A new energy vehicle charging and discharging strategy optimization method

Abstract

The present invention discloses a method for optimizing the charging and discharging strategy of new energy vehicles. The method comprises the following steps: first, status data reported remotely by new energy vehicles is collected, and a data set is constructed through preprocessing; polynomial fitting is performed on the data set, and then the charging efficiency is calculated according to the fitting function, and the optimal charging range is determined according to the charging efficiency; then, the discharge efficiency is calculated by segmented averaging, and the interval with the longest mileage under the same SOC consumption is found; finally, the charging and discharging efficiency and user usage habits are comprehensively considered to find the optimal charging and discharging interval and recommend it 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 for 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, lifespan, and safety. The emergence of DC fast-charging technology has, to a certain extent, addressed the range concerns of new energy vehicles. However, since DC fast-charging is mostly installed in shopping malls and dedicated charging stations, and the charging unit price is relatively high, many car owners with more fixed car usage habits prefer to use slow-charging stations to recharge during off-peak 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 a one-time charge from zero to full. Choosing the appropriate charging and discharging range can extend the battery life, improve battery reliability and safety, 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 power range with the fastest charging speed under the same time can save charging time for some users. During driving, the battery's discharge efficiency is also 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 usage 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 a longer mileage at the same SOC consumption.

[0004] Part 3 of GB / T32960.3-2016 Technical Specifications for Electric Vehicle Remote Service and Management Systems stipulates that new energy vehicles must report vehicle information to the enterprise platform no more than once every 30 seconds. By collecting and statistically analyzing the reported information, it is possible to find the most common charging and discharging scenarios that best suit user preferences, provide users with charging and driving recommendations, 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 existing technology and provide a new energy vehicle charging and discharging strategy optimization method. By analyzing the data reported by the vehicle, the optimal charging and discharging strategy for the vehicle is obtained and recommended to the vehicle.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a method for optimizing charging and discharging strategies of new energy vehicles, characterized by comprising the following steps:

[0007] (1) Data collection and preprocessing:

[0008] Obtain vehicle data reported by a specific vehicle over a period of time from the enterprise platform. Count the number of charges (M), the SOC value at the start and end of each charge, and the time consumed for charging as the charging dataset. Count the end of the last charge as the start of a trip and the start of the next charge as the end of a trip. Count the mileage traveled during this period as the driving dataset.

[0009] (2) Define the fitting function:

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

[0011] Where 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 charging is completed, and a, b, c, d, e, and 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 perform polynomial function fitting, 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 calculate the average SOC increment α:

[0017]

[0018] (3.2) Set the range of x and y values ​​of a specified vehicle after the average charge 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)). The x and y corresponding to the minimum charging time of the specified vehicle are obtained, 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 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;

[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 a specified vehicle:

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

[0030]

[0031] (4.3) Calculate the expected mileage E(m,n) of a 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 for a given 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 mileage max(E(m,n)). The corresponding m and n are the optimal discharge range;

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

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

[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 like this:

[0044] The present invention provides a method for optimizing the charging and discharging strategy of new energy vehicles. The method first collects status data reported remotely by new energy vehicles and constructs a data set through preprocessing. Polynomial fitting is performed on the data set to calculate the charging efficiency according to the fitting function, and the optimal charging range is determined according to the charging efficiency. The method then calculates the discharge efficiency by taking the average of the segmented values ​​to find the interval with the longest mileage under the same state of charge consumption. Finally, the charging and discharging efficiency and user usage habits are comprehensively considered to find the optimal charging and discharging interval and recommend 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-statistically analyzing 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 a diagram of the vehicle's discharge efficiency in each SOC segment. DETAILED DESCRIPTION

[0050] The following describes the specific embodiments of the present invention 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 detailed descriptions of known functions and designs may dilute the main content of the present invention, such 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 report their entire vehicle data to the enterprise platform. Therefore, we can obtain the entire vehicle data reported by a specific vehicle within 6 months from the enterprise platform. We can count the number of charging times M, the SOC value at the start of each charging, the SOC value at the end of each charging, and the time consumed for charging as the charging data set. We also count the mileage traveled during this period as the driving data set, from the end of the last charging to the start of the next charging.

