A method and device for coordinated control of a light storage and charging system

Abstract

The application provides a kind of synergic control method and device of light storage and charging system, method includes: obtaining the real-time state data of light storage and charging system, real-time monitoring external charging request communication message;When identifying charging handshake event, before load current output, according to load demand parameter to generate the active reconstruction instruction of direct current bus voltage, to suppress load impact;Based on the internal temperature and state of charge of battery, the dynamic thermal safety current boundary in the prediction time domain is calculated reversely using battery thermal model;With the boundary as constraint condition, the multi-objective optimization function containing grid transaction cost, battery life loss cost and heat dissipation system operation cost is solved rolling;Finally, the optimal power distribution sequence and heat dissipation control sequence obtained by solving are converted into control instructions and executed.Effectively solve the voltage stability problem under the direct current coupling architecture, while ensuring the thermal safety of battery and prolonging the life, the maximization of system operation economy is realized.

Description

Technical Field

[0001] This invention relates to the field of optical energy storage and charging technology, and in particular to a method and apparatus for coordinated control of an optical energy storage and charging system. Background Technology

[0002] With the increasing popularity of electric vehicles (EVs), integrated photovoltaic-storage-charging stations have been widely adopted due to their ability to effectively solve the problems of photovoltaic power consumption and grid capacity expansion. Among them, DC-coupled photovoltaic-storage-charging systems have higher energy efficiency due to the reduction of AC / DC conversion links, making them the mainstream direction of technological development.

[0003] However, existing photovoltaic energy storage and charging systems face multiple technical challenges in actual operation: In DC-coupled architectures, the connection of electric vehicles often manifests as an impulsive load. Existing control strategies are mostly based on voltage feedback mechanisms (such as voltage droop control or PI control), meaning that the converter output is only adjusted after a voltage drop is detected on the bus. This lag response mechanism is highly susceptible to causing large fluctuations in the DC bus voltage, or even a drop to the undervoltage protection threshold, when faced with the instantaneous startup of a high-power fast charging station, leading to system shutdown.

[0004] Existing energy management systems (EMS) typically focus only on power distribution (such as peak shaving and valley filling), while delegating battery temperature control to a separate thermal management system (TMS). When formulating charge and discharge strategies, EMS often only considers the battery's state of charge (SOC), ignoring the limitations imposed by battery health (SOH) and real-time temperature on power limits. This leads to the EMS potentially issuing high-current commands under high temperatures or poor heat dissipation conditions, forcing the TMS to operate at full speed. This not only wastes energy but also causes battery overheating due to the physical lag in temperature response, accelerating SEI film decomposition and severely shortening battery life.

[0005] Traditional optimization algorithms typically consider only the grid electricity cost in their objective function, neglecting the implicit cost of battery lifespan degradation and the explicit cost of operating the cooling system. In reality, under extreme conditions, the value of battery lifespan reduction caused by high-rate charging and discharging to save a small amount of electricity often far exceeds the electricity cost savings.

[0006] Therefore, there is an urgent need for a collaborative control method for photovoltaic, energy storage, and charging systems that can proactively respond to load shocks, deeply couple thermal constraints to power dispatch, and comprehensively consider equipment lifespan and operating costs. Summary of the Invention

[0007] To overcome the problems existing in related technologies, the purpose of this invention is to provide a collaborative control method and device for a photovoltaic-storage-charging system, which solves the problems of weak DC bus impact resistance, rapid battery life decay due to thermoelectric control interruption, and incomplete economic evaluation of operation in the prior art.

[0008] In a first aspect, the present invention provides a coordinated control method for a photovoltaic energy storage and charging system, comprising: Acquire real-time status data of the photovoltaic energy storage and charging system, wherein the real-time status data includes at least the battery internal temperature, ambient temperature and battery state of charge; The system monitors external charging request communication messages in real time. When a charging handshake event is detected, it parses the load demand parameters of the communication message and generates an active reconfiguration command for the DC bus voltage based on the load demand parameters before the load current is output. The active reconfiguration command is used to drive the bidirectional DC / DC converter to adjust the bus voltage reference value in order to maintain the electrical stability of the photovoltaic energy storage and charging system. Based on the battery's internal temperature, ambient temperature, and state of charge, the dynamic thermal safety current boundary for maintaining the battery temperature within the predicted time domain is calculated using the battery thermal model. Using the dynamic thermal safety current boundary as a constraint, the multi-objective optimization function, which includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs, is solved in a rolling manner to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence. The optimal power allocation sequence and the optimal heat dissipation control sequence are converted into control commands and output.

[0009] Optionally, the real-time monitoring of external charging request communication messages, upon detecting a charging handshake event, parses the load demand parameters of the communication message and generates an active reconfiguration command for the DC bus voltage based on the load demand parameters before outputting the load current, including: Extract the requested voltage and requested current corresponding to the load demand parameters from the communication message, and calculate the estimated load power fluctuation. Based on the energy buffering characteristics of the DC bus capacitor, the target voltage increase value required to maintain the bus voltage drop less than the preset safety threshold during the response delay time is calculated. The reference voltage of the DC bus is set to the sum of the current voltage and the target voltage boost value, and the active reconfiguration command is generated and executed before the load current is output.

