153,155].

-

Diverse Functionalities: Capability to handle step-up, step-down, and bidirectional energy flows tailored to application needs [155,156].

-

Enhanced Safety Mechanisms: Isolated designs ensure user and equipment safety in critical applications [154,156].

-

Versatility Across Frequencies: Operational adaptability from low kHz to MHz facilitates innovative applications [154,156].

Persistent Challenges:

-

Complex Design Requirements: Advanced topologies like resonant and soft-switching converters demand intricate designs and precision [156,157].

-

High Development Costs: Incorporating cutting-edge components like SiC or GaN increases initial expenses [154,156].

-

Thermal Management: Effective heat dissipation strategies are required for consistent high-performance [154,156].

-

Electromagnetic Interference: High switching frequencies pose challenges in EMI compliance [154,156].

DC-DC converters remain a cornerstone of innovation in power electronics. Their unique combination of versatility, efficiency, and adaptability places them at the heart of sustainable technological advancements, bridging the gap between modern applications and the demands of energy-conscious systems. Continued research and technological progress are pivotal in overcoming existing limitations, paving the way for groundbreaking innovations in energy conversion and management.

4.2. DC-AC Inverter

AC-DC converters play a critical role in power electronics by enabling the conversion of alternating current (AC) to direct current (DC) for various applications. These include industrial systems, electric vehicles (EVs), renewable energy systems, telecommunication equipment, and consumer electronics. Their significance lies in improving power quality, enhancing energy efficiency, and enabling precise control of power flow across diverse domains. Advances in AC-DC converter technologies have focused on enhancing efficiency, reducing harmonics, and improving reliability through the integration of modern semiconductor devices and innovative control methods.

The various types of AC-DC converters, along with their features and applications, are summarized in

Table 16. Types of AC-DC Converters.

Type	Description	Applications	References
Unidirectional Converters	Converts AC to DC with a fixed power flow direction.	SMPS, industrial drives, and low-power applications.	[159,160]
Bidirectional Converters	Allows power flow in both directions between AC and DC.	PHEVs, renewable energy systems, and grid energy storage.	[160,161]
Single-Phase Converters	Operates with single-phase AC input.	Residential and small-scale industrial power supplies.	[159,162]
Three-Phase Converters	Operates with three-phase AC input for higher power applications.	HVDC transmission, industrial motor drives, and large-scale renewable systems.	[161,162]
PWM Rectifiers	Employs pulse-width modulation for precise voltage control and low harmonics.	High-performance EV chargers and telecommunication systems.	[159,160]
Resonant Converters	Utilizes resonance for high efficiency switching.	LED drivers and high-frequency power supplies.	[160,163]
Soft-Switching Converters	Achieves Zero Voltage Switching (ZVS) and Zero Current Switching (ZCS) to reduce losses.	Industrial automation and aerospace systems.	[160,164]

:

AC-DC converters find extensive use across multiple domains due to their ability to efficiently convert AC to DC power while maintaining high reliability and performance. In industrial systems, these converters are crucial for providing stable power to industrial drives and automation systems, enabling precise machine operations and energy savings [159,162]. In electric vehicles (EVs), bidirectional AC-DC converters facilitate efficient battery charging from the grid and energy recovery during braking, improving the overall energy efficiency of EVs and plug-in hybrid electric vehicles (PHEVs) [161,164]. Renewable energy systems, such as photovoltaic (PV) setups, employ AC-DC converters for efficient energy conversion and storage, allowing seamless integration with the grid and energy storage systems [159,161].

Key Features:

-

Exceptional Efficiency: Modern converters achieve efficiency levels exceeding 95%, ensuring minimal energy loss [159,161].

-

Enhanced Power Quality: Techniques like PWM enable smooth DC output with reduced harmonics [159,160].

-

Compact and Lightweight Design: Advances in semiconductor integration contribute to smaller footprints and greater portability [160,164].

-

Bidirectional Energy Flow: Ideal for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications, enabling energy storage and retrieval [161,164].

