A charging strategy optimization control method and device

Abstract

This invention relates to a charging strategy optimization control method and apparatus. The control method includes the following steps: establishing a charging demand model for the battery to be charged; establishing a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints; using a genetic algorithm to obtain an optimal charging strategy based on the charging strategy optimization model; and solving the charging strategy optimization objective function based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost. Based on meeting the user's battery swapping needs, and with the objective of minimizing the sum of the total charging cost and the total battery life loss cost within the station, and with battery margin as a constraint, the battery charging strategy within the station is optimized and controlled to improve the charging efficiency of the charging and swapping station.

Description

Technical Field

[0001] This invention relates to the field of battery charging and discharging control technology, and in particular to a charging strategy optimization control method and apparatus. Background Technology

[0002] As industrial development relies heavily on fossil fuels, environmental pollution has become increasingly prominent. Electric heavy-duty trucks, as a product of new energy technologies, play a crucial role in addressing energy consumption in the transportation sector and are an important means to promote my country's energy structure adjustment and achieve its carbon peaking and neutrality goals. However, the development of electric heavy-duty trucks depends on the availability of basic charging and battery swapping infrastructure. The rational planning of charging and battery swapping stations is of great significance for promoting the widespread adoption of electric heavy-duty trucks. Electric heavy-duty truck charging and battery swapping stations adopt a "centralized charging, unified distribution" operation model. This model includes battery swapping, distribution, and charging. When a user finds their electric heavy-duty truck's battery is low, they drive the vehicle to a charging and battery swapping station. A battery swapping robot automatically removes the depleted battery and replaces it with a fully charged battery from the charging compartment. The entire battery swapping process takes approximately 5-10 minutes. The charging and battery swapping stations can combine user battery swapping needs with different charging rates at different times of day and during different electricity price periods to charge the swapped-out batteries, ensuring battery swapping needs are met while maintaining economic efficiency. Once fully charged, the batteries are replaceable and used for heavy-duty truck battery swapping.

[0003] However, the current operation and management of electric heavy-duty truck charging and battery swapping lacks reasonable charging planning. Excessive waiting times for battery swapping services, and even business process stagnation, lead to high operating costs for charging and battery swapping stations, resulting in low profitability or even losses for these stations. Therefore, optimizing and controlling charging strategies while meeting users' battery swapping needs to maintain the smooth operation of charging and battery swapping stations has become an urgent problem to be solved. Summary of the Invention

[0004] Based on the above-mentioned situation in the prior art, the purpose of this invention is to provide a charging strategy optimization control method, including the following steps:

[0005] S101. Establish a charging demand model for the battery to be charged.

[0006] S102. Establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints.

[0007] S103. Use a genetic algorithm to obtain the optimal charging strategy based on the charging strategy optimization model;

[0008] S104. Solve the objective function of the charging strategy optimization based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost.

[0009] Furthermore, the charging demand model is established based on the following formula:

[0010]

[0011] in, Let n represent the probability that the i-th battery awaiting charging arrives at time t′. c (t) represents the battery swapping demand value before time t of a day, E(n) c (t) represents n during the time interval t0 to t. c The expected value of (t); N represents the total number of rechargeable batteries.

[0012] Furthermore, the objective function for optimizing the charging strategy is established according to the following formula:

[0013]

[0014] Among them, L b L represents the cost function of battery life loss; c Represents the battery charging cost function; a v (v c This indicates that the battery charging cutoff voltage is V. c The aging ratio at that time; Let n be the charging rate matrix, representing the charging rate adopted by the i-th battery in the j-th time period; l (x c () indicates the battery charging rate is x c The number of times it can be repeated;

[0015] C f (v c ) indicates that the charging cutoff voltage is V. c The battery level that has already been charged; c e (j) represents the electricity price for time period j; N T N represents the number of planning time periods for the charging strategy, where the length of a single time period is T; b C represents the number of batteries within the station. b This refers to the price of a single new battery.

[0016] Furthermore, the constraints include battery margin constraints and charging rate constraints; the battery margin constraints are established according to the following formula:

[0017] S c (t)=E(n b (t,x c ,v c ))―E(n c (t)≥0

[0018] Among them, S c(t) represents the battery margin in the charging / swapping station at time t; E(n) b (t,x c ,v c )) represents the expected number of fully charged batteries in the station at time t;

[0019] The charging rate constraint is established based on the following formula:

[0020]

[0021] Where, x cmax is a constant, representing the maximum charging rate of the battery in the charging and swapping station.

