Sustainable Management of Energy Storage in Electric Vehicles Involved in a Smart Urban Environment

Abstract

Electric vehicles are increasingly being used for green transportation in smart urban mobility, thus protecting environmental biodiversity and the ecosystem. Energy storage by electric vehicle batteries is a critical point of this ecologically responsible transportation. This storage is strongly linked to the different external managements related to its capacity state. The latter concerns the interconnection of storage to energy resources, charging strategies, and their complexity. In an ideal urban context, charging strategies would use wireless devices. However, these may involve complex frames and unwanted electromagnetic field interferences. The sustainable management of wireless devices and battery state conditions allows for optimized operation and minimized adverse effects. Such management includes the sustainable design of devices and monitoring of complex connected procedures. The present study aims to analyze this management and to highlight the mathematical routines enabling the design and control tasks involved. The investigations involved are closely related to responsible attitude, “One Health”, and twin supervision approaches. The different sections of the article examine the following: electric vehicle in smart mobility, sustainable design and control, electromagnetic exposures, governance of physical and mathematical representation, charging routines, protection against adverse effects, and supervision of complex connected vehicles. The research presented in this article is supported by examples from the literature.

1. Introduction

Throughout history, human progress has sought to improve well-being, including health, safety, and comfort. This has been achieved through several advances in everyday devices and the use of various accessible natural resources. For many years, electromagnetic (EM) energy has occupied an important place, with its importance increasing over time. This energy is obtained from the conversion of different resources, usually fossil constituents. Recently, well-being in an ecological context has been associated with biodiversity and the ecosystem. This can be achieved by using clean resources for conversion into EM energy, managing the storage of this energy, and eco-designing EM devices; this corresponds to a sustainable use of energy inputs and outputs. Thus, the performance of tools is improved, energy is saved through its eco-use and intelligent storage management, and the likely side effects of EM tools on biodiversity in the environment and the ecosystem are reduced; these elements correspond to the Responsible Attitude (RA) approach.

In the context of smart eco-urbanism, the RA approach, via the reduction in the side effects of EM fields (EMF), is associated with the general health of living tissues involved in urban biodiversity. Indeed, EM tools aim at useful objectives (for human well-being) that are generally associated with unsolicited effects linked to parasitic EMFs. These fields can directly disturb the living tissues of biodiversity by producing harmful biological effects (BE) or indirectly disturb them by disrupting the functioning of devices associated with the tissues. The interdependent articulation of the different members of biodiversity highlights the fact that EM tools built by humans would produce, in addition to the projected objectives, negative effects for humans, as well as for biodiversity and the ecosystem. Thus, such health interdependence corresponds to the concept or approach One Health (OH) [1,2].

The abovementioned disorders triggered by EMF exposure can be caused by wireless communication devices, e.g., [3], mobility devices, e.g., [4], or wireless power transfer devices [5,6] commonly used in daily life. Different types of exposures are correlated to the tools concerning the wireless transfer or transmission of EM energy. These categories concern the range of the transfer, the transferred power, the frequency of the EMF, and the character of the exposed object as well as its location. Wireless inductive power transfer (IPT) covers a wide power range, from a few watts for small household tools [7,8] to a few kilowatts for electric vehicles (EVs), including cars, buses, trucks, boats, etc. [9,10]. The transfer distance is relatively small (a few mm to a few cm). The frequency of EMFs is 50 Hz for small household tools, and 85 kHz (as standard but can reach 200 kHz in specific cases) for EVs. Possible EM disturbances could concern objects located at a distance of a few cm for small tools up to about one meter for IPTs aboard EVs.

In this work, we consider the ecological sustainability of energy storage in EVs in a connected smart urban context, taking into account the complexity of the procedures involved. It is important to note that other types of general troubles involving other classes of complexity could be encountered in relation to the sustainability of connected networks or micro-grids, such as, for example, integrated renewable sources and related resilience to failures and attacks [11].

Energy storage by EV batteries is a decisive point for ecologically responsible transport. This form of storage is strongly linked not only to the type of internal composition of the battery, but also to different types of external management related to its capacity status [12,13]. The charging strategy of IPTs in EVs is closely linked to the state of the energy storage (battery cells), the complexity of the infrastructure, the EV autonomy, and the safety concerns regarding biodiversity and the urban ecosystem. In the context of smart urban planning, these strategic elements could be taken into account in an RA approach associated with motoring arrangement, e.g., [14], and connected smart monitoring organization [15,16,17,18,19] based, for example, on the concept of digital twins (DT) [20,21,22,23,24,25,26,27].

