Hybrid power supply power distribution method for extended-range power supply vehicle

Abstract

The invention discloses a hybrid power supply power distribution method for an extended-range power supply vehicle, and relates to the technical field of intelligent power distribution, and the method comprises the steps: determining an external power supply demand state of a current extended-range power supply vehicle; determining the current running state of the extended-range power supply vehicle based on the running state of an engine and the real-time rotating speed of a motor of the extended-range power supply vehicle; obtaining the current state of charge of a power supply battery of the range-extended power supply vehicle and the current state of a range extender oil tank; based on the external power supply demand state, the driving state, the power supply battery charge state and the range extender oil tank state, the power supply mode of the range-extended power supply vehicle is determined; predicting a short-term power demand of the extended-range power supply vehicle according to the real-time vehicle speed of the extended-range power supply vehicle and an external power supply load, inputting the short-term power demand into a preset reinforcement learning model, and determining a hybrid power distribution proportion by taking the lowest energy consumption as a target based on a power supply mode; and adjusting the generated power of the range extender and the output power of the power supply battery based on the hybrid power distribution proportion through the hierarchical state machine.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power distribution technology, and in particular to a hybrid power distribution method for a range-extended power vehicle. Background Art

[0002] With the vigorous development of the new energy industry, the number of distributed power sources is increasing. At the same time, higher requirements are placed on emergency power supply capabilities, requiring power generation equipment with rapid response and flexible deployment. As a flexibly deployable power generation equipment, the extended-range power supply vehicle has important application value in scenarios such as distributed power generation and emergency power supply, and is highly consistent with the development direction of the State Grid. Among them, the extended-range power supply vehicle is a special engineering vehicle that combines extended-range electric vehicle technology with mobile power supply functions. It not only has the long driving range and low emissions characteristics of the extended-range electric vehicle, but can also serve as a mobile power station to provide emergency power support for external equipment or vehicles. It is an ideal choice for emergency, infrastructure, environmental protection and other fields. Its technological maturity and scenario adaptability are driving it to become a key component of new energy infrastructure.

[0003] Existing extended-range power supply vehicles have the following technical problems in energy supply: 1. Extended-range vehicles must meet the dual requirements of driving power and external power supply. However, the power output characteristics of batteries and range extenders differ: Batteries have fast response but limited capacity, while range extenders have high power but slow dynamic response. Traditional rule-based control has difficulty adapting to vehicle speed fluctuations and sudden load changes, which can easily lead to reduced battery life and increased energy consumption. 2. Extended-range electric vehicles require multi-mode switching, which can easily cause power oscillations and conflict between battery protection and external power supply requirements. 3. There is a need for intelligent networking when extended-range power supply vehicles are used for grid-coordinated power supply and supplementary power supply. Summary of the Invention

[0004] In response to the above-mentioned problems, the present invention provides a hybrid power distribution method for a range-extended power vehicle to solve the above-mentioned problems. A hybrid power distribution method for a range-extended power vehicle, comprising: The current sensor based on the distribution of the external power supply load interface determines the current application scenario and external power supply demand status of the extended-range power supply vehicle; Determining the current driving state of the extended-range power supply vehicle based on the engine operating state and the real-time speed of the electric motor of the extended-range power supply vehicle; Obtaining the current state of charge of the power supply battery and the range extender fuel tank status of the range extender power supply vehicle; Determining a power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the state of charge of the power supply battery, and the state of the range-extender fuel tank; Predicting the short-term power demand of the extended-range power supply vehicle based on the real-time speed of the extended-range power supply vehicle and the external power supply load, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption; The hierarchical state machine adjusts the range extender's generated power and the power supply battery's output power based on the hybrid power distribution ratio.

[0005] Preferably, the current sensor based on the distribution of the external power supply load interface determines the current application scenario and external power supply demand state of the extended-range power supply vehicle, including: Detect the connection status and connected device type of each external power interface of the extended-range power supply vehicle; The Hall current sensor on the external power interface of the range-extended power vehicle monitors the load current, voltage and current power data of the external power interface in the connected state in real time; Determining the current application scenario of the extended-range power vehicle based on the connection status of each external power interface and the load current, voltage, and current power data of the external power interface, wherein the application scenarios include: off-grid power generation, grid-connected capacity expansion, grid-connected power protection, microgrid construction, and charging scenarios; The current external power supply demand state is determined based on the application scenario and the type of the connected device.

[0006] Preferably, the determining the current driving state of the extended-range power supply vehicle based on the engine operating state and the real-time speed of the motor of the extended-range power supply vehicle includes: Receive the CAN bus signal of the engine control unit through the vehicle controller, obtain the start / stop flag, determine the engine start / stop state of the extended-range power supply vehicle, and when the engine state is on, obtain the engine operating power, and determine the engine start / stop state and the engine operating power as the engine operating state; Determine the current real-time speed of the motor of the extended-range power supply vehicle by a speed sensor carried by the motor of the extended-range power supply vehicle, and determine the driving speed change curve of the extended-range power supply vehicle based on the data curve of the real-time speed; Determining the operating power of the range extender based on the engine operating state, and determining the total energy consumption power of the current driving based on the driving speed change curve of the range-extended power supply vehicle; The current driving state of the range-extended power supply vehicle is determined based on the driving speed change curve, the operating power of the range extender and the total energy consumption power.

[0007] Preferably, the obtaining of the current state of charge of the power supply battery and the state of the range extender fuel tank of the range-extended power vehicle includes: The battery management system collects a variety of battery pack data in real time, including the total battery pack voltage, battery cell voltage, charge and discharge current, and battery surface temperature; Determine the state of charge of the power supply battery using a multi-parameter fusion correction method based on the multiple battery pack data; The range extender fuel amount and the current fuel consumption rate are determined based on a pressure sensor built into the fuel tank and a fuel consumption model, and the range extender fuel amount and the current fuel consumption rate are determined as the range extender fuel tank state.

[0008] Preferably, the determining of the power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the charge state of the power supply battery and the range-extender fuel tank state includes: Synchronizing the external power supply demand status, the driving status, the power supply battery charge status, and the range extender fuel tank status using a timestamp technology; Determine the current external power supply demand power based on the external power supply demand state, and determine the current internal power supply demand power of the extended-range electric vehicle based on the driving state; Obtaining the state of charge of the power supply battery, determining the current power percentage of the power supply battery, obtaining the state of the range extender fuel tank, and determining the current fuel level of the range extender fuel tank; Based on the external power supply required power, the internal power supply required power, the current power percentage of the power supply battery and the current fuel level of the range extender tank, a corresponding power supply mode is matched according to a preset power supply mode allocation strategy table.

[0009] Preferably, the method of predicting the short-term power demand of the extended-range power supply vehicle based on the real-time vehicle speed and external power supply load of the extended-range power supply vehicle, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption includes: Obtaining the real-time speed of the range-extended power vehicle, and generating a speed curve based on the real-time speed within a preset time window; Obtaining the external power supply load of the extended-range power supply vehicle, and generating an external load curve according to the total power of the external power supply load within a preset time window; Generate a short-term power demand sequence of the range-extended power vehicle within a preset time threshold according to the vehicle speed curve and the external load curve using LSTM time series prediction technology; Obtaining a short-term power demand sequence of the extended-range power supply vehicle, a current battery state of charge, and a fuel tank state, performing normalization processing, generating a state vector, obtaining the power supply mode, and generating a power supply mode code; With minimum energy consumption as a constraint, the state vector and the power supply mode are encoded and input into a preset reinforcement learning model to output the power supply distribution ratio of the range extender and the power supply battery.

