Research on Micro-Mobility with a Focus on Electric Scooters within Smart Cities

: In the context of the COVID-19 pandemic, an increasing number of people prefer individual single-track vehicles for urban transport. Long-range super-lightweight small electric vehicles are preferred due to the rising cost of electricity. It is difﬁcult for new researchers and experts to obtain information on the current state of solutions in addressing the issues described within the Smart Cities platform. The research on the current state of the development of long-range super-lightweight small electric vehicles for intergenerational urban E-mobility using intelligent infrastructure within Smart Cities was carried out with the prospect of using the information learned in a pilot study. The study will be applied to resolving the trafﬁc service of the Poruba city district within the statutory city of Ostrava in the Czech Republic. The main reason for choosing this urban district is the fact that it has the largest concentration of secondary schools and is the seat of the VŠB-Technical University of Ostrava. The project investigators see secondary and university students as the main target group of users of micro-mobility devices based on super-lightweight and small electric vehicles.


Introduction
The main objective of the article is to investigate the current state of the research, analysis, and design of a solution for the maximally efficient and comprehensive concept of urban E-mobility based on small lightweight passenger vehicles (including the possibility of transporting smaller loads). This would bring a qualitatively new level both in terms of the design and in terms the parameters of the vehicles themselves and, at the same time, in terms of their operation, charging, and management.
With the transition to the urban Micro-Mobility (MM) model in Smart-Cities (SCs)-as-aservice with sharing systems, Personal Lightweight Electric Vehicles (PLEVs) are becoming a popular means of transport in cities [1]. Micro-mobility affects first-and last-mile travel in urban areas [2]. In the aftermath of the emergency caused by the COVID-19 pandemic, it has become clear that urban mobility plans need to be modified to reduce the use of public transport and the crowding of people in traffic and, at the same time, avoid traffic congestion by, among other things, encouraging urban residents to stop using private motor vehicles. From this perspective, a reorganization of cities (e.g., Milan) is recommended, both in view of unpredictable environmental sustainability requirements and new mobility needs that require the promotion of bicycles and PLEVs, e.g., electric scooters [3]. PLEVs are a phenomenon that can be currently observed in cities and are intended to be an environmentally friendly form of transport. Analyses conducted show that the dynamic growth of PLEVs in cities will result in an increased demand for electricity distribution, which cities that are developing according to the concept of sustainable development must take into account [4]. Ciociola  A number of questions were identified for this systematic review:

Search Process
The Web of Science scientific database [13] was used for the search. The search process started on 16 March 2022 and ended on 4 July 2022. The search results were stored in the Web of Science database, and the selected publications were uploaded and imported into the Endnote online reference manager. The main search keywords were "Smart Cities", "micro-mobility", "electric vehicle charging while driving", "charger for electric scooter", "management and sharing of electric scooters", and "E-mobility within Smart City (Smart Home)".

Inclusion and Exclusion Criteria
In order to refine the search and select relevant literature, inclusion and exclusion criteria were used in the search (Table 1). Table 1. Inclusion and exclusion criteria.

IC1
The publication contains the keywords "Mobility", "Smart Cities", and "Micro-mobility" in the title, abstract, and topic.
No match with the described criteria, duplicate publication.

IC2
Articles that contain the keywords and match the keywords "charger for electric scooter".
No match with the described criteria, duplicate publication.

IC3
Articles that contain the keywords and match the keywords "management and sharing electric scooter" and "E-mobility within Smart City (Smart Home)".
No match with the described criteria, duplicate publication.

IC4
The publication contains the keywords "electric vehicle charging while driving" and "Smart Cities".
No match with the described criteria, duplicate publication.

IC5
The publication contains the keywords "electric mobility", "Smart Home", and "Smart Cities" in the title, abstract, and topic.
No match with the described criteria, duplicate publication.

Study Selection
The criteria for article selection included a review of the document title, abstract, and skimming of the article. In addition, the inclusion and exclusion criteria were also used in accordance with the PRISMA flowchart ( Figure 1). topic. duplicate publication.

