Dynamic Wireless Charging Systems

• Dynamic Wireless Charging (DWC) is a system that enables electric vehicles to charge on the move using embedded inductive coils and adaptive power transfer mechanisms.
• Key features include ultra-low-latency vehicle-to-infrastructure communication and advanced energy management algorithms that optimize power distribution and extend battery life.
• Implementation leverages modular pavement designs with real-time grid integration and robust security protocols, supporting sustainable urban mobility and reducing range anxiety.

Dynamic Wireless Charging (DWC) refers to the class of power transfer technologies and infrastructure that enable electric vehicles (EVs) or mobile devices to recharge wirelessly while in motion, rather than in stationary charging scenarios. DWC is regarded as a critical enabler for large-scale EV adoption by addressing range limitations and supporting seamless, high-availability electrified mobility in smart cities and urban corridors. Recent infrastructure frameworks integrate modular inductive coils, real-time vehicle-to-infrastructure (V2I) communication, adaptive energy management coordinated with renewable-rich grids, and standards for security, interoperability, and operational efficiency (Agnihotri et al., 14 Dec 2025).

1. Architectural Frameworks and Physical Layer

Large-scale DWC systems are typically conceptualized as Electric Road Systems (ERS) structured into three tightly-integrated architectural layers (Agnihotri et al., 14 Dec 2025):

• Physical Layer: Modular pavement-embedded coil segments (e.g., 2–5 m length, spaced every 10 m) encapsulate primary coils with self-inductance $L_1$, while EVs mount secondary receiver coils ($L_2$). The key transfer parameter is the mutual inductance $M = k\sqrt{L_1 L_2}$, with $k \approx 0.2-0.4$. Inductive power transfer operates at frequencies near 85 kHz (SAE J2954 specification). The instantaneous power transfer is

$$P_\text{transferred} = \frac{\omega^2 M^2 V_1^2}{R_2 (R_1 + R_s)^2 + \omega^2 (L_1 R_2 - M^2)^2}$$

where $V_1$ is the inverter output, $R_1, R_2$ are series resistances, $R_s$ the inverter’s switching resistance, and $\eta_\text{coil} = 88-91\%$ at highway speeds.

• Communication Layer: Ultra-low-latency wireless protocols (5G/6G, IEEE 802.11p) manage discovery, authentication, and real-time V2I negotiation for coil activation. Typical round-trip latency is $<10$ ms for activation/deactivation, with cryptographically secured payloads including location, state-of-charge, requested energy, and billing tokens.
• Control & Grid Integration Layer: A cloud-based Energy Management System (EMS) forecasts demand (often via LSTM or hybrid models), orchestrates coil activation, orchestrates power flow from the grid, local solar, and V2G resources, and mediates dynamic load shedding according to real-time grid conditions, using PMU feedback and time-of-day tariff signals.

Segmented, modular pavement integration with maintenance accessibility (thermal sensors, lateral/lane position sensors) and actuator-enabled covers ensures durability and adaptability to urban maintenance cycles.

2. Adaptive Energy Management and Control Algorithms

Modern DWC frameworks deploy EMS algorithms that dynamically allocate power to EVs according to real-time traffic, grid constraints, and renewable energy availability (Agnihotri et al., 14 Dec 2025). The EMS control law computes per-vehicle priority weights, e.g.,

$$w_i = \alpha \frac{\text{SoC}_\text{min}}{\text{SoC}_i} + \beta \cdot \text{V2G}_i, \quad \alpha + \beta = 1$$

and distributes available power to coil segments subject to per-segment ($P_\text{segment,max}$) and corridor limits. Pseudocode routines capture the real-time allocation:

For each time step t: Acquire EV_list on segment j Forecast P_grid, P_solar, P_V2G For each segment j: Compute weights w_i for EVs i in j For each EV i in j: P_alloc[i] = (w_i / sum(w)) * min(P_available, N_segments * P_segment_max) Activate coils to deliver P_alloc Update SoC_i += eta_coil * P_alloc[i] * dt / Battery_capacity_i End

This algorithmic structure permits coordination so as to maximize charging equity, minimize range anxiety, and prioritize renewable dispatch.

3. Performance Metrics and Comparative Evaluation

An integrated SUMO-MATLAB co-simulation framework is commonly used for operational validation (Agnihotri et al., 14 Dec 2025). Typical corridor-scale parameters:

Metric	Static Fast Charging	ERS Dynamic Charging
Avg. Battery Temp (°C)	45–50	32–36
Deep Discharge Cycles/year	310	125
Estimated Battery Life	$\sim$6 yrs	$\sim$9 yrs
Charging Efficiency (%)	—	88–91 (peak: 90)
Energy per 2 km (kWh)	—	1.6–2.8

Dynamic charging in optimized ERS corridors achieves up to 30–35% reduction in range anxiety versus static charging, reduces annual deep-discharge cycle counts by over 50%, and extends estimated battery life by 3 years due to frequent shallow cycles. Energy cost per km drops to $₹1.7$ ($\sim$2¢) when leveraging 60–70% solar energy, with break-even investments around $1.8$M/km recouped within 6–8 years under active subsidy regimes.