[0010] Optionally, the step of calculating the dynamic thermal safety current boundary for maintaining the battery temperature within the predicted time domain using a battery thermal model, based on the battery's internal temperature, ambient temperature, and battery state of charge, includes: Determine the maximum permissible temperature based on the current battery health status; Based on the discretization inverse solution formula, calculate the first... The dynamic thermal safety current boundary at time t is determined by the discretized inverse solution formula as follows: ; in, This is the highest permissible temperature under the current battery health condition; No. Dynamic thermal safety current boundary at any given moment; This is the current internal temperature of the battery; The current ambient temperature; To predict the step size; The battery thermal time constant; The internal resistance of the battery varies with state of charge and temperature; The equivalent thermal resistance of the battery to the environment; This represents the maximum heat dissipation power that the cooling system can provide at the current moment.

[0011] Optionally, the step of using the dynamic thermal safety current boundary as a constraint to perform a rolling solution on a multi-objective optimization function that includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs, to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence, includes: Construct a multi-objective optimization function that includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs; The multi-objective optimization function is solved using a sequential quadratic programming algorithm, and the optimal power allocation sequence and optimal heat dissipation control sequence that satisfy the constraints are output. The expression for the objective optimization function is as follows: ; in, The formula for calculating battery life loss cost, which incorporates a temperature index term, is as follows: ; in, To predict the length of the time domain; These are the weighting coefficients; For grid transaction costs; For the operating cost of the cooling system; The baseline aging factor; The charging and discharging current of the battery; Current aging factor; Activation energy; It is the gas constant; Real-time battery temperature; Reference temperature; The constraint condition is: .

[0012] Optionally, the operating cost of the heat dissipation system is calculated using the following formula: ; The power grid transaction costs The calculation formula is as follows: ; in, For the first The electricity price on the grid at any given time; This refers to the static standby power consumption of the heat dissipation system. The fluid drive coefficient; For the first The flow rate of the cooling medium at any given time; To control the cycle; For the first Power purchased from the power grid at all times.

[0013] Optionally, the step of converting the optimal power allocation sequence and the optimal heat dissipation control sequence into control commands and outputting them includes: A timing analysis is performed on the optimal heat dissipation control sequence. When a non-zero cooling medium flow rate command is detected before the load current output, a pre-cooling control command is generated to control the heat dissipation system to start in advance before the load is connected. Compare the battery output power in the optimal power allocation sequence with the requested power in the load demand parameter. When the battery output power is less than the requested power, generate a flexible derating command and control the DC charging output power to follow the dynamic thermal safety current boundary by reducing the current. The underlying actuators of the photovoltaic energy storage and charging system are driven to operate according to the pre-cooling control command or the flexible derating command.

[0014] Secondly, the present invention provides a coordinated control device for a photovoltaic energy storage and charging system, comprising: The status sensing module is used to acquire real-time status data of the photovoltaic energy storage and charging system. The real-time status data includes at least the battery internal temperature, ambient temperature, and battery state of charge. The event response and voltage reconstruction module is used to monitor the communication messages of external charging requests in real time. When a charging handshake event is detected, the load demand parameters of the communication message are parsed, and before the load current is output, an active reconstruction command for the DC bus voltage is generated based on the load demand parameters. The thermal safety boundary calculation module is used to calculate the dynamic thermal safety current boundary that keeps the battery temperature within the predicted time domain based on the battery's internal temperature, ambient temperature, and battery state of charge, using the battery thermal model. The multi-objective collaborative optimization module is used to perform rolling solutions on a multi-objective optimization function that includes grid transaction costs, battery life loss costs and heat dissipation system operating costs, with the dynamic thermal safety current boundary as a constraint, to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence. The instruction generation and execution module is used to convert the optimal power allocation sequence and the optimal heat dissipation control sequence into control instructions and output them.

[0015] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the method described in the first aspect above.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect above.

[0017] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0018] The beneficial effects of this invention are as follows: This invention provides a collaborative control method and apparatus for a photovoltaic-storage-charging system. The method utilizes charging pile communication messages (handshake events) as feedforward signals to actively reconstruct the DC bus voltage within a millisecond-level time window before the actual load current output. This event-triggered feedforward mechanism effectively overcomes the lag of traditional feedback control, leverages the energy storage buffer characteristics of the bus capacitor to offset load impacts, and significantly improves the stability of DC microgrid switching between grid and off-grid. Unlike traditional fixed-temperature protection, this invention establishes a reverse solution mechanism based on a battery thermal model to calculate the dynamic thermal safety current boundary. This allows the controller to preemptively limit charging and discharging behaviors that may lead to overheating based on the current heat dissipation capacity and ambient temperature, achieving a shift from passive cooling to active thermal avoidance, effectively extending battery cycle life. This invention introduces battery life loss costs and heat dissipation energy consumption costs into the optimization objective function. It can automatically balance the electricity cost benefits of fast charging with the life loss caused by high-temperature, high-rate charging. This reduces life loss costs during subsequent high-power operation, maximizing the benefits throughout the system's entire life cycle. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the steps of a first embodiment of a coordinated control method for a photovoltaic energy storage and charging system according to the present invention. Figure 2 This is a flowchart illustrating the steps of a second embodiment of the coordinated control method for a photovoltaic energy storage and charging system according to the present invention. Figure 3 This is a structural block diagram of an embodiment of a collaborative control device for a photovoltaic energy storage and charging system according to the present invention; Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] Preferred embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0022] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a collaborative control method for a photovoltaic-energy storage-charging system according to Embodiment 1 of the present invention. The method in this embodiment is mainly applied to a DC-coupled photovoltaic-energy storage-charging system comprising photovoltaic modules, energy storage batteries, DC charging piles, and related control units.