Study Selection
The criteria for article selection included a review of the document title, abstract, and skimming of the article. In addition, the inclusion and exclusion criteria were also used in accordance with the PRISMA flowchart ( Figure 1). Specifically, 3693 publications were found for the keywords "Smart Cities mobility". After specifying "electric mobility", 712 publications were found. After entering the keywords "micro-mobility", 63 publications were chosen, from which 38 publications (Table 2) were selected for the study of the "Smart Cities micro-mobility" topic.  Specifically, 3693 publications were found for the keywords "Smart Cities mobility". After specifying "electric mobility", 712 publications were found. After entering the keywords "micro-mobility", 63 publications were chosen, from which 38 publications (Table 2) were selected for the study of the "Smart Cities micro-mobility" topic.

4.
Management and sharing of electric scooters. 5.
E-mobility within Smart Cities (Smart Homes).
For each of the identified topics, the necessity to address the needs and individual requirements of the above-described area is described in the context of the current state of the solution in the development of super-lightweight small electric vehicles with a long range for intergenerational urban E-mobility concepts within Smart Cities. Furthermore, a table was created in the text-an overview of the tasks addressed within each topic in the development of super-lightweight small electric vehicles with a long range for intergenerational urban E-mobility concepts within Smart Cities.
The data obtained from the selected literature were tabulated in Excel according to the following structure:

Risk of Bias
The risk of the distortion of the objective information obtained from the retrieved publications may be influenced by the specified years of the selected literature in the interval between 2008 and 2022. Another possible factor influencing the distortion is the subjective view of the authors of the article on the area addressed and the chosen selection process. Last but not least, the effect of the bias of selecting literature only from the Web of Knowledge scientific database should also be mentioned.

Results
This section presents the results from the data collected from RQ1 to RQ5 listed in Section 2. RQ1 and RQ2 provide a general perspective on the issues analyzed. The focus on the analyzed area of technological solutions and innovations for the development of the charger for electric scooters and management and sharing of electric scooter concepts is established by RQ3 and RQ4. The context of E-mobility solutions within Smart Homes in Smart Cities is asked by question RQ5.
In the following text, the technical terms PHEV, PLEV, BEV, EV, and HEV are used: "PHEV-Plug-in Hybrid Electric Vehicle", "PLEV-Personal Light Electric Vehicle", "BEV-Battery Electric Vehicle", "EV-Electric Vehicle ", "HEV-Hybrid Electric Vehicle". Electric Vehicles (EVs) move using an electric motor instead of using an Internal Combustion Engine (ICE). Electric vehicles require a charging port and outlet to charge their batteries fully (BEVs). In other vehicles, such as conventional hybrids (HEVs), the engine requires both fuel and electricity to run. This is the same for Plug-in Hybrid Electric Vehicles (PHEVs) [14].

Smart Cities and Micro-Mobility
Interest in publishing on the topic of Smart Cities and micro-mobility according to the number of publications began in 2011. The highest number of publications, 12, was reached in 2019 ( Figure 2). "BEV-Battery Electric Vehicle", "EV-Electric Vehicle ", "HEV-Hybrid Electric Vehicle". Electric Vehicles (EVs) move using an electric motor instead of using an Internal Combustion Engine (ICE). Electric vehicles require a charging port and outlet to charge their batteries fully (BEVs). In other vehicles, such as conventional hybrids (HEVs), the engine requires both fuel and electricity to run. This is the same for Plug-in Hybrid Electric Vehicles (PHEVs) [14].

Smart Cities and Micro-Mobility
Interest in publishing on the topic of Smart Cities and micro-mobility according to the number of publications began in 2011. The highest number of publications, 12, was reached in 2019 ( Figure 2). The total number of 63 publications covering the research area that focus on the described topic includes, among others, the following disciplines: Computer Science, Engineering, Transportation, Telecommunications, Environmental Sciences Ecology, Science Technology Other Topics, and others (Table 3).  The total number of 63 publications covering the research area that focus on the described topic includes, among others, the following disciplines: Computer Science, Engineering, Transportation, Telecommunications, Environmental Sciences Ecology, Science Technology Other Topics, and others (Table 3). In terms of affiliations, the topic is, among others, addressed by: Delft University of Technology, Universidad de Malaga, Czech Technical University Prague, Enzo Ferrari Engn Dept, Univ Nacl Patagonia Austral, Universidad Publica de Navarra (Table 4). Countries/regions that support research on the topic include Spain, the USA, Germany, Italy, England, Mexico, The Netherlands, and others (Table 5). An overview of the solved tasks within the topic "Smart Cities and micro-mobility" is as follows: •

Smart Cities and Electric Vehicle Charging while Driving
The first article on Smart Cities and electric vehicle charging while driving was published in 2011. The number of publications peaked in 2020 ( Figure 3).

