A Discharge Management System and Method for a Mobile Storage and Charging Robot

Abstract

The present invention provides a discharge management system and method for a mobile storage and charging robot, which relates to the technical field of mobile storage and charging robots, and includes a mobile storage and charging robot, a battery management system, a charging interface, a communication module, and a control module; the mobile storage and charging robot is used to charge and discharge the power grid and new energy vehicles; the battery management system is used to monitor and control the charge and discharge states of the rechargeable battery pack and obtain battery monitoring data; the charging interface is used to connect an external charging device; the communication module is used to exchange data with external devices and receive discharge instructions; the control module is used to control the discharge process of the rechargeable battery pack; the control module includes a discharge strategy determination unit, a discharge scheme determination unit, and a control unit; the discharge strategy determination unit is used to determine the discharge strategy; the discharge scheme determination unit is used to determine the discharge scheme; the control unit is used to control the discharge of the mobile storage and charging robot; so as to manage the discharge of the mobile storage and charging robot and improve the discharge efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile storage and charging robots, and in particular, to a discharge management system and method for a mobile storage and charging robot. Background Art

[0002] With the wide application of new energy vehicles, mobile storage and charging robots, as a new type of charging device, have gradually attracted attention. With the development of the power industry, the disordered access of electric vehicles will surely bring a series of problems to the distribution network, such as causing network losses, deterioration of voltage quality, and exacerbation of the peak-valley load difference of the power grid. Mobile storage and charging robots can carry multiple rechargeable battery packs and provide instant charging services where charging is needed. However, the discharge efficiency and safety of existing discharge management systems for mobile storage and charging robots still need to be improved.

[0003] In view of this, the present invention proposes a discharge management system and method for a mobile storage and charging robot to manage the discharge of the mobile storage and charging robot and improve the discharge efficiency and safety. Summary of the Invention

[0004] The purpose of the present invention is to provide a discharge management system for a mobile storage and charging robot, including a mobile storage and charging robot, a battery management system, a charging interface, a communication module, and a control module; the mobile storage and charging robot includes multiple rechargeable battery packs for charging and discharging the power grid and new energy vehicles; the battery management system is used to monitor and control the charging and discharging states of the rechargeable battery packs and obtain battery monitoring data; the battery monitoring data includes the battery voltage, battery current, and battery temperature of the rechargeable battery packs of the mobile storage and charging robot; the charging interface is used to connect to an external charging device to charge the rechargeable battery packs; the communication module is used to exchange data with external devices and receive a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge; the control module is used to control the discharge process of the rechargeable battery packs according to the monitoring data of the battery management system and external instructions; the control module includes a discharge strategy determination unit, a discharge plan determination unit, and a control unit; the discharge strategy determination unit is used to determine a discharge strategy based on the battery monitoring data; the discharge strategy includes the optimal discharge power and the optimal discharge current; the discharge plan determination unit is used to determine a discharge plan based on the discharge instruction and the discharge strategy; the discharge plan includes the total discharge amount, discharge time, and individual discharge amount; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot; the control unit is used to control the discharge of the mobile storage and charging robot according to the discharge plan and the discharge strategy.

[0005] Further, the discharge strategy determination unit includes a battery pack model acquisition component, a discharge power determination component, an injection power acquisition component, and an optimization component. The battery pack model acquisition component is configured to acquire the battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current, and the battery temperature; the discharge power determination component is configured to determine the discharge power of the rechargeable battery pack based on the battery pack model; the injection power acquisition component is configured to acquire the injection power injected by the mobile storage and charging robot into the power grid; the optimization component is configured to optimize the discharge power through a particle swarm optimization algorithm to obtain the optimal discharge power and the optimal discharge current.

[0006] Further, the particle swarm optimization algorithm includes: when the charge state difference of the mobile storage and charging robot is greater than the charge difference threshold, starting the particle swarm optimization algorithm; randomly selecting a plurality of current values within the search range as the initial positions of the particle swarm optimization algorithm; the search range is related to the lowest charge state and the highest charge state of the mobile storage and charging robot; determining the global best position based on the initial positions; the global best position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power; based on the initial positions and the global best position, performing speed update and position update to obtain the updated speed and the updated position; the updated speed is: v i (1) = ωv i (0) + c1r1[g best，i -I i (0)]; the updated position is: I i (1) = I i (0) + v i (1); where v i (1) represents the updated speed of the i-th position; ω represents the inertia weight; v i (0) represents the initial updated speed of the i-th position; c1 represents the first population learning parameter; r1 represents the first random number; g best，i represents the population best position; I i (0) represents the i-th initial position; I i (1) represents the updated position; determining a new global best position based on the updated position; and determining whether the new global best position reaches the iteration stop condition; if the iteration stop condition is not reached, then based on the updated position and the new global best position, performing speed update and position update to obtain the new updated speed and the new updated position, the new updated speed is: The new updated position is: I i (t + 1) = ωI i (t) + (1 - ω)v i (t + 1); vi (t + 1) represents the new update speed obtained in the (t + 1)-th iteration; v i (t) represents the new update speed obtained in the t-th iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t - 1) respectively represent the new update positions obtained in the t-th and (t - 1)-th iterations; p best，i represents the individual best position; g best，i represents the swarm best position; c3 represents the second swarm learning parameter; r3 represents the third random number; Based on the new update position and the new update speed, the update operation of the position is cycled until the update position reaches the iteration stop condition, and this update position is used as the optimal discharge current, and the power corresponding to this optimal discharge current is used as the optimal discharge power.

[0007] Further, the discharge scheme determination unit includes an objective function construction component, a constraint condition construction component, and a solution component; the objective function construction component is used to construct a discharge objective function based on the optimal discharge power; the discharge objective function is related to the storage and charging cost; the constraint condition construction component is used to construct discharge constraint conditions; the discharge constraint conditions include the energy storage capacity constraint of the mobile storage and charging robot, the charge and discharge power constraint, the charge and discharge efficiency constraint, the cyclic charge and discharge constraint of the mobile storage and charging robot, and the power balance constraint; the solution component is used to solve the discharge objective function based on the discharge constraint conditions to obtain the individual discharge amount of the mobile storage and charging robot.