[0023] like Figure 1 As shown, the method in this embodiment includes the following steps: Step S101: Obtain real-time status data of the photovoltaic energy storage and charging system. The real-time status data includes at least the battery internal temperature, ambient temperature, and battery state of charge.

[0024] In this embodiment, the system collects the internal temperature of the battery in real time through temperature sensors deployed inside the energy storage battery module, collects the ambient temperature through external environmental sensors, and reads the current state of charge (SOC) of the battery through the battery management system (BMS). This basic data forms the physical basis for subsequent thermal safety assessments and energy dispatching.

[0025] Step S102: Monitor the communication messages of external charging requests in real time. When a charging handshake event is detected, parse the load demand parameters of the communication message. Before the load current is output, generate an active reconfiguration command for the DC bus voltage based on the load demand parameters. The active reconfiguration command is used to drive the bidirectional DC / DC converter to adjust the reference value of the bus voltage in order to maintain the electrical stability of the photovoltaic energy storage and charging system.

[0026] In this embodiment, the controller monitors the communication bus (e.g., CAN bus) between the charging pile and the electric vehicle in real time. When a specific message during the charging handshake phase is detected (i.e., a charging handshake event is identified), the system determines that a load surge is about to occur. At this time, the controller pre-parses the load demand parameters (such as the vehicle's requested voltage and requested current) contained in the message.

[0027] Unlike existing technologies that only provide feedback regulation after a voltage drop caused by load connection, the key to this step lies in "feedforward control." Within a brief time window before the charging loop closes and the load current is actually output, the controller generates an active reconfiguration command for the DC bus voltage based on the analyzed load demand. This command drives the bidirectional DC / DC converter to adjust the bus voltage reference value in advance to cope with the upcoming power surge and maintain system electrical stability.

[0028] Step S103: Based on the battery's internal temperature, ambient temperature, and battery state of charge, the dynamic thermal safety current boundary for maintaining the battery temperature within the predicted time domain is calculated using the battery thermal model.

[0029] In this embodiment, a fixed current limit value is no longer used; instead, a thermal-electric coupling calculation concept is introduced. The controller has a pre-built battery thermal model that describes the physical relationship between current, internal resistance, heat dissipation, and temperature changes.

[0030] Using the current temperature and SOC state obtained in step S101, the system performs reverse engineering: it sets a maximum permissible temperature target that will not impair battery life, and then calculates the maximum allowable output or input current trajectory of the battery in the future predicted time domain (e.g., the next 10 minutes) to ensure that the battery temperature does not exceed the target. This calculated trajectory is the "dynamic thermal safety current boundary." This boundary transforms the physical thermal limitation into an electrical current constraint.

[0031] Step S104: Using the dynamic thermal safety current boundary as a constraint, the multi-objective optimization function, which includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs, is solved in a rolling manner to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence.

[0032] In this embodiment, model predictive control (MPC) or a similar rolling optimization algorithm is employed. The system constructs a comprehensive multi-objective optimization function designed to minimize the total operating cost of the system.

[0033] Specifically, the total cost consists of three parts: Grid transaction costs: Considering peak and off-peak electricity prices, calculate the cost of purchasing electricity from the grid; Battery life loss cost: The economic cost calculated considering the irreversible damage to battery health caused by high-rate charging and discharging and high temperatures. Cooling system operating costs: Consider the cost of electricity consumed by the liquid cooling pump or fan.

[0034] During the solution process, the "dynamic thermal safety current boundary" calculated in step S103 serves as a hard constraint to ensure that the optimization results do not lead to battery overheating. By solving this mathematical model, the system obtains a set of optimal solutions in the prediction time domain, including how much power the battery should exert (optimal power allocation sequence) and how much the heat dissipation system should operate (optimal heat dissipation control sequence).

[0035] Step S105: Convert the optimal power allocation sequence and the optimal heat dissipation control sequence into control commands and output them.

[0036] In this embodiment, the controller converts the mathematical sequence calculated in step S104 into underlying drive signals. For example, the power distribution sequence is converted into a PWM signal or current command to control the bidirectional DC / DC converter at the battery end, and the heat dissipation control sequence is converted into a drive command to control the liquid cooling pump speed or fan duty cycle. By executing these commands, the system achieves economical operation while ensuring safety and extending lifespan.

