Electric vehicle rapid charging power adaptive distribution system for time-of-use electricity price

Abstract

The invention provides a time-of-use electricity price-oriented electric vehicle rapid charging power adaptive distribution system, and relates to the technical field of new energy vehicle charging, the time-of-use electricity price-oriented electric vehicle rapid charging power adaptive distribution system comprises a distribution system, the distribution system comprises an input layer, a main analysis layer, a distribution layer and a balance layer, wherein the input layer collects time-of-use electricity price signals through a sensor and preprocesses the time-of-use electricity price signals into main control signals, the main analysis layer analyzes the signals through a determinacy comparison algorithm and generates distribution instructions, the distribution layer dynamically divides power through a multi-module circuit, and the balance layer optimizes battery charge state balance and energy recovery through the multi-module circuit. The system improves the precision and anti-interference capability of electricity price signal acquisition, and a photoelectric sensor acquisition module of an input layer is adopted, a photodiode array with high response speed is adopted, an RC filter circuit is matched to smooth signals, high-frequency noise of a power grid is removed, and stable voltage signals are generated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy vehicle charging technology, specifically to a fast charging power adaptive allocation system for time-of-use electricity price. BACKGROUND

[0002] With the popularity of electric vehicles (EV) and the development of smart grid technology, time-of-use electricity price-oriented charging systems are widely used in urban charging stations, residential areas, and industrial parks. These systems monitor the electricity price signal, combine battery state data, and adjust the charging power allocation to optimize charging efficiency, reduce grid load, and extend battery life. Existing technologies mainly include the following aspects: Electricity price signal acquisition: Collecting electricity price signals through voltage or current sensors, some using fiber-optic transmission pulse signals, converting them into analog or digital signals for determining the electricity price period. Devices include photoelectric sensors or voltage transformers with basic filtering circuits.

[0003] Data processing and analysis: Analyzing electricity price signals and battery state of charge (SOC) through embedded microcontrollers or simple algorithms, generating charging priority or allocation instructions based on preset thresholds, and evaluating battery status with BMS feedback.

[0004] Power allocation and control: Using relays, MOSFET switches, or simple thyristor circuits for power shunting, distributing power through fixed rules, supporting parallel charging during low valley periods or current limiting during peak periods.

[0005] Battery state balancing: Adjusting SOC through parallel resistors or simple balancing circuits, with some systems using voltage stabilizing circuits to stabilize charging voltage.

[0006] Despite some progress in existing technologies, there are still problems: Low accuracy and weak anti-interference ability of electricity price signal acquisition: Fixed sampling frequency sensors cannot dynamically adjust, and signals are easily disturbed by grid environment, affecting the accuracy of electricity price determination.

[0007] Insufficient data fusion and analysis capabilities: Only processing single or simple superimposed data, lacking multi-source data fusion, allocation instructions are not precise enough, and it is difficult to adapt to the differences in SOC of multiple vehicles.

[0008] Lack of dynamic and efficient power allocation: Fixed rule-based shunting is inefficient, peak period current limiting is not flexible, and valley period power utilization is insufficient.

[0009] Limited SOC balancing effect and imperfect feedback closed loop: The balancing circuit responds slowly and has low accuracy, lacking efficient closed-loop feedback, affecting optimization and battery life.

[0010] Therefore, a time-of-use electricity price-oriented electric vehicle fast charging power adaptive allocation system is needed to solve the above problems. SUMMARY

[0011] TECHNICAL SOLUTION To achieve the above object, the application is implemented by the following technical solution: a time-of-use electricity price-oriented electric vehicle fast charging power adaptive allocation system, comprising an allocation system, which contains an input layer, a main analysis layer, an allocation layer and a balancing layer, wherein the input layer collects time-of-use electricity price signals through sensors and preprocesses them into main control signals, the main analysis layer analyzes the signals through a deterministic comparison algorithm and generates allocation instructions, the allocation layer dynamically distributes power using a multi-module circuit, and the balancing layer optimizes battery state of charge balancing and energy recovery through a multi-module circuit, the allocation system realizes power adaptive allocation driven by electricity prices through nonlinear circuit characteristics and comparison algorithms, voltage signals, current pulses and capacitive coupled data transmission are selected between layers to support fast charging of multiple electric vehicle batteries.

[0012] Preferably, the input layer comprises the following modules: An optoelectronic sensor acquisition module: collects time-of-use electricity price signals of the power grid (in the form of optical fiber transmitted pulses), converts them into voltage signals, and the specific steps are as follows: S1a, real-time monitoring of optical pulses; S1b, converting the pulses into 0-5V voltage signals; S1c, smoothing the signals through a filter circuit, and outputting the voltage signals through an inductive bridge to the main analysis layer, which receives the voltage signals as electricity price inputs.

[0013] An inductive preprocessing module: uses an inductive coil to preprocess the input signals, and the specific steps are as follows: S2a, receiving the voltage signals of the optoelectronic module through an inductive bridge; S2b, generating an initial current shunt through inductive magnetic flux changes; S2c, outputting 0-10A current pulses through capacitive coupling to the main analysis layer; the main analysis layer receives the current pulses as power references.

[0014] Preferably, the main analysis layer comprises the following modules: A signal analysis module: analyzes the input layer signals through a comparison algorithm (comparing the electricity price signals with preset threshold values) to generate allocation priorities, and the specific steps are as follows: S3a, receiving the voltage signals and current pulses of the input layer; S3b, comparing the signals with the low valley / high peak threshold values (preset 4V / 4.5V); S3c, generating priority signals (0-5V) through voltage lines to the allocation layer; the allocation layer receives the priority signals as shunt references.

[0015] Battery state analysis module: monitor the battery state of charge and generate distribution instructions, the specific steps are: S4a, receive the input layer of current pulse and external battery voltage feedback; S4b, compare the battery state of charge with the priority signal; S4c, output binary distribution instruction (high / low level), through capacitive coupling transmission to the distribution layer; the distribution layer receives the instruction to adjust the power shunt.

[0016] Preferably, the distribution layer includes the following modules: Silicon-controlled pulse distribution module: trigger pulse shunt through silicon-controlled array according to the main analysis layer instruction, the steps are: S5a, receive priority signal and binary instruction through voltage line and capacitive coupling; S5b, adjust the silicon-controlled conduction angle to generate frequency division pulse (10-100 kHz); S5c, shunt power to high priority battery path, low valley period parallel conduction, high peak period current limiting series, output power flow through inductive bridge transmission to the equalization layer; the equalization layer receives the power flow to optimize the distribution.

[0017] Magnetic control switch shunt module: use magnetic control switch to dynamically adjust the shunt impedance according to the instruction, the steps are: S6a, receive binary instruction through capacitive coupling; S6b, apply control current to adjust the switch magnetic field and shunt power; S6c, output shunt power through inductive bridge transmission to the equalization layer, maximize current in low valley period and disperse load in high peak period; the equalization layer receives the shunt power for equalization.

[0018] Preferably, the equalization layer includes the following modules: Electroluminescent voltage stabilization module: stabilize the battery charging voltage through electroluminescent diode, the steps are: S7a, receive the power flow of the distribution layer through the inductive bridge; S7b, clamp the voltage to the stable range through the light-emitting characteristic; S7c, branch the power to the low state of charge battery, and output the remaining energy through capacitive coupling feedback to the input layer; the input layer receives the feedback to adjust the collection.

[0019] Ferroelectric capacitor equalization module: equalize the battery power by polarizing and flipping the ferroelectric capacitor, the steps are: S8a, receive the power flow of the distribution layer and the battery state of charge feedback; S8b, apply electric field to flip the polarization distribution power; S8c, feedback equalization signal through capacitive coupling to the input layer to form a closed loop; the input layer receives the feedback optimization signal collection.

[0020] Preferably, the photoelectric sensor acquisition module acquires the electricity price pulse transmitted by the optical fiber through the photoelectric sensor (response time <1 microsecond), cooperates with the filter circuit (time constant 0.1 millisecond) to output the smooth voltage signal, and the inductive pretreatment module generates 0-10A current pulse through the inductive coil (high saturation magnetic flux density), the signal is transmitted through inductive bridge and capacitive coupling, and the noise resistance of the input layer is adjusted.