[0022] Furthermore, a genetic algorithm is used to obtain the optimal charging strategy based on the charging strategy optimization model, including the following steps:

[0023] S1031. Randomly generate M charging strategies that satisfy the constraints;

[0024] S1032. Genetic algorithm is used to encode the initial value;

[0025] S1033, The fitness value is the sum of battery charging cost and battery life loss;

[0026] S1034. After performing selection, crossover, and mutation operations on the M randomly generated charging strategies that satisfy the constraints, calculate the fitness value.

[0027] S1035. Perform iterative calculations, and when the preset number of iterations is reached, output the optimal charging strategy.

[0028] Furthermore, the optimal charging strategy includes the charging rate of battery i at different stages j and the cutoff voltage of battery i.

[0029] According to another aspect of the present invention, a charging strategy optimization control device is provided, comprising a charging demand model establishment module, a charging strategy optimization model establishment module, an optimal charging strategy acquisition module, and an optimization control module; wherein,

[0030] The charging demand model building module is used to build a charging demand model for the battery to be charged.

[0031] The charging strategy optimization model establishment module is used to establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints.

[0032] The optimal charging strategy acquisition module is used to obtain the optimal charging strategy based on the charging strategy optimization model using a genetic algorithm.

[0033] The optimization control module is used to solve the objective function of the charging strategy optimization based on the optimal charging strategy, so as to obtain the optimal battery charging cost and battery life loss cost.

[0034] Furthermore, the charging demand model is established based on the following formula:

[0035]

[0036] in, Let n represent the probability that the i-th battery awaiting charging arrives at time t′. c (t) represents the battery swapping demand value before time t of a day, E(n) c (t) represents n during the time interval t0 to t. c The expected value of (t); N represents the total number of rechargeable batteries.

[0037] Furthermore, the objective function for optimizing the charging strategy is established according to the following formula:

[0038]

[0039] Among them, L b L represents the cost function of battery life loss; c Represents the battery charging cost function; a v (v c This indicates that the battery charging cutoff voltage is V. c The aging ratio at that time; Let N be the charging rate matrix, representing the charging rate adopted by the i-th battery in the j-th time period; l (x c () indicates the battery charging rate is x c The number of times it can be repeated;

[0040] C f (v c ) indicates that the charging cutoff voltage is V. c The battery level that has already been charged; c e (j) represents the electricity price for time period j; N T N represents the number of planning time periods for the charging strategy, where the length of a single time period is T; b C represents the number of batteries within the station. b This refers to the price of a single new battery.

[0041] Furthermore, the constraints include battery margin constraints and charging rate constraints; the battery margin constraints are established according to the following formula:

[0042] S c (t)=E(n b (t,x c ,v c))―E(e c (t)≥0

[0043] Among them, S c (t) represents the battery margin in the charging / swapping station at time t; E(n) b (t,x c ,v c )) represents the expected number of fully charged batteries in the station at time t;

[0044] The charging rate constraint is established based on the following formula:

[0045]

[0046] Where, x cmax is a constant, representing the maximum charging rate of the battery in the charging and swapping station.

[0047] In summary, this invention provides a charging strategy optimization control method and apparatus. The control method includes the following steps: establishing a charging demand model for the battery to be charged; establishing a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints; using a genetic algorithm to obtain the optimal charging strategy based on the charging strategy optimization model; and solving the charging strategy optimization objective function based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost. Based on meeting the user's battery swapping needs, and with the objective of minimizing the sum of the total charging cost and the total battery life loss cost within the station, and with battery margin as a constraint, the battery charging strategy within the station is optimized and controlled to improve the charging efficiency of the charging and swapping station. Attached Figure Description

[0048] Figure 1 This is a flowchart of the charging strategy optimization control method provided in the embodiments of the present invention;

[0049] Figure 2 This is a schematic diagram of time-of-use electricity pricing for different time periods throughout the day, as described in an embodiment of the present invention.

[0050] Figure 3 This is a flowchart of the optimization algorithm involved in the embodiments of the present invention;

[0051] Figure 4 This is a schematic diagram showing the algorithm optimization results involved in the embodiments of the present invention, as well as the battery margin and battery swapping demand in each time period under the optimal charging rate.