[0008] Further, the discharge objective function is: where C represents the charge and discharge revenue; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharging; M represents the total number of mobile storage and charging robots for discharging; E dis，n (t) represents the discharge amount of the n-th mobile storage and charging robot at time t; C sale (t) represents the reverse power selling price at time t; m represents the variable of the mobile storage and charging robot for charging; M represents the total number of mobile storage and charging robots for charging; E cha，m (t) represents the charge amount of the m-th mobile storage and charging robot at time t; C buy (t) represents the grid power price at time t; t0 represents the start time of time period t; Δt represents the duration of each time period; P dis，n (t) represents the instantaneous discharge power of the n-th mobile storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the loss of the mobile storage and charging robot discharging at the discharge power of P dis，n (t); Indicates integration over time; the energy storage capacity constraint of the mobile charging and discharging robot is: E min (t) ≤ E n 、E m ≤ E max (t); E min (t) = max[E base，min ,E ev，min (t)];

[0009] Among them, E min (t) represents the minimum energy storage power at time t; E n represents the energy storage power of the nth discharging mobile charging and discharging robot; E m represents the energy storage power of the mth charging mobile charging and discharging robot; E max (t) represents the maximum energy storage power at time t; max[*] represents finding the maximum value; E base，min represents the basic minimum energy storage power; E ev，min (t) represents the predicted minimum charging power demand of the electric vehicle at time t; min[*] represents finding the minimum value; E base，max represents the basic maximum energy storage power; E total represents the total capacity of the mobile charging and discharging robot; E ev，reserve (t) represents the power reserved for the electric vehicle at time t; the charging and discharging power constraint is: 0 ≤ P dis，n (t) ≤ P dis，max ; 0 ≤ P cha，m (t) ≤ P cha，max ; Among them, P dis，max represents the maximum discharging power of the mobile charging and discharging robot; P cha，m (t) represents the charging power of the mth mobile charging and discharging robot; P cha，max represents the maximum charging power of the mobile charging and discharging robot; the charging and discharging efficiency constraint is: Among them, E(t + 1) represents the total power at time t + 1; E(t) represents the total power at time t; η cha represents the charging efficiency; P cha，m (t) represents the charging power of the mth mobile charging and discharging robot at time t; η dis represents the discharging efficiency; the mobile charging and discharging robot's cyclic charging and discharging constraint is: Among them, E dis (t) represents the discharging power of the mobile charging and discharging robot at time t; E cha (t) represents the charging power of the mobile charging and discharging robot at time t; E lifetime represents the total cyclic life of the rechargeable battery pack of the mobile charging and discharging robot; the power balance constraint is: Among them, P grid(t) represents the grid demand power during time period t.

[0010] The present invention also provides a discharge management method for a mobile storage and charging robot, including: receiving a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge; obtaining battery monitoring data; the battery monitoring data includes the battery voltage, battery current, and battery temperature of the rechargeable battery pack of the mobile storage and charging robot; based on the battery monitoring data, determining a discharge strategy; the discharge strategy includes the optimal discharge power and the optimal discharge current; based on the discharge instruction and the discharge strategy, determining a discharge plan; the discharge plan includes the total discharge amount, discharge time, and individual discharge amount; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot; controlling the discharge of the mobile storage and charging robot according to the discharge plan and the discharge strategy.

[0011] Further, the determining the discharge strategy includes: obtaining the battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current, and the battery temperature; based on the battery pack model, determining the discharge power of the rechargeable battery pack; obtaining the injection power injected by the mobile storage and charging robot into the power grid; optimizing the discharge power through a particle swarm optimization algorithm to obtain the optimal discharge power and the optimal discharge current.

[0012] Further, the particle swarm optimization algorithm includes: when the charge state difference of the mobile storage and charging robot is greater than the charge difference threshold, starting the particle swarm optimization algorithm; randomly selecting multiple current values within the search range as the initial positions of the particle swarm optimization algorithm; the search range is related to the lowest charge state and the highest charge state of the mobile storage and charging robot; based on the initial positions, determining the global best position; the global best position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power; based on the initial positions and the global best position, performing speed update and position update to obtain the updated speed and the updated position; the updated speed is: v i (1) = ωv i (0) + c1r1[g best，i -I i (0)]; the updated position is: I i (1) = I i (0) + v i (1); where, v i (1) represents the updated speed of the i-th position; ω represents the inertia weight; v i (0) represents the initial updated speed of the i-th position; c1 represents the first group learning parameter; r1 represents the first random number; g best，i represents the group best position; I i(0) represents the i-th initial position; I i (1) represents the updated position; based on the updated position, a new global best position is determined; and it is judged whether the new global best position reaches the iteration stop condition; if the iteration stop condition is not reached, based on the updated position and the new global best position, speed update and position update are performed to obtain a new updated speed and a new updated position, and the new updated speed is: The new updated position is: I i (t + 1) = ωI i (t) + (1 - ω)v i (t + 1); v i (t + 1) represents the new updated speed obtained in the (t + 1)-th iteration; v i (t) represents the new updated speed obtained in the t-th iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t - 1) respectively represent the new updated positions obtained in the t-th and (t - 1)-th iterations; p best，i represents the individual best position; g best，i represents the swarm best position; c3 represents the second swarm learning parameter; r3 represents the third random number; based on the new updated position and the new updated speed, the position update operation is cycled until the updated position reaches the iteration stop condition, and this updated position is used as the optimal discharge current, and the power corresponding to the optimal discharge current is used as the optimal discharge power.