[0010] Preferably, the power supply mode includes: pure electric mode, extended range mode, hybrid power supply mode, emergency power supply mode, braking recovery mode and battery protection mode.

[0011] Preferably, the adjusting the range extender power generation power and the power supply battery output power based on the power allocation ratio by the hierarchical state machine includes: Inputting the power allocation ratio into a hierarchical state machine, and using the hierarchical state machine to generate corresponding range extender control instructions and power supply battery control instructions; Parsing the range extender control command, determining the start / stop state of the range extender and the engine speed of the range extender, and performing power regulation on the range extender; The power supply battery control instruction is parsed to determine the start / stop state, output voltage and output current of the power supply battery, and power regulation is performed on the power supply battery.

[0012] Preferably, the method further comprises obtaining, through a cloud platform, all extended-range power supply vehicles in a preset area and performing intelligent networking of the power grid according to the current external power supply demand: The vehicle positions of multiple extended-range power supply vehicles in a preset area are collected by GPS, and the battery charge state, fuel tank fuel level and range extender available power of each extended-range power supply vehicle are obtained by the status monitoring system of the extended-range power supply vehicle; Uploading the vehicle positions of the plurality of extended-range power supply vehicles, the battery charge state, fuel tank fuel level, and available power of the range extender of each extended-range power supply vehicle to the cloud platform, determining the extended-range power supply vehicle whose available power of the range extender is greater than a preset value as an available device, and building an available device pool; Obtain the total power demand of all power supply nodes and current power supply needs in the preset area through the cloud platform; Obtaining power supply parameters of each node among all power supply nodes, obtaining the total required power, performing power allocation according to the power supply parameters of each node, and determining the ideal power supply power of each node; Determine the current power supply power of each node according to the power supply parameters of each node, obtain the ideal power supply power of each node, and determine the external power supply requirement power of each node according to the difference between the current power supply power and the ideal power supply power; Uploading the supplementary power supply demand power of each node to the cloud platform to build an external power supply demand pool; Determining a range-extended power supply vehicle networking type based on the external power supply demand pool; Obtain the external power supply demand pool and the range extender power supply vehicle networking type, and perform power supply node allocation and power supply parameter design based on the vehicle position and available power of each available device in the available device pool; Use the cloud platform to perform corresponding available device scheduling according to the power supply node allocation, and perform current networking according to the power supply parameter design.

[0013] Preferably, the method further comprises judging the status of each battery in the power supply battery pack of the extended-range power supply vehicle based on the real-time parameters of the power supply battery, and performing battery protection on the abnormal battery when the battery status is judged to be abnormal: The voltage of each battery in the power supply battery pack of the extended-range power supply vehicle is collected in real time through a single-cell voltage sensor, the current of each battery is collected through a Hall current sensor, the instantaneous power and cumulative energy consumption are calculated, and the temperature data is obtained in real time through the patch temperature sensors arranged on the battery surface and electrodes to generate a temperature gradient curve; Synchronize multi-source data timestamps via the bus and use spatial interpolation to fill in the voltage, current, and temperature data collected by the sensors; Measuring the internal resistance of each battery by using the voltage and current of each battery, determining the state of charge (SOH) of each battery based on the rate of change of the internal resistance within a preset time threshold, and determining the state of charge (SOC) of each battery using a Kalman filter method; Inputting the temperature gradient curve, SOH and state of charge of each battery into a preset SOC-SOH coupling model to perform status diagnosis on each battery in the power supply battery pack of the extended-range power supply vehicle, and determining the abnormality type of each battery when an abnormality exists; Mapping the abnormality type of each battery to a three-dimensional battery model, visually locating the abnormal battery, and sending an alarm to the intelligent control interface of the extended-range power supply vehicle; Matching and executing a corresponding emergency treatment strategy in a preset emergency treatment strategy library according to the abnormality type of each battery; Detecting real-time parameters of the abnormal battery, and after the real-time parameters enter a normal threshold, performing an internal resistance test on the abnormal battery to determine the SOH of the abnormal battery; Parameters of the abnormal battery are adjusted according to the SOH of the abnormal battery.

[0014] Through the above technical means, the present invention achieves the following beneficial effects: 1) Based on LSTM time series prediction of vehicle speed fluctuations and external loads, a short-term power demand sequence is generated. Combined with the real-time battery state of charge and fuel tank status normalization processing, a state vector is formed to input into a reinforcement learning model. Under the constraint of minimum energy consumption, the power distribution ratio between the range extender and the battery is optimized online. The reinforcement learning model dynamically optimizes the hybrid ratio of oil and electricity to achieve dual demand balance.

[0015] 2) A multi-dimensional sensor network based on voltage, current, and temperature diagnoses battery anomalies in real time. The SOC-SOH coupling model integrates temperature gradients and electrochemical parameters to accurately identify fault types. The emergency strategy library is linked to execute graded power reduction or forced disconnection, thereby eliminating the risk of voltage mismatch during mode switching, simultaneously resolving conflicts between battery protection and power supply requirements, and improving system robustness.

[0016] 3) Build an available device pool based on GPS positioning and vehicle status, and generate an external power supply demand pool based on the difference between the ideal power and actual power of the power supply node. Dynamically determine the networking type through dual-pool data matching, and achieve cross-regional flexible power supply through cloud platform global optimization and power carrier self-organizing network.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0020] Figure 1 A schematic diagram of a hybrid power distribution method for a range-extended power vehicle provided by the present invention; Figure 2 This is another schematic diagram of a hybrid power distribution method for a range-extended power vehicle provided by the present invention; Figure 3 Another schematic diagram of a hybrid power distribution method for a range-extended power vehicle provided by the present invention; Figure 4 A schematic diagram of the working process of a range-extended power vehicle according to a hybrid power distribution method for a range-extended power vehicle provided by the present invention. DETAILED DESCRIPTION

[0021] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present disclosure.

[0022] With the vigorous development of the new energy industry, the number of distributed power sources is increasing. At the same time, higher requirements are placed on emergency power supply capabilities, requiring power generation equipment with rapid response and flexible deployment. As a flexibly deployable power generation equipment, the extended-range power supply vehicle has important application value in scenarios such as distributed power generation and emergency power supply, and is highly consistent with the development direction of the State Grid. Among them, the extended-range power supply vehicle is a special engineering vehicle that combines extended-range electric vehicle technology with mobile power supply functions. It not only has the long driving range and low emissions characteristics of the extended-range electric vehicle, but can also serve as a mobile power station to provide emergency power support for external equipment or vehicles. It is an ideal choice for emergency, infrastructure, environmental protection and other fields. Its technological maturity and scenario adaptability are driving it to become a key component of new energy infrastructure.