[0037] Example 2 Please see Figure 2 , Figure 2 This is a schematic flowchart of a collaborative control method for a photovoltaic-storage-charging system provided in Embodiment 2 of the present invention. Based on Embodiment 1, this embodiment further refines and expands each step in conjunction with a specific photovoltaic-storage-charging application scenario, detailing the specific implementation process of voltage reconstruction based on a handshake protocol, boundary calculation based on a thermal model, and multi-objective optimization. Figure 2 As shown, the method in this embodiment includes the following steps: Step S201: Obtain real-time status data of the photovoltaic energy storage and charging system. The real-time status data includes at least the battery internal temperature, ambient temperature, and battery state of charge.

[0038] In this embodiment, the system controller periodically collects data through a sensor network and a communication bus. Among these data is the battery internal temperature (…). The temperature is obtained through the cell temperature sampling value reported by the BMS (Battery Management System); ambient temperature ( The battery's state of charge (SOC) and state of health (SOH) are acquired through sensors deployed outside the rack; these are read directly from the BMS. This data will serve as the initial state input for subsequent thermal safety boundary calculations.

[0039] Step S202: Extract the requested voltage and requested current corresponding to the load demand parameters from the communication message, and calculate the estimated load power fluctuation.

[0040] In this embodiment, when the system detects that the charging gun and the electric vehicle (EV) have completed a physical connection and entered the communication handshake stage (e.g., the parameter configuration stage in the GB / T 27930 protocol), the controller will parse the CAN message in real time. The maximum allowable charging voltage sent by the vehicle's BMS is extracted from the message. ) and maximum allowable charging current ( ).

[0041] Based on the extracted parameters, the controller calculates the estimated load power surge. This step is completed before the actual power output, enabling "prediction" of future loads.

[0042] Step S203: Based on the energy buffering characteristics of the DC bus capacitor, calculate the target voltage increase value required to maintain the bus voltage drop below a preset safety threshold within the response delay time.

[0043] In this embodiment, the DC bus capacitance ( It has energy storage characteristics, and the bidirectional DC / DC converter has an inherent physical delay (response delay time) from receiving the command to the current response. To prevent the bus voltage from dropping beyond the safety threshold (e.g., a drop exceeding 5% triggering undervoltage protection) due to the instantaneous connection of a high-power load, the system needs to reserve energy in advance.

[0044] The controller is based on the capacitor energy formula. The differential form is calculated in In order to compensate within a certain period of time The resulting energy gap, while ensuring voltage drop range Within permissible limits, the required target boost value for bus voltage .

[0045] Specifically, assuming the current photovoltaic-storage-charging system's DC bus capacitance... for (Right now ), system rated voltage for The system's preset safety threshold is that the voltage drop can not exceed [a certain value]. (i.e., the minimum is not lower than) ).

[0046] When the system detects an electric heavy truck entering the system, parsing the message reveals a sudden change in its requested power. (Right now The current response delay time of the bidirectional DC / DC converter Approximately (Right now ).

[0047] According to the principle of energy conservation, the energy consumed by the load during the response delay is provided by the capacitor: .

[0048] To ensure that the voltage after discharge is not lower than Initial target voltage Must meet: .

[0049] Substitute the numerical values ​​into the calculation: Solving for ,Right now .

[0050] Therefore, the controller calculates the target voltage boost value. The system will proactively reduce the bus voltage from [a specific voltage setting] before the load is connected. Precharge to Thus ensuring During a load surge, the minimum bus voltage can still be maintained at The above measures are to avoid triggering the undervoltage protection.

[0051] Step S204: Set the reference voltage of the DC bus to the sum of the current voltage and the target voltage boost value, and generate and execute the active reconfiguration command before the load current is output.

[0052] In this embodiment, the controller modifies the reference voltage setting value of the DC bus to... Then, an active reconfiguration command is generated, driving the bidirectional DC / DC converter to operate within a window of a few seconds before the actual load current output, actively increasing the bus voltage. Through this "pre-charge" operation, the bus capacitor is used as a buffer to smooth out subsequent load surges.

[0053] Step S205: Determine the maximum permissible temperature based on the current battery health status.

[0054] In this embodiment, considering that batteries become more sensitive to temperature after aging, the system does not use a fixed upper temperature limit. The controller reads the current battery health (SOH) and determines the maximum allowable temperature under the current state by looking up a table or using a preset function. For example, a new battery may be allowed to operate up to 55°C, while an aged battery with a state of hydration (SOH) below 80% may be limited to 45°C.

[0055] Step S206: Calculate the dynamic thermal safety current boundary at time k based on the discretization inverse solution formula.

