New energy vehicle charging and discharging strategy optimization method

Abstract

The invention discloses a new energy automobile charging and discharging strategy optimization method, which comprises the following steps: firstly, collecting state data remotely reported by a new energy automobile, and constructing a data set through preprocessing; polynomial fitting is carried out through the data set, the charging efficiency is calculated according to a fitting function, and the optimal charging range is determined according to the charging efficiency; then, calculating the discharge efficiency through segmented averaging, and finding out an interval with the longest driving mileage under the same SOC consumption; and finally, by integrating the charging and discharging efficiency and the use habits of the user, finding out an optimal charging and discharging interval and recommending to the user.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy vehicles, and more specifically, relates to a method for optimizing a charging and discharging strategy of a new energy vehicle. Background Art

[0002] The charging and discharging of new energy vehicle batteries is a key issue in the field of new energy vehicle technology, involving aspects such as battery performance, life and safety. At present, the emergence of DC fast charging technology has solved the anxiety of new energy vehicle range to a certain extent, but because DC fast charging is mostly built in shopping malls and dedicated charging stations, and the charging unit price is relatively high, for many car owners with relatively fixed car usage habits, they prefer to use slow charging piles to replenish energy during valley electricity prices. Due to the influence of various factors such as battery characteristics and vehicle usage scenarios, the charging and discharging scenarios of new energy vehicles are not from zero to full value. Choosing the appropriate charging and discharging range can extend the battery life, improve the reliability and safety of the battery, and is one of the important battery management strategies.

[0003] For users who use slow charging / home charging, charging time and cruising range are particularly important concerns. Under the same charging conditions, the battery's SOC percentage value will have a significant impact on the charging time. For example, when the battery is nearly full, the charging power will drop significantly. Therefore, analyzing the battery status and finding the fastest charging range in the same time can save charging time for some users. During driving, the battery discharge efficiency is different at different power levels. It is not only affected by the battery's own characteristics, but also by situations such as uphill and downhill driving and kinetic energy recovery that increase or decrease power consumption, which brings difficulties to collecting power consumption information. Since the use scenarios of the same vehicle are generally relatively fixed, generating a vehicle charging and discharging model from a macro perspective, analyzing the vehicle's charging and discharging scenarios, and generating personalized charging and discharging plans can improve the battery discharge efficiency to a certain extent and achieve longer mileage at the same SOC consumption.

[0004] Part 3 of GB / T32960.3-2016 Technical Specifications for Electric Vehicle Remote Service and Management System stipulates that new energy vehicles must report vehicle information to the enterprise platform no more than once every 30 seconds. By collecting and analyzing the reported information, we can find the most common charging and discharging scenarios that best suit user preferences, provide users with charging and driving suggestions, and improve user satisfaction with the vehicle without battery replacement or hardware upgrades. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for optimizing the charging and discharging strategy of new energy vehicles. By analyzing the data reported by the vehicle, the optimal charging and discharging strategy of the vehicle is obtained and recommended to the vehicle.

[0006] To achieve the above-mentioned purpose of the invention, the present invention provides a method for optimizing the charging and discharging strategy of a new energy vehicle, characterized in that it comprises the following steps:

[0007] (1) Data collection and preprocessing:

[0008] Obtain the vehicle data reported by a specified vehicle within a period of time from the enterprise platform, and count the number of charging times M, the SOC value at the beginning of each charging, the SOC value after the end of charging, and the time consumed for charging within the period as the charging data set; take the end of the last charging as the start of driving and the start of the next charging as the end of driving, and count the mileage within the period as the driving data set;

[0009] (2) Define the fitting function:

[0010] f(x,y)=ax 2 +bx+cxy+dy 2 +ey+f

[0011] Among them, f(x,y) is the charging time in seconds; x is the SOC value at the beginning of charging, y is the SOC value after the end of charging, a, b, c, d, e, f are the coefficients to be fitted;

[0012] Substitute the SOC values ​​at the beginning and end of a single charge in the charging data set into the fitting function, use the least squares method to fit the polynomial function, and obtain the values ​​of coefficients a, b, c, d, e, and f;

[0013] (3) Optimization of charging strategy;

[0014] (3.1) Calculate the average SOC increase α of a given vehicle:

[0015] Traverse the SOC value at the beginning of each charge and the SOC value at the end of each charge in the charging data set, and calculate the difference δ between the SOC at the beginning of each charge and the SOC at the end of each charge i , i=1,2,…,M;

[0016] The difference δ of M SOCs i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC increment α:

[0017]

[0018] (3.2) Set the value range of x and y of the specified vehicle after the average charging increases by α:

[0019]