[0021] Preferably, the signal analysis module generates a priority signal through a comparison algorithm (threshold comparison, preset trough / peak threshold), the battery state analysis module outputs a binary instruction by comparing the battery state of charge with the priority signal, and the signal is transmitted to the distribution layer through voltage lines and capacitive coupling to adjust the adaptive distribution stability.

[0022] Preferably, the silicon-controlled pulse distribution module generates a frequency division pulse through a silicon-controlled array (adjustable conduction angle 0-180 degrees), which is coupled to the magnetic control switch distribution module through an inductive bridge. The magnetic control switch adjusts the magnetic field by controlling the current, maximizes the current injection in the trough period, and limits the current to 50% in the peak period.

[0023] Preferably, the electroluminescent voltage stabilization module stabilizes the branch voltage through the nonlinear light-emitting characteristics of the electroluminescent diode, and the ferroelectric capacitor equalization module equalizes the state of charge of multiple batteries through polarization reversal (reversal time <0.5 milliseconds). The feedback signal is coupled to the input layer through a capacitor to form a closed loop of the whole system.

[0024] Advantages

[0025] The present application provides a fast charging power adaptive distribution system for electric vehicles facing time-of-use electricity prices. It has the following advantages: 1. The system improves the accuracy and anti-interference ability of electricity price signal acquisition. Through the input layer's photoelectric sensor acquisition module, a high-response-speed photodiode array is used, and an RC filter circuit is used to smooth the signal and remove power grid high-frequency noise to generate a stable voltage signal. The inductive bridge transmission enhances the anti-interference ability, and the closed-loop feedback mechanism dynamically adjusts the sensor gain and filter parameters to adapt to electricity price fluctuations, ensuring accurate judgment of trough and peak electricity price periods, and improving signal acquisition reliability and stability.

[0026] 2. The system enhances the accuracy of data fusion and analysis. The main analysis layer fuses multiple source data such as electricity price signal, battery state of charge, voltage, current and temperature through signal analysis module and battery state analysis module, and generates accurate priority signal and distribution instruction by running threshold comparison and state comparison algorithm combined with total database support. Comprehensive evaluation of power grid state and battery demand, adapt to multi-vehicle SOC difference scene, ensure the accuracy of distribution instruction, improve the adaptive ability and decision accuracy.

[0027] 3. The system realizes dynamic and efficient power distribution. The distribution layer dynamically adjusts the conduction angle and impedance through the silicon-controlled pulse distribution module and the magnetic control switch distribution module. In the trough period, high-frequency wide pulse and low impedance are used to maximize power utilization, supporting parallel fast charging; in the peak period, low-frequency narrow pulse and increased impedance are used to flexibly limit current, reducing power grid load. Closed-loop feedback optimizes distribution parameters, responds to electricity price and SOC changes in real time, and improves charging efficiency and power grid protection capability.

[0028] 4. This system optimizes SOC equalization and the closed-loop feedback mechanism. The equalization layer achieves voltage stability and rapid SOC equalization through an electroluminescent voltage regulator module and a ferroelectric capacitor equalization module. The electroluminescent module clamps the voltage, prioritizing power allocation to batteries with low SOC; the ferroelectric capacitor module equalizes SOC through rapid polarization switching, preventing overcharging or undercharging. The closed-loop feedback mechanism optimizes signal acquisition and allocation parameters, and the overall database supports real-time data querying, enhancing SOC equalization accuracy, battery life, and system stability. Attached Figure Description

[0029] Fig. 1 This is a framework diagram of the distribution system of the present invention; Fig. 2 This is a system flowchart of the present invention; Fig. 3 This is a schematic diagram of the system data flow of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1: like Figs. 1-3 As shown, an adaptive power allocation system for fast charging of electric vehicles based on time-of-use pricing includes an allocation system comprising an input layer, a main analysis layer, an allocation layer, and an equalization layer. The input layer collects time-of-use pricing signals through sensors and preprocesses them into main control signals. The main analysis layer parses the signals and generates allocation instructions using a deterministic comparison algorithm. The allocation layer dynamically distributes power using multi-module circuits. The equalization layer optimizes battery state-of-charge balancing and energy recovery using multi-module circuits. The allocation system achieves price-driven adaptive power allocation through nonlinear circuit characteristics and comparison algorithms. Data is transmitted between layers using voltage signals, current pulses, and capacitive coupling, supporting fast charging of batteries in multiple electric vehicles.

[0032] The input layer includes the following modules: Photoelectric sensor acquisition module: Acquires time-of-use electricity price signals from the power grid (in pulse form transmitted via optical fiber) through photoelectric sensors, and converts them into voltage signals. The specific steps are as follows: S1a, Real-time monitoring of light pulses; S1b, Converting pulses into 0-5V voltage signals; S1c, Smoothing the signal through a filter circuit, and transmitting the output voltage signal to the main analysis layer through an inductor bridge. The main analysis layer receives the voltage signal as the electricity price input.

[0033] Inductive pre-processing module: using inductive coil to pre-process input signal, the specific steps are as follows: S2a, receiving voltage signal of photoelectric module through inductive bridge; S2b, generating initial current shunt through inductive magnetic flux change; S2c, outputting 0-10A current pulse through capacitive coupling transmission to main analysis layer; the main analysis layer receives the current pulse as power reference.

[0034] The main analysis layer includes the following modules: Signal analysis module: analyzing input layer signal through comparison algorithm (comparing electricity price signal with preset threshold value) to generate distribution priority, the specific steps are as follows: S3a, receiving voltage signal and current pulse of input layer; S3b, comparing signal with low valley / peak threshold value (preset 4V / 4.5V); S3c, generating priority signal (0-5V) through voltage line transmission to distribution layer; the distribution layer receives the priority signal as shunt basis.

[0035] Battery state analysis module: monitoring battery state of charge and generating distribution instruction, the specific steps are as follows: S4a, receiving current pulse of input layer and external battery voltage feedback; S4b, comparing battery state of charge with priority signal; S4c, outputting binary distribution instruction (high / low level) through capacitive coupling transmission to distribution layer; the distribution layer receives the instruction to adjust power shunt.

[0036] The distribution layer includes the following modules: Silicon controlled pulse distribution module: triggering pulse shunt according to main analysis layer instruction through silicon controlled array, the steps are as follows: S5a, receiving priority signal and binary instruction through voltage line and capacitive coupling; S5b, adjusting silicon controlled turn-on angle to generate frequency division pulse (10-100kHz); S5c, shunting power to high priority battery path, parallel conduction in low valley period, series conduction in peak period, output power flow is transmitted to equalization layer through inductive bridge; the equalization layer receives power flow to optimize distribution.

[0037] Magnetic control switch shunt module: using magnetic control switch to dynamically adjust shunt impedance according to instruction, the steps are as follows: S6a, receiving binary instruction through capacitive coupling; S6b, applying control current to adjust switch magnetic field to shunt power; S6c, outputting shunted power through inductive bridge transmission to equalization layer, maximizing current in low valley period, dispersing load in peak period; the equalization layer receives shunted power for equalization.

[0038] The equalization layer includes the following modules: Electroluminescence voltage stabilization module: Stabilize battery charging voltage by electroluminescence diode, steps are: S7a, receive power flow of distribution layer through inductive bridge; S7b, clamp voltage to stable range by light emitting characteristics; S7c, branch power to low state of charge battery, output remaining energy through capacitive coupling feedback to input layer; input layer receives feedback to adjust acquisition.

[0039] Ferroelectric capacitor equalization module: Equalize battery power by polarization reversal of ferroelectric capacitor, steps are: S8a, receive power flow and battery state of charge feedback of distribution layer; S8b, apply electric field to reverse polarization distribution power; S8c, feedback equalization signal to input layer through capacitive coupling to form closed loop; input layer receives feedback optimization signal acquisition.

[0040] Photoelectric sensor acquisition module acquires electricity price pulse transmitted by optical fiber through photoelectric sensor (response time <1 microsecond), cooperates with filter circuit (time constant 0.1 millisecond) to output smooth voltage signal, inductive preprocessing module generates 0-10A current pulse through inductive coil (high saturation magnetic flux density), signal is transmitted through inductive bridge and capacitive coupling, and noise resistance of input layer is adjusted.

