Bus fast charging station energy management method based on data-driven battery efficiency model

Abstract

The application provides a bus fast charging station energy management method based on a data-driven battery efficiency model, comprising: determining the power demand that meets the operation of a bus line according to the operation time of a charging station, the equipment power limit, the quantity limit, and the bus departure plan; obtaining the battery heat loss through experimental testing, and establishing the relationship between the charging energy conversion efficiency and the actual charging power of the vehicle-mounted energy storage system; taking the minimum actual power consumption of the charging station in a day or the minimum actual electricity cost of the charging station in a day as an objective function, wherein the battery heat loss is considered, and an optimization model is established with the charging station operation time, the charging pile quantity, the maximum power of the charging pile, the power distribution capacity, the electric bus travel power demand, and the continuity of the electric bus charging time as constraint conditions; using piecewise linearization to convert the nonlinear optimization objective function model into a mixed integer convex programming problem, and solving the mixed integer convex programming problem to obtain the optimal charging plan, including the optimal charging time and the corresponding charging power of each vehicle. The method improves the charging energy conversion efficiency, reduces the battery heat loss, and prolongs the service life of the battery, reduces the charging cost of the bus while improving the charging energy conversion efficiency, and improves the economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of public transportation technology, in particular to a bus fast charging station energy management method based on a data-driven battery efficiency model. BACKGROUND

[0002] The operation and charging of electric buses and electric vehicles are quite different. For the same bus operation line, the operation route and operation time are relatively fixed. The battery capacity and charging power of electric buses are much larger than those of electric private cars. One of the mainstream charging modes of electric buses at present is the fast charging mode of returning to the station with piles, that is, the fast charging mode. Large-scale disordered charging will have an adverse effect on the power grid, intensify the peak of the power grid, and also cause the problem of over-planning of charging piles, increasing the construction cost and electricity cost of charging piles.

[0003] The current research on orderly charging of electric vehicles is mainly based on time-of-use electricity prices, aiming to reduce user charging costs or reduce load peak-valley differences to guide electric vehicles to charge orderly, but does not consider the relationship between charging power and battery heat loss, resulting in low actual charging efficiency of the battery when using high-power charging, high actual electricity consumption, causing waste of electric energy and increase of charging cost. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application aims to provide a bus fast charging station energy management method based on a data-driven battery efficiency model.

[0005] To solve the above technical problems, the technical solution provided by the present application is:

[0006] The bus fast charging station energy management method based on a data-driven battery efficiency model comprises the following steps:

[0007] S1. Determine the power demand that meets the operation of the bus line according to the operation time, equipment power limit, quantity limit of the charging station, and bus departure plan;

[0008] S2. Obtain the battery heat loss through experimental testing, and establish the relationship between the charging energy conversion efficiency and the actual charging power of the vehicle-mounted energy storage system;

[0009] The relationship between the charging energy conversion efficiency considering the battery heat loss and the actual charging power of the vehicle-mounted energy storage system in S2 is represented as:

[0010]

[0011] In the formula, t represents any charging time, T represents the number of unit time divided in a day, P t gP represents the actual power consumed by the distribution network. t ch The actual charging power supplied to the on-board energy storage system; η b This represents the energy conversion efficiency of the battery during the charging process;

[0012] S3. The objective function is to minimize the actual electricity consumption of the charging station in a day or minimize the actual electricity cost of the charging station in a day. The optimization model is established with the constraints of the charging station operating time, the number of charging piles, the maximum power of the charging piles, the power distribution capacity, the electricity demand of electric buses, and the continuity of electric bus charging time.

[0013] S4. Piecewise linearization is used to transform the nonlinear optimization objective function model in S3 programming into a mixed integer convex programming problem. Solving the mixed integer convex programming problem yields the optimal charging plan, including the optimal charging time and corresponding charging power for each vehicle.