[0056] In this embodiment, using the first-order thermal resistance-capacitance model of the battery, and given the current state and the maximum allowable temperature, the prediction step size is derived in reverse. The maximum current allowed to pass through is determined using the following discretized inverse solution formula: ; in, The maximum allowable temperature determined in step S205; and These are the current battery temperature and ambient temperature, respectively. The battery thermal time constant; The battery internal resistance is obtained by looking up a table; Equivalent thermal resistance; This refers to the maximum heat dissipation power that the cooling system (such as a liquid-cooled unit) can provide at the current moment.

[0057] The formula calculates This is the "hot safety red line" at the current moment.

[0058] To illustrate the application of this formula more intuitively, let's assume the current time... The state parameters of the battery system are as follows: The current battery health (SOH) is 90%. The maximum allowable temperature is found in the table. ; Internal battery temperature collected by sensor Ambient temperature ; Predicted step size (1 minute) Battery thermal time constant Then the exponent term ; Battery internal resistance under current operating conditions Equivalent thermal resistance ; The liquid cooling system is running at full speed, with maximum heat dissipation power. .

[0059] Substitute the above values ​​into the discretization inverse solution formula: 1. Molecular part (thermal power corresponding to temperature margin): ; Add the active cooling power item: (This is the maximum total power that the battery is allowed to generate heat.)

[0060] 2. Denominator (internal resistance): .

[0061] 3. Final calculation of current boundary: .

[0062] This means that although the battery's rated current may be But at present And only Given the temperature rise margin, the algorithm forcibly limits the current to... Within this range, the battery temperature is precisely guaranteed not to exceed the limit within the next minute. The safety red line.

[0063] Step S207: Construct a multi-objective optimization function that includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs.

[0064] In this embodiment, to achieve synergistic optimization of economy and lifespan, the following multi-objective optimization function is constructed. : ; The function contains three costs: 1. Grid transaction costs Arbitrage by taking advantage of peak-valley electricity price differences; 2. Costs related to battery lifespan degradation This embodiment specifically introduces a temperature index term, the formula of which is:

[0065] This formula demonstrates that the higher the temperature, the higher the lifespan cost under the same current. 3. Operating costs of the cooling system The nonlinear relationship between flow rate and power consumption was taken into account. ; in, To predict the length of the time domain; These are the weighting coefficients; For grid transaction costs; For the operating cost of the cooling system; The baseline aging factor; The charging and discharging current of the battery; Current aging factor; Activation energy; It is the gas constant; Real-time battery temperature; Reference temperature; For the first The electricity price on the grid at any given time; This refers to the static standby power consumption of the heat dissipation system. The fluid drive coefficient; For the first The flow rate of the cooling medium at any given time; To control the cycle; For the first Power purchased from the power grid at all times.

[0066] In this embodiment, the weighting coefficient It has two physical meanings: first, a dimensional normalization factor, used to unify different physical units (such as "yuan" for electricity costs, "SOH decay rate" for battery life, and "joules" for heat dissipation) into a standard "economic cost" unit; second, a strategy preference factor, used to dynamically adjust the control logic according to the user's operating mode.

[0067] (Grid cost weight): Usually set to This serves as a baseline value. When the system is in "demand response" or "power shortage" mode, it can be temporarily increased to [a higher value]. This forces the system to reduce its purchases of electricity from the grid.

[0068] (Aging Cost Weight): This value is proportional to the battery's replacement cost. For expensive energy storage batteries, a larger value means the system would rather pay more for electricity than damage the battery.

[0069] (Heat dissipation cost weight): This value reflects the energy efficiency ratio and maintenance cost of the heat dissipation system.

[0070] To visually demonstrate how weighting coefficients affect the system's decision-making, suppose at a certain moment (e.g., 12:00 noon), the system faces two control options: Option A: with High-current charging eliminates the need to purchase electricity from the grid (it uses solar power entirely), but the battery generates significant heat.

[0071] Option B: with Low-current charging, with any shortfall replenished from the grid, results in only slight battery heating.

[0072] Assuming the original costs of the two options are calculated as follows: Option A: Yuan, Unit (due to high temperature and high current). Yuan (full-speed heat dissipation).

[0073] Option B: Yuan, Unit (low temperature, low current) Yuan (low-speed heat dissipation).

[0074] Scenario 1: Economy-first model (pursuing short-term gains) Set weights: .

[0075] .

[0076] .

[0077] result: Select option B for the controller. Although you will have to pay for electricity, the overall cost is lower.

[0078] Scenario 2: Life-first mode (pursuing long-term asset preservation) Set weights: (Significantly increase battery protection weight) .

[0079] .

[0080] .

[0081] result: The controller decisively selected option B. Because... The aging penalty ( ) far exceeds the cost of purchasing electricity ( The system will actively limit power in order to extend battery life.

[0082] By adjusting The values ​​enable a seamless transition from "simply saving money" to "deep care".

[0083] Assuming a baseline aging factor Yuan / Current aging factor (That is, the loss is proportional to the square of the current).

[0084] Scenario comparison: The system needs to decide whether to use... Charging, or Charge.

[0085] Operating Condition A (Low Temperature): When the battery temperature ( When ), temperature influence factor .

[0086] like Cost per second Yuan (normalized value); like Cost per second Yuan.