[0041] Signal analysis module generates priority signal through comparison algorithm (threshold comparison method, preset trough / peak threshold), battery state analysis module outputs binary instruction by comparing battery state of charge and priority signal, and signal is transmitted to distribution layer through voltage line and capacitive coupling, and adaptive distribution stability is adjusted.

[0042] Silicon controlled pulse distribution module generates frequency division pulse through silicon controlled array (conduction angle 0-180 degrees adjustable), cooperates with inductive bridge to be coupled to magnetic control switch shunt module, magnetic control switch adjusts magnetic field through control current, current injection is maximized in trough period, and current is limited to 50% in peak period.

[0043] Electroluminescence voltage stabilization module stabilizes branch voltage through nonlinear light emitting characteristics of electroluminescence diode, ferroelectric capacitor equalization module equalizes multiple battery states of charge through polarization reversal (reversal time <0.5 millisecond), and feedback signal is coupled to input layer through capacitor to form a closed loop of the whole system.

[0044] In order to enhance the functional integrity and data management capability of the "electric vehicle fast charging power adaptive distribution system oriented to time-of-use electricity price", a total database is arranged in the system, the total database includes an electricity price database, a battery state database and a running record database, and the total database further includes the following contents: Electricity price database: used for storing historical and real-time data of grid time-of-use electricity price signal, including pulse frequency, voltage value and time stamp, providing reference data for signal acquisition of input layer and priority analysis of main analysis layer; Battery status database: used to store the target data of multiple electric vehicle batteries, such as state of charge (SOC), voltage, current and temperature, to provide real-time reference for power distribution and equalization optimization of the distribution layer and the equalization layer; Operation record database: used to store the instruction signals (such as priority signals and binary instructions) of each layer module in the system, power distribution data, equalization feedback signals and system operation history data, to provide basic data support for the closed-loop optimization and cycle training of the adaptive distribution of the system.

[0045] The total database is interconnected with the input layer, the main analysis layer, the distribution layer and the equalization layer through a capacitive coupling interface, and provides real-time data update and data query and feedback support for each module, ensuring the adaptive distribution accuracy and stability of the system under the fluctuation of time-of-use electricity price. Specific embodiment two:

[0047] As Figs. 1-3 shown below is the complete working mode of the "electric vehicle fast charging power adaptive distribution system based on time-of-use electricity price", which describes in detail the whole process from time-of-use electricity price signal collection to final power distribution adjustment. The system includes an input layer, a main analysis layer, a distribution layer and an equalization layer, and integrates a total database. Through the characteristics of nonlinear circuits and deterministic comparison algorithms, the system realizes power adaptive distribution driven by electricity price and supports fast charging (>120kW) of multiple electric vehicles (EV) batteries.

[0048] The working mode covers the whole process of signal collection, preprocessing, analysis, decision-making, distribution, equalization and closed-loop feedback, and clearly defines the functions, data flow interaction and specific operation steps of each layer module, ensuring maximum charging efficiency (>96%) during off-peak electricity price and minimum grid load and battery life protection (state of charge SOC deviation <2%) during peak electricity price. Each layer module transmits data through voltage signals (0-5V), current pulses (0-10A) and capacitive coupling, and cooperates with the total database to provide real-time and historical data support, with a response time of <8ms, forming a closed-loop adaptive control.

[0049] Complete working mode: When the system starts, all modules are initialized, the batteries of multiple electric vehicles are connected to the charging interface, and the battery management system (BMS) provides initial state of charge (SOC), voltage, current and temperature data, which are stored in the battery status database of the total database. The electricity price database of the total database loads historical electricity price data (pulse frequency, voltage value, time stamp), and the operation record database prepares to record instructions, power flow and feedback signals. The system realizes power adaptive distribution through the following continuous steps: Time-of-Use Price Signal Acquisition: The input layer's photoelectric sensor acquisition module monitors the Time-of-Use price signal transmitted by the power grid through an optical fiber in real time using a photoelectric sensor (response time < 1 microsecond). The signal is in the form of a pulse, with the frequency reflecting the price level (high frequency during off-peak hours and low frequency during peak hours). The photoelectric diode array captures the light pulses and converts them into a 0-5V voltage signal. The signal is smoothed by an RC filter circuit (time constant 0.1 milliseconds) to remove high-frequency noise and generate a stable voltage signal (0-5V, reflecting the price level). This voltage signal is transmitted to the main analysis layer's signal analysis module through an inductive bridge (high saturation magnetic flux density inductance, ensuring anti-interference), and is also stored in the price database (recording pulse frequency, voltage value, and timestamp). The main analysis layer receives the voltage signal as the price input for subsequent analysis. The photoelectric sensor module continuously monitors to ensure real-time capture of price fluctuations.

[0050] Signal Preprocessing: The input layer's inductive preprocessing module receives the voltage signal transmitted by the photoelectric sensor acquisition module through the inductive bridge. It uses the magnetic flux change of the inductive coil to generate an initial current diversion. The inductive coil (high saturation magnetic flux density) adjusts the magnetic flux according to the voltage signal strength (0-5V) and outputs a 0-10A current pulse, reflecting the initial reference for power distribution. The current pulse is sent to the main analysis layer's battery state analysis module through capacitive coupling (capacity 10μF, low loss transmission), and is also stored in the price database (recording current value and timestamp). The inductive preprocessing converts the voltage signal into a current form suitable for power distribution through the self-induction effect, enhancing the power characteristics of the signal. The main analysis layer receives the current pulse as the basis for power distribution, ensuring that subsequent decisions are based on stable input signals.

[0051] Price Signal Analysis and Priority Generation: The main analysis layer's signal analysis module receives the voltage signal (through the inductive bridge) and the current pulse (through capacitive coupling) from the input layer, and analyzes the price state through a deterministic comparison algorithm (threshold comparison method). The module queries historical price data (frequency, voltage, timestamp) from the price database, calibrates the preset threshold (4V during off-peak hours and 4.5V during peak hours), compares the current voltage signal with the threshold, and determines the price period (off-peak / peak). If the signal is <4V, it is determined to be during off-peak hours, generating a high-priority signal (5V); if the signal is >4.5V, it is determined to be during peak hours, generating a low-priority signal (0V). The priority signal (0-5V) is transmitted to the thyristor pulse distribution module and the magnetic control switch shunt module of the distribution layer through a low-impedance voltage line, and is also stored in the operation record database (recording priority signal and timestamp). The distribution layer receives the priority signal as the basis for power distribution, ensuring that the distribution strategy is synchronized with the price.

[0052] Battery state monitoring and distribution instruction generation: The battery state analysis module of the main analysis layer receives the current pulse (through capacitive coupling) and the external battery voltage feedback (through the 0-5V signal line of the BMS) of the input layer, and queries the real-time state of charge (SOC), voltage, current and temperature data of multiple EV batteries from the battery state database. The module compares the battery SOC with the priority signal through the comparison algorithm: if it is in the low valley period (high priority signal) and the SOC is less than 50%, a high-level distribution instruction (5V) is generated to preferentially fast charge; if it is in the high peak period (low priority signal) or the SOC is greater than 80%, a low-level distribution instruction (0V) is generated to preferentially limit the current protection. The distribution instruction (high / low level) is transmitted to the thyristor pulse distribution module and the magnetic control switch shunt module of the distribution layer through capacitive coupling (capacity 10μF), and stored in the operation record database (record instruction and SOC data). The distribution layer receives the instruction, adjusts the power shunt strategy, and ensures that the battery demand matches the electricity price.

[0053] Power pulse shunt: The thyristor pulse distribution module of the distribution layer receives the priority signal (through the voltage line) and the binary distribution instruction (through capacitive coupling) of the main analysis layer, and triggers the frequency division pulse (10-100kHz) through the thyristor (SCR) array (conduction angle 0-180 degrees adjustable). The module adjusts the conduction angle according to the priority signal and the distribution instruction: in the low valley period (high priority, high level), the conduction angle is close to 180 degrees, generating a high-frequency wide pulse (100kHz), shunting the maximum power (current>80A) to the high-priority battery path, realizing parallel fast charging; in the high peak period (low priority, low level), the conduction angle is close to 0 degrees, generating a low-frequency narrow pulse (10kHz), serially limiting the power to 50%, reducing the grid load. The shunted power flow (current 0-100A) is transmitted to the electroluminescent voltage stabilization module and the ferroelectric capacitor equalization module of the equalization layer through the inductor bridge (high saturation magnetic flux density), and stored in the operation record database (record pulse parameters and power flow). The equalization layer receives the power flow and performs optimized distribution.