•
The use of MM operation to improve the environment in SCs [37]; • Data collection from sensors deployed in the urban environment in SCs [38]; • Resolving the problem of traffic loads using MM in SCs [39]; • OSU SMOOTH in a Smart City [40]; • The use of MM to support the independent life of disabled persons or seniors in SCs or SHs [41,42].
• Two-Layer Model Predictive Battery Thermal and Energy Management Optimization for Connected and Automated Electric Vehicles [43];

Smart Cities and Electric Vehicle Charging while Driving
The first article on Smart Cities and electric vehicle charging while driving was published in 2011. The number of publications peaked in 2020 ( Figure 3).  Out of the total number of 108 publications covering the research area that focuses on the described topic are included the areas: Engineering, Computer Science, Energy Fuels, Transportation, Telecommunications, Science Technology Other Topics, Environmental Sciences Ecology, Automation Control Systems (Table 6). In terms of affiliations, the topic is, among others, addressed by the United States Department of Energy, Polytechnic University of Turin, University of Zagreb, American University of Sharjah, Chinese Academy of Sciences, Concordia University Canada (Table 7). Countries/regions that support research on the topic include the People's Republic China, the USA, India, Italy, Canada, England, Japan, and South Korea (Table 8).  Figure 4 shows the keywords of selected publications for the topic of Smart Cities and electric vehicle charging while driving.
The automotive industry is currently shifting from traditional fossil fuels to electrification. There is a growing need in the EV industry to provide new infrastructures, services, tools, and solutions to support the use of EVs [44]. It is expected that future EVs will increasingly be able to use a connected driving environment for efficient, comfortable, and safe driving. Due to the relatively slow dynamics associated with the state of charge and temperature response in large battery-electrified vehicles, a long prediction/planning horizon is required to achieve better energy efficiency benefits [45]. The deployment of a Battery Management System (BMS) unit is a key element for monitoring the battery status of an electric car. In turn, the development and assessment of electric vehicle models form the basis for BMS design, as it provides a fast and inexpensive solution for testing optimal battery control logic in the Loop software environment [46]. In modern Smart Cities, mobility is based on EVs and it is considered a key factor in reducing carbon emissions and pollution. However, despite worldwide interest and investment, user adoption is still low, mainly due to a lack of support for charging services [47]. battery control logic in the Loop software environment [46]. In modern Smart Cities, mobility is based on EVs and it is considered a key factor in reducing carbon emissions and pollution. However, despite worldwide interest and investment, user adoption is still low, mainly due to a lack of support for charging services [47].  Lithium ion batteries play a key role in powering electric cars. The battery's Remaining Useful Life (RUL) is fundamentally important to ensure the safety and reliability of vehicles. Due to the complex aging mechanism, RUL prediction for Battery Management Systems (BMSs) is challenging [49]. An accurate prediction of the Remaining Available Energy (E-RAE) of lithium ion batteries is still a challenging problem for electric vehicles, which is of fundamental importance for predicting the remaining range of EVs [50]. Solid and Liquid Electrolyte Lithium ion Batteries (SLELBs) have good commercial applicability in electric vehicles because they combine the safety of solid electrolyte lithium ion batteries with the high ionic conductivity of Liquid Electrolyte Lithium ion Batteries (LELBs) [20]. Feng et al. presented a plug-in hybrid electric vehicle supervisory control strategy based on energy demand prediction and route preview with an adaptive fuel Consumption Minimization Strategy (ECMS) in real-time operation using neural networks [51]. For the implementation of the electrochemical-thermal model in the BMS of electric cars, Gao et al. introduced a control-oriented electrochemical model of lithium ion batteries and its realtime implementation in the EV BMS by simplifying partial differential equations using the Laplace transform and Pade approximation [52].
Gong et al. focused on the study of the performance of Lithium ion (Li ion) batteries depending on the ambient temperature with respect to the available range of electric cars (electrochemical impedance spectroscopy test, dynamic driving plan test, and others) for the prediction of the available range and also for the development of vehicle control for electric cars and PHEVs [53]. Gozukucuk et al. used the Monte Carlo method to probabilistically predict the optimal energy to reach a given route obtained from Google Maps incorporating location and road topology [54]. Guo et al. developed an energy consumption control strategy for an extended-range electric vehicle based on a predictive control model [55]. In order to accurately predict the SOC value of an electric vehicle, He et al. developed a battery state space model suitable for pure EVs [56]. Hoekstra et al. proposed a real-time active cell balancing strategy derived from model-based predictive control [57]. Hu et al. proposed an optimization control strategy for a parallel hybrid electric vehicle with multiple excitation sources to improve torsional stability using the established simplified two-mass nonlinear dynamic model of the HEV powertrain [58]. To improve the energy efficiency and adaptability of pure EV driving conditions in a complex traffic environment, simulation conditions that can dynamically update traffic information based on measured data have been proposed [59]. Huber et al. resolved the problem of the shortest path with a constraint that makes it possible to treat uncertainty-especially the risks resulting from imperfect predictions of energy consumption using an energy reserve from a certain part of the battery capacity provided as such an energy reserve [60]. Jin et al. proposed the parameter estimation of an electrochemistry-based lithium ion battery model using a two-step procedure and parameter sensitivity analysis [61]. Lee et al. presented an adaptive optimal control strategy for SOC balancing based on Pontryagin's minimum principle that can be applied to real vehicles and does not require the prediction of driving patterns using an adaptive SOC balancing concept [62]. Li et al. provided an electrochemical model of a lithium ion battery with variable solid-state diffusion and the identification of parameters over a wide temperature range [63]. To increase the accuracy of E-RDE, Liu et al. introduced a Battery Energy (EB) prediction method based on predictive control theory, in which the combined prediction of the future change of the battery state, the change of the battery model parameters, and the voltage response is implemented on the E-RDE prediction horizon, and the E-RDE is then accumulated and optimized in real-time [64]. Falai et al. focused on assessing the performance and electric range of a two-wheel pure electric scooter in a real driving cycle, where the drive system of the device includes a set of LIBs with an Electric Energy Storage System (EESS) [46].
An overview of the solved tasks within the topic "Smart Cities and Electric Vehicles driving and charging" is given as follows: • Thermal and energy management of Electric Vehicle (EV) batteries to ensure consumption savings through real-time prediction and optimization [45]; • Planning EV mobility routes also on medium and long routes [47]; • Indication of battery degradation using information obtained from discharge voltage on energy from voltage signals to reveal degradation characteristics [49]