[0020] (3.3) Traverse each set of x and y values, substitute them into the fitting function, and find min(f(x,y)) to obtain the x and y corresponding to the minimum charging time of the specified vehicle, which is the optimal charging range;

[0021] (4) Discharge strategy optimization;

[0022] (4.1) Calculate the discharge efficiency;

[0023] Take the mileage d of the i-th driving in the driving data set i , and then the mileage d is calculated at fixed intervals ΔSOC i Divide into k segments, let Indicates d i The mileage of the kth segment in ;

[0024] The discharge efficiency of the kth segment in the i-th mileage is calculated as:

[0025]

[0026] Traverse the driving data set and calculate the average discharge efficiency of the kth segment in the driving data set:

[0027]

[0028] (4.2) Calculate the average SOC consumption value β of the specified vehicle:

[0029] Calculate the difference between the SOC at the beginning and the SOC at the end of each trip i , the difference τ for M SOCs i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC consumption value β:

[0030]

[0031] (4.3) Calculate the expected mileage E(m,n) of the specified vehicle under the average SOC consumption value β;

[0032] Set the SOC value of the specified vehicle at the beginning of each trip to m, and the SOC value at the end of each trip to n, mn = β;

[0033] Calculate expected mileage:

[0034]

[0035] (4.4) Set the range of SOC values ​​m and n of the specified vehicle under the average SOC consumption value β:

[0036]

[0037] (4.5) Traverse each set of m and n values, substitute them into step (4.3), and find the maximum value of the mileage max(E(m,n)), and the corresponding m and n are the optimal discharge range;

[0038] (5) Recommend personalized charging and discharging strategies;

[0039] According to the optimal charging range (x, y) and optimal discharging range (m, n) of the specified vehicle, obtain the recommended charging and discharging range R:

[0040] r1=max(x,n),r2=min(y,m);

[0041]

[0042] Finally, the enterprise platform recommends the recommended charging and discharging range R to the designated vehicle.

[0043] The object of the invention of the present invention is achieved in this way:

[0044] The present invention discloses a method for optimizing a charging and discharging strategy of a new energy vehicle. The method comprises the following steps: firstly, collecting status data reported remotely by the new energy vehicle, and constructing a data set through preprocessing; performing polynomial fitting on the data set, and then calculating the charging efficiency according to the fitting function, and determining the optimal charging range according to the charging efficiency; then calculating the discharge efficiency by segmented averaging, and finding the interval with the longest mileage under the same SOC consumption; and finally finding the optimal charging and discharging interval based on the comprehensive charging and discharging efficiency and the user's usage habits, and recommending it to the user.

[0045] At the same time, the new energy vehicle charging and discharging strategy optimization method of the present invention also has the following beneficial effects:

[0046] (1) For new energy vehicle users who do not have fast charging conditions, the present invention makes full use of the remote data reporting information required by the national standard to calculate the vehicle charging and discharging efficiency and provide personalized suggestions for vehicle driving and charging.

[0047] (2) The present invention can obtain the optimal working range of the battery by macro-statisticing the vehicle's charging and discharging frequency and habits, and provide a reference for the timing of engine power generation in the extended-range hybrid vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a new energy vehicle charging and discharging strategy optimization method of the present invention;

[0049] Figure 2 It is the discharge efficiency diagram of the vehicle in each SOC segment. DETAILED DESCRIPTION

[0050] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0051] Example

[0052] In this embodiment, if Figure 1 As shown, the present invention provides a new energy vehicle charging and discharging strategy optimization method, comprising the following steps:

[0053] (1) Data collection and preprocessing:

[0054] New energy vehicles will report the whole vehicle data to the enterprise platform, so we can obtain the whole vehicle data reported by a specified vehicle within 6 months from the enterprise platform, and count the number of charging times M, the SOC value at the beginning of each charging, the SOC value after the end of charging, and the time consumed for charging in this period as the charging data set; the end of the last charging is regarded as the start of driving, and the start of the next charging is regarded as the end of driving, and the mileage in this period is counted as the driving data set;

[0055] (2) Define the fitting function:

[0056] f(x,y)=ax 2 +bx+cxy+dy 2 +ey+f

[0057] Among them, f(x,y) is the charging time in seconds; x is the SOC value at the beginning of charging, y is the SOC value after the end of charging, a, b, c, d, e, f are the coefficients to be fitted;

[0058] Substitute the SOC values ​​at the beginning and end of a single charge in the charging data set into the fitting function, use the least squares method to fit the polynomial function, and obtain the values ​​of coefficients a, b, c, d, e, and f;