[0013] Further, the determining the discharge scheme includes: constructing a discharge objective function based on the optimal discharge power; the discharge objective function is related to the storage and charging cost; constructing discharge constraint conditions; the discharge constraint conditions include the energy storage capacity constraint of the mobile storage and charging robot, the charge and discharge power constraint, the charge and discharge efficiency constraint, the cyclic charge and discharge constraint of the mobile storage and charging robot, and the power balance constraint; based on the discharge constraint conditions, solving the discharge objective function to obtain the individual discharge amount of the mobile storage and charging robot.

[0014] Further, the discharge objective function is: Where, C represents the charge and discharge revenue; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharging; M represents the total number of mobile storage and charging robots for discharging; E dis，n (t) represents the discharge amount of the n-th mobile storage and charging robot at time t; C sale (t) represents the reverse power selling price at time t; m represents the variable of the mobile storage and charging robot for charging; M represents the total number of mobile storage and charging robots for charging; E cha，m(t) represents the charging power of the m-th mobile energy storage and charging robot in the t-th time period; C buy (t) represents the grid electricity price in the t-th time period; t0 represents the starting time of the t-th time period; Δt represents the duration of each time period; P dis，n (t) represents the instantaneous discharge power of the n-th mobile energy storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the loss of the mobile energy storage and charging robot discharging at the discharge power of P dis，n (t); represents integration over time; the energy storage capacity constraint of the mobile energy storage and charging robot is: E min (t) ≤ E n 、E m ≤ E max (t);

[0015] E min (t) = max[E base，min ,E ev，min (t)];

[0016] Among them, E min (t) represents the minimum energy storage power in the t-th time period; E n represents the energy storage power of the n-th discharging mobile energy storage and charging robot; E m represents the energy storage power of the m-th charging mobile energy storage and charging robot; E max (t) represents the maximum energy storage power in the t-th time period; max[*] represents finding the maximum value; E base，min represents the basic minimum energy storage power; E ev，min (t) represents the minimum charging power demand of the electric vehicle at the predicted t moment; min[*] represents finding the minimum value; E base，max represents the basic maximum energy storage power; E total represents the total capacity of the mobile energy storage and charging robot; E ev，reserve (t) represents the power reserved for the electric vehicle at the t moment; the charge and discharge power constraint is: 0 ≤ P dis，n (t) ≤ P dis，max ; 0 ≤ P cha，m (t) ≤ P cha，max ; Among them, P dis，max represents the maximum discharge power of the mobile energy storage and charging robot; P cha，m (t) represents the charging power of the m-th mobile energy storage and charging robot; P cha，max represents the maximum charging power of the mobile energy storage and charging robot; the charge and discharge efficiency constraint is: Among them, E(t + 1) represents the total power in the (t + 1)-th time period; E(t) represents the total power in the t-th time period; η cha represents the charging efficiency; P cha，m(t) represents the charging power of the m-th mobile storage and charging robot in the t-th period; η dis represents the discharge efficiency; the charging and discharging constraints of the mobile storage and charging robot are: where E dis (t) represents the discharge power of the mobile storage and charging robot in the t-th period; E cha (t) represents the charging power of the mobile storage and charging robot in the t-th period; E lifetime represents the total cycle life of the rechargeable battery pack of the mobile storage and charging robot; the power balance constraint is: where P grid (t) represents the grid demand power in the t-th period.

[0017] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0018] By real-time monitoring the charging and discharging status of the battery pack, intelligent scheduling is performed according to needs, and the discharge strategy is optimized, which can improve the discharge efficiency of the mobile storage and charging robot. At the same time, according to external instructions, the discharge process of the battery pack is controlled, which can ensure the safety of the discharge process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is an exemplary schematic diagram of a discharge management system for a mobile storage and charging robot provided by the present invention;

[0020] Figure 2 is an exemplary flowchart of a discharge management method for a mobile storage and charging robot provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0022] Figure 1 is an exemplary schematic diagram of a discharge management system for a mobile storage and charging robot provided by the present invention. As Figure 1 shown, the discharge management system of the mobile storage and charging robot includes a mobile storage and charging robot 1, a battery management system 2, a charging interface 3, a communication module 4, and a control module 5.

[0023] The mobile storage and charging robot 1 includes a plurality of rechargeable battery packs for charging and discharging the power grid and new energy vehicles.

[0024] The battery management system 2 is used to monitor and control the charging and discharging states of the rechargeable battery pack and obtain battery monitoring data; the battery monitoring data includes the battery voltage, battery current, and battery temperature of the rechargeable battery pack of the mobile storage and charging robot.

[0025] The charging interface 3 is used to connect to an external charging device to charge the rechargeable battery pack.

[0026] The communication module 4 is used to exchange data with an external device and receive a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge.

[0027] The control module 5 is used to control the discharging process of the rechargeable battery pack according to the monitoring data of the battery management system and an external instruction; the control module includes a discharge strategy determination unit, a discharge scheme determination unit, and a control unit; the discharge strategy determination unit is used to determine a discharge strategy based on the battery monitoring data; the discharge strategy includes the optimal discharge power and the optimal discharge current; the discharge scheme determination unit is used to determine a discharge scheme based on the discharge instruction and the discharge strategy; the discharge scheme includes the total discharge amount, the discharge time, and the individual discharge amount; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot; the control unit is used to control the mobile storage and charging robot to discharge according to the discharge scheme and the discharge strategy.

[0028] The discharge strategy determination unit includes a battery pack model acquisition component, a discharge power determination component, an injection power acquisition component, and an optimization component. The battery pack model acquisition component is used to acquire the battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current, and the battery temperature; the discharge power determination component is used to determine the discharge power of the rechargeable battery pack based on the battery pack model; the injection power acquisition component is used to acquire the injection power injected by the mobile storage and charging robot into the power grid; the optimization component is used to optimize the discharge power through a particle swarm optimization algorithm to obtain the optimal discharge power and the optimal discharge current.