[0023] Existing extended-range power supply vehicles have the following technical problems in energy supply: 1. Extended-range vehicles must meet the dual requirements of driving power and external power supply. However, the power output characteristics of batteries and range extenders differ: Batteries have fast response but limited capacity, while range extenders have high power but slow dynamic response. Traditional rule-based control has difficulty adapting to vehicle speed fluctuations and sudden load changes, which can easily lead to reduced battery life and increased energy consumption. 2. Extended-range electric vehicles require multi-mode switching, which can easily cause power oscillations and conflict between battery protection and external power supply requirements. 3. There is a need for intelligent networking when extended-range power supply vehicles are used for grid-coordinated power supply and supplementary power supply.

[0024] A hybrid power distribution method for a range-extended power vehicle, such as Figure 1 Shown, including: Step S101: determining the current application scenario and external power supply demand state of the extended-range power supply vehicle based on the current sensor distributed at the external power supply load interface; In some embodiments, this embodiment is based on a Hall current sensor network distributedly deployed at each external power supply interface of the extended-range power supply vehicle to collect key parameters such as load current, voltage, and power factor in real time. By analyzing current fluctuation characteristics such as mutation amplitude, steady-state accuracy, harmonic distortion rate, and power direction, combined with the interface type and device access status, it accurately identifies application scenarios such as off-grid power generation, grid-connected capacity expansion, and microgrid construction, and quantifies the external power supply demand status, providing high-precision working condition input for subsequent power allocation strategies; Step S102: determining the current driving state of the extended-range power supply vehicle based on the engine operating state and the real-time speed of the motor of the extended-range power supply vehicle; In some embodiments, this embodiment dynamically determines whether the vehicle is in pure electric cruise, hybrid drive, or other driving state based on the engine start / stop state of the extended-range power supply vehicle, such as idling, high-efficiency power generation, or forced shutdown, and the real-time speed of the electric motor, combined with the current battery state of charge (SOC) threshold logic, to provide a real-time operating condition basis for the vehicle's energy allocation strategy; Step S103: obtaining the current state of charge of the power supply battery and the state of the range extender fuel tank of the range extender power supply vehicle; Step S104: determining a power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the charge state of the power supply battery, and the range-extender fuel tank state; In some embodiments, this embodiment dynamically selects the optimal power supply mode based on the real-time external power supply demand state of the range-extended power supply vehicle, such as off-grid voltage stabilization or grid-connected capacity expansion, vehicle driving status, power battery state of charge threshold range, and remaining fuel in the range extender tank. For example, when the SOC is high and the driving load is low, the pure electric mode is enabled to prioritize battery energy consumption; when the SOC is medium or low or the external power supply demand is high, the range-extended mode is switched to use the engine's high-efficiency range to generate electricity and simultaneously balance the fuel consumption in the tank; when the load is extreme, the hybrid mode is activated to coordinate the dual energy output of the range extender and the battery to ensure power supply continuity and avoid the risk of battery over-discharge; Step S105: predicting the short-term power demand of the extended-range power supply vehicle based on the real-time vehicle speed and external power supply load of the extended-range power supply vehicle, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption; In some embodiments, this embodiment uses a time series convolutional network to predict a short-term power demand sequence within a preset time (e.g., 10 seconds) based on the real-time speed fluctuation characteristics of the extended-range power supply vehicle, such as acceleration, and the harmonic distortion rate of the load current of the external power supply interface. This sequence is input into a preset deep reinforcement learning model, which uses the current power supply mode as a state constraint to output a mixed distribution ratio of the engine target power and the battery output power to minimize the system's equivalent fuel consumption rate. Step S106: adjusting the range extender's generated power and the power supply battery's output power based on the hybrid power distribution ratio through a hierarchical state machine.

[0025] In some embodiments, as Figure 4 As shown, this embodiment uses a hierarchical management architecture of a hierarchical state machine to parse the hybrid power distribution ratio output by the reinforcement learning model into the range extender power generation power instruction and the power supply battery output power parameter. The top-level state machine sets the range extender start-stop logic and the battery charge and discharge safety boundary according to the vehicle driving conditions. The bottom-level state machine dynamically fine-tunes the range extender speed range and battery output threshold according to the real-time power distribution ratio to achieve smooth switching and steady-state maintenance of dual-energy coordinated output.

[0026] The working principle of the above technical solution is: first determine the current application scenario of the extended-range power supply vehicle and the external power supply demand status, driving status, power supply battery charge status and range extender fuel tank status; then determine the power supply mode of the extended-range power supply vehicle based on the external power supply demand status, driving status, power supply battery charge status and range extender fuel tank status; predict the short-term power demand of the extended-range power supply vehicle according to the real-time vehicle speed and external power supply load of the extended-range power supply vehicle, input the short-term power demand into the preset reinforcement learning model, and determine the hybrid power distribution ratio with the goal of minimizing energy consumption; finally, adjust the range extender power generation power and power supply battery output power based on the hybrid power distribution ratio through the hierarchical state machine.

[0027] The beneficial effects of the above technical solution are: real-time perception of external load fluctuations through current sensors, combined with the vehicle's driving status and energy status, accurate matching of power supply modes, and avoiding inefficient operation; using a reinforcement learning model with the goal of minimizing energy consumption, dynamically optimizing the power distribution ratio between the range extender and the battery, and synchronously balancing fuel consumption, battery loss, and mode switching penalties; a hierarchical state machine executes power distribution based on priority, and pre-adjusts the range extender speed based on short-term power prediction to achieve rapid response and ensure voltage stability and system robustness.

[0028] In one embodiment, Figure 2 As shown, the current sensor based on the distribution of the external power supply load interface determines the current application scenario and external power supply demand state of the extended-range power supply vehicle, including: Step S201: Detecting the connection status and connected device type of each external power interface of the range-extended power vehicle; In some embodiments, this embodiment is based on multiple high-voltage external power supply interfaces configured on extended-range power supply vehicles. By real-time acquisition of electrical signals from the auxiliary contacts of each interface, such as voltage / current thresholds and bus communication protocols, combined with collaborative analysis by a voltage comparator circuit and a protocol decoding chip, it accurately determines the physical connection status of each interface and the type of connected device: power supply equipment such as grid charging piles, and powered equipment such as emergency rescue tools, providing key input basis for multi-mode switching for the vehicle power management system; Step S202: monitoring the load current, voltage, and current power data of the external power interface in the connected state in real time through a Hall current sensor mounted on the external power interface of the range-extended power vehicle; Step S203: Determine the current application scenario of the extended-range power vehicle based on the connection status of each external power interface and the load current, voltage, and current power data of the external power interface, wherein the application scenarios include: off-grid power generation, grid-connected capacity expansion, grid-connected power protection, microgrid construction, and charging scenarios; In some embodiments, this embodiment accurately determines the current application scenario through multi-dimensional data fusion analysis based on the physical connection signals and real-time load electrical parameters of each external power interface of the extended-range power supply vehicle. For example, when it is detected that the external load is operating independently and there is no grid characteristic signal, the off-grid power generation mode is activated; if it is monitored that the load current phase is synchronized with the grid and the power demand exceeds the baseline, it switches to the grid-connected capacity increase mode; when the grid voltage and frequency are abnormal, it automatically switches to the grid-connected power protection mode to provide voltage stabilization support; when the multi-interface load forms a power balance closed loop, the microgrid construction function is started; when the interface is connected to the charging pile and the battery SOC is lower than the threshold, it prioritizes entering the charging scenario; Step S204: Determine the current external power supply requirement state based on the application scenario and the type of the connected device.