-

High Reliability: Soft-switching reduces stress on critical components, extending operational life [160,164].

Overarching Challenges:

-

Complex Control Mechanisms: Advanced topologies demand intricate algorithms and precision control [159,160].

-

High Manufacturing Costs: Premium materials such as SiC and GaN significantly elevate production expenses [161,164].

-

Thermal Management Issues: High-power densities necessitate innovative cooling strategies [161,164].

-

Electromagnetic Interference: High switching frequencies present EMI challenges that complicate compliance [159,160].

-

Scalability Constraints: Certain designs face limitations in adapting to extreme power levels [160,161].

AC-DC converters remain foundational to the advancement of energy-efficient and sustainable power systems. With ongoing innovation in semiconductor technologies, control methodologies, and thermal solutions, these converters are set to meet the demands of next-generation applications, ranging from smart grids to high-performance EVs, while addressing existing barriers to optimization.

4.3. Comparison of GaN and SiC Devices in Power Converters

The emergence of wide bandgap (WBG) semiconductors, particularly Gallium Nitride (GaN) and Silicon Carbide (SiC), has revolutionized modern power electronics, enabling higher efficiency, compactness, and faster switching operations. A detailed comparison of their properties and applications in converters is provided below

Table 17. Comparison b etween GaN and SiC Devices.

Feature	Gallium Nitride (GaN)	Silicon Carbide (SiC)
Switching Frequency	Extremely high (up to 300 MHz for DC-DC buck converters) [165]	High (typically tens to hundreds of kHz) [166]
Efficiency	Very high due to low parasitic capacitance and fast switching [167]	High efficiency, especially in high-voltage systems [166]
Thermal Management	More challenging; lower thermal conductivity requires careful heat sinking [167]	Superior thermal conductivity allows easier thermal handling [166]
Miniaturization	Enables compact circuits with high power density (up to 1 W/mm2 [165]	Suitable for compact systems, especially in high-power designs [166]
Gate Drive Requirements	Requires precise, low-voltage gate control (e.g., 1.1–1.8 V CML swing) [165]	Compatible with conventional gate drivers (10–20 V swing) [168]

.

In GaN-based converters, switching topologies such as monolithic integrated buck converters operate at exceptionally high frequencies, enabling reduced passive component sizes and improved transient performance [165].

SiC-based designs, such as those used in portable EV chargers, offer higher voltage blocking capabilities and thermal robustness, with operating frequencies generally below 1 MHz but achieving system-level efficiencies around 96% [166]. These designs are well-suited for applications requiring reliable thermal performance and higher voltage levels.

Both technologies offer advantages over traditional silicon devices, but their selection depends on application-specific trade-offs. GaN is optimal for high-frequency, low-voltage, and compact applications; SiC excels in high-power, high-voltage, and thermally intensive systems.

Designers are increasingly adopting hybrid approaches where GaN handles high-frequency sections and SiC supports high-power front-end stages, especially in vehicle chargers and advanced power supply units [168].

4.4. Discussion

Power converters are central to advancing energy-efficient systems in electric vehicles, renewable energy, and consumer electronics. The adoption of wide bandgap semiconductors, particularly SiC and GaN, has enabled significant gains in efficiency, miniaturization, and switching speed. GaN devices are ideal for high-frequency, compact designs, while SiC excels in high-power, thermally demanding environments.

Despite these advantages, challenges such as EMI, thermal management, and design complexity remain. Hybrid approaches combining SiC and GaN are increasingly explored to optimize performance across different voltage and power levels. Ongoing research is crucial to fully leverage these technologies for next-generation sustainable energy solutions.

5. Control Techniques

The control of power electronic interface converters plays a vital role in ensuring the efficient and safe operation of micro electric vehicles (MEVs). As discussed in Section 2, MEVs utilizes several types of electric motors, and can employ various control techniques to manage speed and ensure safety. To mitigate these issues, various control techniques are implemented such as PID control, fuzzy logic, sliding mode, and backstepping controls. The control architecture of micro electric vehicles MEVs involves multiple interconnected subsystems to ensure efficient operation and safety. Figure 16 presents a synoptic diagram of this control structure. This diagram provides a visual overview of how various control techniques, including PID, fuzzy logic, sliding mode control, and backstepping, interact to regulate motor speed, manage energy flow, and enhance vehicle stability.