The present contribution aims to analyze and evaluate different approaches or concepts, namely RA, OH, and DT, involved in the management of EM energy storage of EVs interconnected to the public grid in a smart urban environment. Such management includes optimized performance of the IPT charging tool with minimized adverse effects as well as an optimized charging mode strategy for each specific situation of the EV. The following sections of the paper will analyze and illustrate this management. Section 2 presents an overview of EVs in a smart mobility context. Section 3 concerns the sustainable design and control of EV-IPT for smart mobility. Section 4 concerns the governance of physical phenomena and mathematical equations. Section 5 is devoted to EMF exposure in relation to charging routines and protection, supported by examples of urban bus charging mode analysis and of BE exposure in a human body in the vicinity of an EV. Section 6 concerns the supervision of a complex procedure involving the connected vehicle, the smart environment, and the grid. Section 7 is devoted to the discussion on further details of the notions and concepts involved in the developed analyses. Each topic covered in this article, although stand-alone, is supported by examples from the literature to allow for a deeper understanding.

2. Overview of EVs in Smart Mobility

A typical example of the use of decarbonized energy in transport is related to the replacement of internal combustion engines with EVs equipped with energy storage batteries. This strategy was decided upon in an ecological context in order to reduce pollution, thus preserving biodiversity and the ecosystem. When replacing a traditional mode of transport to preserve biodiversity and the ecosystem, it is mandatory to ensure that the substitution strategy is consistent with this safeguard.

2.1. Energy Transfer and Storage

Smart mobility is therefore in fact linked to the use of EVs in an ecological context. This mobility includes different types of traffic such as urban, road, maritime traffic, etc., and different categories of vehicles for specific uses. For example, urban or road cars, buses, trucks, etc., private or public transport including taxis, urban buses, etc., and short or long distance transport including trains, ships, and planes. The smart management of these situations is different and depends on the transport context related to passengers, goods, raw materials, healthcare, security, etc. The electrical energy required for EV operation comes from the public grid via energy storage batteries aboard the vehicle. This energy transfer can be achieved by cables via chargers involving controlled AC/DC converters aboard a vehicle or wirelessly by means of IPTs (see the next section for their construction). Following the evolution of intelligence, EV storage batteries will eventually be supported wirelessly by IPTs entirely in both charging modes, stationary and running. Moreover, the construction of these IPTs must promote optimal ecology associated with clean energy use and frugality. In such a context, both RA and OH approaches would fulfill their role in the design and management of IPT devices.

2.2. Wireless IPT Constituents

A wireless IPT in an EV is embraced between the power grid and the energy storage battery. The IPT midway component accountable for wireless action is an inductive coupler transformer (ICT) linked to the grid and storage battery across static converters. The ICT is formed of two coils, transmitter and receiver, possessing inductances of L₁ and L₂, and separated by an airgap typifying a mutual inductance of M₁₂. The airgap magnitude exhibits a weak coupling of coils and therefore the required power transfer entails an important reactive power engagement. Thus, the coils are compensated by two capacities of C₁ and C₂. Accordingly, an IPT can achieve a galvanic separated power transfer, with a capacitive compensation of electronics connected to an ICT, allowing their functioning at resonance. Such compensations guarantee reliable efficiency [28,29,30,31,32,33,34,35,36] and can exploit different connection topologies (series or parallel), subject to different factors including the character of the storage battery; for instance, the different connection configurations of the two side topologies would be SS, SP, PS, and PP [29,36]. In the case of an EV, the SS compensation topology appears to be a reliable choice [35,36]. To improve the transfer efficiency corresponding to a better coupling of the ICT coils, magnetic ferrite sheets are generally used to cover these coils. Figure 1 shows representations of an IPT, including its ICT details. The IPT embraces its ICT with ferrite sheets inserted between the power grid, through an AC-DC-AC filtered adjustable frequency–voltage conversion, and the storage battery, through an AC-DC filtered conversion [19].

The ICT structure, sketched in Figure 1a, is described in Figure 1c. It comprises the two ICT coils, ground transmitter, and EV bottom receiver, along with the two magnetic ferrite plates coating the coils. In addition, Figure 1c includes a steel plate representing the EV chassis. The coils of the ICT with their ferrites (pads) are separated by an air gap of distance (d) and coil axes shift (sh).

3. Sustainable Design of EV-IPT in Smart Mobility

As mentioned before, the RA approach contains the sustainable design of the IPT as well as the supervision of the energy storage and its IPT implementation. We review in this section the contributions of the RA and OH approaches in the sustainable design of the IPT components. The supervision of the IPT with the storage battery as a part of RA via the DT concept will be discussed in Section 6.

The sustainable design of the IPT involves the construction of its components, including the ICT, the compensation scheme, the static converters, and the filters (see Figure 1). Such a design allows for enhanced performance [28,29,30,31,32,33,34,35,36,37,38], involving better ICT coupling and reduced stray fields [19,39,40], which illustrate the junction of the RA and OH approaches in the IPT design. Moreover, these two approaches are closely related to the state of charge and battery management [41]. Energy storage management involves the supervision of battery charging scheduling that supports sustainability associated with decarbonized and reasonably priced energy use [42,43]. In fact, EM energy is considered strictly clean if it comes from decarbonized conversion resources [44]. Figure 2 illustrates the two cases of unsustainable (a) and sustainable (b) design of an IPT in the urban context of smart cities (SC) [19].