[0029] In some embodiments, this embodiment is based on the currently identified application scenarios of the extended-range power supply vehicle and the types of equipment connected to each interface. By fusion analyzing the electrical characteristics of the interface through multi-source data, the state parameters of the external power supply demand, such as voltage and current, are determined in real time, thereby dynamically adapting the power supply quality and energy scheduling strategies under different working conditions.

[0030] The beneficial effects of the above technical solution are: through the real-time collection of current, voltage and power dynamic data of the external interface through the Hall current sensor, combined with the device type identification, it can automatically distinguish complex scenarios such as off-grid power generation, grid-connected capacity expansion, microgrid construction, etc., avoiding the misjudgment risk of the traditional single threshold strategy; based on the scenario and device type analysis, it can dynamically generate differentiated power supply demand states, provide high-precision input for subsequent power allocation, and significantly improve the accuracy of the hybrid power distribution method.

[0031] In one embodiment, Figure 3 As shown, the current driving state of the extended-range power supply vehicle is determined based on the engine operating state and the real-time speed of the motor of the extended-range power supply vehicle, including: Step S301: receiving a CAN bus signal from an engine control unit through a vehicle controller, obtaining a start / stop flag, determining the engine start / stop state of the extended-range power supply vehicle, and when the engine state is on, obtaining the engine operating power, and determining the engine start / stop state and the engine operating power as the engine operating state; In some embodiments, this embodiment uses the vehicle controller of the extended-range power supply vehicle to analyze the start / stop flag signal transmitted by the engine control unit via the CAN bus in real time, and combines auxiliary parameters such as engine speed to determine the engine start / stop status, such as shutdown / idling / high-efficiency power generation; when the flag indicates the operating state, the real-time output power data of the engine is synchronously collected, and the start / stop state and the operating power are integrated to construct the engine operating state; Step S302: determining the current real-time speed of the motor of the extended-range power vehicle through a speed sensor carried by the motor of the extended-range power vehicle, and determining a travel speed change curve of the extended-range power vehicle based on a data curve of the real-time speed; In some embodiments, this embodiment uses a Hall effect speed sensor built into the drive motor of a range-extended power vehicle to collect the motor rotor angular velocity signal in real time and transmit it to the vehicle controller via the CAN bus. Combined with the motor reducer transmission ratio and the wheel rolling radius parameter, the angular velocity sequence is mapped into linear vehicle speed data points to generate a continuous vehicle speed change curve, dynamically reflecting the vehicle's driving conditions such as acceleration, constant speed, or braking and coasting. Step S303: determining the operating power of the range extender based on the engine operating state, and determining the total energy consumption of the current driving based on the driving speed change curve of the range-extended power vehicle; In some embodiments, this embodiment dynamically matches the operating power of the range extender based on the real-time operating status of the engine of the range-extended power vehicle, including the start / stop flag, output power, etc., and calculates the total energy consumption under the current driving conditions in real time based on the vehicle speed change curve and integrating parameters such as the drag coefficient, providing a coordinated control benchmark for the range extender power allocation and battery charging and discharging strategy; Step S304: determining the current driving state of the range-extended power supply vehicle based on the driving speed change curve, the operating power of the range extender and the total energy consumption power.

[0032] In some embodiments, this embodiment is based on the real-time speed change curve of the extended-range power vehicle, dynamic characteristics such as acceleration, constant speed or braking coasting, operating power of the range extender and total energy consumption, and accurately determines the current driving state through timing feature analysis and power.

[0033] The beneficial effects of the above technical solution are: obtaining the engine start and stop flag and power data and dynamic information of the motor speed sensor in real time through the vehicle controller, collaboratively analyzing the driving speed change curve and total energy consumption power, accurately determining the vehicle's driving status, and achieving the comprehensive optimization goals of improving energy efficiency, enhancing safety and enhancing equipment reliability of extended-range power supply vehicles in complex scenarios.

[0034] In one embodiment, obtaining the current state of charge of the power supply battery and the state of the range extender fuel tank of the range extender power supply vehicle includes: The battery management system collects a variety of battery pack data in real time, including the total battery pack voltage, battery cell voltage, charge and discharge current, and battery surface temperature; In some embodiments, this embodiment uses a distributed sensor network to collect multi-dimensional battery pack operating parameters in real time: including the total battery pack voltage (reflecting the overall energy state), single cell voltage (monitoring cell consistency through an isolated operational amplifier circuit), bidirectional charge and discharge current (using Hall sensors or shunts to capture the direction and rate of energy flow), and multi-node battery surface temperature (using thermistors or digital temperature sensors to detect thermal distribution anomalies), and transmits this data to the control unit via a high-speed data bus; Determine the state of charge of the power supply battery using a multi-parameter fusion correction method based on the multiple battery pack data; The range extender fuel amount and the current fuel consumption rate are determined based on a pressure sensor built into the fuel tank and a fuel consumption model, and the range extender fuel amount and the current fuel consumption rate are determined as the range extender fuel tank state.

[0035] In some embodiments, this embodiment monitors the oil level in real time based on the pressure sensor built into the fuel tank of the range-extended power vehicle, and dynamically calculates the remaining fuel volume in the tank and the fuel consumption rate per unit time in combination with fuel consumption, which are jointly defined as the range extender tank status, providing a core decision-making basis for the range extender start-stop strategy and power allocation.

[0036] The beneficial effects of the above technical solution are: through the fusion of multi-source battery data and dynamic modeling of fuel quantity, the energy status monitoring accuracy and system collaborative control capabilities of the extended-range power supply vehicle are significantly improved. Based on the multi-dimensional parameters such as the total voltage of the battery pack, single cell voltage, charge and discharge current and surface temperature collected in real time by the battery management system, a multi-parameter fusion correction method is used to dynamically correct the charge state estimation value, effectively overcoming the cumulative deviation problem of the traditional single ampere-hour integration method, and providing high-reliability input for the energy allocation strategy; synchronously, the fuel tank pressure sensor is used to sense the fuel quantity changes in real time, and the current fuel consumption rate is accurately calculated in combination with the fuel consumption model, and the fuel quantity attenuation trajectory and remaining endurance prediction model are constructed, which not only avoids the mechanical lag of the traditional liquid level gauge, but also provides a transient response basis for the start-stop logic and power output of the range extender.