Chargers for Electric Scooters
According to the number of publications, the interest in the charger topic for electric scooters is increasing ( Figure 5). The largest number of publications so far was in 2021.
The total number of 28 publications covering the research area that focus on the described topic include the areas: Engineering, Energy Fuels, Computer Science, Science Technology, Telecommunications, Transportation, and others (Table 9).

•
Use of ANN for PHEV energy prediction within the AER adaptive control strategy [78];

Chargers for Electric Scooters
According to the number of publications, the interest in the charger topic for electric scooters is increasing ( Figure 5). The largest number of publications so far was in 2021. The total number of 28 publications covering the research area that focus on the described topic include the areas: Engineering, Energy Fuels, Computer Science, Science Technology, Telecommunications, Transportation, and others (Table 9).   In terms of affiliations, the topic is, among others, addressed by the Osaka Institute of Technology, Imperial College London, Polytechnic University of Turin, Universiti Malaya, Fuzhou University, King Saud University, and others (Table 10). Countries/regions that support research on the topic include Italy, Japan, England, Malaysia, Taiwan, Australia, the Czech Republic, India, and others (Table 11) (SS-WPT) based on a current converter to achieve higher coupling separation, higher power transfer efficiency, and higher misalignment tolerance than conventional WPT designs with a transmission efficiency of 94% at a 200 mm link distance [83]. Kaneko et al. focused on an electric scooter powered by a powerful electronic motor and its associated new EDLC [84][85][86]. Kwan et al. presented a wireless charging solution for 600 W electric scooters operating at a frequency of 6.78 MHz with a battery charging efficiency of 84% [87] and 65.5% [88]. In order to realize emission-free solutions and clean transportation alternatives, Lin presented a novel frequency-controlled pulsed-DC converter for EVs' or Light Electric Vehicles' (LEVs) battery chargers [89]. Martinez-Navarro et al. designed, built, and commissioned a sustainable e-scooter charging dock using photovoltaic panels and a battery system in Valencia, Spain [90]. Masoud   An overview of the solved tasks within the topic "Chargers for Electric Scooter" is given as follows: • Solution to the design and implementation of a fast charger with high efficiency for lead acid batteries [106].