[0014] The formula for piecewise linearization in S4 is:

[0015]

[0016]

[0017]

[0018]

[0019] In the formula, The actual power that vehicle j charges into the on-board energy storage system at time t; for Corresponding battery efficiency; As an auxiliary constraint parameter, the subscript x corresponds to the x-th segment after linearization. This indicates that the charging power is located in the x-th segment of the piecewise linearized line segment, and the value is... and The power P of segment x is respectively the power of segment interval x. li，x The upper and lower limits of K; η，x and B η，x The linearization coefficient reflects the charging power. Impact W x ={1, 2, 3}.

[0020] Preferred,

[0021] In the charging demand model for electric buses in S1, all buses have an onboard energy storage system whose state of charge before departure is sufficient to complete the entire route:

[0022]

[0023]

[0024] where j is the vehicle number; is the number of buses serving the route of vehicle j; is the first departure time, the last departure time and the departure interval of the route of vehicle j, respectively; j is the length of the route of vehicle j; n j is the number of times of repeating operation of each bus in the route of vehicle j; is the start time of the kth cycle of vehicle j; V j is the energy consumption per kilometer of the route of vehicle j; C p，j is the maximum energy of the vehicle-mounted energy storage system of vehicle j; soc low is the lower limit of SOC for ensuring the safe operation of the vehicle considering the additional loss caused by traffic conditions; represents the SOC of vehicle j before departure in the kth cycle.

[0025] Preferably,

[0026] In S3, the actual power consumption of the charging station in a day is taken as the objective function:

[0027]

[0028] where T represents the number of unit time divided in a day; Δt is the unit time, in this paper, Δt = 60 s; J is the total number of vehicles served by the bus charging station; is the actual charging power of the vehicle-mounted energy storage system of the jth vehicle at t.

[0029] Preferably,

[0030] In S3, the actual electricity cost of the charging station in a day is taken as the objective function:

[0031]

[0032] c(t) is the commercial electricity price of the charging station in the city at t.

[0033] Preferably,

[0034] The constraint condition of the objective function in S3 is:

[0035] S3.1 Time constraint: formula (9) indicates that the bus cannot be charged when it is not at the charging station; formula (10) indicates that all vehicles cannot be charged when the charging station stops business;

[0036]

[0037]

[0038] where d j，t is the state of charge of vehicle j at time t, d j，t ∈ {0, 1}, d j，t = 1 if the vehicle is in charging state, otherwise in non-charging state; is the set of time that bus j is not at charging station, where is the departure time of vehicle j in the kth round, is the return time of vehicle j in the kth round, n j is the total number of rounds that vehicle j needs to circulate in a day; T close is the time that charging station is closed;

[0039] S3.2 Charging station equipment constraints: formula (11) specifies that the number of charging piles in charging at the same time is less than or equal to the total number of charging equipment in the charging station N cs ; formula (12) specifies that the maximum power of a single charging pile cannot exceed the safe power P cs ; formula (13) specifies that the power distribution capacity of the entire charging station cannot exceed the maximum safe power P s of the charging station power distribution network;

[0040]

[0041]

[0042]

[0043] S3.3 Constraints on travel demand:

[0044]

[0045]

[0046]

[0047] Formula (14) represents that the SOC of each vehicle at the departure time of each round should be between the ratio of the energy required for a single trip to the maximum energy and the lower limit of the SOC soc low for battery protection and the set maximum SOC soc max ; formula (15) represents the calculation formula of the SOC of vehicle j at the kth departure time ​The SOC of the vehicle at the moment is the SOC at the initial moment of the day plus the electricity charged into the vehicle before the charging behavior, minus the electricity consumed before the trip; formula (16) represents that the electricity charged in a day meets the sum of the electricity required for the trip in a day, which guarantees the recyclability of the charging strategy;

[0048]

[0049]

[0050] In the formula, y j，t and z j，t are the starting and ending decision variables of the continuous charging controlled by the vehicle numbered j after returning to the station, y j，t , z j，t ∈{0,1}; is the set of time periods of the vehicle j after returning to the station in the charging station, Formulas (17)-(18) guarantee that the charging behavior of each bus is continuous in the time scale by using the charging strategy of the application.