[0029] The particle swarm optimization algorithm includes: when the charge state difference of the mobile storage and charging robot is greater than the charge difference threshold, starting the particle swarm optimization algorithm; randomly selecting multiple current values within the search range as the initial positions of the particle swarm optimization algorithm; the search range is related to the lowest charge state and the highest charge state of the mobile storage and charging robot; based on the initial positions, determining the global best position; the global best position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power; based on the initial positions and the global best position, performing speed update and position update to obtain the updated speed and the updated position; the updated speed is: v i(1) = ωv i (0) + c1r1[g best，i -I i (0)]; The updated position is: I i (1) = I i (0) + v i (1); where v i (1) represents the update speed at the i-th position; ω represents the inertia weight; v i (0) represents the initial update speed at the i-th position; c1 represents the first swarm learning parameter; r1 represents the first random number; g best，i represents the swarm best position; I i (0) represents the i-th initial position; I i (1) represents the updated position; Based on the updated position, determine the new global best position; and determine whether the new global best position reaches the iteration stop condition; if it does not reach the iteration stop condition, then based on the updated position and the new global best position, perform speed update and position update to obtain the new update speed and the new updated position. The new update speed is: The new updated position is: I i (t+) = ωI i (t) + (1 - ω)v i (t + 1); vi(t + 1) represents the new update speed obtained in the (t + 1)-th iteration; v i (t) represents the new update speed obtained in the t-th iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t - 1) represent the new updated positions obtained in the t-th and (t - 1)-th iterations respectively; p best，i represents the individual best position; g best，i represents the swarm best position; c3 represents the second swarm learning parameter; r3 represents the third random number; Based on the new updated position and the new update speed, loop the position update operation until the updated position reaches the iteration stop condition, and take this updated position as the best discharge current, and the power corresponding to this best discharge current as the best discharge power.

[0030] The discharge plan determination unit includes an objective function construction component, a constraint condition construction component, and a solution component. The objective function construction component is used to construct a discharge objective function based on the optimal discharge power; the discharge objective function is related to the storage and charging cost; the constraint condition construction component is used to construct discharge constraint conditions; the discharge constraint conditions include the energy storage capacity constraint of the mobile storage and charging robot, the charge and discharge power constraint, the charge and discharge efficiency constraint, the cyclic charge and discharge constraint of the mobile storage and charging robot, and the power balance constraint; the solution component is used to solve the discharge objective function based on the discharge constraint conditions to obtain the individual discharge amount of the mobile storage and charging robot.

[0031] The discharge objective function is:

[0032] Among them, C represents the charge and discharge revenue; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharging; M represents the total number of mobile storage and charging robots for discharging; Edis,n(t) represents the discharge power of the nth mobile storage and charging robot at time t; C sale (t) represents the reverse power selling price at time t; m represents the variable of the mobile storage and charging robot for charging; M represents the total number of mobile storage and charging robots for charging; E cha，m (t) represents the charging power of the mth mobile storage and charging robot at time t; C buy (t) represents the grid power price at time t; t0 represents the start time of time period t; Δt represents the duration of each time period; P dis，n (t) represents the instantaneous discharge power of the nth mobile storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the loss of the mobile storage and charging robot discharging at the discharge power of P dis，n (t); represents the integration over time;

[0033] The energy storage capacity constraint of the mobile storage and charging robot is:

[0034] E min (t) ≤ E n 、E m ≤ E max (t);

[0035] E min (t) = max[E base，min , E ev，min (t)];

[0036]

[0037] Among them, E min (t) represents the minimum energy storage power at time t; E nThe energy storage capacity of the mobile energy storage and charging robot representing the nth discharge; E m The energy storage capacity of the mobile energy storage and charging robot representing the mth charge; E max (t) represents the maximum energy storage capacity during the t period; max[*] represents finding the maximum value; E base，min Represents the basic minimum energy storage capacity; E ev，min (t) represents the minimum charging amount demand of the electric vehicle at the predicted time t; min[*] represents finding the minimum value; E base，max Represents the basic maximum energy storage capacity; E total Represents the total capacity of the mobile energy storage and charging robot; E ev，reserve (t) represents the power reserved for the electric vehicle at time t;

[0038] The charge and discharge power constraint is:

[0039] 0 ≤ P dis t, n(t) ≤ P dis，max ;

[0040] 0 ≤ P cha，m (t) ≤ P cha，max ;

[0041] Among them, P dis，max Represents the maximum discharge power of the mobile energy storage and charging robot; P cha，m (t) represents the charging power of the mth mobile energy storage and charging robot; P cha，max Represents the maximum charging power of the mobile energy storage and charging robot;

[0042] The charge and discharge efficiency constraint is:

[0043]

[0044] Among them, E(t + 1) represents the total power in the (t + 1) period; E(t) represents the total power in the t period; η cha Represents the charging efficiency; P cha，m (t) represents the charging power of the mth mobile energy storage and charging robot in the t period; η dis Represents the discharge efficiency;

[0045] The cyclic charge and discharge constraint of the mobile energy storage and charging robot is:

[0046]

[0047] Among them, E dis (t) represents the discharge power of the mobile energy storage and charging robot in the t period; E cha (t) represents the charging power of the mobile energy storage and charging robot in the t period; E lifetime Represents the total cycle life of the rechargeable battery pack of the mobile energy storage and charging robot;

[0048] The power balance constraint is as follows:

[0049]

[0050] Among them, P grid (t) represents the power demand of the power grid in the t period.

[0051] Figure 2 is an exemplary flowchart of a discharge management method for a mobile storage and charging robot provided by the present invention. As Figure 2 shown, the discharge management method of the mobile storage and charging robot includes the following:

[0052] Step 1, receiving a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge.