Management and Sharing of Electric Scooters
Interest in publishing on the management and sharing of electric scooters topic started in 2012. The largest number of publications produced so far was in 2021 ( Figure 6).

Management and Sharing of Electric Scooters
Interest in publishing on the management and sharing of electric scooters topic started in 2012. The largest number of publications produced so far was in 2021 ( Figure  6). The total number of 21 publications covering the research area that focus on the described topic include the areas: Transportation, Business Economics, Computer Science, Environmental Sciences Ecology, Telecommunications, and others (Table 12). The total number of 21 publications covering the research area that focus on the described topic include the areas: Transportation, Business Economics, Computer Science, Environmental Sciences Ecology, Telecommunications, and others (Table 12).  Countries/regions that support research on the topic include the USA, Taiwan, Germany, Italy, the People's Republic of China, Poland, Sweden, and Australia (Table 14). Current mobility trends suggest that the popularity of private cars will decline in the near future. One of the reasons for this development is the proliferation of mobility services such as car or bicycle sharing or Mobility-packages-as-a-Service (MaaS) [107]. Electric micromobility is becoming increasingly popular as an urban transport option. It could help reduce transport externalities and provide last-mile solutions and complementary ways to access public transport [108]. Micro-mobility can alleviate many of the problems facing large cities today and offer a path to more sustainable urban transport [109]. Micro-mobility is shaping first-and last-mile journeys in urban areas. Recently, shared dockless electric scooters (e-scooters) have emerged in major cities as an everyday alternative to driving for short-distance commuters due to their affordability, ease of app accessibility, and zero emissions [110]. On-demand mobility services such as bike-sharing, scooter-sharing, and transportation network companies (TNCs, also known as ride-sourcing and ride-hailing) are changing the way people travel by providing dynamic on-demand mobility that can complement public transport and passenger car use [111]. Electric scooter (e-scooter) sharing services have redefined the concept of urban transport and urban development around the world. However, these services have raised concerns about information privacy due to allegations that they are used by companies and even government agencies to collect information [112]. Through the IoT and cloud computing technologies, the marketing and availability of electric motorcycles can be increased [113]. It is also necessary to ensure the management of traffic and traffic lights in connection with the operation of EC [114].
Scorrano et al. found that the purchase price, fuel consumption, annual road tax, insurance costs, range, motor power, country of manufacture, and replaceable battery were consistently statistically significant across different specifications in the application of ES operation [115]. Other important parameters for the wider use of ESs are providing an increase in the lifetime of products, ensuring the exchangeability of batteries, alternative logistics of data collection, as well as ensuring a suitable charging concept that takes into account the protection of the environment [116]. Another important factor in ES operation is the provision of information on the interactions of ES operations with existing transport systems, such as modeling the interaction of e-scooters and bus transit services [117].
An overview of the solved tasks within the topic "Management and sharing electric scooter" is given as follows: • Car or bicycle sharing or mobility-packages-as-a-service [ Measuring the quality of shared mobility [124]; Visualization, analysis, and comparison of the impacts of Smart City policies based on innovative mobility concepts in urban areas [125];

E-Mobility within Smart Cities (Smart Homes)
Interest in publishing on the topic of E-mobility within Smart Cities (Smart Homes) began in 2015. The largest number of publications so far was in 2018 (Figure 7). • Measuring the quality of shared mobility [124];Visualization, analysis, and comparison of the impacts of Smart City policies based on innovative mobility concepts in urban areas [125];

