Electric vehicle charging strategy method based on time and price weights

Abstract

The present invention relates to the field of electric vehicle charging strategies, and in particular to an electric vehicle charging strategy method based on time and price weights. According to the present invention, a charging mode when the charging power of a region cannot satisfy the requirement of full-power charging of all vehicles in the region is determined; information such as the real-time charging power and charging duration of the vehicles is obtained during charging; information entropy is calculated; an actual charging power distribution weight of each vehicle is calculated in real time according to the negotiated charging mode; a real-time charging distribution power of each vehicle is calculated on the basis of the total electrical load condition, the power limitation condition and the like of the current region and according to the weight priority; and the real-time charging power is iteratively adjusted on the basis of the actual charging power and the distribution power. The method can implement ordered charging of electric vehicles when the upper limit of a power limit value is present in a region, thereby improving the power utilization efficiency in the region, and relieving the pressure of a regional power grid.

Description

A Time-Price Weighted Electric Vehicle Charging Strategy Method Technical Field

[0001] This invention belongs to the field of electric vehicle charging strategy technology, specifically relating to an electric vehicle charging strategy method based on time price weight. Background Technology

[0002] The new energy vehicle industry has entered a period of comprehensive upgrading, with the number of new energy vehicles accounting for 5.5% of the total vehicle ownership. Electric vehicles exhibit strong randomness in their charging behavior. As the scale of electric vehicles grows, even small changes in charging randomness can cause drastic changes in local load, seriously affecting the safe and stable operation of the local power distribution network.

[0003] While local governments can guide and encourage users to proactively adjust their vehicle usage and charging habits based on the principle of time-of-use pricing, thus ensuring orderly charging of electric vehicles to some extent, peak, flat, and valley electricity consumption periods can only passively adjust users' charging habits through different pricing methods. Moreover, with the continuous increase in the number of electric vehicles, the uniformity of user behavior will inevitably lead to a large number of electric vehicles connecting for charging during peak load periods, which may result in a series of problems such as distribution network overload, voltage drop, increased distribution network losses, and distribution transformer overload.

[0004] Currently, there is a lack of interaction between electric vehicles and charging piles / stations. Charging power cannot be adjusted, and charging time relies solely on the electric vehicle's reservation function. Charging facilities cannot obtain vehicle charging needs, nor can they formulate corresponding strategies to proactively develop charging strategies and further ensure the orderly charging of electric vehicles.

[0005] Summary of the Invention

[0006] The purpose of this invention is to provide an interactive charging and discharging strategy for electric vehicles based on time and price weights. When an electric vehicle connects to the network, the system acquires the vehicle's battery level parameters and interacts with the user to obtain their preferences, such as "time priority" and "price priority," calculating the entropy value of these preferences and their corresponding weights. Based on the current regional power consumption limit and the information weight ranking, the system iteratively calculates the vehicle's charging power in real time, ensuring orderly charging of electric vehicles under limited regional power conditions and improving utilization.

[0007] The specific technical solution adopted by this invention is as follows:

[0008] Step 1: The user sets the vehicle charging time priority value mt and the price priority value mp, and normalizes them to the 0-1 range according to the maximum and minimum values ​​settable by formula (1). and

[0009] Step 2: Obtain vehicle information at time t, such as the power to be charged, real-time charging power, start charging time, charging duration, initial SOC, etc., and calculate the vehicle information entropy weight value z at time t. i (t).

[0010] First, the vehicle information is standardized.

[0011] X i,j (t) represents the j-th type of real-time information item for the i-th vehicle, where k is the number of information indicators acquired, and j ≤ k. These are standardized values.

[0012] Then, calculate the vehicle information entropy weight value z at time t according to equation (3). i (t).

[0013] In the formula,

[0014] Step 3: Obtain the total power P0(t) of the region at time t, and calculate the charging power of all vehicles and P according to equation (4). f (t), if P f If (t)≤P0(t), then there is no need to allocate charging power according to the weight, charge as needed, and re-enter step two to calculate the charging power allocation for the next time period; otherwise, proceed to step four.

[0015] In the formula p f The charging power of the vehicle.