[0054] Dynamic impedance shunt: The magnetic control switch shunt module of the distribution layer receives the binary distribution instruction (through capacitive coupling), and dynamically adjusts the shunt impedance using the magnetic control switch. The module adjusts the switch magnetic field by controlling the current (0-2A) to change the impedance characteristics: in the low valley period (high level instruction), the impedance is reduced (close to 0 ohm), the current is maximized to the multi-battery path; in the high peak period (low level instruction), the impedance is increased (to 50 ohms), the power is dispersed to protect the grid and the battery. The shunted power flow is transmitted to the electroluminescent voltage stabilization module and the ferroelectric capacitor equalization module of the equalization layer through the inductor bridge, and stored in the operation record database (record impedance value and power flow). The nonlinear magnetic field response of the magnetic control switch ensures the shunt accuracy ±1%, and the equalization layer receives the shunted power for further equalization.

[0055] Voltage stabilization and power distribution: the electroluminescent stabilization module of the balancing layer receives the power flow from the distribution layer (through the inductive bridge) and clamps the charging voltage to a stable range (±0.1V precision) through the non-linear light-emitting characteristics of the electroluminescent diode (ELD). The module adjusts the light-emitting intensity according to the power flow intensity, stabilizes the output voltage, and preferentially distributes power to low state-of-charge batteries (SOC<50%), with the target battery confirmed by querying the battery state database. During the distribution process, the module monitors the battery voltage feedback (through the BMS signal line) to ensure voltage stability. The remaining energy (excess power) is fed back to the input layer's photoelectric sensor acquisition module through capacitive coupling (capacity 10μF) and stored in the operation record database (records voltage values and distribution data). The input layer receives the feedback signal and adjusts the photoelectric sensor acquisition sensitivity (such as the gain factor) to optimize subsequent electricity price signal acquisition.

[0056] Battery state-of-charge balancing and closed-loop feedback: the ferroelectric capacitor balancing module of the balancing layer receives the power flow from the distribution layer (through the inductive bridge) and the battery state-of-charge feedback (through the 0-5V signal line of the BMS), and balances the power of multiple batteries using the polarization reversal characteristics of the ferroelectric capacitor (reversal time <0.5 milliseconds). The module queries the battery state database and compares the SOC of each battery: low SOC batteries (<50%) receive more power, and high SOC batteries (>80%) are limited to prevent overcharging. The module applies an electric field to reverse the polarization state of the capacitor, dynamically distributes power, and balances the SOC deviation to <2%. The feedback signal (0-5V) after balancing is transmitted to the input layer's photoelectric sensor acquisition module through capacitive coupling and stored in the operation record database (records balancing signals and SOC changes). The input layer receives the feedback signal and optimizes the filter circuit parameters (such as time constant 0.05-0.2 milliseconds) to ensure acquisition accuracy.

[0057] Closed-loop optimization and adjustment: the total database updates the electricity price database (stores pulse frequency, voltage value, timestamp), battery state database (stores SOC, voltage, current, temperature), and operation record database (stores instructions, power flow, feedback signals) in real time, supporting each module to query historical data to optimize operation. The system continuously monitors electricity prices and SOC through a closed-loop mechanism and dynamically adjusts parameters: the input layer adjusts the filter circuit time constant (0.05-0.2 milliseconds) to optimize signal smoothing; the main analysis layer updates the comparison threshold (4-4.8V) to adapt to electricity price fluctuations; the distribution layer optimizes the silicon-controlled rectifier conduction angle (0-180 degrees) and the magnetic control switch impedance (0-50 ohms); the balancing layer adjusts the electroluminescent clamping voltage and the ferroelectric capacitor polarization reversal frequency (1-2kHz). The closed loop ensures parallel charging during the low valley period (current >80A, efficiency >96%), serial current limiting during the high peak period (power reduced to 50%), SOC deviation <2%, and response time <8ms. The system continuously optimizes the distribution strategy based on the cyclic training data of the total database (such as historical distribution patterns) to improve long-term stability. Embodiment Three:

[0059] As shown below is the module and algorithm application logic of the time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system: Figs. 1-3 Input layer module and algorithm application logic: Optical-electric sensor acquisition module: Hardware composition and explanation: The core hardware includes a photodiode array (model such as SFH 203, response time <1 microsecond, sensitive wavelength 850 nm, receiving the electricity price pulse signal transmitted by the optical fiber), an RC filter circuit (resistance 10 kΩ, capacitance 0.01 μF, time constant 0.1 millisecond, smoothing signal), an operational amplifier (model such as LM358, gain 1, for signal amplification), and an inductor bridge (high saturation magnetic flux density ferrite inductor, inductance 100 μH, transmitting voltage signal). The photodiode array converts the optical pulse into an electrical signal, the RC filter circuit removes high-frequency noise, the operational amplifier enhances the signal strength, and the inductor bridge ensures anti-interference transmission to the main analysis layer. The hardware is integrated on a single circuit board, with a size of about 50x50 mm, power consumption <0.5 W, and is connected to the optical fiber input port and the inductor bridge output end. Algorithm application logic: Threshold detection algorithm is adopted. The input data is the electricity price pulse signal transmitted by the optical fiber (frequency reflects the electricity price level, high frequency in low valley period and low frequency in high peak period). The algorithm detects the pulse frequency through the photodiode, converts it into a 0-5V voltage signal, compares it with the internal reference voltage (2.5V) to determine the signal validity, and outputs a smoothed voltage signal (0-5V, reflecting the electricity price level). In the system, the algorithm drives the photodiode to collect in real time, the RC filter ensures the signal stability, and the output signal is transmitted to the signal analysis module of the main analysis layer through the inductor bridge, serving as the electricity price input reference and stored in the electricity price database of the total database (recording pulse frequency, voltage value, and time stamp). The algorithm ensures the collection accuracy, high noise immunity, and supports fast electricity price fluctuation monitoring.

[0060] Inductor preprocessing module:

[0061] ​Hardware composition and explanation: The core hardware includes an inductor coil (high saturation magnetic flux density ferrite core, inductance 200 μH, rated current 10 A, generates current pulse), a diode rectifier bridge (model 1N4007, prevents reverse current), a capacitor coupling unit (capacitance 10 μF, voltage resistance 50 V, transmits current pulse), and an operational amplifier (LM358, gain 2, amplifies current signal). The inductor coil receives the voltage signal of the photoelectric module, generates a 0-10 A current pulse through magnetic flux change, the diode rectifier bridge protects the circuit, the capacitor coupling unit transmits the signal to the main analysis layer, and the operational amplifier enhances the signal stability. The hardware is integrated on a circuit board, with a size of about 40x40 mm, power consumption <0.8 W, and connection between the inductor bridge input end and the capacitor coupling output end.

[0062] Algorithm application logic: Signal conversion algorithm is adopted. The input data is a 0-5 V voltage signal of the photoelectric module (through the inductor bridge). The algorithm converts the voltage signal to a 0-10 A current pulse through the magnetic flux change of the inductor coil, compares the current intensity with the preset range (0-10 A) to ensure the signal is effective, and outputs the current pulse as a power reference. In the system, the algorithm drives the inductor coil to generate current shunt, which is transmitted to the battery state analysis module of the main analysis layer through the capacitor coupling, and stored in the electricity price database (records current value and time stamp). The algorithm enhances the power characteristics of the signal, ensures that the main analysis layer receives a stable power reference, and supports subsequent distribution decisions.

[0063] Main analysis layer module and algorithm application logic: Signal analysis module; Hardware composition and explanation: The core hardware includes a comparator circuit (model LM393, compares voltage signal with threshold, response time <1 μs), a microcontroller (model ATmega328, 8-bit, 16 MHz, runs comparison algorithm), an electricity price database interface (EEPROM memory, capacity 256 KB, stores historical electricity price data), and a voltage line output end (low impedance wire, transmits 0-5 V priority signal). The comparator circuit compares the input voltage with the threshold, the microcontroller executes the algorithm and queries the electricity price database, and the voltage line transmits the output signal to the distribution layer. The hardware is integrated on a circuit board, with a size of about 60x60 mm, power consumption <1 W, and connection between the inductor bridge input end and the voltage line output end.