[0051] The beneficial effects of the application are:

[0052] The method of the application improves the energy conversion efficiency of charging, reduces the heat loss of the battery and prolongs the service life of the battery, improves the energy conversion efficiency of charging, reduces the charging cost of the bus, and improves the economy. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, which together with the embodiments of the application are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0054] Figure 1 Flow chart of the bus fast charging station energy management method of the application based on a data-driven battery efficiency model DETAILED DESCRIPTION

[0055] The preferred examples of the application are described below in conjunction with the drawings, and it should be understood that the following examples are given only for the purpose of illustration, and are not intended to limit the scope of the application. Those skilled in the art can make various modifications and replacements to the application without departing from the spirit and principles of the application.

[0056] The application provides a bus fast charging station energy management method based on a data-driven battery efficiency model, and a flow chart is shown in Figure 1 The method comprises the following steps:

[0057] S1. According to the operation time of the charging station, the equipment power limit, the quantity limit and the bus departure plan, a charging demand model of the electric bus is established, and the electric quantity demand meeting the operation of the bus line is determined.

[0058] S2. The battery heating loss is obtained through experimental test, and the relationship between the charging energy conversion efficiency and the actual charging power of the vehicle-mounted energy storage system is established.

[0059] S3. The actual power consumption of the charging station in a day is minimum or the actual electricity fee of the charging station in a day is minimum as the objective function, wherein the battery heating loss is considered, and a nonlinear optimization objective function model is established with the operation time of the charging station, the number of charging piles, the maximum power of the charging pile, the distribution capacity, the electric quantity demand of the electric bus, and the continuity of the charging time of the electric bus as the constraint conditions.

[0060] S4. The nonlinear optimization objective function model in S3 planning is converted into a mixed integer convex programming problem by using piecewise linearization, and the optimal charging plan including the optimal charging time and the corresponding charging power of each vehicle is obtained by solving the mixed integer convex programming problem.

[0061] Specifically, the charging demand model of the electric bus is established in S1, and the electric quantity demand meeting the operation requirement of the electric bus is obtained, which specifically includes the following steps:

[0062] The bus charging station needs to meet the departure demand of multiple bus lines at the same time, and the trip demand of the bus line is determined by the departure table made by the operator. In this case, the departure time table of a certain bus station is provided, as shown in Table 1.

[0063] Table 1

[0064]

[0065] The present application studies the charging demand of the electric bus, and guarantees that the state of charge of the vehicle-mounted energy storage system can meet the energy consumption of walking a complete line before the departure time:

[0066]

[0067]

[0068] In the formula, j is the vehicle number; is the number of vehicles of the bus line served by vehicle j; is the first departure time, the last bus time and the departure interval of the line of vehicle j, respectively; j is the length of the running line of vehicle j; n j is the number of times each bus in the line served by vehicle j needs to be repeatedly operated; V is the vehicle j's velocity at the beginning of the kth cycle; j C is the energy consumption per kilometer of the line served by vehicle j; p，j soc is the maximum energy of the vehicle j's on-board energy storage system; low socmin is the lower limit of the SOC of the vehicle j to ensure safe operation of the vehicle, taking into account additional losses that may be caused by traffic conditions and the like; S0 represents the SOC of the vehicle j before the start of the kth cycle.

[0069] The relationship between the charging energy conversion efficiency of the battery taking into account the heat loss and the actual charging power of the on-board energy storage system in S2 can be expressed as:

[0070]

[0071] where t represents any charging time, T represents the number of unit times divided in a day, and P represents the actual power consumed by the power distribution network. t g P is the actual power consumed by the power distribution network; t ch P is the actual charging power of the on-board energy storage system; η b η represents the energy conversion efficiency of the battery during the charging process, which is the ratio of the actual charging energy of the battery to the input energy of the battery during the charging process, and is expressed in percentage; η b The size of η is related to the charging power of the battery actually charged.