3.1. Charging Modes in Urban Smart Mobility Context

Smart urban mobility using EVs with battery charging via wireless IPTs is suitable for different categories of vehicles. These include shared and autonomous EVs, public buses, trams, shipping vessels, small delivery trucks, etc. The charging modes of IPTs involved in a smart urban environment can be mainly full static, full dynamic, mixed or distant static charging points. A full static mode corresponds to a restricted range and requires a large battery storage. The full dynamic case (electric roads) corresponds, in general, to a given urban trajectory, requires little battery storage (for discontinuous electric roads), and is used for public transport such as electric buses and trams. A mixed static-dynamic mode is mainly suitable for highways and requires medium battery storage. The distant static charging mode concerns public electric buses charging at specific trajectory stops for which the energy storage depends on the number of charging points and the static duration (see next section). Each of these charging modes requires specific protection against EMF exposure.

3.2. Case of an Urban Bus Charging Modes

In general, in the wireless charging of EV storage batteries, the relationship between storage capacity and vehicle range is theoretically organized as follows: neglecting the losses in the IPT-compensated coils, the transferred energy = the capacity of the energy storage batteries = the energy range of the vehicle, so P_t. T_t = n_c. C_c = P_m. T_r. P_t and T_t are the transferred power and charging time, n_c and C_c are the cells number of the battery and cell energy storage capacity, and P_m and T_r are the power of the EV motor and its functioning time. Thus, as a first approximation, for a given transferred power, engine power, and battery cell capacity, the charging time and operating autonomy time are correlated to the number of cells.

Taking the example of an analysis of the charging modes of a public urban bus, the RA-OH selection of an IPT charging routine must take into account not only the energy storage capacity, but also the complexity of the charging infrastructure and the risk of exposure to radiated EMFs of the living tissues of the urban biodiversity. In fact, the bus makes a given number of round trips (circuits) N_c per day. A circuit corresponds to a round trip trajectory between two ends. Each outward or return trajectory includes a given number of stops between its two ends N_s. Charging routines can be practiced at different levels, namely day, circuit, single trajectory or stops; this order corresponds to decreasing storage capacities associated with a corresponding decrease in operating autonomy. These IPT charging routines involve different infrastructure complexities and risks of exposure to stray EMFs. Both of these factors have a significant impact on the choice of charging routine. The most important decision elements are the costs of infrastructure and safety protection shields, as well as the efficiency of charging and the risk to passengers.

Among the charging modes mentioned above, the day, circuit, and one-way charging modes correspond to situations without passengers inside the vehicle; the charging process can take place on the bus platform (garage) or at a final stop–pause of the trajectory. Considering the charging mode at stops, it requires, in addition to a significant number of ground fixed sources, shields for the interior of the bus and precautions to distance the IPT from the bus entrance and the waiting platform. Even if this mode requires a small storage capacity, considering the aforementioned drawbacks, such a solution can be excluded for city buses. To avoid these drawbacks in addition to possible road surface degradation [45,46], a one-way or circuit end stop–pause charging mode using an IPT on the bus roof could be an excellent compromise with respect to battery storage, infrastructure complexity, and exposure safety, as shown in Figure 3. In such a case, a bus wheel engaged in a hatch slot allows for strict vehicle positioning and a simple movable arm including the primary ICT coil could be remotely controlled to cover the secondary coil in a perfectly adapted manner with an offset sh = 0 and the smallest air gap d (see Figure 1c). Thus, stray fields would be totally minimized and maximum efficiency achieved. Moreover, the IPT setting in the front position of the bus roof presents the furthest location from the passenger waiting platform. These conditions ensure flawless performance and health safety.

Considering the typical case of a city bus, the analysis of the charging modes will give the following estimates. Taking the storage capacity for a day mode as 200 cells, 10 circuits (round trips) per day, and 10 stops per single trip, the corresponding number of cells needed for the different modes would be as follows: 200 for a day, 20 for a circuit, 10 for a one-way trip and 1 for stops, excluding the case of stops due to the drawbacks mentioned above. Since the day and circuit modes require the same infrastructure layout with a single bus for the day mode and several buses for the circuit mode, which moreover requires a reduced storage volume, the choice of the circuit mode seems obvious. Indeed, for several buses completing the same circuit, one could be charged while the others are running. The case of the single trajectory mode requires two charging devices with half as many storage cells as the circuit mode, and only a price comparison would decide the situation.