[0064] Algorithm application logic: threshold comparison algorithm is adopted. The input data is the 0-5V voltage signal of the input layer (through the inductive bridge) and the historical data of the electricity price database (pulse frequency, voltage value). The algorithm compares the voltage signal with the preset threshold (4V in the low valley period and 4.5V in the high peak period), queries the historical electricity price data to calibrate the threshold, and generates a 0-5V priority signal (high level 5V represents fast charging in the low valley period, and low level 0V represents current limiting in the high peak period). In the system, the algorithm drives the comparator circuit to determine the electricity price period, and the microcontroller generates a priority signal which is transmitted to the silicon-controlled pulse distribution module and the magnetic control switch shunt module of the distribution layer through the voltage line, and is stored in the operation record database (records the priority signal and the timestamp). The algorithm ensures fast and accurate electricity price analysis and supports the shunt strategy of the distribution layer.

[0065] Battery state analysis module: Hardware composition and explanation: the core hardware includes a comparator circuit (LM393, compares the SOC and the priority signal), a microcontroller (ATmega328, runs the comparison algorithm), a battery state database interface (EEPROM, 256KB, stores the SOC, voltage, current, temperature), a capacitor coupling unit (capacitor 10μF, transmits the binary command), and a signal input end (connects the 0-5V signal line of the BMS, provides the battery feedback). The comparator circuit compares the battery state and the priority signal, the microcontroller generates a distribution command, and the capacitor coupling transmits it to the distribution layer. The hardware is integrated on a circuit board with a size of about 50x50mm and a power consumption of <0.9W, and is connected to the capacitor coupling input / output end.

[0066] Algorithm application logic: state comparison algorithm is adopted. The input data is the 0-10A current pulse of the input layer (through the capacitor coupling), the 0-5V battery voltage feedback of the BMS, and the SOC, voltage, current, and temperature data of the battery state database. The algorithm compares the SOC and the priority signal (5V / 0V), and if it is in the low valley period and the SOC is less than 50%, a high-level command (5V, fast charging) is generated; if it is in the high peak period or the SOC is greater than 80%, a low-level command (0V, current limiting protection) is generated. In the system, the algorithm drives the comparator and the microcontroller to generate a binary distribution command, which is transmitted to the silicon-controlled pulse distribution module and the magnetic control switch shunt module of the distribution layer through the capacitor coupling, and is stored in the operation record database (records the command and the SOC data). The algorithm ensures that the distribution command matches the battery demand and the electricity price state.

[0067] Distribution layer module and algorithm application logic: Silicon-controlled pulse distribution module: Hardware composition and explanation: The core hardware includes thyristor array (model TIC126D, conduction angle 0-180 degrees, rated current 100A, generating frequency pulse), trigger circuit (optocoupler isolation, model MOC3021, triggering thyristor conduction), inductive bridge (ferrite core, inductance 150μH, transmitting power flow), voltage line and capacitive coupling input (receiving priority signal and instruction). The thyristor array adjusts the conduction angle according to the input signal, the trigger circuit ensures accurate pulse, and the inductive bridge transmits power flow to the equalization layer. The hardware is integrated on a circuit board, with a size of about 80x60mm, power consumption <2W, connected to the voltage line, capacitive coupling input and inductive bridge output.

[0068] Algorithm application logic: Pulse modulation algorithm is used. The input data is priority signal (0-5V, through voltage line) and binary allocation instruction (high / low level, through capacitive coupling). The algorithm adjusts the thyristor conduction angle according to the priority and instruction, increases the conduction angle to 180 degrees in the trough period (high level), generates a high-frequency wide pulse (100kHz, current >80A), and shunts the maximum power to the high-priority battery; reduces the conduction angle to 0 degrees in the peak period (low level), generates a low-frequency narrow pulse (10kHz, current drops to 50%). In the system, the algorithm drives the thyristor array to realize parallel or serial shunting, and the power flow is transmitted to the electroluminescent voltage stabilizing module and ferroelectric capacitor equalization module in the equalization layer through the inductive bridge, and stored in the operation record database (records pulse parameters and power flow). The algorithm ensures the shunting accuracy and response speed of the electricity price.

[0069] Magnetic control switch shunt module: Hardware composition and explanation: The core hardware includes magnetic control switch (magnetic sensitive resistor, model MRMS211H, impedance 0-50 ohms, response control current 0-2A), control current circuit (MOSFET drive, model IRF540, generating control current), inductive bridge (ferrite core, 150μH, transmitting power flow), capacitive coupling input (capacitance 10μF, receiving instruction). The magnetic control switch adjusts the magnetic field by adjusting the control current to change the impedance, and the inductive bridge transmits the shunt power. The hardware is integrated on a circuit board, with a size of about 70x50mm, power consumption <1.5W, connected to the capacitive coupling input and the inductive bridge output.

[0070] Algorithm application logic: impedance regulation algorithm is adopted. The input data is the binary allocation instruction (high / low level, coupled through capacitor). The algorithm adjusts the control current according to the instruction, reduces the impedance to 0 ohm in the low valley period (high level) to maximize the current injection; increases the impedance to 50 ohms in the peak period (low level) to disperse power. The output shunt power (current 0-100A) is transmitted to the balancing layer through the inductive bridge, and stored in the operation record database (records impedance value and power flow). In the system, the algorithm drives the magnetic control switch to realize dynamic shunt, cooperates with the silicon controlled module to ensure the shunt accuracy ±1%, and the balancing layer receives the shunt power for optimization.

[0071] Balancing layer module and algorithm application logic: Electroluminescent stabilization module: Hardware composition and explanation: the core hardware includes electroluminescent diode (ELD, model customized, light-emitting threshold 3.5V, clamping voltage ±0.1V), voltage stabilization circuit (including Zener diode 1N4733, auxiliary voltage stabilization), inductive bridge input end (receives power flow), capacitor coupling output end (capacitor 10μF, feedbacks remaining energy), BMS signal interface (0-5V, receives battery voltage feedback). The ELD clamps the voltage through the light-emitting characteristic, the voltage stabilization circuit assists in stabilization, and the capacitor coupling feedbacks to the input layer. The hardware is integrated on the circuit board, with a size of about 60x50mm, power consumption <1.2W, and is connected with the inductive bridge input end and the capacitor coupling output end.

[0072] Algorithm application logic: voltage clamping algorithm is adopted. The input data is the power flow of the allocation layer (through the inductive bridge) and the SOC and voltage data of the battery state database. The algorithm adjusts the ELD light-emitting intensity according to the power flow intensity, clamps the voltage to the stable range, preferentially branches the power to the low SOC battery (<50%), and queries the database to confirm the target battery. The output stable voltage power flow is output to the battery, and the remaining energy is feedback to the input layer through the capacitor coupling, and is stored in the operation record database (records voltage value and branch data). In the system, the algorithm drives the ELD to stabilize the charging voltage, the feedback signal optimizes the input layer collection sensitivity, and ensures the safe charging of the battery.

[0073] Ferroelectric capacitor balancing module: Hardware composition and explanation: the core hardware includes ferroelectric capacitor array (model customized, polarization flip time <0.5ms, capacity 50μF), electric field control circuit (MOSFET drive, IRF540, applies electric field), inductive bridge input end (receives power flow), capacitor coupling output end (capacitor 10μF, feedbacks balancing signal), BMS signal interface (receives SOC feedback). The ferroelectric capacitor balances the power through polarization flip, and the electric field control circuit adjusts the allocation. The hardware is integrated on the circuit board, with a size of about 60x50mm, power consumption <1.3W, and is connected with the inductive bridge input end and the capacitor coupling output end.

[0074] Algorithm application logic: Polarization distribution algorithm is adopted. The input data is the power flow of the distribution layer (through the inductive bridge), the SOC feedback of the BMS, and the SOC data of the battery state database. The algorithm compares the SOC of each battery, applies an electric field to flip the polarization state of the ferroelectric capacitor, and the low SOC battery (<50%) gets more power, while the high SOC battery (>80%) is limited, and the SOC deviation is balanced to <2%. The output balancing signal (0-5V) is fed back to the input layer through capacitive coupling and stored in the operation record database (records balancing signal and SOC change). In the system, the algorithm drives the ferroelectric capacitor to achieve dynamic balancing, and the feedback signal optimizes the input layer acquisition parameters.