[0055] (2) Define the fitting function:

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

[0057] Where 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 charging is completed, and a, b, c, d, e, and 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 perform polynomial function fitting, 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 values ​​of the af coefficients are obtained as [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 randomness 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 calculate the average SOC increment α:

[0065]

[0066] (3.2) Set the range of x and y values ​​of a specified vehicle after the average charge 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)). The x and y corresponding to the minimum charging time of the specified vehicle are obtained, which is the optimal charging range.

[0069] In this embodiment, the average SOC value of the vehicle charging increase is calculated as 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. The x and y values ​​for the minimum charging time 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's onboard system, providing charging recommendations for users. Within this range, the same charging habits can be used to recharge more energy in less time. This charging strategy can be further optimized based on big data, by acquiring data from multiple vehicles of the same model and the actual performance parameters of the battery of that model, thereby improving 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 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;

[0076] In this embodiment, assuming that the fixed interval ΔSOC=1%, 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 a specified vehicle:

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

[0084]

[0085] (4.3) Calculate the expected mileage E(m,n) of a 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 for a given 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 mileage max(E(m,n)). 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 mileage value max(E(m,n))=269.49 is obtained under the possible value range, and the corresponding m=87 and n=16 are the optimal discharge range;

[0095] Consuming the same SOC within the optimal discharge range maximizes driving range. Therefore, the enterprise platform can push this optimal discharge range to the user's vehicle system, reminding them when and how much to replenish for maximum driving efficiency. This discharge strategy can also be further optimized based on big data, by collecting data from multiple vehicles of the same model to reduce individual errors.

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

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

[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 charging range is: starting charging SOC value x=13, ending charging SOC value y=84; the optimal discharging range is: starting driving SOC value m=87, ending driving SOC value n=16. Taking the intersection interval as the center, the starting and ending SOC values ​​are supplemented to obtain 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 car 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 reach 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. Rational use of the vehicle according to this range can avoid damage to the battery from extreme charging and discharging, save charging time, and improve vehicle range. It can reduce user range anxiety while maximizing battery life.

[0105] Although the above describes the illustrative specific embodiments of the present invention to facilitate understanding of the present invention by those skilled in the art, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concepts 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 vehicle data reported by a specified vehicle over a period of time from the enterprise platform. Count the number of charges (M), the SOC value at the start and end of each charge, and the time taken to charge during this period as the charging dataset. Count the end of the last charge as the start of a trip and the start of the next charge as the end of a trip. Count the mileage during this period as the driving dataset. (2) Define the fitting function: f(x,y)=ax 2 +bx+cxy+dy 2 +ey+f Where 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 charging is completed, and a, b, c, d, e, and f are all 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 perform polynomial function fitting, 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) Recommendation of personalized charging and discharging strategies; According to the optimal charging range (x, y) and optimal discharging range (m, n) of the specified vehicle, the recommended charging and discharging range R is obtained: r1=max(x,n),r2=min(y,m); Finally, the enterprise platform recommends the recommended charging and discharging range R to the designated vehicle; The optimal charging range is obtained by: (2.1) Calculate the average charge increase SOC of a given vehicle α: 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 calculate the average SOC increment α: (2.2) Set the range of x and y values ​​of a specified vehicle after the average charge increases by α: (2.3) Traverse each set of x and y values, substitute them into the fitting function, and calculate 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; The optimal discharge range is obtained by: (3.1) Calculate the discharge efficiency; Take the mileage d after the i-th charge 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; The discharge efficiency of the kth segment of the mileage after the i-th charge 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 τ between the SOC at the beginning and the SOC at the end of each trip i , the difference τ of M SOCs i Sort by removing the largest 10% and the smallest 10%, and then calculate the average SOC consumption value β: (3.3) Calculate the expected mileage E(m,n) of a 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 for a given 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 calculate the maximum value of the mileage max(E(m,n)), where the corresponding m and n are the optimal discharge range.

Images