[0016] Step four: Based on the charging vehicle's time priority value mt, price priority value mp, and information weight value z... i The power allocation weight value e at time t+1 is calculated according to equation (5). i And calculate the vehicle's distributable power p according to equation (6). h .

[0017] in zt max and zt min The highest and lowest time priority weight ratios are given by zp. max and zp min The weighting ratios are the highest and lowest price priority.

[0018] Step 5, according to p h and Iterative regression is used to adjust the actual charging power p of the vehicle. Vehicles are grouped; if p... fi ≤p i If the number is 'a', then it is recorded as group a, and the vehicle number 'a' is recorded. x =i, 1≤x≤u, u is p fi ≤p i The number of vehicles is used to adjust the actual charging power of the vehicle to p. fi If p fi >p i If the number is 'b', then it is recorded as group b, and the vehicle number 'b' is recorded. x =i, 1≤x≤v, v is p fi >p i The number of vehicles is equal to the total number of vehicles currently charging, i.e., u + v = n.

[0019] The real-time charging power of vehicle group B is obtained after time Δt. And calculate the power difference. Until the difference is less than p after time t1 thre If the vehicle's charging power is considered stable and cannot be increased further, then the vehicle's reassignable residual power can be allocated to vehicles in group a, and the total reassignable residual power P of group b can be calculated according to formula (7). rem p thre This is the threshold for dynamic changes in charging power.

[0020] Group A vehicles adjust real-time charging power according to formula (8).

[0021] Then, proceed to step two to calculate the charging power allocation for the next time period.

[0022] Furthermore, as described in S1 and The value is related to the charging method when the available charging power in the area is insufficient for all vehicles to charge at full power. A value of 1 indicates the highest time priority, meaning the charging demand is the most urgent; a value of 0 indicates the least urgent, meaning the charging power allocation can be fully utilized. A value of 1 indicates the highest price priority, meaning no price adjustments will be accepted; a value of 0 indicates the lowest price priority, meaning any price adjustment will be accepted. Value and The value can be associated with different price coefficient values ​​to ultimately determine the charging cost.

[0023] Furthermore, the vehicle time-t information mentioned in S2 includes, but is not limited to, the power to be charged, the real-time charging power, the start time of charging, the charging duration, and the initial SOC index. Additional index items can be added as needed.

[0024] Furthermore, the time and price weighting function f described in S4 can be set according to requirements, such as:

[0025] f(x,y)=ft(x)×fp(y)

[0026] ft(x)=(zt max -zt min )x+zt min

[0027] fp(y)=(zp min -zp max )y+zp max

[0028] In the formula zt max and zt min The highest and lowest time priority weight ratios are given by zp. max and zp min The weighting ratios are the highest and lowest price priority.

[0029] Furthermore, Δt, t1, and p mentioned in S5 thre All can be set according to the application, and t1 must be less than the time interval between time t+1 and time t as described in S4.

[0030] The technical effect achieved by this invention is that by introducing users' "time" and "price" preference settings, the preference weights of time and price are associated with the allocable charging power. When the total charging power of the area is limited, but the total charging power of all vehicles is greater than the upper limit, the information entropy weight under fair weight is first calculated based on vehicle information, and then multiplied by the time and price weight function to obtain a new weight. The charging power of all vehicles is then allocated according to the new weight, ensuring on-demand allocation and the maximum orderly charging under the condition of limited resources. Attached Figure Description

[0031] Figure 1 is a schematic diagram of the electric vehicle charging strategy method based on time price weight proposed in this invention; Detailed Implementation

[0032] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0033] The present invention will be further described below with reference to the accompanying drawings:

[0034] As shown in Figure 1, a time-price weighted electric vehicle charging strategy method is as follows:

[0035] Step 1: The user sets the vehicle charging time priority value mt and the price priority value mp, and normalizes them to the 0-1 range according to the maximum and minimum values ​​settable by formula (1). and

[0036] In actual use, MT and MP can be set to 6 levels, namely 0, 0.2, 0.4, 0.6, 0.8 and 1 respectively.

[0037] Step 2: Obtain vehicle information at time t, including five indicators: power to be charged, real-time charging power, start charging time, charging duration, and initial SOC. Calculate the vehicle information entropy weight value z at time t. i (t).