[0052] Figure 5 This is a schematic diagram illustrating the optimization process of the average total cost of individuals in the population and the total cost of the optimal individual in the population during the iterative process of the genetic algorithm in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0054] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. An embodiment of the present invention provides a charging strategy optimization control method, which is applicable to, for example, electric heavy-duty truck charging and swapping stations. Figure 1 The flowchart of the control method is shown below, including the following steps:

[0055] S101. Establish a charging demand model for the battery to be charged. For example, in this embodiment, a battery swapping demand model for electric heavy-duty trucks is established by collecting user battery swapping information from electric heavy-duty truck charging and swapping stations. This charging demand model can be established according to the following formula:

[0056]

[0057] Where N represents the total number of batteries to be charged, for example, the total number of batteries to be charged can be obtained by the total number of electric heavy truck customers, and the behavior of each customer is independent and identically distributed. Let n represent the probability that the i-th battery awaiting charging arrives at time t′. c (t) represents the battery swapping demand value before time t of a day, E(n) c (t) represents n during the time interval t0 to t. c The expected value of (t) can be expressed by the following formula:

[0058]

[0059] in, This indicates the time slot, where the customer entry rate varies at different times.

[0060] S102. Establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints. The charging strategy optimization objective function can be established according to the following formula:

[0061]

[0062] Among them, L b L represents the cost function of battery life loss; c Represents the battery charging cost function; a v (v c This indicates that the battery charging cutoff voltage is V. c The aging ratio at that time; Let N be the charging rate matrix, representing the charging rate adopted by the i-th battery in the j-th time period; l (x c () indicates the battery charging rate is x c The number of times it can be repeated;

[0063] C f (v c ) indicates that the charging cutoff voltage is V. c The battery level that has already been charged; c e (j) represents the electricity price for time period j; N T N represents the number of planning time periods for the charging strategy, where the length of a single time period is T; b C represents the number of batteries within the station. b This refers to the price of a single new battery.

[0064] Let the starting time of the charging strategy optimization be t0, and the battery life loss cost function be L. b This can be expressed by the following formula:

[0065]

[0066] Where, N b C represents the number of batteries within the station. b Price per new battery

[0067] Charging rate matrix This can be expressed by the following formula:

[0068]

[0069] The battery charging cutoff voltage is V c Aging ratio a v (v c This can be expressed by the following formula:

[0070]

[0071] a v (v c ) is a piecewise function. Indicates a v (v c The value of ) in the i-th time period. For the cutoff voltage setting, N v The number of gears.

[0072] N l (x c () indicates the battery charging rate is x c The number of cycles that can occur can be expressed by the following formula:

[0073]

[0074] N c This is a constant, representing the number of charging rate levels. N is a constant representing the cutoff voltage level. l (x c The right boundary of the i-th time period.

[0075] Battery charging cost function L c This can be expressed by the following formula:

[0076]

[0077] Among them, c e (j) can be represented as:

[0078]

[0079] Figure 2 The diagram shows the time-of-use electricity prices for different times of the day.

[0080] The above analysis shows that the decision variable is the charging rate matrix. The charging cutoff voltage v of the i-th battery c That is, the independent variable is

[0081] The constraints include battery margin constraints and charging rate constraints; the battery margin constraints are established according to the following formula:

[0082] S c (t)=E(n b (t,x c ,v c ))―E(n c (t)≥0

[0083] Among them, S c (t) represents the battery margin in the charging / swapping station at time t; E(n) b (t,x c ,v c The number of fully charged batteries in the station at time t is represented by the following formula:

[0084]

[0085]

[0086] Among them, a i (t) indicates whether battery i is fully charged at time t; if it is, it is 1, otherwise it is 0. Indicates the moment when battery i begins charging; Indicates according to charging strategy The time required to fully charge the i-th battery; find the answer. The expression requires solving an integral equation:

[0087]

[0088] In the above formula,

[0089]

[0090] γ v (v c The quantity () is a function of the charge with respect to the cutoff voltage, and can be expressed by the following formula:

[0091]

[0092] The charging rate constraint is the charging rate used by battery i in time period j. Less than the maximum charging rate that the charging case can provide (x) cmax It can be established based on the following formula:

[0093]

[0094] Where, x cmax is a constant, representing the maximum charging rate of the battery in the charging and swapping station.