[0087] Operating Condition B (High Temperature): When the battery temperature rises to... ( When ), temperature influence factor May increase to (Assumed value, depends on activation energy) ).

[0088] If at this point we still... Charging costs skyrocket every second. Yuan.

[0089] During the solution process in step S208, the optimizer will find that under condition B, The "lifetime cost (80 yuan)" incurred from charging may far exceed the "profit gained from the peak-valley price difference in the power grid (e.g., 50 yuan)". Therefore, the algorithm will automatically output a "flexible derating" solution (e.g., reducing to...). ), or in If cost allows, first turn on the liquid cooling at full speed to bring the temperature back down. Then, high-current charging is performed.

[0090] Step S208: Solve the multi-objective optimization function using a sequential quadratic programming algorithm to output the optimal power allocation sequence and the optimal heat dissipation control sequence that satisfy the constraints.

[0091] In this embodiment, the controller employs a sequential quadratic programming (SQP) algorithm to solve the aforementioned nonlinear optimization problem in a rolling manner. During the solution process, the constraints are strictly enforced. (i.e., the boundary calculated in step S206).

[0092] The output of the solution includes two sets of sequences: 1. Optimal power allocation sequence: The power values ​​that the battery should charge and discharge at the next N time points; 2. Optimal heat dissipation control sequence: the target flow rate of the cooling medium (e.g., coolant) at the next N time points. .

[0093] Step S209: Perform timing analysis on the optimal heat dissipation control sequence. When a non-zero cooling medium flow rate command is detected before the load current output, a pre-cooling control command is generated to control the heat dissipation system to start in advance before the load is connected.

[0094] In this embodiment, the controller performs a proactive analysis on the heat dissipation sequence output in step S208. If the optimization results show that, in order to cope with the upcoming high-power load and reduce overall costs (especially reducing lifespan costs), The cooling system needs to be activated before the load is connected (i.e., If the system generates a "pre-cooling control command", it will generate a "pre-cooling control command". This makes the heat dissipation system no longer passively respond to high temperatures, but actively "store cold" in order to save money and extend its life.

[0095] Step S210: Compare the battery output power in the optimal power allocation sequence with the requested power in the load demand parameters. When the battery output power is less than the requested power, generate a flexible derating command and control the DC charging output power to follow the dynamic thermal safety current boundary by reducing the current.

[0096] In this embodiment, the system determines whether the battery's output capacity (determined by the optimal power allocation sequence) is sufficient to meet the vehicle's requested power, provided that thermal safety and economic optimization are met (extracted by step S202).

[0097] If insufficient (e.g., battery overheating), By limiting the output, the system will not force an output that would cause overheating and shutdown. Instead, it will generate a "flexible derating command." This command, through the charging pile communication protocol, instructs the electric vehicle's BMS to reduce the required current, so that the actual charging curve closely follows the dynamic thermal safety current boundary, achieving flexible control of "uninterrupted power supply but reduced speed."

[0098] Step S211: Drive the underlying actuator of the photovoltaic energy storage and charging system to operate according to the pre-cooling control command or the flexible derating command.

[0099] In this embodiment, the controller translates the generated high-level strategy into driving signals for the underlying hardware: If a precooling command is issued, the liquid cooling pump and compressor will start in advance; If a flexible derating instruction is issued, adjust the current loop setpoint of the DC / DC converter; At the same time, it executes the optimal power allocation sequence to control the energy flow between photovoltaics, cells, and the grid.

[0100] Example 3 Please see Figure 3 , Figure 3 This is a structural block diagram of an embodiment of a coordinated control device for a photovoltaic energy storage and charging system according to the present invention. The device includes: The status sensing module 301 is used to acquire real-time status data of the photovoltaic energy storage and charging system. The real-time status data includes at least the battery internal temperature, ambient temperature, and battery state of charge. The event response and voltage reconstruction module 302 is used to monitor the communication messages of external charging requests in real time. When a charging handshake event is detected, the load demand parameters of the communication message are parsed, and before the load current is output, an active reconstruction command for the DC bus voltage is generated according to the load demand parameters. The thermal safety boundary calculation module 303 is used to calculate the dynamic thermal safety current boundary that keeps the battery temperature within the predicted time domain based on the battery internal temperature, ambient temperature and battery state of charge, using the battery thermal model. The multi-objective collaborative optimization module 304 is used to perform rolling solutions on a multi-objective optimization function that includes grid transaction costs, battery life loss costs and heat dissipation system operating costs, with the dynamic thermal safety current boundary as a constraint, to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence. The instruction generation and execution module 305 is used to convert the optimal power allocation sequence and the optimal heat dissipation control sequence into control instructions and output them.

[0101] In an optional embodiment, the event response and voltage reconstruction module 302 includes: The load parameter extraction unit is used to extract the requested voltage and requested current from the communication message (e.g., GB / T 27930 protocol message) and calculate the estimated load power fluctuation. The target voltage calculation unit is used for the energy buffer characteristic formula based on the DC bus capacitance. Calculate the target voltage increase required to keep the bus voltage drop below a preset safety threshold within the response delay time; The instruction generation unit is used to set the reference voltage of the DC bus to the sum of the current voltage and the target voltage boost value, and to generate the active reconfiguration instruction before the load current is output and send it to the bidirectional DC / DC converter.