4. Governing Physical Phenomena and Mathematical Equations

The main physical phenomena concerned in the sustainable management in EVs are related to electric motoring, energy storage, and the connection to the electric grid. The electrochemical phenomena involved in the battery storage are out of the scope of the present contribution; thus, a battery is considered as a form of EM energy storage. The dominant physical phenomena in the design of an EM device as an electric motor or an IPT are related to the EM and electrical circuit domains. The interaction of the EMFs radiated by a wireless EM device using IPT with the exposed substances produces, in the latter, induced fields and possible unwanted thermal effects, which are governed by EM and heat transfer (HT) phenomena, coupled with the dissipated EM power (P_d) in these materials. In the case of exposure to EMFs of living tissues, a biological heat (BH) phenomenon will govern the temperature rise created by a P_d heat source. These different physical phenomena are governed by consistent mathematical equations, which are presented in the next lines.

4.1. Ruling Equations

Established on Maxwell’s microscopic local behavior, under their differential form, the general EMF four equations [47] are presented by the following:

∇ × E = −∂_t B (Maxwell–Faraday)

(1)

∇ × H = σ E + ∂_t D (Maxwell–Ampère)

(2)

∇ · D = ρ_e (Maxwell–Gauss)

(3)

∇ · B = 0 (Maxwell–Thomson)

(4)

The HT equation in its differential form is given by the following:

c ρ ∂T/∂t = ∇ · (k ∇T) + P_d

(5)

In the case of EMF exposure of matters, the EMF harmonic fields and heat source P_d equations are given as follows:

∇ × H = J

(6)

J = J_e + σ E + j ω D

(7)

E = −∇ V − j ω A

(8)

B = ∇ × A

(9)

P_d = ω · ε″ · E²/2 or = σ · E²/2

(10)

In the case of EMF exposure in living tissues, Equation (5) will be extended to the BH using the following equation:

c ρ ∂T/∂t = ∇ · (k ∇T) + P_d + P_t + c_f ρ_f p_f (T_f − T)

(11)

In (1)–(11), H and E are the magnetic and electric fields vectors in A/m and V/m, B and D are the magnetic and electric inductions vectors in T and C/m², and A and V are the magnetic vector potential and electric scalar potential in Wb/m and volt. J and J_e are the total and source current densities vectors in A/m². The electric conductivity is denoted by σ in S/m, the volume density of electric charges by ρ_e in C/m³, and the angular frequency by ω = 2πf, with the frequency f in Hz. The character ∇ is a partial derivative vector operator. The character ∂_t is the partial time derivative operator. The behavior magnetic and electric laws, respectively, given by B/H and D/E are signified by the permeability μ in H/m and the permittivity ε in F/m. The symbol ε″ denotes the imaginary part of the complex permittivity (j ω D = j ω (ε’ − j ε″) E), and ρ represents the material density in kg/m³. E represents the electric field strength (absolute peak value) in V/m, c represents the substance specific heat (at constant pressure) in J/(kg °C), k the thermal conductivity in W/(m °C), and T the temperature in °C. The power dissipation in W/m³ given by Equation (10) relates to the foremost dielectric heating of EMF energy loss. Notice that the imaginary part ε″ of the (frequency-dependent) permittivity ε is a measure of the ability of a dielectric material to convert EMF energy into heat. The volume density of power dissipations given by Equation (10) will be used in the coupling of EMF and BH equations. In the case of living tissues, we consider a self-tissue heat source P_t, convective heat transfer via irrigating fluid of tissue, and external heat source related to the EMF exposure P_d. The quantities P_t and P_d are heat sources in W/m³, T_f, and T are, respectively, the fluid temperature and the local temperature of tissue in °C, and c_f, ρ_f, and p_f are, respectively, fluid, specific heat in J/(kg °C), density in kg/m³, and perfusion rate in 1/s.

Note that in the EMF equations, the source term is the excitation current density J_e = σ E_e. Moreover, the SAR in dielectric materials (living tissues) is given by P_d/ρ in W/kg. The P_d given by (10) is a function of σ or/and ω · ε″, respectively, in electric conducting or dielectric behaviors of matter. At low frequencies, conductive behavior is dominant while at high frequencies it becomes negligible.

Equation (11) relates to the BH behavior of tissues considering exposure to EMFs and is similar to the Penne BH equation [48,49] associated with living animal tissues, accounting for convective heat transfer in blood. Animal blood acts as the sap fluid in plants. Similarly, veins and arteries, which contain blood, act as phloem and xylem containing sap. The P_t term in (11) is related to animal metabolic heat (from Penne BH equation) and corresponds to the internal heat of plant tissues. In addition, the last term in (11) corresponds to convective heat transfer in the fluid related to plant sap or animal blood. The source term in Equations (6)–(9) corresponds to an excitation current provided by an electrical circuit. Thus, in the analysis of an EM device, including electric circuits, the EM domain must be coupled to the circuit domain with a general circuit equation of the form:

v = 1/C ∫ i dt + r i + L di/dt + dΨ/dt + ᴕ

(12)

In (12), “v” is the source voltage, “i” is the circuit current, “r” is the total circuit resistance, “L” a linear inductance, “C” a capacitance, “ᴕ” a nonlinear voltage drop (e.g., a semiconductor) in the electrical circuit, and “Ψ ” the flux linkage. The equations describing the EM and circuit domains to be solved are therefore (Equations (6)–(9) and (12)).

