Dynamic wireless charging and vehicle infrastructure collaborative optimization control method

Abstract

The invention belongs to the technical field of intelligent traffic and electric vehicle wireless charging, and particularly relates to a dynamic wireless charging and vehicle-road collaborative optimization control method. The invention discloses an optimization control method named as a dynamic wireless charging and vehicle infrastructure collaborative optimization control method, and aims to solve the problems that energy supply and traffic operation are separated, a collaborative mechanism is lacked and the adaptability to heterogeneous vehicles is insufficient in the prior art. According to the method, a multi-source fusion sensing node is deployed to collect vehicle state and traffic flow information, a refined energy consumption prediction model is constructed, an effective charging window is determined in combination with dynamic wireless charging facility parameters, and a traffic signal control strategy is incorporated into a multi-target cost function, so that the energy consumption prediction accuracy is improved. A personalized speed adjustment suggestion and a power distribution scheme are generated by adopting a distributed rolling time domain optimization algorithm, and global collaborative optimization of the charging efficiency and the passing efficiency is realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and electric vehicle wireless charging technology, specifically, it relates to a dynamic wireless charging and vehicle-road cooperative optimization control method. Background Technology

[0002] With the deep integration of electrification and intelligentization, dynamic wireless charging technology and vehicle-road cooperative control systems are gradually becoming core pillars for building next-generation intelligent transportation infrastructure. Dynamic wireless charging enables electric vehicles to continuously replenish power during driving through contactless energy transfer between road-embedded transmitters and on-board receivers, effectively alleviating range anxiety. Vehicle-road cooperation, relying on roadside perception, communication, and control units, achieves real-time interaction and coordinated regulation of vehicle operating status, traffic flow evolution trends, and signal timing strategies, thereby improving traffic efficiency and operational safety. If these two technologies can be deeply coupled, it is expected that energy supply efficiency and traffic system performance can be simultaneously optimized in open road environments, forming a new intelligent transportation paradigm integrating "vehicle-road-energy." However, current research and engineering practices are still largely limited to the local optimization of single subsystems, and a joint modeling and cooperative control mechanism capable of coordinating the dynamic characteristics of both energy flow and traffic flow has not yet been established.

[0003] Specifically, existing technologies have attempted to achieve preliminary collaboration in specific scenarios. For example, patent CN118552008B proposes a method for energy-saving speed planning and dynamic wireless charging facility deployment for public transportation systems. It constructs a multi-objective optimization model with the goals of minimizing energy consumption and maximizing charging coverage, and uses a hybrid multi-objective artificial hummingbird algorithm to solve the Pareto front, achieving collaborative design of bus fleet operation strategies and charging facility layout under fixed routes. This method has good applicability in closed, predictable bus lane scenarios. Its core logic relies on strong constraints such as fixed routes, regular service frequencies, and uniform vehicle models, thus simplifying the complexity of vehicle behavior modeling and energy demand prediction. Correspondingly, another patent, CN116631757B, focuses on improving the physical layer performance of dynamic wireless charging systems. Through innovative design of the stacked structure of the transmitting coils and its collaborative excitation control circuit, it significantly enhances the electromagnetic coupling stability and energy transmission efficiency of the system under lateral vehicle offset or longitudinal speed fluctuations. This solution effectively addresses the issue of energy transfer robustness at the hardware level, providing a fundamental guarantee for high-efficiency dynamic charging.