5.1. PID Technique

MEVs benefit from PID controllers, vital for stability and efficiency across various conditions. The PID controller continuously calculates the difference between the desired speed and the mesured speed of the motor. This difference, known as the error (e(t)), is then used to calculate the necessary voltage adjustments. The PID controller can be presented by Equation (17):

$$U\left(t\right)=K_{p}e\left(t\right)+K_i\int e\left(\tau \right)d\tau +K_d{\displaystyle \frac{de\left(t\right)}{dt}}$$

(17)

where U(t) is the control variable (voltage), e(t) is the control error (difference between desired and mesured speed), $K_p$ is the proportional gain, $K_i$ is the integral gain, and $K_d$ is the derivative gain.

The paper [169] presents the design and control system of an innovative foldable MEV designed to address urban transportation challenges and improve parking efficiency. The authors employ a PID control technique to regulate the folding motor, which raises and lowers the rear section of the vehicle. This PID controller operates by continuously calculating the error between the desired and actual positions of the motor, enabling precise and responsive folding motion. In [170], the authors investigate micro-mobility autonomous vehicles by detailing the design and implementation of a comprehensive autonomous system for micro-mobility. They analyze the vehicle’s limitations and explore various strategies to overcome these challenges. The study employs two PID controllers to regulate braking and acceleration, adjusting inputs based on the error between the target speed and the vehicle’s current speed. The authors highlight that their PID controllers incorporate safeguards to prevent wind-up effects, which can occur when there is a significant discrepancy between the desired and actual speeds. To minimize current ripple in a BLDC motor, M. N. Yuniarto et al. in article [171] implemented a two-level cascaded control system, comprising a speed control loop at the first level and a current control loop at the second. Using a PID-based approach, the authors demonstrated that the technique effectively reduced current ripple, making the controller highly suitable for electric scooter applications. The paper [172] examines the impact of PID controllers on the performance and energy efficiency of an electric scooter. The authors demonstrate the advantages of PID control through MATLAB-Simulink simulations and real-world experiments, comparing the scooter’s performance under PID and non-PID control. The results show that with PID control, the temporary speed reduction is less pronounced, and the scooter returns to a steady speed more quickly, enhancing overall stability and responsiveness.

PID controllers are widely utilized for their simplicity and effectiveness, but their performance heavily relies on the accurate tuning of their parameters. Proper tuning is essential to achieve the desired system performance, and a variety of techniques are available, ranging from traditional methods to more advanced approaches.

Moreno-Suarez et al. in article [173] present a Hardware-in-the-Loop (HIL) simulation to assess the performance of linear PID controllers, specifically tuned using Genetic Algorithms (GAs), for controlling a BLDC motor in an electric scooter. In their study, GAs are employed to optimize the PID gains within a two-level cascaded control loop. The results demonstrate that the GA-tuned PID controllers effectively maintained the desired voltage and speed, even under challenging conditions. Notably, the GA-tuned controllers achieved a 20% to 60% reduction in mean squared error (MSE) compared to traditionally tuned PID controllers, highlighting their superior performance.

5.2. Fuzzy Logic Control (FLC) Technique

Fuzzy Logic Control (FLC) has emerged as a powerful alternative to traditional control strategies for MEVs, offering enhanced adaptability and performance in complex and dynamic environments. FLC stands out for its ability to mimic human expertise rather than relying on an exact mathematical model of the system [108]. Unlike PID controllers, FLC excels in managing nonlinearities and uncertainties, making it particularly advantageous in scenarios with variable or unpredictable system dynamics. The FLC achieves this by interpreting inputs, applying linguistic rules, and generating precise outputs. Figure 17 illustrates this process, highlighting how FLC replicates human decision-making to handle control tasks effectively.