[0059] In this embodiment, by substituting the data sampled from a vehicle within 6 months into the fitting function, the value of the af coefficient is [10, -608, -5, 7, -410, 19362], and the final fitting function is:

[0060] f(x,y)=10x 2 -608x-5xy+7y 2 -410y+19362

[0061] (3) Optimization of charging strategy;

[0062] (3.1) Calculate the average SOC increase α of a given vehicle:

[0063] Traverse the SOC value at the beginning of each charge and the SOC value at the end of each charge in the charging data set, and calculate the difference δ between the SOC at the beginning of each charge and the SOC at the end of each charge i , i=1,2,…,M;

[0064] Due to the randomness and contingency of charging behavior, in order to obtain the mean value in most cases in the data set, the trimmed mean is used to calculate the SOC increment; therefore, we calculate the difference δ of M SOCs. i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC increment α:

[0065]

[0066] (3.2) Set the value range of x and y of the specified vehicle after the average charging increases by α:

[0067]

[0068] (3.3) Traverse each set of x and y values, substitute them into the fitting function, and find min(f(x,y)) to obtain the x and y corresponding to the minimum charging time of the specified vehicle, which is the optimal charging range;

[0069] In this embodiment, the average SOC value of the vehicle charging increase is calculated to be α=71, and the values ​​of x and y that can be obtained are as follows:

[0070]

[0071] Substituting the above x and y values ​​into the fitting function, we can find min(f(x,y))=26093, and obtain that x and y when the charging time is the shortest are (13, 84), which is the optimal charging range for the vehicle.

[0072] The enterprise platform can push the optimal charging range to the vehicle system to provide reference suggestions for users. Within this charging range, the same charging habits can be used to replenish more energy in less time. The charging strategy can be further optimized based on big data, by obtaining data from multiple vehicles of the same model, as well as the actual performance parameters of the battery of the model, to improve the accuracy and versatility of the charging strategy.

[0073] (4) Discharge strategy optimization;

[0074] (4.1) Calculate the discharge efficiency;

[0075] Take the mileage d of the i-th driving in the driving data set i , and then the mileage d is calculated at fixed intervals ΔSOC iDivide into k segments, let Indicates d i The mileage of the kth segment in ;

[0076] In this embodiment, assuming that the fixed interval ΔSOC=1%, then the SOC value at the beginning of each driving segment minus the SOC value at the end of the driving segment is 1%, and the mileage of the vehicle under the condition of consuming 1% is the mileage of one segment;

[0077] The discharge efficiency of the kth segment in the i-th mileage is calculated as:

[0078]

[0079] Traverse the driving data set and calculate the average discharge efficiency of the kth segment in the driving data set:

[0080]

[0081] Figure 2 is the discharge efficiency diagram corresponding to each SOC segment; Figure 2 It can be seen that when the vehicle's SOC is in the range of 30-80, the calculated discharge efficiency is significantly better.

[0082] (4.2) Calculate the average SOC consumption value β of the specified vehicle:

[0083] Calculate the difference between the SOC at the beginning and the SOC at the end of each trip i , the difference τ for M SOCs i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC consumption value β:

[0084]

[0085] (4.3) Calculate the expected mileage E(m,n) of the specified vehicle under the average SOC consumption value β;

[0086] Set the SOC value of the specified vehicle at the beginning of each trip to m, and the SOC value at the end of each trip to n, mn = β;

[0087] Calculate expected mileage:

[0088]

[0089] (4.4) Set the range of SOC values ​​m and n of the specified vehicle under the average SOC consumption value β:

[0090]

[0091] (4.5) Traverse each set of m and n values, substitute them into step (4.3), and find the maximum value of the mileage max(E(m,n)), and the corresponding m and n are the optimal discharge range;

[0092] In this embodiment, the average SOC consumption value β=71, then mn=71, so the value ranges of m and n are as follows:

[0093]

[0094] Finally, the maximum value of the mileage that can be driven within the possible value range is obtained, max(E(m,n))=269.49, and the corresponding m=87, n=16 is the optimal discharge range;

[0095] Consuming the same SOC in the optimal discharge range can achieve the longest driving range, so the enterprise platform can push the optimal discharge range to the user's vehicle system to remind the user when to replenish energy and how much energy to replenish for the highest driving efficiency. The discharge strategy can also be further optimized based on big data, by obtaining data from multiple vehicles of the same model to reduce individual errors.