[0037] In one embodiment, determining the power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the charge state of the power supply battery, and the range-extender fuel tank state includes: Synchronizing the external power supply demand status, the driving status, the power supply battery charge status, and the range extender fuel tank status using a timestamp technology; Determine the current external power supply demand power based on the external power supply demand state, and determine the current internal power supply demand power of the extended-range electric vehicle based on the driving state; In some embodiments, this embodiment accurately calculates the current external power supply demand based on the real-time status of the extended-range power supply vehicle's external power supply interface and the type of connected equipment, such as industrial equipment requiring constant voltage and frequency and charging piles requiring bidirectional energy interaction. At the same time, the internal power supply demand is dynamically analyzed based on the vehicle's driving status (such as a sharp increase in driving power during rapid acceleration and negative power output during braking recovery), providing a power benchmark input for vehicle energy scheduling. Obtaining the state of charge of the power supply battery, determining the current power percentage of the power supply battery, obtaining the state of the range extender fuel tank, and determining the current fuel level of the range extender fuel tank; In some embodiments, this embodiment uses a multi-parameter fusion correction method to accurately calculate the state of charge (SOC) of the power supply battery based on multi-dimensional parameters such as the total battery pack voltage, single cell voltage, charge and discharge current, and node surface temperature collected by the battery management system of the range-extended power vehicle, and converts it into an intuitive current power percentage. At the same time, based on the fuel static pressure data monitored in real time by the built-in pressure sensor in the fuel tank, combined with the operating power of the range extender, the remaining fuel volume in the range extender tank is dynamically analyzed and the current fuel level is accurately output; Based on the external power supply required power, the internal power supply required power, the current power percentage of the power supply battery and the current fuel level of the range extender tank, a corresponding power supply mode is matched according to a preset power supply mode allocation strategy table.

[0038] In some embodiments, this embodiment dynamically matches the optimal power supply mode based on the real-time external power supply demand power, internal power supply demand power, battery charge percentage (SOC) and current fuel tank oil level parameters of the extended-range power supply vehicle through a preset power supply mode allocation strategy table (mapping relationship such as: high SOC + low power demand pure electric mode; low SOC + high external demand extended-range dominant mode), triggers the hybrid power supply strategy under extreme working conditions (such as dual energy shortage, high load output), and finally outputs a power supply mode instruction that adapts to the current energy status and demand intensity.

[0039] The beneficial effects of the above technical solution are: through the multi-source state coordination and dynamic power matching mechanism of timestamp synchronization, the energy utilization efficiency and multi-scenario power supply reliability of the extended-range power supply vehicle are significantly improved, and the timestamp technology is used to achieve accurate synchronization of the external power supply demand status, driving status, battery charge status and fuel tank oil level status, eliminating the timing deviation caused by traditional multi-system independent collection, and providing millisecond-level consistent data basis for power supply mode decision-making; based on the real-time analysis of the external power supply demand power and the vehicle's internal power supply demand power, combined with the battery SOC threshold and the fuel tank oil level attenuation model, the preset power supply strategy table is dynamically matched, thereby improving the intelligence of the power supply strategy.

[0040] In one embodiment, the method of predicting the short-term power demand of the extended-range power supply vehicle based on the real-time vehicle speed and external power supply load of the extended-range power supply vehicle, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption includes: Obtaining the real-time speed of the range-extended power vehicle, and generating a speed curve based on the real-time speed within a preset time window; In some embodiments, the preset time window in this embodiment is 1 minute; Obtaining the external power supply load of the extended-range power supply vehicle, and generating an external load curve according to the total power of the external power supply load within a preset time window; In some embodiments, the preset time window in this embodiment is 1 minute; Generate a short-term power demand sequence of the extended-range power supply vehicle within a preset time threshold according to the vehicle speed curve and the external load curve using LSTM time series prediction technology; In some embodiments, this embodiment uses LSTM time series prediction technology to construct a multivariate input sequence based on the vehicle speed curve and external load curve collected in real time by the extended-range power supply vehicle. The LSTM gating mechanism is used to synchronously capture the relationship between vehicle speed inertia changes and load step responses, and to make rolling predictions for short-term power demand sequences within a fixed time in the future, such as 30 seconds. Obtaining a short-term power demand sequence of the extended-range power supply vehicle, a current battery state of charge, and a fuel tank state, performing normalization processing, generating a state vector, obtaining the power supply mode, and generating a power supply mode code; In some embodiments, this embodiment uses a normalization algorithm to scale multi-source heterogeneous data to the [0,1] interval based on the short-term power demand sequence of the extended-range power supply vehicle (predicted and generated by the LSTM model), the current battery state of charge (SOC percentage), and the fuel tank status (remaining fuel volume and consumption rate) to construct a state vector with a unified dimension. At the same time, based on the current power supply mode (e.g., pure electric mode is coded as 01, extended-range mode is coded as 10, and hybrid mode is 11), the discrete mode labels are converted into coded vectors to form a standardized input feature matrix for the reinforcement learning model. With minimum energy consumption as a constraint, the state vector and the power supply mode are encoded and input into a preset reinforcement learning model, and the power supply distribution ratio of the range extender and the power supply battery is output.

[0041] In some embodiments, this embodiment is based on the normalized state vector and power supply mode encoding of the extended-range power supply vehicle, with the lowest system equivalent fuel consumption rate as the constraint target, and inputs a preset deep reinforcement learning model: analyzes the relationship between state characteristics and mode encoding, iteratively optimizes the policy gradient in the continuous action space, and outputs the optimal action value, that is, the target power ratio of the range extender and the output power ratio of the power supply battery.

[0042] The beneficial effects of the above technical solution are: generating a short-term power demand sequence through LSTM time series prediction of real-time vehicle speed and external power supply load, combining the normalized vector of battery state of charge and fuel tank state with power supply mode encoding, inputting the reinforcement learning model to dynamically optimize the power distribution ratio of the range extender and the power supply battery, improving the energy efficiency and economy of the system, and based on the coordinated prediction of the vehicle speed curve and the external load curve, accurately capturing the transient demand fluctuations in driving and power supply scenarios, avoiding the power response lag or redundancy caused by traditional static threshold control; through the reinforcement learning model, online optimization is carried out with the minimum energy consumption as the constraint, and the oil-electric hybrid ratio is adaptively adjusted to ensure that the range extender continues to operate in the high-efficiency range; integrating the power supply mode encoding to enhance the adaptability of working conditions, quickly switching the power distribution strategy under sudden load changes or acceleration requirements, and eliminating the power interruption or energy consumption surge caused by the mode switching deadlock in traditional rule control.

[0043] In one embodiment, the power supply mode includes: pure electric mode, extended range mode, hybrid power supply mode, emergency power supply mode, braking recovery mode and battery protection mode.

[0044] In some embodiments, the pure electric mode is used to use the battery for independent power supply; the extended-range mode is used to use the range extender to generate electricity for power supply, and use the battery to supplement the power supply peak; the hybrid power supply mode is used to use the battery and the range extender for joint power supply; the emergency power supply mode is used to use the range extender and the battery to supply power at full load in an emergency power supply scenario; the braking recovery mode is used to recover kinetic energy by reversing the motor in the braking scenario of the extended-range power supply vehicle, and reversely charge the battery; the battery protection mode is used to use the range extender to charge the battery when the battery state of charge is lower than a preset threshold.

[0045] In one embodiment, adjusting the range extender power generation power and the power supply battery output power based on the power allocation ratio by a hierarchical state machine includes: Inputting the power allocation ratio into a hierarchical state machine, and using the hierarchical state machine to generate corresponding range extender control instructions and power supply battery control instructions; In some embodiments, this embodiment uses a hierarchical processing architecture of a layered state machine to parse the power allocation ratio output by the reinforcement learning model into executable instructions: the top-level state sets the range extender start-stop logic and battery charge and discharge safety boundaries based on driving conditions; the bottom-level state dynamically generates range extender control instructions and power supply battery control instructions based on the ratio value, and uses state guard conditions to intercept the oscillation risk caused by power mutation, thereby achieving steady-state maintenance of dual-energy coordinated output; Parsing the range extender control command, determining the start / stop state of the range extender and the engine speed of the range extender, and performing power regulation on the range extender; In some embodiments, the range extender controller of this embodiment determines the start / stop state of the range extender by deconstructing the start / stop logic signal and the target speed command output by the hierarchical state machine and combining it with the real-time engine speed feedback; at the same time, the generator speed is dynamically adjusted through the engine controller; The power supply battery control instruction is parsed to determine the start / stop state, output voltage and output current of the power supply battery, and power regulation is performed on the power supply battery.