[0072] The present application obtains η by experimental testing b and the specific steps for obtaining the relationship between P t and η are as follows: ch

[0073] 1) The different P t ch corresponding to η b are measured by experiment. The efficiency η b corresponding to a certain charging power and the actual charging power P t ch The specific operation is as follows: the battery is charged at a constant current, the energy input to the battery during the entire charging process is recorded, the actual charging energy of the battery is obtained through a small current discharging experiment after the charging is completed, and then the charging power P t ch of the battery actually charged in the charging process is calculated. b

[0074] 2) The data obtained in step 1) are fitted to obtain a model describing the relationship between η b and P t ch , i.e. η b = f(P​t ch )。

[0075] The daily required power of the electric bus charging station in S3 is determined by the bus departure table, which can be expressed as:

[0076]

[0077] The daily required power is determined by the departure rule and the length of the line, and the charging station energy conversion efficiency η within a single day can be expressed as:

[0078]

[0079] In the formula (5), C ostg is the actual power consumption of the charging station in a day.

[0080] According to formula (5), the charging station energy conversion efficiency is the highest, which is equivalent to finding the minimum actual power consumption, so it is equivalent to taking the minimum actual power consumption of the charging station in a day as the objective function under the premise of considering the charging energy conversion efficiency.

[0081] In order to reflect the influence of the ordered charging energy management strategy of the bus fast charging station considering dynamic charging loss, this case compares four different scenarios:

[0082] Scenario one: without any scheduling strategy, the bus charges as soon as it arrives at the station;

[0083] Scenario two: ordered charging with the minimum power consumption as the target, the objective function is:

[0084]

[0085] In the formula, T represents the number of unit time divided in a day; Δt is the unit time, Δt = 60s in this paper; J is the total number of vehicles served by the bus charging station; P t ch is the actual charging power of the jth vehicle at time t.

[0086] Scenario three: with the minimum daily operating electricity fee as the target, considering the battery energy conversion efficiency during the charging process of the electric bus, the objective function is:

[0087]

[0088] In the formula, c(t) is the commercial electricity price of the charging station operating city at time t.

[0089] Scenario four: with the minimum daily operating electricity fee as the target, without considering the battery energy conversion efficiency during the charging process of the electric bus, P t g = P tch The objective function is:

[0090]

[0091] The constraints of the above optimization problem are as follows:

[0092] S3.1 Time constraints: Equation (9) indicates that the bus cannot be charged when it is not at the charging station; Equation (10) indicates that all vehicles cannot be charged when the charging station stops operating;

[0093]

[0094]

[0095] In the formula, d j，t is the charging state of vehicle j at time t, d j，t ∈{0,1}, d j，t =1 vehicle is in charging state, otherwise in non-charging state; is the set of time when the bus j is not at the charging station,

[0096] wherein is the departure time of vehicle j in the kth round, l jk is the back station time of vehicle j in the kth round, n j is the total number of rounds that vehicle j circulates in a day; T close is the time when the charging station is closed.

[0097] S3.2 Charging station equipment constraints: Equation (11) stipulates that the number of charging piles simultaneously in charging is less than or equal to the total number of charging equipment in the charging station N cs ; Equation (12) stipulates that the maximum power of a single charging pile cannot exceed the safe power P cs ; Equation (13) stipulates that the power distribution capacity of the entire charging station cannot exceed the maximum safe power P s of the charging station power distribution network;

[0098]

[0099]

[0100]

[0101] S3.3 Constraints on travel demand:

[0102]

[0103]

[0104]