E-Mobility within Smart Cities (Smart Homes)
Interest in publishing on the topic of E-mobility within Smart Cities (Smart Homes) began in 2015. The largest number of publications so far was in 2018 (Figure 7). The total number of 24 publications covering the research area that focus on the described topic include the areas: Computer Science, Engineering, Energy Fuels, Telecommunications, Automation Control Systems, and others (Table 15). The total number of 24 publications covering the research area that focus on the described topic include the areas: Computer Science, Engineering, Energy Fuels, Telecommunications, Automation Control Systems, and others (Table 15). In terms of affiliations, the topic is, among others, addressed by the Eindhoven University of Technology, Ku Leuven, Abdus Salam International Center for Theoretical Physics ICTP, AGH University of Science Technology, Alma Digit Res Labs, Beijing Jiaotong University, and others (Table 16). Countries/regions that support research in the given topic include, among others: People's Republic of China, the USA, India, Italy, The Netherlands, Belgium, and others (Table 17). Ideas about what the cities of the future will look like are currently met by the Smart Cities concept, which is an effective platform that ensures the optimization of resources and services through monitoring and communication technologies [126,127]. By 2030, the number of urban areas is expected to triple, housing 60% of the world's population [128]. The Internet of Things (IoT) is expected to provide the basic infrastructure for Smart Cities and make ICT a technology to address major challenges related to climate change, energy efficiency, mobility, and future services [129]. Urban mobility is a multidimensional characteristic of cities, which is seen as layers of interconnected infrastructures, places, people, and information. Therefore, the study of networks such as electrical and transportation systems should go beyond the individual network and connect with other networks in the SC [130].
There are six possible tools to make urban mobility more environmentally friendly: stricter rules for urban transport policy solutions, taking advantage of shared vehicles, improving the urban fabric, proposing alternatives to car use, new financial resources to change driver behavior, and a business model for those cities that want to have a green image [131]. To resolve poor air quality and greenhouse gas emissions in cities, it is proposed that the use of electric cars, electric bicycles, and electric scooters be established [132]. For that reason, it is necessary to focus on new methods of electricity production and storage (photovoltaics, fuel cells, battery storage systems) [133] using the current structure of SH solutions within building automation [134]. With the transition to the Internet of Things (IoT), there is a significant increase in stationary and mobile IoT sensing and computing devices that continuously generate a huge amount of contextual information [135]. Based on the obtained data, it is possible to more easily provide information modeling the behavior of residents on the scale of Smart Cities [136]. The emerging paradigm of the Internet of Things is based on intelligent objects that will be able to communicate with the surrounding environment using ubiquitous connectivity [137]. An overview of the solved tasks within the topic "E-mobility within Smart Cities (Smart Home)" is given as follows: • Secure data collection using the IoT within SCs [128,138] [141,142].

Discussion
The main objective of the article was to investigate the current state of research, analysis, and design of a solution for the maximally efficient and comprehensive concept of urban E-mobility to bring a qualitatively new level both in terms of the design and in terms of the parameters of the ESs themselves and, at the same time, in terms of their operation, charging, and management within SCs.
The aim of the systematic review was to determine the possible solutions for the development of long-range super-lightweight small electric vehicles for intergenerational urban E-mobility in the SC concept. The steps to determine the current status of the solution were to analyze the requirements and solutions needed for the development of the: "MM concept in SC, the concept of EV charging while driving, the ES charger concept, the management and sharing of the ES concept, of the concept of E-mobility within SC (SH)".
A number of questions were identified for this systematic review: "RQ1 What technological solutions and innovations can be used to develop the concept of micro-mobility in Smart Cities?", "RQ2 What technological solutions and innovations can be used for the development of the concept of electric vehicle charging while driving?", "RQ3 What technological solutions and innovations can be used to develop the electric scooter charger concept?", "RQ4 What technological solutions and innovations can be used to develop the management and sharing of the electric scooter concepts?", "RQ5 What technological solutions and innovations can be used to develop the E-mobility within Smart Cities (Smart Home) concept?".
The PRISMA studies and the Kofod-Petersen method were used to extract useful information from the presented systematic review.
To answer RQ1 related to the solution of the "Smart Cities and micro-mobility" concept requires addressing the following needs, which are listed in Table 18. The most mentioned tasks for solving are "traffic regulations, public safety, PLEVs parking", "MM and PLEV shared micro-mobility", "PLEVs analysis for urban public transport", "IoT, LoRaWAN, SC", "connection of MM with traffic load in SC and configuration and provision of SC infrastructure", and "support for seniors and disabled people for an active life with the help of MM". Table 18. Overview-topics addressed within the "SC and MM" concept.