The electric circuits involved in the IPT are those relative to the coils of the ICT (see Figure 1a). The involved coupling coefficient k and the resonant frequency ω_o (for coils with SS compensation) are given by the following:

k = M₁₂ (L₁ L₂)^−1/2

(13)

ω_o = (L₁ C₁)^−1/2 = (L₂ C₂)^−1/2

(14)

The ICT structure defined in Figure 1a and presented in Figure 1c will be considered in the design by Equations (6)–(9) and (12), accounting for (13) and (14) in a strong coupled EM and electric circuit domains.

The living tissues BEs corresponding to induced fields and temperature rise, produced by a source matching to stray fields, can be obtained by solving Equations (6)–(9) and (11) with a heat source P_d relative to (10). These EM and BH domains will be coupled in a weak manner.

4.2. Numerical Computations

The solution of the various equations stated in the last section relating to the EM, BH (or HT), and electric circuit domains, (6)–(9), (11), and (12), respectively, must take into account the particular characteristics of the configurations involved. These are the geometric sophistication, the inhomogeneity of the matter, the nonlinear behaviors of the variables, and the interdependence of the different domains, which imply advanced computational strategies [50]. Satisfying these characteristics imposes a local solution in the matter, advocating the use of discretized 3D procedures such as the finite element method (FEM) or equivalent approaches [51,52,53,54,55,56,57] associated with appropriate equation coupling strategies. Thus, an iterative weak coupling (due to distant time constants) of the EM and BH domains would be obtained thanks to (10). In the case of taking into account the domain of electric circuits, the solution of the equations would be simultaneous, reflecting a strong coupling (due to close time constants) [58,59,60]. Actually, the equations given above are the base of eco-design (Section 3) of wireless charging systems through (6)–(9) and (12)–(14). The same equations are used in the DT control (storage management—Section 6). The exposure of living tissues by EMFs (Section 5) is governed by (6–11). The involvement of these models in a real-world setting is indicated through many referenced works given in the related sections and [51,52,53,54,55,56,57,58,59,60].

5. EMF Exposure vs. Charging Routines and Living Tissues Protection

The characteristics of the ICT stray fields are directly related to the 3D relative positions of the two ICT coils (see Figure 1a,c) fixed to the ground (transmitter) and on the underside of the vehicle (receiver) and therefore strongly affected by the positioning in the vehicle space. Thus, the exposure to the EMFs produced by the ICT stray fields is affected by the charging mode (static or dynamic) and the characteristics of the 3D coil air gap (distances d and sh—Figure 1c).

The effects of these fields on living tissues are related to their position relative to the location of the ICT and to the 3D characteristics of the air gap of its coils. In dynamic charging mode, the stray fields are variable depending on the progressive position of the lower receiver coil of the EV relative to that of the ground-mounted transmitter. In the case of static mode, the stray fields are invariable but strongly depend on the 3D characteristics of the ICT air gap (distances d and sh).

5.1. Exposures and Charging Modes

In the case of charging modes involved in the operation of EVs, whether dynamic charging or remote static charging points (such as bus stops), the potentially affected living tissues are those of the passenger compartment inside the vehicle. In this case, passenger protection could be ensured by protecting the upper part, above the ICT of the vehicle, using shields of appropriate shape and materials [5,61].

In the case of the charging modes concerned by stationary EVs, whether it is classic static charging (at the station or at home) or static charging in pause (at the end-of-travel terminals of buses), the vehicle is normally empty and the living tissues potentially affected are those located outside the vehicle and near the ICT. These tissues can be of humans, animals, birds, plants, etc., that could be affected by stray fields through the open space below the vehicle, coming from the ICT during the stationary static charging. The shielding of the ICT in this open space is problematic due to its 3D configuration with coils separated by an air gap between the ground and the underside of the vehicle. This difficulty would be amplified for larger twisted air gaps linked to the distances d and sh, which are difficult to control. In such conditions, the only way to preserve biodiversity is to bypass access under or near the vehicle for an excessive duration; this action is part of the RA practiced by public authorities. Thus, for the ICT under the vehicle, static charging could be carried out in closed places or surrounded areas in the case of open spaces. For the static point charging of buses, in addition to the shielding of the passenger compartment, the ICT must be placed far from the entrance and the waiting platform of the bus passengers. A smart RA-OH approach solution for this last case is the organization proposed in Section 3.2 (Figure 3), relating to the charging of buses during the break at the end of the trajectory, where the bus is stopped without any passengers and has the ICT on its roof. This corresponds to efficient performance and safety, in addition to the economic efficiency linked to the removal of the shielding.

