Economical routing method, device, vehicle and storage medium for electric vehicle fleet

Abstract

The present invention proposes an economical routing method, device, vehicle and storage medium for an electric vehicle fleet, wherein the routing method comprises the following steps: Step 1: constructing a map model including charging stations, warehouses and customer locations to realize the intersection and road section division of the map; Step 2: adding constraints to the map model established in Step 1 to generate a path cost set; the constraints include the acceleration, speed, terrain and power system efficiency of the vehicle at all road sections; Step 3: calculating the energy consumption cost between the customer location, charging station and warehouse, finding the path with the minimum energy consumption between each two, and constructing a customer demand library; Step 4: selecting the optimal path to achieve the purpose of minimizing the final total energy cost of distributing vehicles from the warehouse. The present invention takes into account the actual terrain, power system efficiency, and energy consumption accurate models of speed changes including acceleration and braking, and integrates energy consumption estimation into the integrated management of electric vehicle path problems, with high-precision integration characteristics.

Description

Technical Field

[0001] The invention discloses an economical routing method, device, vehicle and storage medium for electric vehicle fleets, which can find the optimal path with the lowest energy consumption for urban cargo delivery for electric commercial vehicle fleets under the condition of ensuring customer time and vehicle battery power. The method has strong real-time performance and belongs to the field of economical routing for electric vehicle fleets. Background Art

[0002] Nowadays, large freight companies have begun to use electric vehicles for customer cargo distribution, but electric vehicles have a high purchase cost and limited driving range. In addition, it takes a relatively long time to charge electric vehicles, and there is almost no charging infrastructure. This requires more careful route planning and accurate energy consumption prediction to ensure that the vehicle's battery does not run out of power during driving. Finding the path with the lowest energy consumption and reducing vehicle distribution costs is an urgent problem that needs to be solved. Recently, the field of economic routing has become increasingly prominent. The current research on this issue is mainly due to the difficulty in estimating vehicle energy consumption. Most of them use approximate methods, which usually assume that energy consumption is linearly related to distance and ignore dynamic conditions such as load. However, this approximation leads to the lack of necessary accuracy in planning reliable routes for electric vehicles. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to find the optimal path with the lowest energy consumption for urban cargo delivery for electric commercial vehicle fleets under the conditions of ensuring customer time and vehicle battery power, to propose an energy consumption model that takes into account the actual road section inclination, power system efficiency, and speed changes including acceleration and braking, and to integrate energy consumption estimation into the integrated management of electric vehicle routing problems.

[0004] Technical solution: In order to solve the above problems, the technical solution adopted by the present invention is:

[0005] An economical routing method for an electric vehicle fleet, characterized in that it comprises the following steps:

[0006] Step 1: Build a map model including charging stations, warehouses, and customer locations to realize the intersection and road section division of the map;

[0007] Step 2: Add constraints to the map model established in step 1 to generate a path cost set; the constraints include the acceleration, speed, terrain and power system efficiency of the vehicle on all road sections;

[0008] Step 3: Taking into account the energy consumption of vehicle auxiliary equipment, calculate the energy consumption cost between the customer site, charging station and warehouse, find the path with the minimum energy consumption between each pair, and build a customer demand database;

[0009] Step 4: Ensure that all vehicles have sufficient power during the mission and meet customer time requirements, select the optimal path, and minimize the final total energy cost of distributing vehicles from the warehouse.

[0010] Furthermore, a city map is selected. The map data can be obtained through relevant open source databases. The additional information of traffic lights and traffic signs comes from Open Street Maps software and is established as a directed graph. The nodes Indicates the intersections and customer locations of each road in the city The set of warehouses {0} and charging stations S, namely Arc Length Represents the road section between two intersections (two nodes).

[0011] Furthermore, taking into account the vehicle's acceleration, speed, terrain, and power system efficiency on all road sections, the vehicle longitudinal dynamics model is first established:

[0012]

[0013] Where m is the total mass of the vehicle (kg), a(t) is the instantaneous acceleration (m / s 2 ), F t (t) is the force generated by the powertrain or brake (N). When driving on a non-level road, F g (t) is the slope resistance (N), F r (t) is the rolling friction (N), F a (t) is the acceleration resistance (N), g is the acceleration due to gravity (9.81m / s 2 ), θ(t) is the instantaneous slope angle (°), C r is the rolling resistance coefficient. d is the drag coefficient, A is the front surface area of ​​the vehicle (m 2 ), ρ is the air density (kg / m 3 ), v(t) is the instantaneous velocity (m / s).

[0014] The instantaneous power p on the wheels when the vehicle is moving m (t)(W) can be calculated by the following formula:

[0015] p m (t)=mav(t)+mgv(t)sinθ(t)+mgv(t)C r cosθ(t)+0.5C d Aρv(t) 3 (2)

[0016] For electric vehicles, the power on the wheels comes from the motor, which consumes battery power and can recover energy during braking. m (t)≥0, the vehicle is in traction state, p m (t)<0, the vehicle is in the regenerative braking state. Considering the efficiency of the vehicle power system, the instantaneous power of the motor p e (t)(W) is calculated as follows:

[0017]

[0018] Where η + (t) is when p m (t)≥0, when the vehicle is in traction state, the efficiency of the vehicle power system and battery, η - (t) is p m (t)<0, when the vehicle is in regenerative braking state, the efficiency of the vehicle power system and battery, etc.