[0053] Step 2, obtaining battery monitoring data; the battery monitoring data includes the battery voltage, battery current, battery temperature, etc. of the rechargeable battery pack of the mobile storage and charging robot.

[0054] Step 3, determining a discharge strategy based on the battery monitoring data; the discharge strategy includes the optimal discharge power, the optimal discharge current, etc.

[0055] Determining the discharge strategy includes: obtaining the battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current, and the battery temperature. The battery pack model can be obtained by methods such as the performance of the rechargeable battery pack or experimental data.

[0056] Based on the battery pack model, determine the discharge power of the rechargeable battery pack. The discharge power refers to the rate at which the battery releases electrical energy during discharge. The battery voltage at different discharge currents can be determined through the battery pack model, and the product of the battery current and the obtained battery voltage is used as the discharge power.

[0057] Obtain the injection power injected by the mobile storage and charging robot into the power grid. The injection power refers to the electrical energy power actually delivered by the mobile storage and charging robot to the power grid. It can be calculated by installing a power meter at the connection point between the mobile storage and charging robot and the power grid and measuring the voltage and current output to the power grid in real time.

[0058] Through the particle swarm optimization algorithm, optimize the discharge power to obtain the optimal discharge power and the optimal discharge current. The optimal discharge power can refer to the power that maximizes the electrical energy injected into the power grid. The optimal discharge current can refer to the discharge current corresponding to the optimal discharge power. For example, according to the battery pack model, calculate the battery voltage corresponding to the optimal discharge current, and take the product of the optimal discharge current and the battery voltage as the optimal discharge power.

[0059] The particle swarm optimization algorithm includes: when the difference in charge state of the mobile storage and charging robot is greater than the charge difference threshold, the particle swarm optimization algorithm is started. The difference in charge state refers to the difference between the charge state of the rechargeable battery at the end of the previous optimization and the current charge state. The charge difference threshold is used to determine whether to start the optimization algorithm. When the difference in charge state exceeds the charge difference threshold, the discharge strategy is optimized; otherwise, it is not. The charge difference threshold can be set according to requirements or experience. For example, 10%.

[0060] Randomly select multiple current values within the search range as the initial positions of the particle swarm optimization algorithm; the search range is related to the minimum charge state and the maximum charge state of the mobile storage and charging robot. For example, where, I min represents the minimum discharge current; I base represents the basic discharge current; k represents the adjustment factor; SOC current represents the current charge state; SOC min represents the minimum charge state; SOC max represents the maximum charge state; I max represents the maximum discharge current; I rated represents the rated discharge current of the battery. So that the minimum discharge current can decrease as the charge state increases, ensuring that a smaller discharge current can also be obtained at a high charge state; similarly, by setting the formula for the maximum discharge current, the maximum discharge current can increase as the charge state increases, allowing a larger discharge current to be obtained at a high charge state. Based on the initial positions, determine the global best position; the global best position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power. The optimization objective function is: where, f(I) represents the objective function, and the larger the function value, the better; β1 represents the first weight parameter; β2 represents the second weight parameter; P dis represents the discharge power; P inj represents the injection power. Based on the initial positions and the global best position, perform velocity update and position update to obtain the updated velocity and updated position; the updated velocity is: v i (1) = ωv i (0) + c1r1[g best，i -I i (0)]; the updated position is: I i (i1) = I i (0) + v i (1); where, v i (1) represents the updated velocity of the i-th position; ω represents the inertia weight; v i(0) represents the initial update speed at the i-th position; c1 represents the first swarm learning parameter; r1 represents the first random number; g best，i represents the swarm best position; I i (0) represents the i-th initial position; I i (1) represents the updated position. Based on the updated position, a new global best position is determined; and it is judged whether the new global best position reaches the iteration stop condition. The iteration stop condition may refer to that the number of iterations reaches a threshold or the value of the objective function is less than a preset optimization threshold. The preset optimization threshold may refer to the maximum value set in advance for stopping the iteration. When the value of the objective function is less than this preset optimization threshold, the iteration stops. If the iteration stop condition is not reached, based on the updated position and the new global best position, speed update and position update are performed to obtain a new updated speed and a new updated position. The new updated speed is: The new updated position is: I i (t + 1) = ωI i (t)+(1 - ω)v i (t + 1); v i (t + 1) represents the new updated speed obtained in the (t + 1)-th iteration; v i (t) represents the new updated speed obtained in the t-th iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t - 1) represent the new updated positions obtained in the t-th and (t - 1)-th iterations respectively; p best，i represents the individual best position; g best，i represents the swarm best position; c3 represents the second swarm learning parameter; r3 represents the third random number; Based on the new updated position and the new updated speed, the position update operation is cycled until the updated position reaches the iteration stop condition, and this updated position is used as the best discharge current, and the power corresponding to this best discharge current is used as the best discharge power. By the difference between the previous best position and the next best position, the optimization step size is adjusted, which improves the convergence speed of the optimization algorithm.

[0061] Step 4, based on the discharge instruction and the discharge strategy, determine the discharge plan; the discharge plan includes the total discharge amount, the discharge time, the individual discharge amount, etc.; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot.

[0062] Determine the discharge plan, including: constructing a discharge objective function based on the optimal discharge power; the discharge objective function is related to the storage and charging cost; constructing discharge constraint conditions; the discharge constraint conditions include the energy storage capacity constraint of the mobile storage and charging robot, the charge and discharge power constraint, the charge and discharge efficiency constraint, the cyclic charge and discharge constraint of the mobile storage and charging robot, and the power balance constraint; based on the discharge constraint conditions, solve the discharge objective function to obtain the individual discharge amount of the mobile storage and charging robot.