5.2. Example of Exposure BEs in Human Body Nearby an EV

In this section we will study an example of an assessment of the effects of EMF exposure, corresponding to the determination of the fields induced in living human tissues and the verification of their compliance with safety standards.

5.2.1. Evaluation and Control of BEs

The abovementioned EMF exposure situations can be assessed and controlled by solving the equations given in Section 4.1, allowing to check the values of the fields induced in the living tissues of the object corresponding to the standard thresholds [62,63,64,65]. Thus, the 3D calculations of the EM domain (6)–(9) would take into account, in the previously mentioned charging mode routines, the specific corresponding geometries involving the ICT configuration, the shield arrangement, and the physical properties of the living tissues concerned. Numerical models (phantoms) could represent the living tissue object inside the vehicle or located nearby as appropriate. The substantial characteristics of these models should correspond to the consistency of the physical and biological properties, the realistic shape, and the reliability with the numerical methodology used. Different body models and living tissue characteristics can be found in the literature, e.g., for human tissues [66,67,68,69,70,71]. Figure 4 shows a structural human body and its various organs and tissues [48].

5.2.2. Case of Human Body BEs Due to ICT EMF Exposure

This section concerns an illustrative example of exposure to EMFs from an ICT relative to the situation of a human body positioned horizontally on the ground next to a statically charged electric vehicle [48]. Figure 5 presents the distributions of the body-induced B and E fields due to exposure to EMFs produced by the ICT. The high-resolution body model of Figure 4, which corresponds well to the numerical 3D FEM approach used, was used in the induced field calculations. The field distributions in Figure 5 are relative to a source field in the solved Equations (6)–(9), corresponding to the stray fields of a 3 kW ICT at 30 kHz. The results obtained were compared with the thresholds of the safety standards [62,63] fields. In fact, the body (model) of Figure 4 was positioned related to the ICT EMF source and the 3D fields were calculated in the different EMF mesh points of the body. The different obtained values of the induced electric field E and the magnetic induction B in the FEM mesh points were checked against the safety thresholds of 4.05 V/m and 27 μT, respectively. For the obtained results shown in Figure 5, these thresholds were not antagonistic. Thus, the correspondence check in the present example of exposure to EMFs, for the actual power and frequency of the ICT on the human body in its position, reflected body-induced fields that are in accordance with these safety standards.

Note that the example given in Figure 5 of a human laying alongside a static charging EV corresponds to the only situation without a possibility of protection by shielding. The other types of charging, such as dynamic charging with onboard passengers (EV ground shielded) or static bus roof charging without inside or near-ICT passengers, do not pose problems.

6. Supervision of Complex Connected Vehicle–Smart Environment–Grid

As foreseen by RA and OH approaches, appropriate eco-design, clean energy use, and risk assessment are required. Thus, they allow the reduction of harmful effects and preserve biodiversity and ecosystem. Moreover, RA could be involved in energy management between EV battery storages and the grid. Thus, control algorithms could be used for grid-to-vehicle (G2V), vehicle-to-grid (V2G) [72,73], or vehicle-to-vehicle (V2V) functional approaches [74]. Moreover, RA includes ensuring interoperability between different sides of the IPT coil (ground and vehicle) [36,75] and the consideration of an appropriate charging profile [76]. Moreover, connectivity and autonomous driving control [77,78], intelligent power monitoring in EV charging [79], consideration of battery storage [80] or optimization via control [81] will make EVs much more reliable. In brief, the complexity of charging schemes involves, for a given EV, in addition to the charging infrastructure, the EV engine, the IPT, the energy storage batteries, and the network connected to the SC. This complexity must be managed for each vehicle. Moreover, this management must be integrated with the other vehicles connected to the network, as mentioned before.

6.1. Management of Energy Storage Associated with IPT

As discussed before, the driving range of an EV is a critical issue related to the vehicle autonomy and strongly associated with the energy storage capacity and charging strategies of the IPT. Moreover, the storage state of the battery effectively shapes the design and operation of its IPT and the connected inverter. The targets involve energy efficiency, storage volume capacity, storage longevity, external management cost, vehicle weight, and autonomy. Such intentions are closely connected to vehicle type, its use and its environmental context. Monitoring the storage battery state, life cycle prediction, and fault detection are important related tasks. Energy storage management through such monitoring could be realized with the technology of connected digital tools, by matching complex procedures with their virtual models, namely DTs. The concept of DTs has been used for monitoring complex systems in many fields, including the automotive industry [82,83]. This concept could be described as a simple data integration between a physical complex procedure and its virtual image in a bidirectional manner [84]. DTs for EV driving routines are typically used for system condition monitoring, diagnosis, prediction, optimization, resource allocation, task offloading, and risk assessment [85,86,87].