[0075] Total database and algorithm application logic: Total database: Hardware composition and explanation: The core hardware includes EEPROM memory (model AT24C256, capacity 256KB, stores electricity price, battery status and operation record), microcontroller (ATmega328, manages data reading and writing), capacitive coupling interface (capacitor 10μF, connects each layer module), I2C communication interface (connects microcontroller and each module). EEPROM stores electricity price database (pulse frequency, voltage value, timestamp), battery state database (SOC, voltage, current, temperature) and operation record database (instruction, power flow, feedback signal). The hardware is integrated on a circuit board, with a size of about 50x40mm, power consumption <0.5W, and connected to each layer through capacitive coupling.

[0076] Algorithm application logic: Data management algorithm is adopted. The input data is the output signal of each module (voltage, current, instruction, power flow, feedback signal). The algorithm stores data in EEPROM in real time, the electricity price database supports input layer and main analysis layer query, the battery state database supports main analysis layer and balancing layer decision, and the operation record database supports closed-loop optimization. The output is the query result (such as historical electricity price threshold, SOC data), which is transmitted to each module through the I2C interface. In the system, the algorithm ensures real-time data update and query, supports closed-loop optimization, adjusts parameters (such as filter time constant 0.05-0.2 milliseconds, threshold 4-4.8V), improves distribution accuracy and stability.

[0077] Overall operating logic: After system startup, the photoelectric sensor acquisition module acquires the electricity price pulse through photodiodes and RC filters, outputting a 0-5V voltage signal to the inductor preprocessing module; the inductor module generates a 0-10A current pulse through the inductor coil and transmits it to the main analysis layer. The signal analysis module analyzes the electricity price through a comparator and microcontroller, generating a priority signal; the battery state analysis module compares the SOC, generates allocation instructions, and transmits them to the allocation layer. The thyristor pulse allocation module adjusts the conduction angle to generate a frequency-divided pulse, and the magnetic switch shunt module adjusts the impedance to shunt the power, transmitting it to the equalization layer. The electroluminescent voltage regulator module clamps the voltage, and the ferroelectric capacitor equalization module flips the polarization to equalize the SOC, feeding back the signal to the input layer. The overall database stores and queries data, supports closed-loop optimization, dynamically adjusts parameters, ensuring efficient charging during off-peak hours and current limiting protection during peak hours, with an SOC deviation of <2% and a response time of <8ms. Specific Implementation Example 4:

[0079] like Figs. 1-3 As shown, the following are specific use cases of this solution system: Case 1: Dynamic Charging Management of Urban Fast Charging Stations in Response to Peak and Off-Peak Electricity Price Switching Scenario Description: A busy urban fast-charging station, located in a commercial area, serves multiple electric vehicles (12 EVs, including cars and electric buses). The power grid transmits time-of-use pricing signals via fiber optic cables, with signal frequencies reflecting electricity prices (higher frequency during off-peak hours and lower frequency during peak hours). The charging station experiences off-peak electricity prices from late night to early morning and peak electricity prices during the day. Power allocation needs to be dynamically adjusted during price transitions to meet the different State of Charge (SOC) requirements of various vehicles (e.g., buses have lower SOC and require priority fast charging), ensuring efficient charging, grid stability, and battery protection.

[0080] Detailed usage instructions: Upon system startup, all modules are initialized, and the batteries of 12 EVs are connected to the charging interface. The Battery Management System (BMS) provides initial SOC, voltage, current, and temperature data via the 0-5V signal line, storing them in the battery status database of the overall database. The electricity price database loads historical electricity price data (pulse frequency, voltage value, timestamp) from the past week, and the running record database prepares to record commands, power flow, and feedback signals.

[0081] In the late night valley period (such as 2:00), the photoelectric sensor acquisition module captures the high-frequency electricity price pulse transmitted by the optical fiber of the power grid through the photodiode array (SFH203, response time <1 microsecond), and converts it into a 0-5V voltage signal. The signal is smoothed by an RC filter circuit (resistance 10kΩ, capacitance 0.01μF, time constant 0.1 milliseconds) to remove high-frequency noise and generate a stable voltage signal, which is transmitted to the signal analysis module (LM393 comparator, ATmega328 microcontroller) of the main analysis layer through the inductor bridge (ferrite inductor, 100μH). The voltage signal is also stored in the electricity price database, recording the pulse frequency and timestamp. The inductor preprocessing module receives the voltage signal (through the inductor bridge), generates a 0-10A current pulse through the inductor coil (200μH, rated current 10A), and prevents reverse current through the diode rectifier bridge (1N4007). The current pulse is transmitted to the battery state analysis module through capacitive coupling (10μF) and stored in the electricity price database.

[0082] The signal analysis module receives the voltage signal, runs the threshold comparison algorithm, compares the signal with the valley threshold (4V), queries the electricity price database to calibrate the threshold, determines the valley period, generates a high-priority signal (5V), and transmits it to the silicon-controlled pulse distribution module (TIC126D) and magnetic control switch shunt module (MRMS211H) of the distribution layer through a low-impedance voltage line. The battery state analysis module receives the current pulse and BMS feedback, queries the battery state database, finds that the SOC of 4 buses is <40% and the SOC of other cars is >60%, runs the state comparison algorithm, generates a high-level distribution instruction (5V), and prioritizes fast charging for buses, which is transmitted to the distribution layer through capacitive coupling and stored in the operation record database.

[0083] The silicon-controlled pulse distribution module receives the priority signal and instruction, runs the pulse modulation algorithm, adjusts the silicon-controlled conduction angle to near 180 degrees, generates a high-frequency wide pulse (100kHz) through the trigger circuit (MOC3021), and shunts the maximum power (current >80A) to the bus battery path to realize parallel charging. The power flow is transmitted to the equalization layer through the inductor bridge (150μH). The magnetic control switch shunt module receives the instruction, runs the impedance adjustment algorithm, applies a control current (0-2A) through the MOSFET drive (IRF540) to reduce the impedance to near 0 ohms, maximizes the current injection, and transmits the power flow to the equalization layer, which is stored in the operation record database.

[0084] The equalization layer's electroluminescent voltage stabilization module receives power flow, runs a voltage clamping algorithm through electroluminescent diodes (ELD, custom, threshold 3.5V), stabilizes voltage (±0.1V), queries the battery state database, prioritizes power to low SOC bus batteries, and the remaining energy is fed back to the input layer through capacitive coupling (10μF). The ferroelectric capacitor equalization module receives power flow and BMS feedback, runs a polarization distribution algorithm, applies an electric field to flip the polarization state of the ferroelectric capacitor (50μF, flip time <0.5ms), equalizes SOC, and feeds back a signal (0-5V) to the input layer through capacitive coupling, storing it in the operation record database. The input layer receives feedback, adjusts the gain of the photoelectric sensor, and optimizes the sensitivity of the acquisition.

[0085] During the daytime peak period (e.g., 10:00 am), the photoelectric sensor detects low-frequency pulses, generating a lower voltage signal, and the main analysis layer determines the peak period, generating a low-priority signal (0V) and a low-level command. The silicon-controlled module reduces the conduction angle to near 0 degrees, generating a low-frequency narrow pulse (10kHz), and serially limits power to 50%; the magnetic control switch module increases impedance to 50 ohms, disperses power, and reduces grid load. The equalization layer continues to stabilize voltage, prioritizes charging for low SOC batteries, and optimizes input layer parameters through feedback signals. The total database (AT24C256, 256KB) updates electricity prices, battery status, and operation records in real time through the I2C interface, dynamically adjusts filter constants, thresholds, conduction angles, and polarization frequencies, and ensures closed-loop optimization.

[0086] Effectiveness proof: The system improves charging efficiency during the low valley period, prioritizing fast charging for low SOC buses; during the peak period, it reduces grid load and protects batteries from overcharging. The total database supports real-time data queries and closed-loop optimization, equalizing SOC, improving the operating efficiency of the charging station, and the coordination of multiple vehicle charging, adapting to frequent electricity price switching.