[0063] The discharge objective function is:

[0064] where C represents the charge and discharge revenue; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharging; M represents the total number of mobile storage and charging robots for discharging; E dis，n (t) represents the discharge power of the nth mobile storage and charging robot at time t, that is, the optimal discharge power; C sale (t) represents the reverse power selling price at time t; m represents the variable of the mobile storage and charging robot for charging; M represents the total number of mobile storage and charging robots for charging; E cha，m (t) represents the charging power of the mth mobile storage and charging robot at time t; C buy (t) represents the grid power price at time t; t0 represents the start time of time period t; Δt represents the duration of each time period; P dis，n (t) represents the instantaneous discharge power of the nth mobile storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the loss of the mobile storage and charging robot discharging at the discharge power of P dis，n (t); represents integration over time. The robots used for charge and discharge can be determined according to the charge state of the mobile storage and charging robot. The mobile storage and charging robots can be classified by the highest charge state and the lowest charge state. For example, the mobile storage and charging robots with a charge state less than the lowest charge state can be assigned as the robots to be charged; calculate the robots with a charge state greater than the highest charge state and calculate the total excess power. If the total excess power is greater than the grid demand, then the mobile storage and charging robots with a charge state greater than the highest charge state are used as the discharging robots, otherwise the mobile storage and charging robots with a charge state greater than the lowest charge state are used as the discharging robots. Among them, the lowest charge state and the highest charge state can be set according to the needs of new energy vehicles.

[0065] The energy storage capacity constraint of the mobile storage and charging robot is:

[0066] E min (t) ≤ E n 、E m ≤ E max (t);

[0067] E min E(t)=max[E base，min ,E ev，min (t)];

[0068]

[0069] Among them, E min E(t) represents the minimum energy storage power in the t period; E n represents the energy storage power of the nth discharging mobile energy storage and charging robot; E m represents the energy storage power of the mth charging mobile energy storage and charging robot; E max E(t) represents the maximum energy storage power in the t period; max[*] represents finding the maximum value; E base，min represents the basic minimum energy storage power; E ev，min E(t) represents the predicted minimum charging demand of the electric vehicle at time t; min[*] represents finding the minimum value; E base，max represents the basic maximum energy storage power; E total represents the total capacity of the mobile energy storage and charging robot; E ev，reserve E(t) represents the power reserved for the electric vehicle at time t.

[0070] The charge and discharge power constraint is:

[0071] 0≤P dis， n(t)≤P dis，max ;

[0072] 0≤P cha，m E(t)≤P cha，max ;

[0073] Among them, P dis，max represents the maximum discharge power of the mobile energy storage and charging robot; P cha，m E(t) represents the charging power of the mth mobile energy storage and charging robot; P cha，max represents the maximum charging power of the mobile energy storage and charging robot.

[0074] The charge and discharge efficiency constraint is:

[0075]

[0076] Among them, E(t + 1) represents the total power in the (t + 1) period; E(t) represents the total power in the t period; η cha represents the charging efficiency; P cha，m E(t) represents the charging power of the mth mobile energy storage and charging robot in the t period; η dis represents the discharge efficiency.

[0077] The cyclic charge and discharge constraint of the mobile energy storage and charging robot is:

[0078]

[0079] Among them, E dis (t) represents the discharge power of the mobile storage and charging robot in the t period; E cha (t) represents the charging power of the mobile storage and charging robot in the t period; E lifetime represents the total cycle life of the rechargeable battery pack of the mobile storage and charging robot.

[0080] The power balance constraint is:

[0081]

[0082] Among them, P grid (t) represents the grid demand power in the t period.

[0083] Step 5, control the discharge of the mobile storage and charging robot according to the discharge plan and the discharge strategy. For example, control the discharge of the mobile storage and charging robot according to the optimal discharge power.

[0084] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile storage and charging robot discharge management system, characterized in that: It includes a mobile storage and charging robot, a battery management system, a charging interface, a communication module and a control module; The mobile storage and charging robot includes a plurality of rechargeable battery packs for charging and discharging to the power grid and new energy vehicles; The battery management system is used to monitor and control the charging and discharging state of the rechargeable battery pack and obtain battery monitoring data; the battery monitoring data includes the battery voltage, battery current and battery temperature of the rechargeable battery pack of the mobile storage and charging robot; The charging interface is used to connect an external charging device to charge the rechargeable battery pack; The communication module is used to exchange data with external equipment and receive a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge; The control module is used to control the discharge process of the rechargeable battery pack according to the monitoring data and external instructions of the battery management system; the control module includes a discharge strategy determination unit, a discharge scheme determination unit and a control unit; the discharge strategy determination unit is used to determine the discharge strategy based on the battery monitoring data; the discharge strategy includes an optimal discharge power and an optimal discharge current; the discharge scheme determination unit is used to determine the discharge scheme based on the discharge instruction and the discharge strategy; the discharge scheme includes a total discharge amount, a discharge time and an individual discharge amount; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot; the control unit is used to control the discharge of the mobile storage and charging robot according to the discharge scheme and the discharge strategy; The discharge strategy determination unit includes a battery pack model acquisition component, a discharge power determination component, an injection power acquisition component and an optimization component; The battery pack model acquisition component is used to acquire the battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current and the battery temperature; The discharge power determination component is used to determine the discharge power of the rechargeable battery pack based on the battery pack model; The injection power acquisition component is used to obtain the injection power injected into the power grid by the mobile storage and charging robot; The optimization component is used to optimize the discharge power by a particle swarm optimization algorithm to obtain an optimal discharge power and an optimal discharge current; the particle swarm optimization algorithm includes: When the charge state difference of the mobile storage and charging robot is greater than the charge difference threshold, the particle swarm optimization algorithm is started; the charge state difference refers to the difference between the charge state of the rechargeable battery at the end of the previous optimization and the current charge state; Randomly selecting multiple current values ​​within a search range as the initial position of the particle swarm optimization algorithm; the search range is related to the lowest charge state and the highest charge state of the mobile storage and charging robot; Based on the initial position, a global optimal position is determined; the global optimal position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power; Based on the initial position and the global optimal position, speed update and position update are performed to obtain update speed and update position; the update speed is: v i (1) = ωv i (0)+c1 r1[g best，i -I i (0)]; the update position is: I i (1)=I i (0)+v i (1), where v i (1) represents the update speed of the i-th position; ω represents the inertia weight; v i (0) represents the initial update speed of the i-th position; c1 represents the first group learning parameter; r1 represents the first random number; g best，i Indicates the optimal position of the group; I i (0) indicates the i-th initial position; I i (0) indicates the update position; Based on the updated position, determining a new global optimal position; and judging whether the new global optimal position reaches an iteration stop condition; If the iteration stop condition is not reached, the speed update and position update are performed based on the updated position and the new global optimal position to obtain a new update speed and a new update position, and the new update speed is: ; The new update position is: I i (t+1)=ωI i (t)+(1-ω)v i (t+1); v i (t+1) represents the new update speed obtained in the t+1th iteration; v i (t) represents the new update speed obtained in the tth iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t-1) represents the new updated position obtained in the t-th and t-1-th iterations respectively; p best，i represents the best position of an individual; g best，i represents the best position of the group; c3 represents the learning parameter of the second group; r3 represents the third random number; Based on the new update position and the new update speed, the position update operation is cyclically performed until the update position reaches the iteration stop condition, and the update position is used as the optimal discharge current, and the power corresponding to the optimal discharge current is used as the optimal discharge power.