[0004] However, with the large-scale deployment of intelligent connected electric vehicles on open urban roads, the inherent limitations of the aforementioned technological approaches at the principle level are becoming increasingly apparent. Fundamentally, this stems from the lack of a systematic characterization of the inherent coupling relationship between the two dynamic processes of "energy supply" and "traffic operation" within their optimization framework. Furthermore, when the application scenario expands from closed bus routes to open road networks containing various types of heterogeneous vehicles such as private cars, taxis, and logistics vehicles, vehicle arrival times, speeds, energy consumption characteristics, and charging demands all exhibit high randomness and time-varying characteristics, which existing bus-oriented models cannot effectively represent. Simultaneously, the signal timing adjustments, lane-level guidance, and speed guidance provided by vehicle-to-everything (V2X) systems essentially constitute key external variables affecting whether vehicles can efficiently pass through charging sections. However, current wireless charging control strategies generally treat these as static boundary conditions, failing to establish a dynamic feedback loop between charging power scheduling and traffic control commands. Building upon this, if energy transfer efficiency is improved solely at the hardware level while neglecting whether vehicles are in optimal traffic conditions (such as whether they are lingering in misaligned areas due to red lights), it may result in high power output but inefficient utilization, or even system thermal stress accumulation and lifespan degradation due to frequent start-stop cycles. This secondary contradiction arising from the disconnect between "charging" and "traffic" objectives is particularly prominent in high-density, highly heterogeneous urban traffic scenarios: on the one hand, guiding vehicles to slow down or change lanes to maximize charging benefits may exacerbate local congestion; on the other hand, ignoring charging windows to ensure traffic efficiency weakens the return on investment for dynamic charging infrastructure. Therefore, existing technologies, when addressing the complex needs of multi-source information fusion, multi-objective dynamic trade-offs, and multi-agent collaborative decision-making in open road environments, reveal deep-seated bottlenecks such as insufficient modeling granularity, lack of feedback mechanisms, and a single control dimension.

[0005] In summary, there is an urgent need to overcome the limitations of existing technologies, such as the closed nature of application scenarios, the single optimization dimension, and the lack of control linkage, and to construct a unified and collaborative optimization framework that can integrate real-time traffic conditions, refined vehicle energy consumption models, wireless charging facility operating parameters, and roadside active control strategies. Therefore, how to achieve deep coupling and global optimization of the dynamic wireless charging process and vehicle-road cooperative control commands while ensuring traffic efficiency has become a key challenge and a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] This invention provides a dynamic wireless charging and vehicle-road cooperative optimization control method, aiming to solve the technical problems in existing technologies such as the disconnect between energy supply process and traffic operation status, lack of cooperative mechanisms, single control dimension, and insufficient adaptability to various types of heterogeneous vehicles in open road environments. To achieve the above-mentioned objectives, this invention constructs a unified cooperative optimization framework that integrates real-time traffic flow state perception, refined vehicle energy consumption modeling, dynamic wireless charging facility operation control, and roadside active traffic control command generation. By establishing a two-way coupled feedback mechanism between energy flow and traffic flow, it achieves the global cooperative goal of maximizing charging efficiency and optimizing traffic efficiency.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: a dynamic wireless charging and vehicle-road cooperative optimization control method, the method comprising the following steps: First, deploying intelligent sensing nodes with multi-source information fusion capabilities on the roadside. These intelligent sensing nodes integrate millimeter-wave radar, video recognition units, geomagnetic detectors, and vehicle-road communication modules to collect real-time data on the position coordinates, speed, acceleration, vehicle type, battery charge state, and historical trajectory of all vehicles within the road segment. Second, based on the collected multi-dimensional vehicle state information, constructing a refined energy consumption prediction model for heterogeneous traffic flow. This model is based on vehicle dynamics equations and incorporates external disturbances such as rolling resistance, air resistance, slope correction factors, and air conditioning load to calculate the instantaneous power demand and cumulative energy consumption of each vehicle within a future preset time window. Third, spatiotemporally integrating the energy consumption prediction results with the physical layout parameters of the dynamic wireless charging road segment. The matching process involves using physical layout parameters, including the spatial distribution density of the transmitting coils, the upper limit of the rated output power, the electromagnetic field coverage range, and the thermal management threshold, to determine the effective charging window and corresponding schedulable power range for each vehicle when traversing the charging area. Furthermore, the phase timing plan, green wave speed guidance strategy, and lane-level path guidance instructions provided by the traffic signal control system are introduced as collaborative control variables and incorporated into the constraint set of the joint optimization objective function, forming a multi-objective cost function that includes energy replenishment benefits, travel time costs, queuing delay penalties, and system thermal load balance. Finally, a distributed rolling time-domain optimization algorithm is used to solve the multi-objective cost function, generating personalized speed profile adjustment suggestions for each connected vehicle and a dynamic power allocation scheme for the corresponding charging segment. Control commands are then synchronously sent to the on-board terminal and the roadside charging controller via the vehicle-to-infrastructure communication link, completing closed-loop collaborative execution.