[0038] First, the vehicle information is standardized.

[0039] X i,j (t) represents the j-th type of real-time information item for the i-th vehicle, where i≤n, j≤5, and n is the total number of vehicles. These are standardized values.

[0040] The information entropy weight value z of the vehicle at time t is calculated according to equation (3). i (t).

[0041] In the formula,

[0042] Step 3: Obtain the total power P0(t) of the region at time t, and calculate the charging power of all vehicles and P according to equation (4). f (t), if P f If (t)≤P0(t), then there is no need to allocate charging power according to the weight, charge as needed, and re-enter step two to calculate the charging power allocation for the next time period; otherwise, proceed to step four.

[0043] In the formula p f The charging power of the vehicle.

[0044] Step four: Based on the charging vehicle's time priority value mt, price priority value mp, and information weight value z... i The power allocation weight value e at time t+1 is calculated according to equation (5). i And calculate the vehicle's distributable power p according to equation (6). h In practice, the time interval from t to t+1 can be set to 5 minutes.

[0045] in zt max and zt min The highest and lowest time priority weight ratios are given by zp. max and zp min This sets the weighting ratio for the highest and lowest price priority. (zt can be set.) max =2.0, zt min =0.1, zp max =2.5, zp min =0.5, then

[0046] Step 5, according to p h and Iterative regression is used to adjust the actual charging power p of the vehicle. Vehicles are grouped; if p... fi ≤p i If the number is 'a', then it is recorded as group a, and the vehicle number 'a' is recorded. x =i, 1≤x≤u, u is p fi ≤p i The number of vehicles is used to adjust the actual charging power of the vehicle to p. fi If p fi >p i If the number is 'b', then it is recorded as group b, and the vehicle number 'b' is recorded. x =i, 1≤x≤v, v is p fi >p i The number of vehicles is equal to the total number of vehicles currently charging, i.e., u + v = n.

[0047] The real-time charging power of vehicle group B is obtained after time Δt. And calculate the power difference. Until the difference is less than p after time t1 thre If the vehicle's charging power is considered stable and cannot be increased further, then the vehicle's reassignable residual power can be allocated to vehicles in group a, and the total reassignable residual power P of group b can be calculated according to formula (7). rem p thre This is the threshold for dynamic changes in charging power. During implementation, Δt is set to 5 seconds and t1 to 25 seconds, meaning the power difference is calculated 5 times.

[0048] Group A vehicles adjust real-time charging power according to formula (8).

[0049] Then, proceed to step two to calculate the charging power allocation for the next time period.

[0050] S1 as described and The value is related to the charging method when the available charging power in the area is insufficient for all vehicles to charge at full power. A value of 1 indicates the highest time priority, meaning the charging demand is the most urgent; a value of 0 indicates the least urgent, meaning the charging power allocation can be fully utilized. A value of 1 indicates the highest price priority, meaning no price adjustments will be accepted; a value of 0 indicates the lowest price priority, meaning any price adjustment will be accepted. Value and The value can be associated with different price coefficient values ​​to ultimately determine the charging cost.

[0051] The vehicle time-t information mentioned in S2 includes, but is not limited to, power to be charged, real-time charging power, start charging time, charging duration, and initial SOC index. Additional index items can be added as needed.

[0052] The time and price weighting function f described in S4 can be set according to requirements, such as:

[0053] f(x,y)=ft(x)×fp(y)

[0054] ft(x)=(zt max -zt min )x+zt min

[0055] fp(y)=(zp min -zp max )y+zp max

[0056] In the formula zt max and zt min The highest and lowest time priority weight ratios are given by zp. max and zp min The weighting ratios are the highest and lowest price priority.

[0057] Δt, t1, and p as described in S5 thre All can be set according to the application, and t1 must be less than the time interval between time t+1 and time t as described in S4.