2. The mobile storage and charging robot discharge management system according to claim 1 is characterized in that: The discharge scheme determination unit includes an objective function construction component, a constraint condition construction component and a solution component; The objective function building component is used to build a discharge objective function based on the optimal discharge power; the discharge objective function is related to the storage and charging cost; The constraint condition building component is used to build the discharge constraint condition; the discharge constraint condition includes the mobile storage and charging robot energy storage capacity constraint, the charge and discharge power constraint, the charge and discharge efficiency constraint, the mobile storage and charging robot cycle charge and discharge constraint and the power balance constraint; The solving component is used to solve the discharge objective function based on the discharge constraint condition to obtain the individual discharge amount of the mobile storage and charging robot.

3. The mobile storage and charging robot discharge management system according to claim 2 is characterized in that: The discharge objective function is: Where C represents the charge and discharge benefit; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharge; M represents the total number of mobile storage and charging robots for discharge; E dis,n (t) represents the discharge power of the nth mobile storage and charging robot in time period t; C sale (t) represents the reverse electricity price in period t; m represents the variable of the mobile storage and charging robot; M represents the total number of mobile storage and charging robots; E cha，m (t) represents the charging capacity of the mth mobile storage and charging robot in time period t; C buy (t) represents the bid price of the t period; t0 represents the starting time of the t period; Δt represents the duration of each time period; P dis,n (t) represents the instantaneous discharge power of the nth mobile storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the mobile storage and charging robot with P dis,n (t) Discharge power loss; represents the integration of time; The energy storage capacity constraint of the mobile storage and charging robot is: E min (t)≤E n 、E m ≤E max (t); E min (t)=max[E base，min ,E ev，min (t)]; The max (t)=min[E base,max ,E total -E ev，r，eserve (t)]; Among them, E min (t) represents the minimum energy storage capacity in period t; E n E represents the energy storage capacity of the nth discharged mobile charging station; m Represents the energy storage capacity of the mth mobile storage robot: E max (t) represents the maximum energy storage capacity in time period t, and max[*] represents the maximum value; E base，min Indicates the basic minimum energy storage capacity; E ev，min (t) represents the predicted minimum charging demand of electric vehicles at time t; min[*] represents the minimum value; E base，max Indicates the basic maximum energy storage capacity; E total Represents the total capacity of the mobile storage and charging robot; E ev，reserve (t) represents the power reserved for the electric vehicle at time t; The charge and discharge power constraints are: 0≤P dis，n (t)≤P dis，max ; 0≤P cha，m (t)≤P cha,max ; Among them, P dis，max Indicates the maximum discharge power of the mobile storage and charging robot; P cha，m (t) represents the charging power of the mth mobile storage and charging robot; P cha，max Indicates the maximum charging power of the mobile storage and charging robot; The charge and discharge efficiency constraint is: Where, E(t+1) represents the total electricity in the t+1 period; E(t) represents the total electricity in the t period; η cha Indicates charging efficiency; P cha，m (t) represents the charging power of the mth mobile storage and charging robot in time period t; η dis It indicates the discharge efficiency; The cyclic charging and discharging constraints of the mobile storage and charging robot are: Among them, E dis (t) represents the discharge power of the mobile storage and charging robot in time period t; E cha (t) represents the charging capacity of the mobile storage and charging robot in time period t; E lifetime Indicates the total cycle life of the rechargeable battery pack of the mobile storage and charging robot; The power balance constraint is: Among them, P grid (t) represents the grid demand power during period t.