[0102] In an optional embodiment, the thermal safety boundary calculation module 303 includes: The temperature limit determination unit is used to determine the maximum permissible temperature under the current aging condition based on the currently read battery health (SOH). ; Boundary solving elements are used to solve the problem based on the discretized inverse formula, combined with the maximum power of the current heat dissipation system. , calculate in reverse Dynamic thermal safety current boundary at any moment The solver unit ensures that the calculated current boundary ensures that the battery's temperature trajectory in the predicted time domain remains below the maximum allowable temperature.

[0103] In an optional embodiment, the multi-objective collaborative optimization module 304 includes: Cost function building unit, used to construct functions that include grid transaction costs Battery lifespan depletion costs and operating costs of the cooling system The multi-objective optimization function. Wherein, the battery life loss cost... Includes an index term reflecting the effect of temperature on the aging rate. The operating cost of the heat dissipation system Includes cubic terms reflecting the nonlinear relationship between flow rate and power consumption. ; The sequential solution unit is used to perform rolling solutions to the multi-objective optimization function using the sequential quadratic programming (SQP) algorithm, and outputs results that satisfy the constraints. The optimal power allocation sequence and the optimal heat dissipation control sequence.

[0104] In an optional embodiment, the instruction generation and execution module 305 includes: The precooling logic unit is used to perform timing analysis on the optimal heat dissipation control sequence. When a non-zero cooling medium flow rate command is detected before the load current output, a precooling control command is generated to control the liquid cooling system or air cooling system to start in advance before the load is connected. The flexible derating unit is used to compare the battery output power in the optimal power allocation sequence with the requested power in the load demand parameters. When the battery output power is less than the requested power, a flexible derating instruction is generated, and the DC charging output power is controlled to reduce the current through the charging communication protocol so that it follows the dynamic thermal safety current boundary. The underlying drive unit is used to drive the underlying actuators (such as inverter controllers and thermal management controllers) of the photovoltaic energy storage and charging system to operate according to the pre-cooling control command or the flexible derating command.

[0105] Example 4 Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device, including: one or more processors 401, a memory 402, and one or more communication interfaces 403.

[0106] The memory 402 stores one or more computer programs, which, when executed by the one or more processors 401, cause the one or more processors 401 to implement the collaborative control method of the optical storage and charging system as described in any of the above embodiments. The communication interface 403 is connected between the processor 401 and the memory 402, and is configured to enable information interaction between the processor and the memory and other external devices (such as BMS, charging pile controller, and power grid dispatch center).

[0107] Among them, processor 401 is a device with data processing capabilities, including but not limited to central processing unit (CPU), digital signal processor (DSP), microcontroller (MCU) or field programmable gate array (FPGA); memory 402 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH).

[0108] In some embodiments, the processor 401, memory 402, and communication interface 403 are interconnected via bus 404, thereby connecting to other components of the computing device. Communication interface 403 is specifically configured to support CAN bus, RS485, or Ethernet protocols to adapt to the industrial fieldbus environment of the optical storage and charging system.

[0109] Example 5 This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by the processor, implements the steps of the coordinated control method for the optical storage and charging system described in any embodiment.

[0110] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] Example 6 This invention also provides a computer program product having a computer program stored thereon, wherein when the computer program is executed by the processor, it implements the steps of the coordinated control method for the optical storage and charging system described in any embodiment.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the methods, apparatuses, electronic devices, and storage media disclosed in this invention can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0117] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this application. Any specific values ​​in all examples shown and discussed herein should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0118] Furthermore, it should be noted that the use of terms such as "first" and "second" is merely for ease of distinction, and unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A coordinated control method for a photovoltaic energy storage and charging system, characterized in that, include: Acquire real-time status data of the photovoltaic energy storage and charging system, wherein the real-time status data includes at least the battery internal temperature, ambient temperature and battery state of charge; The system monitors external charging request communication messages in real time. When a charging handshake event is detected, it parses the load demand parameters of the communication message and generates an active reconfiguration command for the DC bus voltage based on the load demand parameters before the load current is output. The active reconfiguration command is used to drive the bidirectional DC / DC converter to adjust the bus voltage reference value in order to maintain the electrical stability of the photovoltaic energy storage and charging system. Based on the battery's internal temperature, ambient temperature, and state of charge, the dynamic thermal safety current boundary for maintaining the battery temperature within the predicted time domain is calculated using the battery thermal model. Using the dynamic thermal safety current boundary as a constraint, the multi-objective optimization function, which includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs, is solved in a rolling manner to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence. The optimal power allocation sequence and the optimal heat dissipation control sequence are converted into control commands and output; The real-time monitoring of external charging requests communication messages, upon recognizing a charging handshake event, parses the load demand parameters of the communication message and, before outputting the load current, generates an active reconfiguration command for the DC bus voltage based on the load demand parameters, including: Extract the requested voltage and requested current corresponding to the load demand parameters from the communication message, and calculate the estimated load power fluctuation. Based on the energy buffering characteristics of the DC bus capacitor, the target voltage increase value required to maintain the bus voltage drop less than the preset safety threshold during the response delay time is calculated. The reference voltage of the DC bus is set to the sum of the current voltage and the target voltage boost value, and the active reconfiguration command is generated and executed before the load current is output. The step of converting the optimal power allocation sequence and the optimal heat dissipation control sequence into control commands and outputting them includes: A timing analysis is performed on the optimal heat dissipation control sequence. When a non-zero cooling medium flow rate command is detected before the load current output, a pre-cooling control command is generated to control the heat dissipation system to start in advance before the load is connected. Compare the battery output power in the optimal power allocation sequence with the requested power in the load demand parameter. When the battery output power is less than the requested power, generate a flexible derating command and control the DC charging output power to follow the dynamic thermal safety current boundary by reducing the current. The underlying actuators of the photovoltaic energy storage and charging system are driven to operate according to the pre-cooling control command or the flexible derating command.