Topic of the Article Reference Number Observations
Battery charging MPC of Electric Vehicles (EVs) in RT [45,69] Battery, EV, MPC, HESS Battery State Of Charge (SOC) estimation optimization model [65,66,70] SOC, HEV, OML-SOCE, Fast charging optimization to maximize battery cycle life [67] ML, fast charging Prediction, EV range optimization [47,68] EV, range Indication of battery degradation during battery charging [ To answer RQ3 related to the solution of the "Chargers for Electric Scooter" concept requires addressing the following needs, which are listed in Table 20. The most mentioned tasks are "Wireless Power Transfer (WPT) using Wireless Charger", "EDLC charger solution (Electric Double Layer Capacitor)", "Optimization of MESN solutions for ES", "Design and solution of BC for ES", and "FC with high efficiency for LAB".
To answer RQ4 related to the solution of the "Management and sharing of electric scooters" concept requires addressing the following needs, which are listed in Table 21. The most mentioned tasks are "The connection between EC operation and environmental protection", "Elimination of accidents and injuries", and "EC traffic problems, traffic regulations". Table 20. Overview-resolved topics within the "Chargers for ES" concept.

Topic of the Article Reference Number Observations
Car or bicycle sharing or mobility-packages-as-a-service [107] ES, sharing, MaaS Last-mile operation solutions from the point of view of the private operator and from the point of view of the user [108] ES, mobility, sharing Use of hybrid energy systems of fuel cells and batteries with increased efficiency [118] ES, electric batteries The connection between EC operation and environmental protection [119,120] ES, sharing, environment Sustainability of urban transport in connection with the operation of ESs [109] ES, sustainability of transport EC traffic problems, traffic regulations, public safety, parking regulations, liability issues, accident, and injury analysis [110] ES, rules of the road Elimination of accidents and injuries [121,122] ES, elimination of injuries Monitoring of e-scooter routes during goods delivery [123] ES, route monitoring ES shared mobility quality measurement, MMQUAL [124] ES, shared mobility quality Ensuring ES operation flexibility and affordability [111] ES, TNC, flexibility, price EC operation, IT development, data protection [112] ES, IT, data secure Ensuring safety risk analysis of ES operation [122] ES, safety, analysis Ensuring visualization, analysis, and comparison of the impacts of SC policies based on innovative mobility concepts in urban areas [125] ES, visualization, traffic impact analysis Ensuring service and maintenance of batteries and charging stations for ESs [113] ES, service and maintenance, IoT, cloud computing Ensuring the control of operation, traffic, and traffic lights [114] ES, traffic control Provision of analysis of possibilities for expansion of ES operation [115] ES, expansion of operation, impact analysis Ensuring the analysis of the impact of ES operations on the improvement of the environment [116] ES, operation, environment Providing a solution to the problem of modeling the interaction of e-scooters and bus transit services. [117] ES, interaction with the operation of electric buses To answer RQ5 related to the solution to the development of the concept of "E-mobility within Smart Cities (Smart Homes)" requires addressing the following needs, which are listed in Table 22 The most mentioned tasks are "Secure data collection using IoT within SC", "Optimization of resources and services through monitoring and IT in SC", and "Implementation of Smart Metering in Smart Homes". Table 22. Overview-topics addressed within the "E-mobility within SC (SH)" concept.

Topic of the Article Reference Number Observations
Secure data collection using IoT within SCs [128,138] IoT, SC, data Wireless networks, long-distance data transmission in SCs [137] SC, Wireless networks, data Localization of persons and objects in SCs [139] Localization, SC Electricity consumption prediction in connection with e-transport in SCs [130] SC, consumption of electricity, prediction, e-transport Monitoring and modeling the behavior of residents for realistic proposals for predicting electricity consumption; energy in SCs Some limitations of the EV use in SCs (SHs) [140] arise from the dependence of electricity generation on weather conditions and the utility of EVs [139] depending on the range of vehicle use with respect to the following [138]:

•
The variation in vehicle energy consumption by season (winter/summer); • The actual charging profile of the EV; • The parking periods required to achieve the target range for the user.
The analyses showed that the most important factors in the operation of the electric shared mobility market are prices, the condition of the fleet, the replacement of vehicles, rental area, legal requirements, the location of parking spaces, and operational safety [139].