4. A discharge management method for a mobile storage and charging robot, characterized in that: include: Receiving a discharge instruction; the discharge instruction is used to instruct the mobile storage and charging robot to discharge; Get battery monitoring data; The battery monitoring data includes the battery voltage, battery current and battery temperature of the rechargeable battery pack of the mobile storage and charging robot; Determining a discharge strategy based on the battery monitoring data; the discharge strategy includes an optimal discharge power and an optimal discharge current; Based on the discharge instruction and the discharge strategy, a discharge plan is determined; the discharge plan includes a total discharge amount, a discharge time, and an individual discharge amount; the individual discharge amount refers to the discharge amount of each mobile storage and charging robot; According to the discharge scheme and the discharge strategy, the mobile storage and charging robot is controlled to discharge; the discharge strategy is determined, including: Acquire a battery pack model of the mobile storage and charging robot; the battery pack model is used to represent the relationship between the battery voltage, the battery current and the battery temperature; Determining the discharge power of the rechargeable battery pack based on the battery pack model; Obtaining the injected power injected into the power grid by the mobile storage and charging robot; The discharge power is optimized by a particle swarm optimization algorithm to obtain an optimal discharge power and an optimal discharge current; the particle swarm optimization algorithm includes: When the charge state difference of the mobile storage and charging robot is greater than the charge difference threshold, the particle swarm optimization algorithm is started; the charge state difference refers to the difference between the charge state of the rechargeable battery at the end of the previous optimization and the current charge state; Randomly selecting multiple current values ​​within a search range as the initial position of the particle swarm optimization algorithm; the search range is related to the lowest charge state and the highest charge state of the mobile storage and charging robot; Based on the initial position, a global optimal position is determined; the global optimal position is related to the discharge power and the output efficiency; the output efficiency is the ratio of the injection power to the discharge power; Based on the initial position and the global optimal position, speed update and position update are performed to obtain update speed and update position, and the update speed is: v i (1) = ωv i (0)+c1 r1[g best，i -I i (0)]; the update position is: I i (1)=I i (0)+v i (1) Where v i (1) represents the update speed of the i-th position; ω represents the inertia weight; v i (0) represents the initial update speed of the i-th position; c1 represents the first group learning parameter; r1 represents the first random number; g best，i Indicates the optimal position of the group; I i (0) indicates the i-th initial position; I i (1) indicates the updated position; Based on the updated position, determining a new global optimal position; and judging whether the new global optimal position reaches an iteration stop condition; If the iteration stop condition is not reached, the speed update and position update are performed based on the updated position and the new global optimal position to obtain a new update speed and a new update position, and the new update speed is: ; The new update position is: I i (t+1)=ωI i (t)+(1-ω)v i (t+1); v i (t+1) represents the new update speed obtained in the t+1th iteration; v i (t) represents the new update speed obtained in the tth iteration; c2 represents the individual learning parameter; r2 represents the second random number; I i (t) and I i (t-1) represents the new updated position obtained in the t-th and t-1-th iterations respectively; p best，i represents the best position of an individual; g best，i represents the best position of the group; c3 represents the learning parameter of the second group; r3 represents the third random number; Based on the new update position and the new update speed, the position update operation is cyclically performed until the update position reaches the iteration stop condition, and the update position is used as the optimal discharge current, and the power corresponding to the optimal discharge current is used as the optimal discharge power.

5. The mobile storage and charging robot discharge management method according to claim 4 is characterized in that: The determining of the discharge plan comprises: Based on the optimal discharge power, a discharge target function is constructed; the discharge target function is related to the storage and charging cost; Constructing discharge constraints; the discharge constraints include mobile storage and charging robot energy storage capacity constraints, charging and discharging power constraints, charging and discharging efficiency constraints, mobile storage and charging robot cycle charging and discharging constraints and power balance constraints; Based on the discharge constraint conditions, the discharge objective function is solved to obtain the individual discharge capacity of the mobile storage and charging robot.

6. The mobile storage and charging robot discharge management method according to claim 5, characterized in that: The discharge objective function is: Where C represents the charge and discharge benefit; max represents maximization; t represents the time period variable; T represents the total number of time periods; n represents the variable of the mobile storage and charging robot for discharge; M represents the total number of mobile storage and charging robots for discharge; E dis,n (t) represents the discharge power of the nth mobile storage and charging robot in time period t; C sale (t) represents the reverse electricity price in period t; m represents the variable of the mobile storage and charging robot; M represents the total number of mobile storage and charging robots; E cha，m (t) represents the charging capacity of the mth mobile storage and charging robot in time period t; C buy (t) represents the electricity price in the t period; t0 represents the starting time of the t period; Δt represents the duration of each time period; P dis，n (t) represents the instantaneous discharge power of the nth mobile storage and charging robot, that is, the optimal discharge power; Loss[P dis，n (t)] represents the mobile storage and charging robot with P dis，n (t) Discharge power loss; represents the integration of time; The energy storage capacity constraint of the mobile storage and charging robot is: E min (t)≤E n 、E m ≤E max (t); E min (t)=max[E base，min ,E ev,min (t)]; The max (t)=min[E base,max ,E total -E ev，reserve (t)]; Among them, E min (t) represents the minimum energy storage capacity in period t; E n E represents the energy storage capacity of the nth discharged mobile charging station; m Represents the energy storage capacity of the mth charging mobile storage robot: E max (t) represents the maximum energy storage capacity in time period t, and max[*] represents the maximum value: E base，min Indicates the basic minimum energy storage capacity; E ev，min (t) represents the predicted minimum charging demand of electric vehicles at time t; min[*] represents the minimum value; E base，max Indicates the basic maximum energy storage capacity; E total Represents the total capacity of the mobile storage and charging robot; E ev，reserve (t) represents the power reserved for the electric vehicle at time t; The charge and discharge power constraints are: 0≤P dis，n (t)≤P dis,max ; 0≤P cha，m (t)≤P cha，max ; Among them, P dis，max Indicates the maximum discharge power of the mobile storage and charging robot; P cha，m (t) represents the charging power of the mth mobile storage and charging robot; P cha，max Indicates the maximum charging power of the mobile storage and charging robot; The charge and discharge efficiency constraint is: Where, E(t+1) represents the total electricity in the t+1 period; E(t) represents the total electricity in the t period; η cha Indicates charging efficiency; P cha，m (t) represents the charging power of the mth mobile storage and charging robot in time period t; η dis It indicates the discharge efficiency; The cyclic charging and discharging constraints of the mobile storage and charging robot are: Among them, E dis (t) represents the discharge power of the mobile storage and charging robot in time period t; E cha (t) represents the charging capacity of the mobile storage and charging robot in time period t; E lifetime Indicates the total cycle life of the rechargeable battery pack of the mobile storage and charging robot; The power balance constraint is: Among them, P grid (t) represents the grid demand power during period t.

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