[0096] (5) Recommend personalized charging and discharging strategies;

[0097] According to the optimal charging range (x, y) and optimal discharging range (m, n) of the specified vehicle, obtain the recommended charging and discharging range R:

[0098] r1=max(x,n),r2=min(y,m);

[0099]

[0100] Finally, the management platform recommends the recommended charging and discharging range R to the designated vehicle;

[0101] In this embodiment, α=β=71, so the optimal range of charging is: starting charging SOC value x=13, ending charging SOC value y=84; the optimal range of discharging is: starting driving SOC value m=87, ending driving SOC value n=16: Take the intersection interval as the center, complete the starting and ending SOC, and get the recommended range R:

[0102] r1=max(x,n),r2=min(y,m);

[0103]

[0104] From the above calculations, we can know that the SOC difference of the vehicle's usual power consumption is about 71. In the usual vehicle usage scenario, the user charges to an SOC of 86, and then drives to an SOC of 15. Under the same SOC charging and consumption, the charging time is the shortest, taking 435 minutes, and the driving range is the longest, which can travel 269.5km. R is the optimal charging and discharging SOC range that meets the vehicle's daily charging and discharging consumption habits. The enterprise platform can push personalized healthy power usage ranges for the vehicle based on this range and the battery type. Reasonable use of the vehicle according to this range can avoid damage to the battery due to extreme charging and discharging, save charging time, and improve vehicle range, alleviating user range anxiety while maximizing battery life.

[0105] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A method for optimizing charging and discharging strategies for new energy vehicles, characterized in that: The following steps are involved: (1) Data collection and preprocessing: Obtain the vehicle data reported by a specified vehicle within a period of time from the enterprise platform, and count the number of charging times M, the SOC value at the beginning of each charging, the SOC value after the end of charging, and the time consumed for charging within the period as the charging data set; take the end of the last charging as the start of driving and the start of the next charging as the end of driving, and count the mileage within the period as the driving data set; (2) Define the fitting function: f(x,y)=ax 2 +bx+cxy+dy 2 +ey+f Among them, f(x,y) is the charging time in seconds; x is the SOC value at the beginning of charging, y is the SOC value after the end of charging, a, b, c, d, e, f are the coefficients to be fitted; Substitute the SOC values ​​at the beginning and end of a single charge in the charging data set into the fitting function, use the least squares method to fit the polynomial function, and obtain the values ​​of coefficients a, b, c, d, e, and f; (3) Obtain the optimal charging range for a specified vehicle by optimizing the charging strategy; (4) Obtaining the optimal discharge range of a specified vehicle by optimizing the discharge strategy; (5) Recommend personalized charging and discharging strategies; According to the optimal charging range (x, y) and optimal discharging range (m, n) of the specified vehicle, obtain the recommended charging and discharging range R: r1=max(x,n),r2=min(y,m); Finally, the enterprise platform recommends the recommended charging and discharging range R to the designated vehicle.

2. A new energy vehicle charging and discharging strategy optimization method according to claim 1, characterized in that: The optimal charging range acquisition method is: (2.1) Calculate the average amount of SOC increase α for a given vehicle: Traverse the SOC value at the beginning of each charge and the SOC value at the end of each charge in the charging data set, and calculate the difference δ between the SOC at the beginning of each charge and the SOC at the end of each charge i , i=1,2,…,M; The difference δ of M SOCs i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC increment α: (2.2) Set the value range of x and y of the specified vehicle after the average charging increases by α: (2.3) Traverse each set of x and y values, substitute them into the fitting function, and find min(f(x,y)). The x and y corresponding to the minimum charging time of the specified vehicle are obtained, which is the optimal charging range.

3. A new energy vehicle charging and discharging strategy optimization method according to claim 1, characterized in that: The optimal discharge range acquisition method is: (3.1) Calculate the discharge efficiency; Take the mileage d of the i-th driving in the driving data set i , and then the mileage d is calculated at fixed intervals ΔSOC i Divide into k segments, let Indicates d i The mileage of the kth segment in ; The discharge efficiency of the kth segment in the i-th mileage is calculated as: Traverse the driving data set and calculate the average discharge efficiency of the kth segment in the driving data set: (3.2) Calculate the average SOC consumption value β of the specified vehicle: Calculate the difference between the SOC at the beginning and the SOC at the end of each trip i , the difference τ for M SOCs i Sort by removing the largest 10% and the smallest 10%, and then find the average SOC consumption value β: (3.3), calculate the expected mileage E(m,n) of the specified vehicle under the average SOC consumption value β; Set the SOC value of the specified vehicle at the beginning of each trip to m, and the SOC value at the end of each trip to n, mn = β; Calculate expected mileage: (3.4) Set the range of SOC values ​​m and n of the specified vehicle under the average SOC consumption value β: (3.5) Traverse each set of m and n values, substitute them into step (3.3), and find the maximum value of the mileage max(E(m,n)), and the corresponding m and n are the optimal discharge range.

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