In the field of MEVs, FLC has been widely explored in numerous studies. For instance, in [174], the authors present the design and implementation of an FLC system to regulate the complementary torque provided by a servo motor in an electric power-assisted bicycle (Elebike). The system aims to enhance the rider’s experience by seamlessly supplementing their pedaling force to achieve the desired speed and acceleration. While the paper provides a comprehensive overview of the FLC design and methodology, it lacks experimental results or performance data to validate the system’s effectiveness. The authors highlight the system’s adaptability to various riders and terrain conditions, facilitated by adjustments to normalization factors, partitioning, and membership functions. Additionally, they emphasize the use of a dynamic bike model for realistic simulations. However, the absence of concrete results leaves the practical performance of the FLC system open to further investigation.

The article [175] proposes a fuzzy logic-optimized threshold-based energy management strategy (EMS) for a fuel cell hybrid E-bike. The authors highlight that E-bikes have a simple structure and compact size, making the rule-based EMS easy to implement and highly effective for this application. Simulations were conducted to evaluate the performance of the proposed EMS, with results compared to a deterministic rule-based strategy. The authors conclude that the fuzzy logic-optimized threshold-based EMS is a promising approach for enhancing the performance of fuel cell hybrid E-bikes.

The authors of [176] investigate the application of FLC to develop a solar-powered e-bike battery charging system, with a primary focus on maintaining a stable charging current. The FLC is implemented on an ATmega8535 microcontroller, demonstrating its suitability for embedded systems in e-bike technology. The study showcases the successful application of FLC in achieving a stable charging current, ensuring the battery is charged safely and efficiently. Furthermore, the optimized controller performance highlights opportunities for further advancements, such as reducing charging time and improving energy efficiency. This work underscores the potential of FLC for fine-tuning and optimizing various aspects of e-bike operation.

Sohrab Ebnealipour et al. in article [177] propose a FLC approach to energy management in e-bikes, focusing on optimizing the interaction between human and electric power sources. The study aims to design a smart switch controller that dynamically manages the transition between these power sources to conserve battery charge while accounting for the rider’s physical exertion and fatigue. The research introduces an adaptive power source switching mechanism that minimizes a cost function by balancing battery usage with calorie burning. Extensive simulations are performed using various driving cycles to evaluate the controller’s performance under different conditions, demonstrating its adaptability and robustness. The results highlight the effectiveness of the optimized switch controller, showing a significant reduction in battery usage while maintaining calorie burning within acceptable limits for the rider. This underscores the success of the FLC strategy in achieving energy efficiency and rider comfort.

The paper [47] compares different control methods for e-bikes using a BLDC motor. The researchers wanted to find the method that produced the best transient response, which refers to how the system behaves when it changes from one state to another. The researchers concluded that combining intelligent control methods like FLC with optimization algorithms can significantly improve the transient response of BLDC motors in e-bikes.The paper [178] focuses on improving the performance of a DC motor drive. It explores the advantages of using cascaded current control within a variable speed control system. The researchers designed and implemented two different control systems, testing each in a closed-loop configuration and a cascaded current control configuration. The results demonstrated that adding cascaded current control significantly reduced speed ripple, regardless of whether a Fuzzy Logic or PI controller was used for speed control. Specifically, FLC reduced speed ripple by 70%. However, PI cascaded control reduced speed ripple by 53%. The [179] describes the design and implementation of an Electronic Differential System (EDS) for an e-scooter using a fuzzy FLC for speed control. The EDS is responsible for controlling the speed of each drive wheel independently, enabling the scooter to navigate curves more effectively. The goal of the research is to demonstrate the superior performance of the FLC compared to a conventional PI controller. The EDS is simulated on a road with straight segments and left and right curves, mimicking real-world driving conditions. The results demonstrate the superiority of the FLC in achieving faster rise time, shorter settling time, and minimal overshoot compared to the PI controller, especially when the scooter is subject to disturbances like curves or inclines. The FLC effectively manages the variations in load on the motor during these maneuvers, ensuring smoother and more responsive speed control.