[0058] The innovation of this invention lies in introducing the "time" and "price" preferences of charging vehicles, which, together with the vehicle information entropy weight, form a new vehicle charging power allocation weight. When the regional charging power is limited but the total vehicle power exceeds the limit, the charging power of each vehicle can be allocated according to the weight. When a vehicle has a higher time priority value and a lower price priority value, it receives a larger charging power allocation weight and is thus allocated more charging power; conversely, it receives a lower charging power allocation weight during peak charging times and is thus allocated less charging power. This method can achieve orderly and fair charging of vehicles under conditions of limited regional power resources.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A time price weighted electric vehicle charging strategy method, characterized in that, The method comprises the following steps: S1, the user sets a vehicle charging time priority value mtand a price priority value mp, and normalizes them to the 0 to 1 range according to the maximum and minimum values by Equation (1) and S2, acquire vehicle t time information, to be charged power, real-time charging power, start charging time, charging duration, initial SOC index, calculate vehicle t time information entropy weight value z i (t), The vehicle information is standardized according to formula (2), X i,j (t) denotes the jth real-time information item of the ith vehicle, k is the number of information index items obtained, j≤k, The standardization value is The vehicle t-time information entropy weight value z is calculated according to formula (3) i (t), In the formulae, S3, obtain the total power P0(t) of the region at the time of charging, and calculate the charging power of all vehicles and P f (t) according to formula (4) f (t)≤P0(t), no charging power needs to be distributed according to the weight, charging as needed, and re-entering S2 to calculate the charging power distribution of the next time period; otherwise, entering S4, wherein p f is the power to be charged for the vehicle, S4, based on the charging vehicle time priority value mt, price priority value mp, and information weight value z i The power allocation weight value e at time t+1 is calculated according to equation (5). i And calculate the vehicle's distributable power p according to equation (6). h , wherein The time and price weight function S5, according to p h and Iterative regression is used to adjust the actual charging power p of the vehicles, and the vehicles are grouped. If p fi ≤p i If the number is 'a', then it is recorded as group a, and the vehicle number 'a' is recorded. x =i, 1≤x≤u, u is p fi ≤p i The number of vehicles is used to adjust the actual charging power of the vehicle to p. fi If p fi >p i If the number is 'b', then it is recorded as group b, and the vehicle number 'b' is recorded. x =i, 1≤x≤v, v is p fi >p i The number of vehicles, the total number of vehicles in both groups equals the current total number of vehicles charging, i.e., u + v = n. acquiring the real-time charging power of the vehicles in group b once every Δt time and calculating the power difference Difference is less than p after t1 time thre , it is considered that the vehicle charging power is stable and cannot be further improved, at which time the residual power that can be redistributed by the vehicle can be distributed to the a group of vehicles, and the total residual power P that can be redistributed by the b group of vehicles is calculated according to formula (7) rem . p thre is a dynamic change threshold of charging power, The group a vehicles adjust the real-time charging power according to formula (8) Then re-enter S2 to calculate the charging power allocation of the next time period.

2. The time price weighted electric vehicle charging strategy method according to claim 1, characterized in that, S1 in the above description and the value is related to the charging mode when the available charging power of the area does not meet the full power charging of all vehicles, 1 indicates that the time priority is the highest, i.e. the charging demand is the most urgent; 0 means the least urgent, i.e. can fully receive the charging power allocation schedule, 1 means the highest price priority, i.e. does not accept price adjustment; 0 means the lowest price priority, i.e. can receive any price adjustment, The value and The value can be associated with different price coefficient values and ultimately determine the charging cost.

3. The time price weighted electric vehicle charging strategy method according to claim 1, wherein, The vehicle t time information in S2 includes but is not limited to the to-be-charged power, the real-time charging power, the start charging time, the charging duration, the initial SOC index, and index items can be added according to actual needs.

4. The time price weighted electric vehicle charging strategy method according to claim 1, wherein, The time and price weight function f in S4 can be set according to demand, such as: f(x, y) = ft(x) x fp(y) ft(x) = (zt max - zt min )x + zt min fp(y) = (zp min - zp max )y + zp max where zt max and zt min are the highest and lowest weight proportions of time priority, zp max and zp min are the highest and lowest weight proportions of price.

5. The time price weighted electric vehicle charging strategy method according to claim 1, wherein, Δt, t1 and p in S5 thre All can be set according to application, and satisfy t1 is less than the time interval between t+1 moment and t moment in S4.

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