[0105] Formula (14) represents that the SOC of each vehicle at the departure time of each cycle should be between the ratio of the energy required for a single trip and the maximum energy plus the lower limit of the SOC of the protected battery soc low and the set maximum SOC soc max ; Formula (15) represents the calculation formula of the SOC of vehicle j at the kth departure time The SOC of the vehicle at the time is the SOC at the initial time of the day plus the amount of electricity charged into the vehicle before that, minus the amount of electricity consumed before the trip; Formula (16) represents that the amount of electricity charged in a day can meet the sum of the required energy for a day's trip, which ensures the recyclability of the charging strategy;

[0106]

[0107]

[0108] In the formula, y j，t and z j，t are the starting and ending decision variables of the continuous charging process of the vehicle numbered j after returning to the station, y j，t , z j，t ∈{0,1}; is the set of time periods of vehicle j after returning to the station, Formulas (17)-(18) ensure that when the charging strategy in this paper is adopted, the charging behavior of each vehicle at each time during the charging process of the bus is continuous in the time scale.

[0109] Since the measured battery efficiency model exhibits nonlinear characteristics, the optimization objectives (6) and (7) established in step 3 are nonlinear optimization models, and the invention uses piecewise linearization for processing. The formula for piecewise linearization is:

[0110]

[0111]

[0112]

[0113]

[0114] In the formula, P is the actual power charged into the vehicle energy storage system of vehicle j at time t; is the battery efficiency corresponding to P ; is an auxiliary constraint parameter, and subscript x corresponds to the xth segment after linearization​ represents the charging power located at the xth segment of the piecewise linearization after the broken line segment, and the value is and are the upper and lower limit values of the xth segment of the piecewise interval power P li，x ; K η，x and B η，x are linearization coefficients, reflecting the influence of the charging power ; and W x ={1, 2, 3}.

[0115] Further, the optimization objective obtained in the foregoing step is a mixed integer convex programming problem, and solving the mixed integer convex programming problem obtains an optimal charging plan, including an optimal charging time and a corresponding charging power P t ch and a corresponding P t g This case compares four scenarios, and the results show that the power consumption of scenario two is reduced compared to scenario one, and the cost of scenario three is lower than that of scenario four. Through the comparison results, it can be known that the energy management strategy of the bus fast charging station based on the battery dynamic charging loss model data driving can reduce the power consumption, improve the charging energy conversion efficiency, reduce the heat loss of the battery and prolong the service life of the battery, at the same time, reduce the charging cost of the electric bus and improve the economy.

[0116] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0117] Finally, it should be noted that: the above only describes the preferred examples of the present application, and is not used to limit the present application, although the present application has been described in detail with reference to the foregoing examples, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for energy management of a bus fast-charging station based on a data-driven battery efficiency model, characterized in that Includes the following steps: S1. Determine the electricity demand required to meet the operation of bus routes based on the charging station's operating time, equipment power limitations, quantity limitations, and bus departure schedule; S2. By experimentally testing the battery heat loss, establish the relationship between the charging energy conversion efficiency and the actual charging power of the on-board energy storage system; The relationship between the charging energy conversion efficiency considering battery heat loss and the actual charging power delivered by the on-board energy storage system in S2 is expressed as follows: In the formula, t represents any charging time, T represents the number of unit time divided in a day; P t g P is the actual consumed power of the power distribution network; P t ch P is the actual charged power of the vehicle-mounted energy storage system; η b represents the energy conversion efficiency of the battery during charging; S3. The objective function is to minimize the actual electricity consumption of the charging station in a day or minimize the actual electricity cost of the charging station in a day. The optimization model is established with the constraints of the charging station operating time, the number of charging piles, the maximum power of the charging piles, the power distribution capacity, the electricity demand of electric buses, and the continuity of electric bus charging time. S4. Piecewise linearization is used to transform the nonlinear optimization objective function model in S3 programming into a mixed integer convex programming problem. Solving the mixed integer convex programming problem yields the optimal charging plan, including the optimal charging time and corresponding charging power for each vehicle. The formula for piecewise linearization in S4 is: In the formula, is the actual power charged into the vehicle energy storage system at time t for vehicle j; is the corresponding battery efficiency; is the auxiliary constraint parameter, subscript x corresponds to the xth segment after linearization, indicates that the charging power is located in the xth segment of the piecewise linear segment, and the value is and are the upper and lower limit values of the xth segment of the segmented interval power P li，x ; K η，x and B η，x are linearization coefficients, reflecting the influence of the charging power ; W x ={1, 2, 3}.