[0046] In some embodiments, this embodiment decodes the electric energy parameter encoding in the instruction through the battery management system, and dynamically determines the start / stop state based on the real-time state of charge (SOC) of the battery pack, and adjusts the switching frequency of the power device through the voltage / current dual closed-loop control of the bidirectional DC / DC converter using the parsed target voltage / current parameters.

[0047] The beneficial effects of the above technical solution are: utilizing the hierarchical structure of the hierarchical state machine to convert the power distribution ratio into the start and stop instructions and speed parameters of the range extender, the start and stop instructions of the battery and the voltage and current parameters, reducing redundant judgments through the state inheritance mechanism, and ensuring the real-time nature of instruction parsing and execution; at the same time, through state jumps, predicting and intercepting system conflict risks, eliminating the response delay or instruction oscillation problems caused by logic dispersion in traditional control.

[0048] In one embodiment, the method further includes obtaining, through a cloud platform, intelligent networking of the power grid for all extended-range power supply vehicles in a preset area according to current external power supply requirements: The vehicle positions of multiple extended-range power supply vehicles in a preset area are collected by GPS, and the battery charge state, fuel tank fuel level and range extender available power of each extended-range power supply vehicle are obtained by the status monitoring system of the extended-range power supply vehicle; Uploading the vehicle positions of the plurality of extended-range power supply vehicles, the battery charge state, fuel tank fuel level, and available power of the range extender of each extended-range power supply vehicle to the cloud platform, determining the extended-range power supply vehicle whose available power of the range extender is greater than a preset value as an available device, and building an available device pool; In some embodiments, this embodiment aggregates multi-source heterogeneous data to a cloud platform in real time through a wireless communication module based on dynamic parameters such as vehicle location (GPS coordinates), battery state of charge (SOC percentage), fuel tank level (remaining fuel volume), and available power of the range extender (maximum output capacity under current operating conditions) collected in real time by the on-board terminal of the range extender power supply vehicle. The cloud service dynamically filters the uploaded data based on a preset range extender power threshold (e.g., ≥50kW), marks the range extender power supply vehicles that meet the conditions as available devices, and builds an available device pool. Obtain the total power demand of all power supply nodes and current power supply needs in the preset area through the cloud platform; In some embodiments, the regional energy dispatching system based on the cloud platform of this embodiment collects the topology of all power supply nodes in a preset area in real time through the power grid interface, synchronously obtains the real-time load data of each node, and dynamically calculates the total required power of the current power supply demand in combination with the historical load curve; Obtaining power supply parameters of each node among all power supply nodes, obtaining the total required power, performing power allocation according to the power supply parameters of each node, and determining the ideal power supply power of each node; In some embodiments, this embodiment obtains the real-time operating parameters of all power supply nodes in a preset area based on a cloud platform and simultaneously integrates the total power demand. By combining the equal incremental rate principle with the node power supply capacity upper limit and voltage stability margin constraints, the ideal power supply power of each node is dynamically calculated to achieve the global optimal allocation of power gaps. Determine the current power supply power of each node according to the power supply parameters of each node, obtain the ideal power supply power of each node, and determine the external power supply requirement power of each node according to the difference between the current power supply power and the ideal power supply power; In some embodiments, this embodiment dynamically calculates the current power supply power of each node based on the power supply node operating parameters collected in real time by the cloud platform, combined with network loss correction and load forecast data, and simultaneously obtains the ideal power supply power, accurately quantifies the external power supply demand power of each node, and provides a dynamic target value for the power gap compensation of the extended-range power supply vehicle; Uploading the supplementary power supply demand power of each node to the cloud platform to build an external power supply demand pool; In some embodiments, this embodiment uses a regional energy dispatch system based on a cloud platform to aggregate the supplementary power demand power (including node ID, power gap value and timestamp) of each power supply node in real time to build a structured external power demand pool; Determining a range-extended power supply vehicle networking type based on the external power supply demand pool; In some embodiments, this embodiment dynamically determines the optimal networking type through a topology optimization algorithm based on the spatial distribution characteristics of node power gaps, time series fluctuation characteristics, and power constraints (voltage / frequency compensation requirements) recorded in the external power supply demand pool, combined with the real-time location distribution and power output capacity of the extended-range power supply vehicles in the available equipment pool. This includes a centralized star topology (single-point centralized power compensation), a distributed mesh topology (multi-node collaborative power supply), or a hybrid hierarchical topology. Obtain the external power supply demand pool and the range extender power supply vehicle networking type, and perform power supply node allocation and power supply parameter design based on the vehicle position and available power of each available device in the available device pool; In some embodiments, this embodiment dynamically allocates power supply nodes and designs power supply parameters through a spatial matching algorithm based on the external power supply demand pool and the network type of the range-extended power supply vehicle, such as a star / mesh topology, combined with the real-time location of each vehicle in the available device pool and the available power of the range extender; Use the cloud platform to perform corresponding available device scheduling according to the power supply node allocation, and perform current networking according to the power supply parameter design.

[0049] In some embodiments, this embodiment is based on an intelligent scheduling system on a cloud platform, which dynamically schedules extended-range power supply vehicles in the available equipment pool according to the spatial distribution of power supply nodes and power gaps, matches the real-time location of the vehicle with the available power of the range extender, and designs a current networking solution based on node requirements.

[0050] The beneficial effects of the above technical solution are as follows: The real-time status of range-extended power vehicles and regional power supply needs are dynamically integrated through the cloud platform to build a globally optimized mobile microgrid networking system. Based on GPS positioning and vehicle status monitoring systems, an available device pool is constructed. The external power demand pool is dynamically generated by combining the difference between the current power and ideal power of the power supply node. The network type is accurately determined by matching the data from the two pools. Relying on the cloud platform's global optimization algorithm, power supply nodes are dynamically allocated and power supply parameters are designed based on vehicle location distribution and the available power of the range extender. Current networking instructions are used to dispatch available devices to generate the optimal power supply topology, enabling remote scheduling and grid-connected power supply of cross-regional power supply vehicles in disaster environments.