[0095] S103. Using a genetic algorithm, the optimal charging strategy is obtained based on the charging strategy optimization model. The optimal charging strategy includes the charging rate of battery i at different stages j and the cutoff voltage of battery i. Since the optimization problem involved in this embodiment of the invention is a complex, multi-dimensional optimization problem with multiple nonlinear constraints, a genetic algorithm is used as the solution algorithm because it has strong robustness for solving this type of optimization problem, has a fast iteration speed, and does not require setting a large number of parameters. Figure 3 The flowchart of the optimization algorithm involved in the embodiment of the present invention is shown. A genetic algorithm is used to obtain the optimal charging strategy based on the charging strategy optimization model, including the following steps:

[0096] S1031. Randomly generate M charging strategies that satisfy the constraints.

[0097] S1032. Encode the initial value using a genetic algorithm; here, the initial value refers to the value of the objective function min{L}. b +L c In the context of}, the charging rate matrix of the i-th battery during the j-th time period. and the charging cutoff voltage v of the i-th battery c The assigned random initial value.

[0098] S1033, The fitness value is the sum of battery charging cost and battery life loss.

[0099] S1034. After performing selection, crossover, and mutation operations on the M randomly generated charging strategies that meet the constraints, calculate the fitness value. The selection, crossover, and mutation operations are performed using the "roulette wheel selection," "two-point crossover," and "binary mutation operator" methods from genetic algorithms, respectively. These are mature operation methods in genetic algorithms and will not be elaborated further here. The fitness value is calculated based on the sum L of battery charging cost and battery life loss. b +L c The values ​​are assigned. For example, if the population size is M, and i is the position of an individual in the population, i = 1, 2, 3, ..., M, individuals are sorted from largest to smallest according to their objective function values. Let SP be the selection pressure, and the formula for calculating individual fitness is:

[0100]

[0101] S1035. Perform iterative calculations, and when the preset number of iterations is reached, output the optimal charging strategy.

[0102] Figure 4 The diagram shows the algorithm optimization results and the battery margin and battery swapping demand in each time period under the optimal charging rate. It shows the optimal charging rate adopted by battery i in different time periods j under the premise of meeting the user's battery swapping demand, so as to minimize the sum of battery charging cost and battery life loss cost of the charging and swapping station. It can be seen that the number of fully charged batteries in each time period can meet the user's battery swapping demand. Figure 5 This refers to the optimization process of the average total cost of individuals in the population and the total cost of the best individual in the population during the iteration process of the genetic algorithm. The total cost of the best individual continuously decreases during the iteration process and is always less than the average total cost of individuals in the population.

[0103] S104. Solve the objective function of the charging strategy optimization based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost.

[0104] Embodiments of the present invention also provide a charging strategy optimization control device, including a charging demand model establishment module, a charging strategy optimization model establishment module, an optimal charging strategy acquisition module, and an optimization control module; wherein,

[0105] The charging demand model building module is used to build a charging demand model for the battery to be charged.

[0106] The charging strategy optimization model establishment module is used to establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints.

[0107] The optimal charging strategy acquisition module is used to obtain the optimal charging strategy based on the charging strategy optimization model using a genetic algorithm.

[0108] The optimization control module is used to solve the objective function of the charging strategy optimization based on the optimal charging strategy, so as to obtain the optimal battery charging cost and battery life loss cost.

[0109] The process by which each module in the charging strategy optimization control device of this invention realizes its function is the same as the steps in the charging strategy optimization control method of the above embodiments, and will not be repeated here.

[0110] In summary, this invention relates to a charging strategy optimization control method and apparatus. The control method includes the following steps: establishing a charging demand model for the battery to be charged; establishing a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints; using a genetic algorithm to obtain the optimal charging strategy based on the charging strategy optimization model; and solving the charging strategy optimization objective function based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost. Based on meeting the user's battery swapping needs, and with the objective of minimizing the sum of the total charging cost and the total battery life loss cost within the station, and with battery margin as a constraint, the battery charging strategy within the station is optimized and controlled to improve the charging efficiency of the charging and swapping station.

