Charging control method for electric vehicle equipped with energy storage pile

Abstract

The invention is suitable for the technical field of electric vehicle charging, and provides a charging control method for an electric vehicle equipped with an energy storage pile, which comprises the following steps: acquiring data such as energy storage pile remaining capacity, power grid real-time electricity price, power grid load rate, electric vehicle battery remaining capacity and the like in real time through a control module; judging to start an energy storage priority mode, a power grid priority mode or a hybrid power supply mode, and calculating power supply power in the corresponding mode; dynamic power adjustment, economic dispatch optimization and SOC prediction are carried out in the charging process, and charging is stopped and related data are recorded when the electric quantity of the battery reaches a full-charge threshold value. The method can balance the load of a power grid, reduces the charging cost of a user, improves the charging efficiency and the energy utilization efficiency, and has a remarkable practical value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric vehicle charging, and particularly relates to a charging control method for an electric vehicle equipped with an energy storage stack. BACKGROUND

[0002] With the continuous increase in the number of electric vehicles, the influence of their charging demand on the power grid is increasingly significant. In the traditional charging mode, electric vehicles directly take power from the power grid, while some existing charging control technologies can achieve certain orderly charging.

[0003] In the traditional charging mode, electric vehicles directly take power from the power grid, and during the peak power consumption period, a large number of electric vehicles charging simultaneously will cause a sharp increase in the power grid load, which may cause problems such as voltage fluctuation and power supply stability decline. Moreover, the power grid price differs in different periods, and the charging cost is higher during the peak period, which brings great economic pressure to users. Although some existing charging control technologies can achieve a certain degree of orderly charging, there is still a lack of effective solutions based on quantitative calculation in terms of fine and intelligent charging control using energy storage stacks to balance the power grid load and reduce user costs. SUMMARY

[0004] The purpose of the present application is to provide a charging control method for an electric vehicle equipped with an energy storage stack, which aims to solve the technical problems existing in the prior art identified in the background.

[0005] The present application is implemented as follows: a charging control method for an electric vehicle equipped with an energy storage stack, the charging system of which comprises an energy storage stack, a power grid, an electric vehicle charging interface, and a control module. The energy storage stack is used to store electric energy, the power grid is the main source of electric energy, the electric vehicle charging interface is used to connect the electric vehicle and the charging system, and the control module realizes intelligent control of the entire charging process based on a preset mathematical formula and algorithm.

[0006] The specific steps of the control method are as follows:

[0007] Real-time data acquisition: the control module acquires the following data in real time:

[0008] The remaining capacity of the energy storage stack, unit: %, represents the percentage of the remaining capacity of the energy storage stack at time t.

[0009] The real-time price of the power grid, unit: yuan / kWh, reflects the power supply price of the power grid at time t.

[0010] The load rate of the power grid, defined as the ratio of the current load to the maximum load, is used to measure the load status of the power grid.

[0011] The remaining capacity of the electric vehicle battery, unit: %, represents the percentage of the remaining capacity of the electric vehicle battery at time t.

[0012] Electric vehicle desired full time, unit: hour, the desired time to complete charging set by the user or estimated by the vehicle system.

[0013] Electric vehicle maximum charging power, unit: kW, represents the maximum charging power that the vehicle can withstand.

[0014] Charging mode judgment, including:

[0015] Energy storage priority mode, grid priority mode, hybrid power supply mode.

[0016] The power supply power of the energy storage priority mode, the grid priority mode, and the hybrid power supply mode is calculated respectively.

[0017] Charging process monitoring and adjustment:

[0018] Dynamic power adjustment: real-time adjustment of charging power according to changes in grid load.

[0019] Economic dispatch optimization: calculate the optimal charging strategy based on the predicted price curve.

[0020] SOC prediction model: use Kalman filter algorithm to predict battery SOC.

[0021] Charging end judgment and processing: when the electric vehicle battery reaches the set full charge threshold, the control module controls the charging system to stop charging, and records the relevant data of this charging, including charging time, charging capacity, proportion of using energy storage and grid electricity, charging cost, etc. for user query and subsequent data analysis. The charging cost can be calculated.

[0022] The beneficial effects of the present application are:

[0023] Quantitative load adjustment capability: quantitative adjustment of the peak and valley load difference of the power grid is achieved, which can reduce the peak load demand, effectively alleviate the power supply pressure during the peak period of the power grid, and improve the stability of the power grid operation.

[0024] Optimal economic dispatch: based on the model established by the economic dispatch optimization formula, the charging cost can be optimized according to the fluctuation of the grid price and the state of the energy storage stack, which can reduce the charging cost. At the same time, reasonable charging and discharging strategy helps to prolong the service life of the energy storage stack and reduce the replacement cost of equipment.