DTs can be shaped at different levels, system, subsystem, specific component, etc. Thus, the scheduled energy exchange between the EV and the grid or other sources, and the adapted IPT charging profile could be supervised by a DT as part of the battery monitoring via the RA approach. Moreover, such supervision could be supported in the context of the smart management involved in SCs using DT administration. In fact, the recently increased number of established interconnected SCs reflects an increase in the need for skills as DTs, which could contribute to the progression of these SCs [88]. In a DT SC, detection sensors, observation cameras, and other digital tools related to the city’s resource distribution, infrastructure, logistics, people movement, and all types of EV-related concerns could collect different specific data. Thus, the SC organization would be supervised and administered more efficiently [89].

6.2. DT Monitoring EV-IPT-Energy Storage Grid

To emphasize the role of the DT concept in supervising complex procedures involving EV-IPT-energy storage batteries connected to a SC grid, we will detail at this point its main features [90]. A DT is constituted of a physical real–virtual model pair, allowing a self-adapting to be conducted. Its real part conveys processed detected data to its virtual part, while the latter spreads control instructions to the real side. This matching decreases uncertainties as well as unwanted and menacing irregularities. The data that was processed by the real part of the model distributes sensed detections, matched and adjusted by exterior data (IoT) and learned history. The conforming outcome would, once trained, be communicated under a data analysis form. Such indications, accompanied by a suitable reduced model, will progress to the virtual part. It is clear that a swift matching demands a reliable virtual replica with minimal computation time. This can be achieved by condensing the numerical model while preserving a realistic physical portrait using, for example, surrogate models [91]. Management with such a pair permits an adaptive command for a given operational procedure [92]. Figure 6 recapitulates the abovementioned features of a DT for the management of EV-IPT-battery storage grid connected to a SC.

The previous analysis demonstrated the opportunity to use the RA approach in strategies regarding a scheduled energy exchange between the grid and the EV via a configurable IPT charging profile. This could be achieved through control algorithms managed by DT connected in the context of battery storage monitoring. Several applications have been published in this area, including energy storage, battery management system, state-of-charge estimation, etc.; see, for example [93,94,95,96].

7. Discussion

This study has analyzed the management of sustainable IPT devices as well as the associated energy storage and has highlighted the mathematical routines enabling the design and control tasks involved. The analyses involved are related to the RA, OH, and DT approaches. Following the different analyses, several points deserve further discussion:

• Innovations: As mentioned above, biodiversity and ecosystem protection could be achieved through RA and OH approaches materialized by the sustainable design of EM devices. The more sophisticated the device, the more crucial the role of these approaches becomes. In fact, sophistication is generally associated with greater side effects; for example, in the present work, a faster-charging IPT device would produce higher stray EMFs. Thus, more innovations should always be accompanied by more control and adaptation. However, when it comes to innovation in general, there is a big gap between paranoia and naivety. The precautionary principle must therefore be remembered.
• Mixed mobility: We have discussed at different places in the article about charging routines and their adapted uses. In large forms of urban public transport like trams, a mix of charging routines and motorizations could be used in a given trajectory adapted according to the topology involved. Thus, during tram operation, the energy source could come from battery storage or from the connected grid. Similarly, this could come from direct charging from the grid during operation or static IPT charging at the terminal stop. With these possibilities, the EV could operate in some parts of the trajectory, without grid connection (battery storage source) and other parts with grid connection (grid source for operation and direct charging) and possible static charging at the terminal. A typical example could be a tram with a trajectory partly on the surface or underground. For underground transport, the grid connection is simple and can be used for both motorization and direct charging. For surface transport, where grid connection requires more complex infrastructure, the motorization would use battery storage. In addition, the terminal break stop could be used for IPT static charging without passengers. Figure 7 illustrates, in this case, the two charging modes: direct (underground) and indirect via IPT (the terminal break stop), as well as the motorization modes’ direct grid (underground) and energy storage (surface).