[0087] Case two: Nighttime concentrated charging and SOC equalization optimization in suburban residential areas Scenario description: In a suburban residential area, equipped with a community charging pile network, serving 6 households of electric vehicles (6 EVs, including family cars and small electric SUVs), the residents concentrate on charging at night during the low valley electricity price period (22:00-06:00), and the vehicle SOC difference is large (partially <30%, partially >70%). The system needs to efficiently utilize low electricity prices, fast charging, while equalizing SOC, preventing some batteries from overcharging or undercharging, and ensuring the next day's travel needs.

[0088] Detailed use process: System starts, connects 6 EVs, BMS provides SOC, voltage, current and temperature data through 0-5V signal line, stores to battery state database. Electricity price database loads historical data of night valley electricity price, running record database prepares to record. Photovoltaic sensor acquisition module (SFH 203) captures high-frequency electricity price pulse, converts into 0-5V voltage signal, smoothes through RC filter circuit (10kΩ, 0.01μF), generates stable voltage signal, transmits to signal analysis module (LM393, ATmega328) through inductance bridge (100μH), stores to electricity price database. Inductance preprocessing module receives voltage signal, generates 0-10A current pulse through inductance coil (200μH), diode rectifier bridge (1N4007) protection circuit, current pulse transmits to battery state analysis module through capacitor coupling (10μF), stores to electricity price database.

[0089] Signal analysis module runs threshold comparison algorithm, compares voltage signal with valley threshold (4V), queries electricity price database, generates high priority signal (5V), transmits to thyristor pulse distribution module (TIC126D) and magnetic control switch shunt module (MRMS211H) through voltage line. Battery state analysis module receives current pulse and BMS feedback, queries battery state database, finds that 2 SUVs SOC<30% and 4 cars SOC>70%, runs state comparison algorithm, generates high-level distribution instruction (5V), preferentially charges SUVs, transmits to distribution layer through capacitor coupling, stores to running record database.

[0090] Thyristor pulse distribution module runs pulse modulation algorithm, adjusts conduction angle to close to 180 degrees, generates high-frequency wide pulse (100kHz) through trigger circuit (MOC3021), shunts maximum power to SUV battery, realizes parallel charging, power flow transmits to equalization layer through inductance bridge (150μH). Magnetic control switch shunt module runs impedance adjustment algorithm, reduces impedance to 0 ohm through MOSFET drive (IRF540), maximizes current injection, power flow transmits to equalization layer, stores to running record database.

[0091] Electroluminescent voltage stabilization module receives power flow, runs voltage clamping algorithm through ELD (threshold 3.5V) and voltage stabilization circuit (1N4733), stabilizes voltage, queries battery state database, preferentially distributes power to low SOC SUV battery, remaining energy is fed back to input layer through capacitor coupling. Ferroelectric capacitor equalization module receives power flow and BMS feedback, runs polarization distribution algorithm, flips polarization state of ferroelectric capacitor (50μF), preferentially distributes power to low SOC battery, equalizes SOC, feedback signal is coupled to input layer through capacitor, stores to running record database.

[0092] When the SUV battery SOC approaches 60%, the battery state analysis module detects the narrowing of the SOC difference and generates a low-level command. The distribution layer reduces the conduction angle and increases the impedance, limiting the high SOC car power, and the balancing layer continues to optimize the SOC deviation. The total database updates the data through the I2C interface, supports the module to query the historical SOC and electricity price data, dynamically adjusts the filter constant (0.05-0.2 milliseconds), threshold and polarization frequency, ensures efficient and balanced night charging. The input layer adjusts the photoelectric sensor gain based on feedback to optimize the collection accuracy.

[0093] Effectiveness proof: The system improves charging efficiency during the night valley period, prioritizes charging for low SOC SUVs, and effectively balances the SOC to prevent overcharging or undercharging. The total database supports closed-loop optimization, reduces charging time, improves resident charging experience and battery life, and adapts to centralized charging needs.

[0094] Case three: Charging scheduling under mixed load and electricity price fluctuations in an industrial park Scenario description: In an industrial park, equipped with charging stations serving mixed loads (4 commercial electric trucks and 3 passenger EVs), the truck SOC is low (<40%), and needs to be prioritized for charging to support logistics operations, and the passenger car SOC is high (>60%). The electricity price is high during the day (08:00-17:00) and low during the evening and night (17:00-22:00). The system needs to reduce grid load during peak hours to protect the battery and charge quickly during the valley period to balance the SOC and ensure the next day's operational needs.

[0095] Detailed usage process: System initialization, connect 7 EVs, BMS provides SOC, voltage, current and temperature data, stored in the battery state database. The electricity price database loads historical data, and the record database is ready for recording. During the day peak period (such as 10:00), the photoelectric sensor acquisition module (SFH 203) detects low-frequency electricity price pulses and converts them into lower 0-5V voltage signals, which are smoothed through the RC filter circuit (10kΩ, 0.01μF) and transmitted to the signal analysis module (LM393, ATmega328) through the inductor bridge (100μH), and stored in the electricity price database. The inductor preprocessing module generates 0-10A current pulses through the inductor coil (200μH) and transmits them to the battery state analysis module through capacitive coupling (10μF), and stores them in the electricity price database.

[0096] The signal analysis module runs a threshold comparison algorithm, compares the voltage signal with the peak threshold (4.5V), queries the electricity price database, generates a low-priority signal (0V), and transmits it to the thyristor pulse distribution module (TIC126D) and the magnetic control switch shunt module (MRMS211H) through the voltage line. The battery state analysis module receives the current pulse and BMS feedback, queries the battery state database, identifies the SOC of the truck <40% and the SOC of the passenger car >60%, runs a state comparison algorithm, generates a low-level distribution instruction (0V), and preferentially allocates limited power to the truck, which is transmitted to the distribution layer through capacitive coupling and stored in the operation record database.

[0097] The thyristor pulse distribution module runs a pulse modulation algorithm, reduces the conduction angle to near 0 degrees, generates a low-frequency narrow pulse (10kHz) through a trigger circuit (MOC3021), and serially shunts limited power to the truck battery, reducing the grid load. The magnetic control switch shunt module runs an impedance adjustment algorithm, increases the impedance to 50 ohms through a MOSFET driver (IRF540), disperses power, and the power flow is transmitted to the balancing layer through an inductive bridge (150μH), and stored in the operation record database.

[0098] The electroluminescent voltage stabilization module receives the power flow, runs a voltage clamping algorithm through an ELD (threshold 3.5V), stabilizes the voltage, queries the battery state database, preferentially branches power to the low-SOC truck battery, and the remaining energy is fed back to the input layer through capacitive coupling. The ferroelectric capacitor balancing module receives the power flow and BMS feedback, runs a polarization distribution algorithm, flips the polarization state of the ferroelectric capacitor (50μF), limits the power of the high-SOC passenger car, balances the SOC, and feeds back the signal to the input layer through capacitive coupling, and stores it in the operation record database.

[0099] In the evening low valley period (such as 18:00), the photoelectric sensor detects high-frequency pulses, generates a high-voltage signal, and the main analysis layer generates a high-priority signal (5V) and a high-level instruction. The thyristor module increases the conduction angle to 180 degrees, generates a high-frequency wide pulse (100kHz), shunts maximum power to the truck battery; the magnetic control switch module reduces the impedance to 0 ohms, maximizes the current injection. The balancing layer stabilizes the voltage, preferentially charges the truck, balances the SOC, and the feedback signal optimizes the input layer's acquisition sensitivity. The total database updates the data through the I2C interface, supports module queries, dynamically adjusts the filter constant, threshold, conduction angle, and polarization frequency, and ensures closed-loop optimization.

[0100] Effectiveness proof: The system reduces the grid load during peak hours and preferentially charges low-SOC trucks; during the low valley period, it improves charging efficiency and quickly replenishes the truck battery energy. The total database supports data queries, optimizes hybrid load distribution strategies, balances the SOC, improves park operation efficiency and battery service life, and adapts to electricity price fluctuations and mixed load demands.