[0008] In a preferred embodiment of the present invention, the refined energy consumption prediction model uses a piecewise linearization method to approximate the nonlinear air resistance term, specifically in the form: when the vehicle speed is less than a threshold v low At that time, the air drag coefficient is taken as a constant C. d1 When the vehicle speed is between vlow With v high Between v, the air drag coefficient increases linearly with a slope k1; when the vehicle speed is greater than v... high At that time, the air drag coefficient is taken as a constant C. d2 , where v low v high C d1 C d2 Both k1 and k2 are fixed parameters calibrated based on experimental data and are not adjusted according to changes in operating conditions.

[0009] Furthermore, the determination of the effective charging window depends on the relative positional relationship between the vehicle trajectory and the geometric center of the transmitting coil. The lateral offset distance between the i-th vehicle and the j-th transmitting coil at any time t is defined as d. ij (t), with a longitudinal overlap length of l ij (t), then the instantaneous coupling efficiency η of the vehicle under the action of the coil. ij (t)

[0010] It is given by the following empirical formula:

[0011]

[0012] Where, η max D represents the maximum coupling efficiency under ideal alignment conditions. safe For the maximum permissible lateral safety offset distance, L coil The effective length of a single transmitting coil; all three are pre-set system constants.

[0013] As one of the key innovations of this invention, the multi-objective cost function is constructed in a weighted sum form, and its mathematical expression is as follows:

[0014]

[0015] Where N represents the total number of controlled vehicles in the current optimization period, E charge,i T represents the actual amount of electricity charged by the i-th vehicle during this trip. travel,i For its total travel time, Q i H is the waiting time it generates at intersections or bottleneck sections. j Let w1, w2, w3, and w4 be the cumulative heat load index of the j-th charging unit in this round of scheduling, and w1, w2, w3, and w4 be preset weight coefficients that satisfy w1+w2+w3+w4=1 and each weight value remains unchanged after being configured according to the operation priority during the system initialization phase.

[0016] Furthermore, the distributed rolling time-domain optimization algorithm adopts a two-layer iterative structure. The outer loop is responsible for updating the global traffic situation estimate and re-dividing the optimization sub-regions. The inner loop independently solves the local optimal control sequence in each sub-region. Adjacent sub-regions exchange information and maintain consistency constraints by sharing the state variables of boundary vehicles. After each optimization cycle, only the instruction corresponding to the first control step is issued and executed, and the rest is retained for the rolling update of the next cycle, ensuring that the system has the online adaptability to cope with sudden traffic events.

[0017] As another important feature of this invention, after receiving the power scheduling command from the central collaborative optimization engine, the roadside charging controller adjusts the inverter output frequency and duty cycle through the pulse width modulation drive circuit to ensure that the amplitude of the transmitting coil current accurately tracks the target power curve. Simultaneously, it monitors the feedback signal from the coil temperature sensor. If the temperature of any coil unit exceeds a preset upper limit T, the controller will immediately take action. limit The power reduction protection logic is immediately activated, and a thermal overload alarm is sent to the central collaborative optimization engine, triggering a new round of global re-optimization process.

[0018] In addition, after receiving the speed profile adjustment suggestion, the vehicle terminal converts it into target commands for throttle opening and braking pressure through the CAN bus interface, which are then executed by the vehicle longitudinal controller. If the vehicle fails to strictly follow the suggested trajectory due to driver intervention or other reasons, the vehicle terminal continuously uploads the actual driving deviation to the roadside perception node, which then corrects the prediction model input of subsequent vehicles, thereby improving the overall coordination accuracy.

[0019] As a specific implementation of the present invention, the intelligent sensing node is connected to the central collaborative optimization engine through an optical fiber backbone network to ensure low-latency and high-reliability data transmission; the central collaborative optimization engine interacts with the roadside charging controller and traffic signal controller using the industrial Ethernet protocol; the vehicle-road communication module supports C-V2X PC5 direct communication mode to ensure that basic collaborative functions can still be maintained in areas without cellular network coverage.

[0020] This invention effectively solves the problem of the separation between energy supply and traffic operation in existing technologies by constructing a four-layer closed-loop collaborative architecture of "sensing-model-optimization-execution".