• EMF Exposures: We have discussed the effects of direct exposure on living tissues of biodiversity (including humans). Exposure to EMF can also indirectly affect these living tissues through wearable or implanted tools onboard the tissues, which therefore need to be protected [97].
• Complexity: In Section 6, we referred to the complexity of procedures. A complex procedure comprises several interacting constituents typified by various phenomena acting together in an interdependent manner, which is related to the temporal and 3D spatial behaviors of the phenomena involved. The closer the time constants and the higher the local nonlinear behavior of matter in the phenomena, the deeper their interdependence and thus the greater the complexity. For distant time constants and linear behaviors, this interdependence is considerably reduced and thus is the associated complexity. The notion of complexity exists in many natural and artificial occurrences [91]. Moreover, such complexity can be treated mathematically by reflecting its multiple interacting constituents through the coupling of the equations governing the interdependent interacting phenomena (see Section 4). The more complex the procedure, the higher the complexity of the coupled model will be. For greater interdependence (closer time constants and higher nonlinearities), the equations will be strongly coupled (simultaneous solution). For weak interdependence (distant time constants and linear behavior), the coupled solution will be weak (iterative) [60].
• History of DT: The concept of DT discussed in Section 6 was first presented by Michael Grieves in 2002 [90], although its application predates this. An example of its use was by NASA, who used it to safely manage a spacecraft following a disruptive oxygen tank explosion on the Apollo 13 mission in 1970. The mission subsequently modified simulators to accommodate real spacecraft conditions; this was probably the first realistic use of a DT. Furthermore, the exercise of real–virtual correspondence is related to the virtual reasoned deduction related to the observation of a phenomenon. Thus, the association of an observable and its virtual image has been and is still experimented with in frequent natural and artificial events. Members of biodiversity often rely on observation and sensory exercises, using reasoning and imitation to ensure their self-protection and survival. Logical reasoning, coupled with observation, is the most primitive of natural cognitive abilities. For example, in flora and fauna, life safety is based on observation, and experiences of imitation tactical strategy are common, which occurs through camouflage [98]. This allows living creatures to blend into their environment through adaptive matching. Figure 8 shows (as does Figure 6) a representation of camouflage, emphasizing real environmental observation, the imitation tactic of obscuration, and their bidirectional link for a changing environment using real-time matching. Such obscuration allows hiding to protect one’s life for prey as well as to attack for invaders. Changing environments and unexpected disturbances are matched with adaptive imitation strategies confirmed by training. Matching training helps to mitigate the uncertainties of observation, detection, and imitation strategies.

• In this work, we considered the ecological sustainability of energy storage in EVs in a connected smart urban context, taking into account the complexity of the procedures involved. Regarding the connected grid integrating renewable sources, we may encounter other types of complexities related to environmental conditions that require advanced control mechanisms to further improve the resilience and sustainability of the grid, e.g., [99].
• Scaling down of ICT: A reduction in the size and cost of ICT coils could be achieved by replacing wound coils with printed circuit board (PCB) technologies; see, for example [100,101,102]. This technology could be further investigated with a view to optimizing the coupling efficiency of ICT coils, minimizing coil resistance, reducing stray fields, etc.
• In several points of the paper, we mentioned EMF exposure risks and shielding strategies. In the application concerned in the paper, there are two fundamental techniques using ferrites for magnetic flux directorial and metals plates (under the transmitter and upper the receiver) for field leakage minimization. Shielding barriers must come in adequate sizes and forms at the least possible cost and with the highest shielding effectiveness for different applications [61]. There are more sophisticated shielding materials that can be used in shields in general. Actually, shielding materials are a vast domain related to material and nanomaterial scientific topics; see, for example [103].
• Regarding the frequencies used in wireless charging devices, they generally have a wide range. In the case of EVs, as mentioned in the introduction, the inductive wireless charging frequency is around 85 kHz (this is standard, but it can reach 200 kHz in some cases). Such wireless inductive charging is suitable for the power used in EVs. The frequency may vary depending on the power of the vehicle type, EMC restrictions, and the preservation of certain biodiversity species, for example, those that use guidance and echolocation strategies, such as bats.

8. Conclusions

The analyses performed in this paper have illustrated the role of RA, OH, and DT approaches in the sustainable management of EVs energy storage with their associated IPTs, allowing the optimized protection of biodiversity and the urban ecosystem., consequently leading to increased energy savings, reduced exposure to EMFs, optimized energy storage capacity, and complexity of charging infrastructures.

The outcomes and general recommendations can be summarized in the following points:

• In public transport using IPT static charging, the choice of a small number of short charging periods in terminal stops without passengers should be encouraged. Thus, the following elements are optimized: passenger safety, energy storage volume, infrastructure complexity, shielding, etc.
• Mixed modes of grid energy use adapted to the public transport trajectory with the characteristics described in Figure 7 should be preferred.
• For EVs other than public transport such as passenger cars or taxis, the necessary static IPT charging of a battery should be carried out in closed rooms or bounded areas in open spaces. Such a static charging mode corresponds to the only situation without possibility of protection by shielding and must be controlled for living tissues that are close by in general. The other types of charging, such as dynamic charging with onboard passengers (EV ground shielded) or static bus roof charging without inside or near-ICT passengers, do not pose problems.

For possible future work, researchers can consider the following:

• Complexities related to extreme environmental conditions.
• DT control mechanisms to further improve the resilience and sustainability of the connected grid, particularly in scenarios involving renewable energy integration.
• Reducing the size and cost of ICT coils by replacing wound coils with printed circuit boards that optimize the coupling efficiency of ICT coils, minimize coil resistance, and reduce stray fields.
• Enhancing communications between EV and smart grid, ensuring uninterrupted service.
• Improving IPT efficiency through coupling coils more closely via more sophisticated strategies by means of field strengthening using, for example, in-between coils.