[0025] Adaptive charging control: the control mechanism composed of dynamic power adjustment and SOC prediction formulas can adjust the charging power in real time according to the grid load and battery state, improve the charging efficiency, and reduce the battery loss caused by overcharging, overdischarging, etc., prolong the service life of the battery. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0027] A charging control method for an electric vehicle equipped with an energy storage stack, the method comprising:

[0028] Real-time power data is collected, including:

[0029] Residual energy of the energy storage stack , unit: %, representing the percentage of residual energy of the energy storage stack at time t;

[0030] Real-time electricity price of the power grid , unit: yuan / kWh, reflecting the power supply price of the power grid at time t;

[0031] Load rate of the power grid , defined as the ratio of the current load to the maximum load, i.e. , used to measure the load state of the power grid;

[0032] Residual energy of the electric vehicle battery , unit: %, representing the percentage of residual energy of the electric vehicle battery at time t;

[0033] Desired full charging time of the electric vehicle , unit: hours, set by the user or estimated by the vehicle system;

[0034] Maximum charging power of the electric vehicle , unit: kW, the maximum charging power that the vehicle can withstand;

[0035] According to the collected power data, a charging mode is selected, and the power supply power under the corresponding charging mode is calculated:

[0036] Energy storage priority mode: enabled when the following conditions are met:

[0037]

[0038] wherein, is the first energy threshold, which is the lowest energy percentage that the energy storage stack can supply preferentially; is the peak electricity price threshold, used to determine whether the power grid is in the peak electricity price period; is the load threshold, used to determine whether the power grid is in a high load state;

[0039] The energy storage stack power supply power is calculated:

[0040]

[0041] wherein, Pmax is the maximum discharging power of the energy storage stack, unit: kW; Qreq is the required charging amount of the electric vehicle, wherein Qbat is the battery capacity of the electric vehicle, Qtarget is the target charging amount percentage; η is the discharging efficiency of the energy storage stack, value range: 0 to 1;

[0042] Grid priority mode: enabled when any of the following conditions is met:

[0043]

[0044] wherein, Qth2 is the second electric quantity threshold, lower than the first electric quantity threshold, is the electric quantity standard for the energy storage stack to be charged or the grid to be powered preferentially; Qth3 is the low valley electricity price threshold, used to determine whether the grid is in a low valley electricity price period; Qth4 is the low valley load threshold, used to determine whether the grid is in a low load state; this formula indicates that the energy storage stack output power takes the minimum value among the maximum discharging power of the energy storage stack, the power required to meet the desired charging time, and the maximum discharging power considering the discharging efficiency, to ensure safe and efficient discharging.

[0045] Calculate the grid power supply power:

[0046]

[0047] wherein, Pmax_grid is the maximum power supply power of the grid, unit: kW. This formula indicates that the grid power supply power takes the minimum value among the maximum power supply power of the grid, the power required to meet the desired charging time, and the maximum charging power of the electric vehicle, to ensure the safety of the charging process and not to exceed the bearing capacity of the grid and the vehicle.

[0048] Hybrid power supply mode: when the energy storage electric quantity is in the middle interval and the grid state is normal, the power is allocated according to the charging urgency:

[0049]

[0050] wherein, η is the energy storage power supply proportion coefficient, value range: 0 to 1; Tref is the reference charging time, which is a pre-set standard charging time; and Qmax and Qmin are the upper and lower limit electric quantity percentages allowed by the battery, respectively. This formula calculates the appropriate proportion of energy storage power supply by considering the desired charging time and the remaining battery electric quantity;

[0051] Calculate the hybrid power supply power:

[0052]

[0053] The power supply of the energy storage stack and the power grid in the hybrid power supply mode is calculated respectively.

[0054] Dynamic power adjustment is performed to control the charging power in real time, while economic dispatch optimization is performed to minimize the total cost, and Kalman filtering algorithm is used to predict the battery SOC to control the stop of charging:

[0055] The charging power is adjusted in real time according to the change of the grid load:

[0056]

[0057] In the formula, is the original charging power, i.e. the charging power before adjustment; is the adjustment coefficient, which is set according to the actual situation, and is used to control the amplitude of power adjustment; is the normal load rate, which is the standard load rate when the power grid is normally operated; is the maximum load rate. This formula can adjust the charging power in proportion according to the degree of deviation of the grid load from the normal level, to avoid overload of the grid.