[0021] The above solution achieves global collaborative optimization of "vehicle-road-energy": Unlike existing technologies that treat traffic control as a static boundary, this solution incorporates variables such as signal timing and green wave guidance into a multi-objective cost function. This avoids inefficient charging caused by vehicles queuing at red lights and stopping in misaligned areas, achieving simultaneous optimization of traffic efficiency and energy replenishment.

[0022] It possesses high adaptability to complex and heterogeneous traffic flows: Existing solutions (such as dedicated bus lanes) mostly rely on strong constraints of uniform vehicle types and fixed routes. The refined energy consumption model constructed in this solution considers rolling resistance, air resistance, and auxiliary system load, and performs piecewise linearization of nonlinear terms, which can accurately predict the randomized demand of various heterogeneous vehicles such as private cars and logistics vehicles in open road networks.

[0023] Refined charging window and power matching: By calculating the lateral offset distance and longitudinal overlap length between the vehicle's center of gravity and the coil in real time, the coupling efficiency η is dynamically determined. ij (t). This spatiotemporal matching mechanism ensures that the roadside coils can accurately allocate power according to the actual alignment state during vehicle operation, significantly improving the effectiveness of energy transmission.

[0024] Full lifecycle system thermal safety assurance: This solution innovatively incorporates the "cumulative heat load index" into the optimization objective and sets up hardware-level power reduction protection logic. This not only prevents equipment damage caused by coil overheating but also triggers global re-optimization through a feedback mechanism, ensuring the continuous stability of the system under high loads.

[0025] In summary, this invention, by constructing a closed-loop collaborative architecture encompassing four levels—perception, modeling, optimization, and execution—achieves deep spatiotemporal integration of the dynamic wireless charging process and vehicle-road cooperative control strategies. This not only resolves the conflict between charging and traffic objectives in traditional solutions but also significantly improves the overall operational efficiency and energy utilization efficiency of diverse, heterogeneous vehicle groups in open road environments. Specifically:

[0026] 1. Real-time response capabilities resulting from distributed scrolling time-domain optimization.

[0027] This algorithm adopts a two-layer iterative structure, with the outer layer responsible for global situation estimation and the inner layer responsible for finding local optima in sub-regions.

[0028] Computational advantages: By dividing long road segments into multiple sub-regions, the high-dimensional nonlinear problem is reduced in dimensionality, significantly shortening the solution time and meeting the real-time requirements of second-level control of open roads.

[0029] Dynamic robustness: The system executes only the instructions for the first step and continuously updates. This means that when sudden traffic events such as emergency braking or temporary lane changes occur on the road segment, the system has the ability to adapt and re-optimize online.

[0030] 2. The closed-loop feedback correction mechanism significantly improves control accuracy.

[0031] This solution not only issues instructions but also establishes a feedback loop of "deviation upload - model correction".

[0032] Anti-interference capability: If the driver does not strictly follow the recommended speed profile, the on-board terminal will upload the driving deviation in real time.

[0033] Self-healing: The roadside sensing nodes correct the predicted input of subsequent vehicles, offsetting the accumulated errors caused by human driving uncertainties or sensor noise, and ensuring the accuracy of closed-loop control.

[0034] 3. Multi-mode communication architecture ensures deterministic transmission of instructions.

[0035] The system adopts a multi-level communication scheme of "fiber optic + industrial Ethernet + C-V2X".

[0036] Extremely low latency: End-to-end latency of less than 10ms is achieved through the fiber optic backbone network, ensuring instantaneous response of vehicle-road cooperation;

[0037] High-reliability coverage: Support for C-V2X PC5 direct connection mode ensures that the system can still maintain basic collaborative charging functions in extreme environments such as tunnels and underground passages where there is no cellular network coverage. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the overall system architecture of the dynamic wireless charging and vehicle-road cooperative optimization control method described in this invention.

[0039] Figure 2 This is a schematic diagram of the multi-source information fusion and data acquisition module of the intelligent sensing node in this invention.

[0040] Figure 3 This is a schematic diagram illustrating the geometric relationship for calculating the effective charging window and coupling efficiency when a vehicle passes through a dynamic wireless charging section in this invention.

[0041] Figure 4 This is a schematic diagram of the two-layer iterative solution process of the distributed rolling time-domain optimization algorithm used in this invention.