[0111] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A charging strategy optimization control method, characterized in that, Including the following steps: S101. Establish a charging demand model for the battery to be charged; the charging demand model is established according to the following formula: in, This indicates that the i-th battery to be charged is in t ′ The probability of coming to charge at any time, n c (t) represents the battery swapping demand value before time t of a day, E(n) c (t) represents n during the time interval t0 to t. c The expected value of (t); N represents the total number of batteries to be charged; S102. Establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints; the charging strategy optimization objective function is established according to the following formula: Among them, L b L represents the cost function of battery life loss; c Represents the battery charging cost function; a v (v c This indicates that the battery charging cutoff voltage is V. c The aging ratio at that time; Let N be the charging rate matrix, representing the charging rate adopted by the i-th battery in the j-th time period; l (x c () indicates the battery charging rate is x c The number of loops at time C; f (v c ) indicates that the charging cutoff voltage is V. c The battery level that has already been charged; c e (j) represents the electricity price for time period j; N T N represents the number of planning time periods for the charging strategy, where the length of a single time period is T; b C represents the number of batteries within the station. b The price is for a single new battery; the constraints include battery margin constraints and charging rate constraints; the battery margin constraints are established according to the following formula: S c (t)=E(n b (t,x c ,v c ))-E(n c (t))≥0 Among them, S c (t) represents the battery margin in the charging / swapping station at time t; E(n) b (t,x c ,v c )) represents the expected number of fully charged batteries in the station at time t; The charging rate constraint is established based on the following formula: Where, x cmax This is a constant, representing the maximum charging rate of the battery in the charging and swapping station; S103. Use a genetic algorithm to obtain the optimal charging strategy based on the charging strategy optimization model; S104. Solve the objective function of the charging strategy optimization based on the optimal charging strategy to obtain the optimal battery charging cost and battery life loss cost.

2. The method according to claim 1, characterized in that, The optimal charging strategy is obtained using a genetic algorithm based on the charging strategy optimization model, including the following steps: S1031. Randomly generate M charging strategies that satisfy the constraints; S1032. Genetic algorithm is used to encode the initial value; S1033, The fitness value is the sum of battery charging cost and battery life loss; S1034. After performing selection, crossover, and mutation operations on the M randomly generated charging strategies that satisfy the constraints, calculate the fitness value. S1035. Perform iterative calculations, and when the preset number of iterations is reached, output the optimal charging strategy.

3. The method according to claim 2, characterized in that, The optimal charging strategy includes the charging rate of battery i at different stages j and the cutoff voltage of battery i.

4. A charging strategy optimization control device, characterized in that, It includes a charging demand model establishment module, a charging strategy optimization model establishment module, an optimal charging strategy acquisition module, and an optimization control module; among which, The charging demand model building module is used to build a charging demand model for the battery to be charged; the charging demand model is built according to the following formula: in, This indicates that the i-th battery to be charged is in t ′ The probability of coming to charge at any time, n c (t) represents the battery swapping demand value before time t of a day, E(n) c (t) represents n during the time interval t0 to t. c The expected value of (t); N represents the total number of batteries to be charged; The charging strategy optimization model establishment module is used to establish a charging strategy optimization model, which includes a charging strategy optimization objective function and constraints; the charging strategy optimization objective function is established according to the following formula: Among them, L b L represents the cost function of battery life loss; c Represents the battery charging cost function; a v (v c This indicates that the battery charging cutoff voltage is V. c The aging ratio at that time; Let N be the charging rate matrix, representing the charging rate adopted by the i-th battery in the j-th time period; l (x c () indicates the battery charging rate is x c The number of loops at time C; f (v c ) indicates that the charging cutoff voltage is V. c The battery level that has already been charged; c e (j) represents the electricity price for time period j; N T N represents the number of planning time periods for the charging strategy, where the length of a single time period is T; b C represents the number of batteries within the station. b The price is for a single new battery; the constraints include battery margin constraints and charging rate constraints; the battery margin constraints are established according to the following formula: S c (t)=E(n b (t,x c ,v c ))-E(n c (t))≥0 Among them, S c (t) represents the battery margin in the charging / swapping station at time t; E(n) b (t,x c ,v c )) represents the expected number of fully charged batteries in the station at time t; The charging rate constraint is established based on the following formula: Where, x cmax This is a constant, representing the maximum charging rate of the battery in the charging and swapping station; The optimal charging strategy acquisition module is used to obtain the optimal charging strategy based on the charging strategy optimization model using a genetic algorithm. The optimization control module is used to solve the objective function of the charging strategy optimization based on the optimal charging strategy, so as to obtain the optimal battery charging cost and battery life loss cost.

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