5.3. Sliding Mode Control

The sliding mode control (SMC) is a famous control algorithm, especially in the field of electric drives and motors. It is a variable structure nonlinear control approach, that is discontinuous and is characterized by accuracy, robustness, and ease of implementation and tuning [180]. This control combines two main steps:

• Designing a sliding hyper-surface s so that the design specifications are met;
• Selecting a suitable control law u that guarantees that the system state will be attracted to the sliding surface.

The main goal of the control approach described is to make the reference signal (speed, position …) track the desired value in a way that makes the error e its derivatives tend toward zero. The control input u for the second-order sliding mode is presented in Equation (18):

$$u=-k\mathrm{sgn}\left(s\right)$$

(18)

where u is the control input, k is the constant parameter, s is the switching function and sgn(·) is the sign function.

A chattering phenomenon can happen in cases of high-frequency oscillations. This is due to the high-frequency switching between functions and can negatively affect the performance of the system. To mitigate this, the control law can be modified to Equation (19):

$$u=-k\mathrm{sat}(s/\psi )$$

(19)

where $\psi$ is defined as the thickness of the boundary layer around s, and $\mathrm{sat}(s/\psi )$ is a saturation function defined as:

$$\mathrm{sat}(s/\psi )=\left\{\begin{array}{cc}{\displaystyle \frac{s}{\psi }}\hfill & \mathrm{if}\left|{\displaystyle \frac{s}{\psi }}\right|\le 1,\hfill \\ \mathrm{sgn}(s/\psi )\hfill & \mathrm{if}\left|{\displaystyle \frac{s}{\psi }}\right|>1.\hfill \end{array}\right.$$

This control law guarantees that all of the trajectories of the system will be attracted toward the sliding surface, s = 0, and settle there from any initial condition. It is important to note that the control law must meet the reaching Lyapunov stability condition:

$$s\dot{s}<0$$

(20)

SMC has received limited attention in the field of MEVs, despite its well-established robustness and ability to handle nonlinearities and system uncertainties. In [180]. The authors propose a method for designing two adaptive SMC to achieve self-balancing and yaw control for a two-wheeled human transportation vehicle (HTV). These controllers are developed based on the vehicle’s nonlinear mathematical model. The self-balancing controller employs the sliding mode technique to regulate the HTV’s angular position, ensuring it remains upright. To validate their approach, the researchers conducted both simulations and experiments, evaluating the performance of the proposed SMC laws under various scenarios and terrains. The results demonstrate that the HTV can be successfully ridden while maintaining stability, with the controller achieving the desired angle and yaw values rapidly and effectively. Some improvements to the SMC are covered in [35]. The study introduces Integral Sliding Mode Control (ISMC) as one of the proposed control strategies. ISMC, an advanced variant of SMC, is recognized for its fast dynamic response and robustness against external disturbances. The results demonstrate that ISMC outperforms other controllers by maintaining a speed closer to the desired 20 km/h, even in the presence of disturbances. Additionally, the authors highlight the ISMC’s ability to provide a consistent and reliable speed range under varying operating conditions. The combination of SMC with other techniques has been widely explored in the literature. For instance, Ref. [181] integrates SMC with FLC to control the torque of a BLDC motor located in the front wheel of a hybrid bicycle, while [182] employs a Genetic Algorithm (GA) alongside SMC for the control of a surface-mounted permanent magnet (SPM) motor used in an e-bike application. These hybrid approaches capitalize on SMC’s robustness in managing nonlinearities and uncertainties while leveraging the adaptability of FLC and the optimization capabilities of GA to further enhance system performance. Both studies demonstrate the ability of these control systems to maintain stable operation under varying conditions. Specifically, Ref. [181] highlights effective control of terrain and speed changes, with the system responding appropriately to pedaling variations. Similarly, Ref. [182] showcases stable speed control and adaptability, even in challenging environments, underscoring the effectiveness of these hybrid strategies in EMM applications.