[0058] The optimal charging strategy is calculated based on the predicted price curve:

[0059]

[0060] Constraints:

[0061]

[0062] In the formula, is the grid price at time ; is the energy storage discharge cost; is the energy storage stack capacity; is the time interval; and are the minimum and maximum percentage of energy allowed by the energy storage stack respectively. Through this optimization model, the total charging cost is minimized under the conditions of meeting the charging demand and the energy storage stack energy limit.

[0063] Kalman filtering algorithm is used to predict the battery SOC:

[0064]

[0065] In the formula, is the charging current; is the rated capacity of the battery; and are the process and measurement noise; OCV and SOC; R is the internal resistance of the battery; through the algorithm, the SOC of the battery is accurately predicted, and a reliable basis is provided for charge control.

[0066] Charging end judgment and processing: when the electric vehicle battery power reaches the set full charge threshold , the control system stops charging, and records the relevant data of this charging, including charging time, charging power, the proportion of power used by energy storage stacks and power grid, charging cost, etc., so as to query and analyze the subsequent data.

[0067] Calculate the charging cost :

[0068] .

[0069] The technical features of the above-mentioned embodiments can be combined arbitrarily, in order to make the description simple, not all possible combinations of the technical features in the above-mentioned embodiments are described, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the present application.

[0070] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

[0071] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application, any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A charging control method for an electric vehicle equipped with an energy storage stack, characterized in that: The method comprises: Real-time collection of power data, including: remaining power of energy storage stack , real-time electricity prices of power grids , grid load rate , Remaining power of electric vehicle battery , electric vehicles are expected to be full of time , Maximum charging power of electric vehicles ; Select the charging mode based on the collected power data and calculate the power supply under the corresponding charging mode; Dynamic power adjustment is performed to control charging power in real time, while economic scheduling optimization is performed to minimize total cost. The Kalman filter algorithm is used to predict battery SOC and control charging to stop.

2. The method according to claim 1, characterized in that The step of selecting a charging mode according to the collected power data includes: Energy storage priority mode: Enabled when the following conditions are met: in, The first power threshold is the minimum power percentage that the energy storage stack can supply first. The peak electricity price threshold is used to determine whether the power grid is in the peak electricity price period; is the load threshold, used to determine whether the power grid is in a high load state; Calculate the power supply of the energy storage stack: Where, is the maximum discharge power of the energy storage stack, unit: kW; The amount of charging required for electric vehicles, ,in is the battery capacity of electric vehicles, Charge the target power percentage; is the energy storage stack discharge efficiency, ranging from 0 to 1; Grid priority mode: Enabled when any of the following conditions are met: in, The second power threshold is lower than the first power threshold and is the power standard at which the energy storage stack needs to be charged or the grid prioritizes power supply; The low-valley electricity price threshold is used to determine whether the power grid is in the low-valley electricity price period; is the valley load threshold, used to determine whether the power grid is in a low load state; Calculate the grid power supply: Where, The maximum power supply to the grid; Hybrid power supply mode: When the energy storage capacity is in the middle range and the grid status is normal, power is allocated according to the charging urgency: Where, The energy storage power supply ratio coefficient ranges from 0 to 1; The benchmark charging time is the pre-set standard charging time; and They are the upper and lower limit percentages of battery power allowed respectively; Calculate hybrid power: The power supply of the energy storage stack and the power grid in the hybrid power supply mode are calculated respectively.

3. The method according to claim 2, characterized in that The dynamic power adjustment is performed to control the charging power in real time: Adjust charging power in real time according to grid load changes: Where, is the original charging power, i.e. the charging power before adjustment; It is the adjustment coefficient, which is set according to the actual situation and is used to control the amplitude of power adjustment; Normal load rate is the load rate standard when the power grid is operating normally; is the maximum load rate.

4. The method according to claim 3, characterized in that The economic dispatch optimization is performed to minimize the total cost: Calculate the optimal charging strategy based on the predicted electricity price curve: Constraints: Where, For the moment Grid electricity prices; is the energy storage discharge cost; is the energy storage stack capacity; is the time interval; and They are the minimum and maximum charge percentages allowed for the energy storage stack.

5. The method according to claim 4, characterized in that The Kalman filter algorithm is used to predict the battery SOC and control the charging to stop: Use Kalman filter algorithm to predict battery SOC: Where, is the charging current; is the rated capacity of the battery; and is process and measurement noise; is the functional relationship between open circuit voltage and SOC; is the internal resistance of the battery; Charging end judgment and processing: When the electric vehicle battery reaches the set full charge threshold When the charging system stops charging, the charging data will be recorded. Calculating charging costs : 。

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