Conclusions
The main contribution of the article was a systematic overview and discussion related to the research on the current status of the development of long-range super-lightweight small electric vehicles for intergenerational urban E-mobility within the Smart Cities concept for a pilot study of a new way of resolving the traffic management of the urban district of Poruba within the statutory city of Ostrava in the Czech Republic. Another of the goals was to help researchers and other workers dealing with the described area to identify the most feasible solution for the use of electric scooters as part of micro-mobility solutions in Smart Cities.
After applying the above-described inclusion and exclusion criteria to articles generated from the scientific research database Web of Science on the subject of the "Smart Cities and micro-mobility", "Smart Cities and Electric Vehicles charging while driving", "chargers for electric scooters", "Management and sharing of electric scooters", and "E-mobility within Smart Cities (Smart Homes)", 134 articles in the period from 2008 to 2022 were selected. The selected articles made it possible to achieve the set goals and answer the research questions of this systematic review. Most of the mentioned studies were conducted in the USA (44), followed by contributions by the People's Republic of China (36), Italy (31), India and England (15), Germany (12), Canada (11), Japan (10), The Netherlands (7), South Korea (6), Mexico (4) and the Czech Republic (4).
A summary of the findings of this research review is provided for each research question: RQ1: As part of the search for an answer to RQ1, the largest number of articles dealt with the issue of shared PLEV micro-mobility (6). Other articles dealt with the provision of the IoT in connection with the operation of MM in Smart Cities (5), resolving traffic regulations and public safety together with ensuring the provision of parking (4), and providing data collection within MM (2) in connection with solving traffic loads (2). Supporting the active life of seniors and disabled people with the help of MM (2) is also an important topic to address. RQ2: In response to RQ2, the articles dealt with topics describing the solution of the battery behavior model (5) and the optimization of the estimation of the battery SOC (3). The topics of Model-Predictive-Control-based battery charging control (MPC) of Electric Vehicles (EVs) in Real-Time (RT) (2) and EV range optimization solutions (2) were also important topics. RQ3: In the search for answers to RQ3, most of the articles dealt with the issue of wireless charger solutions for Wireless Power Transfer (WPT) (7) and solutions for the design of an EDLC (3). Other articles dealt with the optimization of the MESN solution for ESs (3) and the design of the battery charger for electric scooters (6). RQ4: In order to find the answer to RQ4, articles were examined that dealt with the connection between the operation of electric scooters and environmental protection (2), as well as the elimination of accidents and injuries during the operation of ESs (2), ES operation, IT development, data security, the provision of visualization within ES operation (service and maintenance), the IoT, and cloud computing RQ5: In the search for answers to RQ5, articles were found describing the optimization of resources and services through monitoring and communication technologies in SCs (2), the implementation of Smart Metering in Smart Homes (2), the use of wireless networks and long-distance data transmission in SCs, the localization of persons and objects in SCs, and the prediction of electricity consumption in connection with e-transportation in SCs using monitoring and modelling of the behavior of residents for realistic suggestions for predicting electricity consumption in SCs.
Future research should focus on the development of charging stations and the investigation of models of the behavior of conventional and wireless batteries in connection with the operation of PLEVs in SCs, their sharing, visualization, and monitoring using long-term measured data, and their analysis using SW tools within the IoT to ensure the long-range certainty.
In spite of all the answers obtained, there are also limitations in the present study. The defined inclusion and exclusion criteria limited the scope of this study. Consequently, this systematic review does not provide the details of the described technological systems that do not include environmental parameters. Additionally, publications were retrieved from only one database (Web of Science) [136], and the search was limited to publications between 2008 and 2022.
Author Contributions: J.V. proposed the system concept; J.V. wrote the manuscript; J.V. and P.B. edited the manuscript; J.V. designed and implemented the classification methods; J.V. and P.B. critically evaluated the scientific validity of the proposed system, acquired the vital data, and performed the final edits. All authors have read and agreed to the published version of the manuscript.

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