[0101] Summarize: These three case studies detail the system's applications in urban charging stations, suburban residential areas, and industrial parks, validating its effectiveness in scenarios involving electricity price switching, centralized charging, and mixed loads. The system achieves adaptive power allocation through the coordinated use of photoelectric sensor data acquisition, inductor preprocessing, thyristor pulse distribution, magnetic switch branching, electroluminescent voltage regulation, and ferroelectric capacitor balancing. This is combined with threshold comparison, state comparison, pulse modulation, impedance adjustment, voltage clamping, and polarization allocation algorithms, along with data support from a comprehensive database. The case studies demonstrate that the system improves charging efficiency, reduces grid load, balances state of charge (SOC), extends battery life, and enhances system stability, making it suitable for diverse charging scenarios. Specific Implementation Example 5:

[0103] like Figs. 1-3 As shown in the table below, the key performance indicators of the system in three scenarios are presented. The data is generated based on the module functions and case operation logic assumptions of the system implementation examples, covering performance during both off-peak and peak periods:

[0104] The following is a description of the table above: Charging efficiency: This refers to the utilization rate of the charging power of the system during a given electricity price period. Parallel charging efficiency is high during off-peak hours, while current limiting during peak hours results in moderate efficiency.

[0105] SOC deviation: reflects the deviation after the state of charge of multiple EV batteries is balanced. The system maintains a low deviation through the ferroelectric capacitor balancing module.

[0106] Response time: The system's response speed from acquiring electricity price signals to power allocation reflects the rapid processing capabilities of photoelectric sensors and algorithms.

[0107] Grid load: This represents the system's power demand on the grid. High load is allowed during off-peak periods, while the load is reduced during peak periods through current limiting.

[0108] Priority allocation accuracy: reflects the accuracy of the main analysis layer in generating priority signals and allocation instructions based on electricity price and SOC.

[0109] Data source: The data is hypothetical and is derived based on the module functions (photoelectric sensor acquisition, inductor preprocessing, comparison algorithm, thyristor shunt, magnetic switch impedance adjustment, electroluminescent voltage regulation, ferroelectric capacitor balancing) and case operation logic (urban charging station, suburban residential area, industrial park) of the system in specific embodiments.

[0110] It is to be understood that the phrases "including", "containing", or any other similar phrases, are intended to be non-exclusive, such that a process, method, article, or apparatus that includes items also includes items not specifically listed, or items not specifically listed but inherent in such process, method, article, or apparatus. It is also to be understood that the use of relational terms such as "first" and "second", and / or three or more terms, are used solely to distinguish one from another entity or action without necessarily implying a sequence or order of such entities or actions.

[0111] While the embodiments of the application have been shown and described, it is to be understood that the application is not limited to these embodiments. Rather, it is the intention that all variations and modifications, which fall within the spirit and scope of the application, are to be embraced by the appended claims.

Claims

1. A power adaptive allocation system for electric vehicle fast charging oriented to time-of-use electricity price, comprising an allocation system, characterized in that: The distribution system comprises an input layer, a main analysis layer, a distribution layer and a balancing layer. The input layer collects time-of-use electricity price signals through sensors and pre-processes them into main control signals. The main analysis layer analyzes the signals through a deterministic comparison algorithm and generates distribution instructions. The distribution layer uses multi-module circuits to dynamically distribute power. The balancing layer uses multi-module circuits to optimize battery state of charge balancing and energy recovery. The distribution system uses nonlinear circuit characteristics and comparison algorithms to achieve power adaptive distribution driven by electricity prices. Voltage signals, current pulses and capacitive coupling are used to transmit data between layers to support fast charging of multiple electric vehicle batteries.

2. The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system according to claim 1, characterized in that: The input layer comprises the following modules: An optical sensor acquisition module acquires grid time-of-use electricity price signals through an optical sensor and converts them into voltage signals. The specific steps are as follows: S1a, real-time monitoring of light pulses; S1b, converting the pulses into 0-5V voltage signals; S1c, smoothing the signals through a filter circuit, and outputting the voltage signals through an inductive bridge to the main analysis layer, which receives the voltage signals as electricity price inputs; An inductive pre-processing module uses an inductive coil to pre-process input signals. The specific steps are as follows: S2a, receiving voltage signals from the optical sensor acquisition module through an inductive bridge; S2b, generating an initial current shunt through inductive magnetic flux changes; S2c, outputting 0-10A current pulses through capacitive coupling to the main analysis layer, which receives the current pulses as power references. 3.The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system of claim 1, wherein: The main analysis layer comprises the following modules: A signal analysis module analyzes input layer signals through a comparison algorithm and generates distribution priorities. The specific steps are as follows: S3a, receiving voltage signals and current pulses from the input layer; S3b, comparing the signals with low valley / high peak thresholds; S3c, generating priority signals through voltage lines to the distribution layer, which receives the priority signals as shunt references; A battery state analysis module monitors battery state of charge and generates distribution instructions. The specific steps are as follows: S4a, receiving current pulses from the input layer and external battery voltage feedback; S4b, comparing battery state of charge with priority signals; S4c, outputting binary distribution instructions through capacitive coupling to the distribution layer, which receives the instructions to adjust power shunts.

4. The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system according to claim 1, wherein: The distribution layer comprises the following modules: A silicon-controlled pulse distribution module triggers pulse shunts according to main analysis layer instructions through a silicon-controlled array. The steps are as follows: S5a, receiving priority signals and binary instructions through voltage lines and capacitive coupling; S5b, adjusting silicon-controlled conduction angles to generate frequency division pulses; S5c, shunting power to high-priority battery paths, with parallel conduction during low valley periods and series conduction during high peak periods, and outputting power flow through an inductive bridge to the balancing layer, which receives the power flow for optimized distribution; A magnetic control switch shunt module uses magnetic control switches to dynamically adjust shunt impedance according to instructions. The steps are as follows: S6a, receiving binary instructions through capacitive coupling; S6b, applying control currents to adjust switch magnetic fields and shunt power; S6c, outputting shunted power through an inductive bridge to the balancing layer, which receives the shunted power for balancing.

5. The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system according to claim 1, wherein: The balancing layer comprises the following modules: The electroluminescent voltage stabilizing module stabilizes the battery charging voltage through electroluminescent diodes, and the steps are: S7a, receiving the power flow of the distribution layer through the inductive bridge; S7b, clamping the voltage to the stable range through the light-emitting characteristic; S7c, branching the power to the low state of charge battery, and outputting the remaining energy through the capacitive coupling feedback to the input layer; the input layer receives the feedback adjustment collection; The ferroelectric capacitor equalization module equalizes the battery power through the polarization reversal of the ferroelectric capacitor, and the steps are: S8a, receiving the power flow and the battery state of charge feedback of the distribution layer; S8b, applying an electric field to reverse the polarization distribution power; S8c, feeding back the equalization signal to the input layer through capacitive coupling to form a closed loop; the input layer receives the feedback optimization signal collection.

6. The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system according to claim 2, wherein: The photoelectric sensor acquisition module acquires the electricity price pulse transmitted by the optical fiber through the photoelectric sensor, and cooperates with the filter circuit to output a smooth voltage signal. The inductive pretreatment module generates a 0-10A current pulse through an inductive coil, and the signal is transmitted through inductive bridge and capacitive coupling to adjust the noise immunity of the input layer.

7. The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system according to claim 3, characterized in that: The signal analysis module generates a priority signal through a comparison algorithm, and the battery state analysis module outputs a binary instruction by comparing the battery state of charge with the priority signal. The signal is transmitted to the distribution layer through voltage lines and capacitive coupling to adjust the adaptive distribution stability. 8.The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system of claim 4, wherein: The silicon-controlled pulse distribution module generates a frequency division pulse through a silicon-controlled array, which is coupled to a magnetic control switch shunt module through an inductive bridge. The magnetic control switch adjusts the magnetic field by controlling the current, maximizes the current injection in the trough period, and limits the current to 50% in the peak period. 9.The time-of-use electricity price oriented electric vehicle fast charging power adaptive allocation system of claim 5, wherein: The electroluminescent voltage stabilizing module stabilizes the branch voltage through the nonlinear light-emitting characteristic of the electroluminescent diode, and the ferroelectric capacitor equalization module equalizes the battery state of charge through the polarization reversal. The feedback signal is coupled to the input layer through the capacitor to form a full-system closed loop.

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