[0042] Figure 5 This is a schematic diagram of the instruction issuance and closed-loop execution path from the central collaborative optimization engine to roadside equipment and vehicle terminals in this invention. Detailed Implementation

[0043] This invention provides a dynamic wireless charging and vehicle-road cooperative optimization control method. Its core lies in constructing a closed-loop cooperative architecture covering four levels: perception, modeling, optimization, and execution, to achieve deep integration of energy flow and traffic flow in the spatiotemporal dimensions. The specific embodiments of this invention will be described in detail below with reference to the accompanying drawings and technical details.

[0044] See Figure 1 , Figure 2At the system deployment level, several intelligent sensing nodes are first deployed along the target road segment. Each intelligent sensing node integrates millimeter-wave radar, a video recognition unit, a geomagnetic detector, and a vehicle-to-infrastructure (V2I) communication module. The millimeter-wave radar is used for all-weather, high-precision ranging and speed estimation. The video recognition unit uses a deep learning model to classify vehicle types and recognize license plates. The geomagnetic detector uses magnetic field disturbances caused by vehicles to help determine the vehicle's presence and axle information. The V2I communication module supports the C-V2X PC5 direct communication protocol, ensuring basic data interaction capabilities are maintained even in areas without cellular network coverage. The data collected by these multi-source sensors is time-synchronized and spatially registered by the embedded processor within the node, generating a vehicle state vector in a unified spatiotemporal coordinate system. This vector includes each vehicle's position coordinates, speed, acceleration, vehicle type, battery state of charge, and a sequence of historical trajectory points from the most recent few seconds. All intelligent sensing nodes are connected to a central collaborative optimization engine via a fiber optic backbone network, ensuring millisecond-level latency and high-reliability data transmission.

[0045] After receiving real-time traffic flow data from various intelligent sensing nodes, the central collaborative optimization engine initiates a refined energy consumption prediction model for heterogeneous traffic flow. This model is based on the classical vehicle longitudinal dynamics equations and is expressed as follows:

[0046]

[0047] Where m is the vehicle mass, g is the acceleration due to gravity, and f r Let P be the rolling resistance coefficient, θ be the road slope angle, ρ be the air density, A be the vehicle's frontal area, v(t) and a(t) be the vehicle speed and acceleration at time t, respectively. aux This refers to the load power of auxiliary systems such as air conditioning and lighting. For the nonlinear term C... d v(t) 2 This invention employs a piecewise linearization strategy: when v(t) <v low At that time, the air drag coefficient is taken as a constant C. d1 When v low ≤v(t)≤v high At that time, C d C increases linearly with slope k1, i.e. d (v)=C d1 +k1(vv low When v(t) > v high At that time, C d Take the constant C d2 Parameter v low v high C d1 C d2Both k1 and k2 are calibrated and determined based on measured wind tunnel test data and are fixed in the model parameter library, not dynamically adjusted according to operating conditions. Based on this model, the system can predict the instantaneous power demand P of each vehicle within a preset time window in the future. req,i (t) and cumulative electricity consumption

[0048] Simultaneously, the system acquires the physical layout parameters of the dynamic wireless charging section, including the spatial distribution density of the transmitting coils along the lane centerline, the upper limit of the rated output power of a single coil group (e.g., 30kW), the effective lateral coverage range of the electromagnetic field (±15cm), and the thermal management threshold. Combining the predicted vehicle trajectory with the coil geometry, the effective charging window for each vehicle when crossing the charging area is calculated. Figure 3 As shown, the lateral offset distance between the i-th vehicle and the j-th transmitting coil at any time t is defined as d. ij (t), which is the vertical distance between the vehicle's center of gravity projection point and the coil centerline; longitudinal overlap length l ij (t) is defined as the length of the intersection of the projections of the vehicle chassis receiving coil and the roadside transmitting coil in the direction of travel. Based on this, the instantaneous coupling efficiency η ij (t) is given by an empirical formula:

[0049]

[0050] Where, η max For ideal alignment state (d ij =0,l ij =L coil The maximum coupling efficiency under these conditions is typically 0.92; D safe The maximum permissible lateral safety offset distance is set to 20cm; L coil The effective length of a single transmitting coil is typically 40 cm. Therefore, the theoretical charging power that the i-th vehicle can obtain at time t is P. charge,i (t)=∑ j η ij (t)·P tx,j (t), where P tx,j (t) represents the actual output power of the j-th transmitting coil at time t.