5.4. Model Predictive Control

Model Predictive Control (MPC) is an advanced control strategy that uses a process model to predict and optimize the future behavior of a controlled system [183]. Over the past three decades, MPC has gained significant attention in both academia and industry due to its versatility and effectiveness. This method directly utilizes an explicit and identifiable model, enabling precise control. MPC is particularly recognized for its ability to deliver high-performance electric drive systems and is considered more reliable than traditional approaches such as Field-Oriented Control (FOC) and Direct Torque Control (DTC) [184]. While MPC offers high performance, flexibility, and robust handling of system constraints, it does have some limitations, including higher current ripples and a variable switching frequency. An overview of MPC is illustrated in Figure 18.

This review highlights the role of MPC in enhancing the stability and performance of light vehicles in urban networks.For instance, studies [185,186] investigate the application of MPC in stabilizing and controlling bicycle systems. These efforts aim to reduce rider effort and improve stability, particularly during cornering, by accounting for centrifugal forces. MPC’s strength lies in its ability to handle system constraints while optimizing control inputs for dynamic and complex scenarios, making it a vital tool in improving safety and performance in such applications. In article [187] introduces a novel battery management approach for pedal-electric bicycles (Pedelecs) utilizing a nonlinear Model Predictive Controller (NMPC). The main objective is to optimize motor assistance levels to maintain a user-defined state of charge (SoC) by the end of a route, while simultaneously minimizing cyclist fatigue. This is achieved by factoring in route characteristics, such as elevation profiles, and cyclist-specific parameters. The NMPC was implemented on a BeagleBone^® Blue equipped with a GPS module. The system successfully meets the target SoC, though with a slight trade-off in increased cyclist fatigue during steep climbs. Additionally, the results demonstrate adaptability to riders of varying masses by appropriately adjusting the model parameter for total mass. In [188], the authors propose a novel method for controlling the speed of cargo e-bikes by leveraging a Model Predictive Controller (MPC) integrated with a battery thermal lumped model. This innovative approach not only enhances motor control but also accounts for the battery’s thermal conditions to ensure efficient and safe operation. The models are validated using experimental data, and the study examines the impact of various MPC parameters, such as weight factors and prediction horizons, on the performance of the control system. The results demonstrate that the proposed control model effectively maintains the desired rotational speed while ensuring the battery surface temperature remains within safe and optimal limits.

5.5. Discussion

The control of power electronic interface converters in MEVs is essential for efficiency, safety, and performance, utilizing techniques like PID, fuzzy logic, sliding mode, and model predictive control (MPC). PID controllers are widely applied for their simplicity and effectiveness but require precise tuning for optimal results, with advanced methods like Genetic Algorithms enhancing their performance. FLC excels in handling nonlinearities and uncertainties by mimicking human expertise, making it ideal for dynamic scenarios such as energy management. SMC provides robust control for nonlinear systems, addressing challenges like self-balancing and speed regulation, though hybrid methods are often needed to mitigate chattering effects. MPC, known for optimizing multivariable systems under constraints, has shown great potential in battery management and vehicle stability. Hybrid approaches combining these techniques leverage their strengths, offering innovative solutions for complex MEV control challenges.

6. Conclusions

EMM represents a promising avenue for addressing urban transportation challenges by offering compact, efficient, and environmentally friendly mobility solutions. This study underscores the critical role of individual components—electric motors, batteries, power converters, and advanced control techniques in shaping the performance, safety, and adaptability of EMM systems. However, it is the integration and optimization of these components that ultimately determine the success of micromobility solutions.

Despite the fragmented and limited availability of technical and conceptual literature on EMM, this review consolidates insights from over 190 studies to provide a roadmap for advancing this field. Future research should focus on refining control algorithms, improving energy storage technologies, and enhancing component synergy to address existing challenges such as scalability, cost-efficiency, and environmental impact. By leveraging advances in technology and innovative design paradigms, EMM systems can unlock their full potential, fostering safer, more sustainable, and efficient urban transportation ecosystems. Ultimately, EMM holds the promise of paving the way for greener and more accessible urban mobility landscapes, transforming the way we navigate cities.