2. The coordinated control method for a photovoltaic energy storage and charging system according to claim 1, characterized in that, The step of calculating the dynamic thermal safety current boundary for maintaining the battery temperature within the predicted time domain based on the battery's internal temperature, ambient temperature, and state of charge using a battery thermal model includes: Determine the maximum permissible temperature based on the current battery health status; Based on the discretization inverse solution formula, calculate the first... The dynamic thermal safety current boundary at time t is determined by the discretized inverse solution formula as follows: ； in, This is the highest permissible temperature under the current battery health condition; No. Dynamic thermal safety current boundary at any given moment; This is the current internal temperature of the battery; The current ambient temperature; To predict the step size; The battery thermal time constant; The internal resistance of the battery varies with state of charge and temperature; The equivalent thermal resistance of the battery to the environment; This represents the maximum heat dissipation power that the cooling system can provide at the current moment.

3. The coordinated control method for a photovoltaic energy storage and charging system according to claim 1, characterized in that, The process involves using the dynamic thermal safety current boundary as a constraint to perform a rolling solution on a multi-objective optimization function that includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs, thereby obtaining the optimal power allocation sequence and the optimal heat dissipation control sequence, including: Construct a multi-objective optimization function that includes grid transaction costs, battery life loss costs, and heat dissipation system operating costs; The multi-objective optimization function is solved using a sequential quadratic programming algorithm, and the optimal power allocation sequence and optimal heat dissipation control sequence that satisfy the constraints are output. The expression for the objective optimization function is as follows: ； in, The formula for calculating battery life loss cost, which incorporates a temperature index term, is as follows: ； in, To predict the length of the time domain; These are the weighting coefficients; For grid transaction costs; For the operating cost of the cooling system; The baseline aging factor; The charging and discharging current of the battery; Current aging factor; Activation energy; It is the gas constant; Real-time battery temperature; Reference temperature; The constraint condition is: .

4. The coordinated control method for the photovoltaic energy storage and charging system according to claim 3, characterized in that, The operating cost of the heat dissipation system is calculated using the following formula: ； The power grid transaction costs The calculation formula is as follows: ； in, For the first The electricity price on the grid at any given time; This refers to the static standby power consumption of the heat dissipation system. The fluid drive coefficient; For the first The flow rate of the cooling medium at any given time; To control the cycle; For the first Power purchased from the power grid at all times.

5. A coordinated control device for a photovoltaic energy storage and charging system, characterized in that, The method for implementing the coordinated control of the optical storage and charging system according to any one of claims 1-4, wherein the coordinated control device for the optical storage and charging system comprises: The status sensing module is used to acquire real-time status data of the photovoltaic energy storage and charging system. The real-time status data includes at least the battery internal temperature, ambient temperature, and battery state of charge. The event response and voltage reconstruction module is used to monitor the communication messages of external charging requests in real time. When a charging handshake event is detected, the load demand parameters of the communication message are parsed, and before the load current is output, an active reconstruction command for the DC bus voltage is generated based on the load demand parameters. The thermal safety boundary calculation module is used to calculate the dynamic thermal safety current boundary that keeps the battery temperature within the predicted time domain based on the battery's internal temperature, ambient temperature, and battery state of charge, using the battery thermal model. The multi-objective collaborative optimization module is used to perform rolling solutions on a multi-objective optimization function that includes grid transaction costs, battery life loss costs and heat dissipation system operating costs, with the dynamic thermal safety current boundary as a constraint, to obtain the optimal power allocation sequence and the optimal heat dissipation control sequence. The instruction generation and execution module is used to convert the optimal power allocation sequence and the optimal heat dissipation control sequence into control instructions and output them.

6. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the collaborative control method of the optical storage and charging system according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the collaborative control method of the optical storage and charging system according to any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the collaborative control method of the optical storage and charging system according to any one of claims 1-4.

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