[0051] Furthermore, the system incorporates phase timing plans, green wave speed guidance strategies, and lane-level path guidance instructions provided by the traffic signal control system as cooperative control variables. These variables are formalized as constraints and incorporated into the mathematical model of the joint optimization problem. The optimization objective function is constructed as a weighted sum of multi-objective cost functions:

[0052]

[0053] Where N is the total number of controlled vehicles in the current optimization cycle; T represents the actual amount of electricity charged by the i-th vehicle while it is traversing the charging section; travel,i Q: The total travel time from entrance to exit; i For the waiting time in queues caused by red lights or congestion at intersections or bottleneck sections; H j Let the cumulative heat load index of the j-th charging unit in this round of scheduling be defined as follows: Where T coil,j (t) represents the real-time temperature of the coil, T amb For ambient temperature, T limit The preset temperature upper limit is set. The weighting coefficients w1, w2, w3, and w4 satisfy w1 + w2 + w3 + w4 = 1. Their values ​​are configured according to the operation strategy during the system initialization phase. For example, when focusing on traffic efficiency during peak hours, w2 = 0.5 and w1 = 0.3 can be set, while when focusing on energy replenishment during off-peak hours, w1 = 0.6 and w2 = 0.2 can be set. After configuration, they remain unchanged within a single operating cycle.

[0054] To solve the aforementioned high-dimensional nonlinear optimization problem, this invention employs a distributed rolling time-domain optimization algorithm. For example... Figure 4 As shown, the algorithm employs a two-layer iterative structure. The outer loop runs at a fixed period (5 seconds) and is responsible for updating the global traffic situation estimate, including vehicle queue length, average speed field, and charging segment occupancy rate. Based on this, the entire road is divided into several optimization sub-regions, each covering several consecutive transmitting coils and their corresponding lane segments. The inner loop runs independently within each sub-region, using the local vehicle set as the optimization object to solve for the locally optimal control sequence that satisfies boundary consistency constraints. Adjacent sub-regions exchange information by sharing the state variables of boundary vehicles (speed and position when entering the next region), ensuring the continuity of the global solution. After each optimization cycle, only the instruction corresponding to the first control step (1 second) is issued and executed; the remaining predicted control sequences are retained for rolling updates in the next cycle, thus giving the system online adaptability to sudden events (emergency braking, temporary lane changes).

[0055] The optimization results generate two types of control commands: one is a personalized speed profile adjustment suggestion for each connected vehicle, and the other is a dynamic power allocation scheme for the corresponding charging segment. The former is sent to the on-board terminal via the vehicle-to-infrastructure communication link in C-V2X message format, while the latter is transmitted to the roadside charging controller via the industrial Ethernet protocol. After receiving the speed profile suggestion, the on-board terminal parses the target speed-time curve and converts it into target commands for throttle opening and braking pressure via the CAN bus interface, which are then executed by the vehicle's longitudinal controller. If the vehicle fails to strictly follow the suggested trajectory due to driver intervention, sensor noise, or other reasons, the on-board terminal continuously uploads the actual driving deviation (including position error, speed deviation, and acceleration fluctuation) via the PC5 interface. The roadside sensing node receives this and uses it as a feedback correction item to update the initial conditions for trajectory prediction of subsequent vehicles, thereby improving the overall coordination accuracy.

[0056] After receiving power scheduling commands from the central collaborative optimization engine, the roadside charging controller drives the pulse width modulation (PWM) inverter circuit to adjust the output frequency and duty cycle, ensuring that the transmitter coil current amplitude accurately tracks the target power curve. The controller has a built-in temperature monitoring module that collects thermistor signals from each coil unit in real time. If the temperature of any coil exceeds the preset upper limit T... limit The system immediately initiates a power reduction protection logic: the output power of the coil is linearly reduced to a safe level at a preset slope, while a thermal overload alarm flag is sent to the central collaborative optimization engine. Upon receiving the flag, the central engine sets the upper limit of the coil's available power to zero in the next optimization cycle and reallocates the load of surrounding coils, triggering a new round of global re-optimization to ensure system thermal stability.