Grid disturbance is tightly bounded (voltage/frequency deviation $<$ ±1.5%) by EMS-driven integration of solar (61% of supply), grid (35%), and V2G (4%) resources.

4. Communication, Security, and Privacy Protocols

Highly reliable, low-latency communication is essential for dynamic power delivery. Standard architectural flows consist of four phases: discovery, authentication & negotiation, charging control (including real-time coil alignment feedback), and conclusion (Ma et al., 25 Mar 2024). Protocol stacks employ DSRC (IEEE 802.11p), C-V2X LTE-PC5, or 5G NR-V2X, offering sub-10 ms end-to-end latencies and $>$99.9% reliability over several hundred meters.

Security and privacy are addressed by advanced schemes such as QEVSEC (low-latency hash/xor authentication, 0.27 ms per pad) (Bianchi et al., 2022) and post-quantum protocols (DynamiQS, RLWE-based IBE, 281 ms initial handshake) (Bianchi et al., 2023). These mechanisms ensure session unlinkability, prevent free-rider/replay attacks, resist adversary impersonation, and scale efficiently for high-density pad arrays.

Billing and metering leverage blockchain or private distributed ledger technology (DLT) for settlement and fraud resistance, though consensus delays remain a consideration for real-time operations.

5. Coil Alignment, Efficiency Maximization, and Physical Constraints

Efficient DWC is strongly dependent on precise coil alignment and adaptive frequency control. Lateral misalignments as small as 10 cm can degrade coupling by 10% or reduce aggregate efficiency by $5-15\%$. RFID-based phase-coherent sensing systems achieve sub–10 cm misalignment measurement accuracy using maximum likelihood grid-search over high-rate phase data, reducing average error below 5 cm even under field conditions (Sun et al., 2023). This enables adaptive inverter tuning and possible micro-steering feedback to vehicle control systems.

Multi-layer LSTM networks integrated into DWC infrastructure predict and control the optimal lateral positioning for maximal inductive coupling, providing up to 162.3% higher charging efficiency versus simple center-of-lane controllers (Das et al., 2022). Adaptive frequency-tracking algorithms based on real-time mutual inductance estimation can recover $>30\%$ more power than static-matching schemes in variable-coupling environments (Jun-Kim, 2023).

6. Grid Planning, Voltage Stability, and Scalability

Traffic-aware grid planning frameworks link macroscopic traffic models (e.g., Cell Transmission Model) to AC Optimal Power Flow (OPF) microgrid sizing, leveraging spatiotemporal EV flow, speed distributions, and load profiles to optimize distributed PV and energy storage siting (Ghose et al., 2 Nov 2025). Compared to worst-case or flat-design approaches, such traffic-aware planning reduces necessary grid infrastructure by 40–70% while maintaining $\geq99\%$ service reliability.

Voltage stability for electrified roads with mobile PQ loads is analyzed via continuation power flow: unique voltage profile signatures ("half-leaf vein" for one-way and "harp-like" for two-way roads) arise, with stability constraints dictating the maximum feeder length ($L_\text{max}$) and fleet size ($N_\text{max}$) and the effect of on-board PV/capacitive compensation explicitly quantified (Wang et al., 2019).

7. Deployment, Economics, and Future Directions

Phased deployment strategies (pilot → scale-up → corridor-scale) coupled with innovative financing (green bonds, PPPs) reduce capital risk and operational disruption (Agnihotri et al., 14 Dec 2025). Case studies (e.g., Delhi Outer Ring Road) demonstrate energy delivery up to 9.8 GWh/year, 33,000 tCO₂/year emission reduction, payback in 6–8 years, and significant battery life extension. Key open questions involve long-term pavement–coil durability, deployment at urban megascale, integration of second-life batteries as buffers, and quantification of DWC's socio-economic impact on transit and last-mile logistics.

Technical challenges such as durable modular coil construction, wireless interoperability standards, multi-party billing, and grid resilience under DWC load pulses remain active research foci. Future research is projected to explore adaptive beamforming, advanced physical-layer security, joint optimization of energy-infrastructure investment, and integration of DWC into holistic urban mobility and energy management ecosystems (Agnihotri et al., 14 Dec 2025, Ma et al., 25 Mar 2024).