2. The energy management method for bus fast charging stations based on a data-driven battery efficiency model according to claim 1, characterized in that, In the charging demand model for electric buses in S1, all buses have an onboard energy storage system whose state of charge before departure is sufficient to complete the entire route: where j is the vehicle number; the number of buses serving the route for vehicle j; the first departure time, the last departure time and the departure interval of the route for vehicle j, respectively; j the length of the route for vehicle j; j the number of times each bus needs to repeat the route for vehicle j; the start time of the kth cycle for vehicle j; j the energy consumption per kilometer for the route for vehicle j; p，j the maximum energy of the energy storage system for vehicle j; low the lower limit of the SOC for the safe operation of the vehicle, taking into account the additional loss caused by the traffic conditions; the SOC of vehicle j before the start of the kth cycle.

3. The energy management method for bus fast charging stations based on a data-driven battery efficiency model according to claim 1, characterized in that, In S3, the objective function is to minimize the actual electricity consumption of the charging station in a day. In the formula, T represents the number of time units in a day; Δt is the unit of time, which in this paper is Δt = 60s; J is the total number of vehicles served by the bus charging station; Pj(t) is the actual charging power of the jth vehicle at time t.

4. The energy management method for bus fast charging stations based on a data-driven battery efficiency model according to claim 1, characterized in that, In S3, the objective function is to minimize the actual electricity cost of the charging station throughout the day. c(t) represents the commercial electricity price at time t in the city where the charging station operates.

5. The energy management method for bus fast charging stations based on a data-driven battery efficiency model according to claim 3 or 4, characterized in that, The constraints on the objective function in S3 are as follows: S3.1 Time constraints: Equation (9) means that buses cannot be charged when they are not at a charging station; Equation (10) means that all vehicles cannot be charged when the charging station is closed. where d j，t is the state of charge of vehicle j at time t, d j，t ∈ {0, 1}, d j，t = 1 if the vehicle is in charging state, otherwise in non-charging state; is the set of time when bus j is not at charging station, where is the departure time of vehicle j in the kth round, is the back time of vehicle j in the kth round, n j is the total number of rounds that vehicle j has to circulate in a day; T close is the time when charging station is closed; S3.2 Charging station device constraints: formula (11) stipulates that the number of charging piles simultaneously in charging is less than or equal to the total number of charging devices N in the charging station cs ; formula (12) stipulates that the maximum power of a single charging pile must not exceed the safety power P cs ; formula (13) stipulates that the power distribution capacity of the entire charging station must not exceed the maximum safety power P s of the charging station power distribution network; S3.3 Constraints on travel demand: Formula (14) means that the SOC of each vehicle at the start time of each cycle should be between the ratio of the energy required for a single trip to the maximum energy plus the minimum SOC limit of the protection battery. low and the set maximum SOC (soc) max Between; Formula (15) represents the time when vehicle j departs for the kth time. The formula for calculating the State of Charge (SOC) of a vehicle at that time. The SOC of a vehicle at any given time is the SOC at the beginning of the day plus the amount of electricity charged into the vehicle during the charging activities that occurred before that time, minus the amount of electricity consumed during the previous trips; Formula (16) represents the sum of the electricity charged in a day to meet the energy required for a day's trips, ensuring the cyclicality of the charging strategy. where y j，t and z j，t are the start and end decision variables of controlling continuous charging during the charging process after the vehicle numbered j returns to the station, y j，t , z j，t ∈{0,1}; is the set of time periods of the vehicle j after returning to the station, formulae (17)-(18) ensure that the charging strategy of the present application is continuous in time scale for each bus charging behavior.

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