[0057] As a specific implementation of the present invention, the entire system adopts a layered communication architecture. The intelligent sensing nodes are connected to the central collaborative optimization engine via single-mode optical fiber, with a transmission bandwidth of not less than 1Gbps and an end-to-end latency of less than 10ms. The central collaborative optimization engine communicates with the roadside charging controller and traffic signal controller using the Industrial Ethernet (IEEE 802.3) protocol, supporting Time-Sensitive Networking (TSN) characteristics to ensure deterministic transmission of control commands. The vehicle-to-infrastructure communication module operates in the 5.9GHz ITS band, supports C-V2X PC5 Mode 4 as defined in 3GPP Release 14, and has broadcast, multicast, and unicast capabilities, with a communication range of not less than 300 meters and a message update frequency of not less than 10Hz.

[0058] In summary, this invention constructs a scalable, adaptable, and highly efficient vehicle-road cooperative charging optimization system by deeply integrating real-time traffic perception, refined energy consumption modeling, dynamic charging regulation, and active traffic control. Its technical solution not only achieves spatiotemporal coordination between the charging process and traffic operation, but also ensures broad applicability and engineering feasibility for various types of heterogeneous vehicles in open road environments through a distributed optimization architecture and closed-loop feedback mechanism.

[0059] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A dynamic wireless charging and vehicle-road cooperative optimization control method, characterized in that, Includes the following steps: Step 1: Deploy intelligent sensing nodes on the roadside to collect real-time data on the location coordinates, speed, acceleration, vehicle type, battery charge status, and historical trajectory of all vehicles within the road segment; Step 2: Based on the collected multi-dimensional vehicle status information, construct a refined energy consumption prediction model to calculate the instantaneous power demand and cumulative energy consumption of each vehicle within a preset time window in the future; Step 3: Spatiotemporally match the energy consumption prediction results with the physical layout parameters of the dynamic wireless charging section. The physical layout parameters include the spatial distribution density of the transmitting coil, the upper limit of the rated output power, the electromagnetic field coverage range and the thermal management threshold, so as to determine the effective charging window and the corresponding schedulable power range of each vehicle when passing through the charging area. Step 4: Introduce the phase timing plan, green wave speed guidance strategy and lane-level path guidance instructions provided by the traffic signal control system as cooperative control variables, and incorporate them into the constraint set of the joint optimization objective function to form a multi-objective cost function that includes energy replenishment benefits, travel time costs, queuing delay penalties and system heat load balance. Step 5: Use a distributed rolling time-domain optimization algorithm to solve the multi-objective cost function, and generate personalized speed profile adjustment suggestions and dynamic power allocation schemes for the corresponding charging segments for each connected vehicle. The personalized speed profile adjustment suggestions are sent to the vehicle terminal via the vehicle-to-infrastructure communication link, and the dynamic power allocation scheme is sent to the roadside charging controller to complete closed-loop collaborative execution.

2. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, In step 1, the intelligent sensing node integrates millimeter-wave radar, video recognition unit, geomagnetic detector, and vehicle-to-infrastructure (V2I) communication module. The millimeter-wave radar is used for ranging and speed estimation, the video recognition unit is used for vehicle type classification and license plate recognition, the geomagnetic detector is used to determine the vehicle's presence status and axle count information, and the V2I communication module supports the C-V2X PC5 direct communication protocol. The intelligent sensing node is connected to the central collaborative optimization engine through a fiber optic backbone network.

3. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, In step 2, the refined energy consumption prediction model is based on the vehicle's longitudinal dynamics equations and combines rolling resistance, air resistance, slope correction factor and auxiliary system load power to calculate instantaneous power demand. Specifically, the nonlinear air resistance term is processed using a piecewise linearization method: when the vehicle speed is less than a first speed threshold, the air resistance coefficient is a first constant; when the vehicle speed is between the first speed threshold and a second speed threshold, the air resistance coefficient increases linearly with a first slope; when the vehicle speed is greater than the second speed threshold, the air resistance coefficient is a second constant. The first speed threshold, the second speed threshold, the first constant, the second constant, and the first slope are all fixed parameters calibrated based on measured wind tunnel test data.

4. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, In step 3, the determination of the effective charging window depends on the relative positional relationship between the vehicle trajectory and the geometric center of the transmitting coil. The lateral offset distance and longitudinal overlap length between the target vehicle and the target transmitting coil are defined at any given time. The instantaneous coupling efficiency of the target vehicle under the action of the target transmitting coil is determined as follows: if the lateral offset distance is less than the maximum permissible lateral safe offset distance and the longitudinal overlap length is greater than zero, then the instantaneous coupling efficiency is equal to the maximum coupling efficiency multiplied by a first compensation coefficient and then by a second compensation coefficient. The first compensation coefficient is 1 minus the ratio of the lateral offset distance to the maximum permissible lateral safe offset distance, and the second compensation coefficient is the ratio of the longitudinal overlap length to the effective length of a single transmitting coil. Otherwise, the instantaneous coupling efficiency is zero.

5. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, In step 4, the multi-objective cost function is constructed as a weighted sum, and its mathematical expression includes the weighted sum of the following four terms: the total actual charging power obtained by all controlled vehicles during the trip, the total travel time of all controlled vehicles, the total queuing time of all controlled vehicles at intersections or bottleneck sections, and the maximum value of the cumulative heat load index of all charging units in the scheduling cycle; the weight coefficients of each term are preset values, and the sum of all weight coefficients is 1. Specifically, its mathematical expression is as follows: wherein N represents the total number of controlled vehicles in the current optimization period, E charge,i represents the actual charging power obtained by the ith vehicle in this trip, T travel,i is the total travel time thereof, Q i is the queuing waiting time thereof at the intersection or bottleneck section, H j is the cumulative thermal load index of the jth charging unit in this round of scheduling, w1, w2, w3, and w4 are preset weight coefficients, satisfying w1+w2+w3+w4=1 and each weight value remains unchanged after being configured according to the operation priority in the system initialization phase.

6. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 5, characterized in that, The cumulative heat load index is calculated as follows: for the target charging unit, the ratio of the difference between its real-time temperature and ambient temperature to the difference between the preset upper temperature limit and ambient temperature is calculated by integrating within the scheduling cycle.

7. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, In step 5, the distributed rolling temporal optimization algorithm adopts a two-layer iterative structure. The outer loop runs at a fixed period and is responsible for updating the global traffic situation estimate and re-dividing the optimization sub-regions. The inner loop runs independently in each optimization sub-region, taking the local vehicle set as the optimization object, and solving for the local optimal control sequence that satisfies the boundary consistency constraint. Adjacent optimization sub-regions exchange information by sharing the state variables of the boundary vehicles. After each optimization cycle, only the instruction corresponding to the first control step size is issued and executed, and the remaining predicted control sequences are retained for the rolling update of the next cycle.

8. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, After receiving the dynamic power allocation scheme, the roadside charging controller adjusts the inverter output frequency and duty cycle through the pulse width modulation drive circuit to ensure that the amplitude of the transmitting coil current accurately tracks the target power curve. At the same time, it monitors the temperature sensor feedback signals of each coil unit in real time. Once the temperature of any coil unit exceeds the preset temperature limit, it immediately activates the power reduction protection logic and sends a thermal overload alarm flag to the central collaborative optimization engine to trigger a new round of global re-optimization process.

9. The dynamic wireless charging and vehicle-road cooperative optimization control method according to claim 1, characterized in that, After receiving the personalized speed profile adjustment suggestion, the vehicle terminal converts it into target commands for throttle opening and braking pressure via the controller area network bus interface, which are then executed by the vehicle longitudinal controller. If the vehicle fails to strictly follow the suggested trajectory due to driver intervention or other reasons, the vehicle terminal continuously uploads the actual driving deviation to the roadside sensing node, which then corrects the prediction model input for subsequent vehicles accordingly.

10. The dynamic wireless charging and vehicle-road cooperative optimization control method according to any one of claims 1 to 9, characterized in that, The central collaborative optimization engine interacts with the roadside charging controller and traffic signal controller using the industrial Ethernet protocol; the vehicle-road communication link supports C-V2X PC5 direct communication mode.

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