[0051] In one embodiment, the method further includes determining the status of each battery in the power supply battery pack of the extended-range power supply vehicle based on real-time parameters of the power supply battery, and performing battery protection on the abnormal battery when the battery status is determined to be abnormal: The voltage of each battery in the power supply battery pack of the extended-range power supply vehicle is collected in real time through a single-cell voltage sensor, the current of each battery is collected through a Hall current sensor, the instantaneous power and cumulative energy consumption are calculated, and the temperature data is obtained in real time through the patch temperature sensors arranged on the battery surface and electrodes to generate a temperature gradient curve; Synchronize multi-source data timestamps via the bus and use spatial interpolation to fill in the voltage, current, and temperature data collected by the sensors; In some embodiments, this embodiment uses bus synchronization technology to achieve timestamp alignment of multi-source sensors, such as voltage, current, and temperature acquisition units, to eliminate timing deviations caused by hardware clock drift or transmission delays. On this basis, spatial interpolation is used to reconstruct complete data for missing data points in the sensor network, combined with spatial correlation characteristics (such as voltage gradients and temperature field distributions of adjacent nodes); Measuring the internal resistance of each battery by using the voltage and current of each battery, determining the state of charge (SOH) of each battery based on the rate of change of the internal resistance within a preset time threshold, and determining the state of charge (SOC) of each battery using a Kalman filter method; In some embodiments, this embodiment measures the battery internal resistance using a DC discharge method based on the real-time acquisition of each battery's voltage and current. A temperature compensation model is used to eliminate environmental interference. A battery aging curve is mapped based on the internal resistance change rate within a preset time threshold (here, within 5 days) to quantitatively assess the state of health (SOH) of each battery. A Kalman filter method is used to construct a first-order equivalent circuit model of the battery. Using voltage and current as observation inputs, the SOC of each battery is estimated in real time through state equation iteration and covariance adaptive correction. Inputting the temperature gradient curve, SOH and state of charge of each battery into a preset SOC-SOH coupling model to perform status diagnosis on each battery in the power supply battery pack of the extended-range power supply vehicle, and determining the abnormality type of each battery when an abnormality exists; In some embodiments, the present embodiment simultaneously inputs the temperature gradient curve, SOH, and state of charge (SOC) of each battery in the extended-range electric vehicle battery pack into a preset SOC-SOH coupling model, and performs battery status diagnosis through multi-dimensional residual analysis. If an abnormality is detected, such as a sudden change in temperature gradient, accelerated SOH decay, or SOC jump, the fault tree model is combined to accurately locate the abnormality type of each battery. Mapping the abnormality type of each battery to a three-dimensional battery model, visually locating the abnormal battery, and sending an alarm to the intelligent control interface of the extended-range power supply vehicle; In some embodiments, based on the real-time diagnostic results of the battery management system of the extended-range power vehicle, this embodiment binds the abnormality type of each battery, such as thermal runaway precursors, internal short circuits, or electrode aging, to the three-dimensional grid model of the battery pack through a coordinate mapping algorithm, visually locates the abnormal cell in the form of a heat map, and simultaneously transmits the alarm signal and the heat map to the intelligent control interface; Matching and executing a corresponding emergency treatment strategy in a preset emergency treatment strategy library according to the abnormality type of each battery; In some embodiments, this embodiment dynamically matches graded response measures based on the real-time diagnosis of abnormalities by the battery management system of the extended-range power supply vehicle, such as thermal runaway precursors, internal short circuits, or electrode aging, through a preset emergency treatment strategy library. For example, in response to the risk of thermal runaway, the liquid cooling system is immediately triggered to cool down at full power and cut off the charge and discharge circuits; in the event of an internal short circuit fault, the corresponding module is isolated and the output power is limited; in the event of an abnormal electrode aging, the pulse repair mode is automatically enabled and the charging current limit threshold is adjusted; Detecting real-time parameters of the abnormal battery, and after the real-time parameters enter a normal threshold, performing an internal resistance test on the abnormal battery to determine the SOH of the abnormal battery; In some embodiments, this embodiment is based on real-time monitoring of the battery management system of the extended-range power vehicle. When the real-time parameters of the abnormal battery (such as voltage mutation, abnormal temperature rise) return to the preset normal threshold range, the system automatically triggers the internal resistance test process; Parameters of the abnormal battery are adjusted according to the SOH of the abnormal battery.

[0052] In some embodiments, this embodiment dynamically adjusts the charge and discharge parameters of the extended-range power vehicle based on the abnormal battery SOH value diagnosed by the battery management system. For example: for batteries with low SOH values, the charge and discharge cut-off voltage threshold is lowered and the SOC operating window is narrowed; for batteries with medium SOH values ​​but significantly increased internal resistance, the power limit threshold is increased to avoid local overload; at the same time, the battery pack energy distribution weight is reconstructed according to the SOH distribution, and high SOH battery cells are called first to achieve flexible load regulation of aging batteries.

[0053] The beneficial effects of the above technical solution are as follows: through multi-source data fusion and SOC-SOH coupled diagnosis, refined safety management and control of battery packs for extended-range electric vehicles is achieved. Based on a multi-dimensional sensor network for voltage, current, temperature, etc., single-cell-level data is collected in real time, and spatial interpolation is used to fill data gaps and synchronize timestamps to ensure the spatiotemporal consistency of the monitoring data. The battery state of health (SOH) is dynamically tracked by combining the internal resistance change rate, and the Kalman filter algorithm is used to correct the state of charge (SOC) estimation error in real time, effectively solving the problem of missed early anomalies in traditional single-parameter monitoring. The SOC-SOH coupled model integrates temperature gradient curves and electrochemical parameters to achieve accurate identification of anomaly types, and simultaneously maps the anomaly type to the battery three-dimensional model to achieve spatial positioning visualization, significantly shortening fault location time. Relying on a preset emergency strategy library, protection measures are dynamically matched, and after the parameters return to normal, internal resistance retesting is performed to verify the SOH attenuation.

[0054] Those skilled in the art should understand that the first and second in the present invention simply refer to different application stages.

[0055] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0056] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A hybrid power distribution method for a range-extended power vehicle, characterized in that: include: The current sensor based on the distribution of the external power supply load interface determines the current application scenario and external power supply demand status of the extended-range power supply vehicle; Determining the current driving state of the extended-range power supply vehicle based on the engine operating state and the real-time speed of the electric motor of the extended-range power supply vehicle; Obtaining the current state of charge of the power supply battery and the range extender fuel tank status of the range extender power supply vehicle; Determining a power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the state of charge of the power supply battery, and the state of the range-extender fuel tank; Predicting the short-term power demand of the extended-range power supply vehicle based on the real-time speed of the extended-range power supply vehicle and the external power supply load, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption; The hierarchical state machine adjusts the range extender's generated power and the power supply battery's output power based on the hybrid power distribution ratio.

2. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The current sensor based on the distribution of the external power supply load interface determines the current application scenario and external power supply demand state of the extended-range power supply vehicle, including: Detect the connection status and connected device type of each external power interface of the extended-range power supply vehicle; The Hall current sensor on the external power interface of the range-extended power vehicle monitors the load current, voltage and current power data of the external power interface in the connected state in real time; Determining the current application scenario of the extended-range power vehicle based on the connection status of each external power interface and the load current, voltage, and current power data of the external power interface, wherein the application scenarios include: off-grid power generation, grid-connected capacity expansion, grid-connected power protection, microgrid construction, and charging scenarios; The current external power supply demand state is determined based on the application scenario and the type of the connected device.

3. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The determining of the current driving state of the extended-range power supply vehicle based on the engine operating state and the real-time speed of the electric motor of the extended-range power supply vehicle includes: Receive the CAN bus signal of the engine control unit through the vehicle controller, obtain the start / stop flag, determine the engine start / stop state of the extended-range power supply vehicle, and when the engine state is on, obtain the engine operating power, and determine the engine start / stop state and the engine operating power as the engine operating state; Determine the current real-time speed of the motor of the extended-range power supply vehicle by a speed sensor carried by the motor of the extended-range power supply vehicle, and determine the driving speed change curve of the extended-range power supply vehicle based on the data curve of the real-time speed; Determining the operating power of the range extender based on the engine operating state, and determining the total energy consumption power of the current driving based on the driving speed change curve of the range-extended power supply vehicle; The current driving state of the range-extended power supply vehicle is determined based on the driving speed change curve, the operating power of the range extender and the total energy consumption power.

4. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The obtaining of the current state of charge of the power supply battery and the state of the range extender fuel tank of the range extender power supply vehicle includes: The battery management system collects a variety of battery pack data in real time, including the total battery pack voltage, battery cell voltage, charge and discharge current, and battery surface temperature; Determine the state of charge of the power supply battery using a multi-parameter fusion correction method based on the multiple battery pack data; The range extender fuel amount and the current fuel consumption rate are determined based on a pressure sensor built into the fuel tank and a fuel consumption model, and the range extender fuel amount and the current fuel consumption rate are determined as the range extender fuel tank state.

5. The hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The determining of the power supply mode of the range-extended power supply vehicle based on the external power supply demand state, the driving state, the state of charge of the power supply battery, and the state of the range-extender fuel tank includes: Synchronizing the external power supply demand status, the driving status, the power supply battery charge status, and the range extender fuel tank status using a timestamp technology; Determine the current external power supply demand power based on the external power supply demand state, and determine the current internal power supply demand power of the extended-range electric vehicle based on the driving state; Obtaining the state of charge of the power supply battery, determining the current power percentage of the power supply battery, obtaining the state of the range extender fuel tank, and determining the current fuel level of the range extender fuel tank; Based on the external power supply required power, the internal power supply required power, the current power percentage of the power supply battery and the current fuel level of the range extender tank, a corresponding power supply mode is matched according to a preset power supply mode allocation strategy table.

6. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The method includes predicting the short-term power demand of the extended-range power supply vehicle based on the real-time vehicle speed and external power supply load of the extended-range power supply vehicle, inputting the short-term power demand into a preset reinforcement learning model, and determining the hybrid power distribution ratio based on the power supply mode with the goal of minimizing energy consumption, including: Obtaining the real-time speed of the range-extended power vehicle, and generating a speed curve based on the real-time speed within a preset time window; Obtaining the external power supply load of the extended-range power supply vehicle, and generating an external load curve according to the total power of the external power supply load within a preset time window; Generate a short-term power demand sequence of the range-extended power vehicle within a preset time threshold according to the vehicle speed curve and the external load curve using LSTM time series prediction technology; Obtaining a short-term power demand sequence of the extended-range power supply vehicle, a current battery state of charge, and a fuel tank state, performing normalization processing, generating a state vector, obtaining the power supply mode, and generating a power supply mode code; With minimum energy consumption as a constraint, the state vector and the power supply mode are encoded and input into a preset reinforcement learning model to output the power supply distribution ratio of the range extender and the power supply battery.

7. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The power supply modes include: pure electric mode, extended range mode, hybrid power supply mode, emergency power supply mode, braking recovery mode and battery protection mode.

8. The hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The adjusting the range extender power generation power and the power supply battery output power based on the power allocation ratio by the hierarchical state machine includes: Inputting the power allocation ratio into a hierarchical state machine, and using the hierarchical state machine to generate corresponding range extender control instructions and power supply battery control instructions; Parsing the range extender control command, determining the start / stop state of the range extender and the engine speed of the range extender, and performing power regulation on the range extender; The power supply battery control instruction is parsed to determine the start / stop state, output voltage and output current of the power supply battery, and power regulation is performed on the power supply battery.

9. The hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The method further includes obtaining, through a cloud platform, intelligent networking of the power grid for all extended-range power supply vehicles in a preset area according to current external power supply requirements: The vehicle positions of multiple extended-range power supply vehicles in a preset area are collected by GPS, and the battery charge state, fuel tank fuel level and range extender available power of each extended-range power supply vehicle are obtained by the status monitoring system of the extended-range power supply vehicle; Uploading the vehicle positions of the plurality of extended-range power supply vehicles, the battery charge state, fuel tank fuel level, and available power of the range extender of each extended-range power supply vehicle to the cloud platform, determining the extended-range power supply vehicle whose available power of the range extender is greater than a preset value as an available device, and building an available device pool; Obtain the total power demand of all power supply nodes and current power supply needs in the preset area through the cloud platform; Obtaining power supply parameters of each node among all power supply nodes, obtaining the total required power, performing power allocation according to the power supply parameters of each node, and determining the ideal power supply power of each node; Determine the current power supply power of each node according to the power supply parameters of each node, obtain the ideal power supply power of each node, and determine the external power supply requirement power of each node according to the difference between the current power supply power and the ideal power supply power; Uploading the supplementary power supply demand power of each node to the cloud platform to build an external power supply demand pool; Determining a range-extended power supply vehicle networking type based on the external power supply demand pool; Obtain the external power supply demand pool and the range extender power supply vehicle networking type, and perform power supply node allocation and power supply parameter design based on the vehicle position and available power of each available device in the available device pool; Use the cloud platform to perform corresponding available device scheduling according to the power supply node allocation, and perform current networking according to the power supply parameter design.

10. A hybrid power distribution method for a range-extended power vehicle according to claim 1, characterized in that: The method further includes determining the status of each battery in the power supply battery pack of the range-extended power supply vehicle based on real-time parameters of the power supply battery, and performing battery protection on the abnormal battery when the battery status is determined to be abnormal: The voltage of each battery in the power supply battery pack of the extended-range power supply vehicle is collected in real time through a single-cell voltage sensor, the current of each battery is collected through a Hall current sensor, the instantaneous power and cumulative energy consumption are calculated, and the temperature data is obtained in real time through the patch temperature sensors arranged on the battery surface and electrodes to generate a temperature gradient curve; Synchronize multi-source data timestamps via the bus and use spatial interpolation to fill in the voltage, current, and temperature data collected by the sensors; Measuring the internal resistance of each battery by using the voltage and current of each battery, determining the state of charge (SOH) of each battery based on the rate of change of the internal resistance within a preset time threshold, and determining the state of charge (SOC) of each battery using a Kalman filter method; Inputting the temperature gradient curve, SOH and state of charge of each battery into a preset SOC-SOH coupling model to perform status diagnosis on each battery in the power supply battery pack of the extended-range power supply vehicle, and determining the abnormality type of each battery when an abnormality exists; Mapping the abnormality type of each battery to a three-dimensional battery model, visually locating the abnormal battery, and sending an alarm to the intelligent control interface of the extended-range power supply vehicle; Matching and executing a corresponding emergency treatment strategy in a preset emergency treatment strategy library according to the abnormality type of each battery; Detecting real-time parameters of the abnormal battery, and after the real-time parameters enter a normal threshold, performing an internal resistance test on the abnormal battery to determine the SOH of the abnormal battery; Parameters of the abnormal battery are adjusted according to the SOH of the abnormal battery.

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