[0019] When a certain approximate acceleration / deceleration is given Initial velocity v i , the total mechanical energy e of a vehicle traveling on a certain length d section of road m (Wh) can be calculated as follows:

[0020]

[0021] Where, t f is the time consumed by the entire section (S), v f is the final speed. Based on this, the total amount of battery consumption in the vehicle, e(Wh), can be calculated:

[0022]

[0023] Since the motor mode is transient, It is also an approximate value of the overall power system efficiency. At the same time, because the length of the given section is d, there must be changes in acceleration during the section, and it is impossible to always be a constant value. The speed changes at t f It is also difficult to linearly change from v i Change to v f In order to be closer to reality and achieve high-precision calculation of path costs, the overall power system efficiency, acceleration and speed are refined. The approximate efficiency of the entire power system depends on several parameters, the most relevant of which are wheel speed, torque, power and gear. Here, the efficiency function model is established by regression analysis of a specific reference driving condition of the vehicle. The model is based on the current approximate speed of the vehicle. As input, the model is more robust, the function is simpler and the accuracy is higher.

[0024]

[0025] Where λ1, λ2 and λ3 are regression coefficients, λ1 = 4.5 × 10 -4 ,λ2=1.02×10 -3 , λ3=0.5.

[0026] To refine the impact of acceleration, the model takes into account stops at traffic lights and intersections as well as speed changes. The vehicle's motion state can be divided into an acceleration phase, a constant speed phase, and a deceleration phase, and the acceleration in the acceleration and deceleration phases is specified. At a given initial velocity v i , constant speed in constant speed phase (usually the maximum speed limit of the vehicle) and the terminal speed v at point b f , the battery energy consumption e of the road section (a, b) during the acceleration phase, constant speed phase and deceleration phase can be calculated ↑ 、e → and e ↓ .

[0027]

[0028] In the formula, ↑, → and ↓ represent the acceleration phase, constant speed phase and deceleration phase of motion. is the instantaneous slope angle between (a, b). The vehicle travel distance d during the acceleration and deceleration phases ↑ and d ↓ The calculation is as follows:

[0029]

[0030] So far, the total battery energy consumption between the road section (a, b) is e ab for:

[0031] e ab =e ↑ +e → +e ↓ (9)

[0032] In actual situations, the distance between the road section (a, b) and the distance between the road section (a, b) and the distance between the road section (b ...b) and the distance between the road section (a, b) and the ab If the distance is shorter, the vehicle cannot complete the above three stages. The above method still applies, and the energy consumption of the unfinished stage is zero.

[0033] From the above energy consumption calculation formula are all input variables, but mass m is the only decision variable that affects energy consumption and is linear, which mainly depends on the vehicle load and the order of visiting customer nodes. The energy cost coefficient α is introduced here ab and β ab, the above energy consumption cost calculation process is linearly transformed as follows.

[0034] e ab =α ab m+β ab (10)

[0035] The energy cost coefficient α ab and β ab The calculation of is as follows:

[0036]

[0037] At the same time, in order to ensure the time requirement for reaching the customer's location, the travel time t of the road section (a, b) is calculated. ab , the calculation formula is as follows:

[0038]

[0039] Where, t p The time the vehicle is stopped.

[0040] Based on the above formula, the energy costs of all road sections in the directed graph in step 1 can be calculated to generate a path cost set.

[0041] Furthermore, the energy consumption of vehicle auxiliary equipment (such as air conditioners, cabin heaters, refrigerators, etc.) is taken into account to calculate the energy consumption cost between the customer, charging station and warehouse. Based on step 1, for any starting point i to destination j, there must be a series of continuous paths between (i, j). Here, based on the path cost set in step 2, the power of the vehicle auxiliary equipment is set to p aux (W), calculate the cost of all paths between (i, j) and find the path with the lowest energy consumption

[0042]

[0043] The relevant parameter constraints are as follows:

[0044]

[0045] In the formula, the binary decision variable x ab Indicates whether the road segment (a, b) is part of the path. Based on the above formula, the Bellman-Ford method can easily solve the path with the minimum energy consumption. The final energy of the path with the minimum energy consumption is e ij The calculation is as follows:

[0046]

[0047]

[0048] In the formula, once the path initial point is determined, the vehicle mass m will no longer change, then the total energy cost coefficient α of the road section (a, b) is ij , β ij It can be calculated by the above formula. In these coefficients, it embeds all the details of the road, such as the slope of the road section, the efficiency of the power system, and the speed changes including acceleration and braking, which greatly improves the accuracy of the vehicle energy consumption calculation, so that the customer demand library reaching all customer locations can be determined.

[0049] Furthermore, to ensure that all vehicles have sufficient power during the mission and meet customer time requirements, the optimal path is selected to achieve the goal of minimizing the final total energy cost of distributing vehicles from the warehouse. Here, the total power of the vehicle battery is represented by B (Wh), and the minimum power of a single battery L (Wh) ≥ 20% B. Taking into account the actual situation of vehicle charging, the vehicle has a specific charging rate r at each charging station. i (W), and assume that all vehicles leave the charging station with fully charged batteries. To represent the time requirements of customers and charging stations, f i is the earliest service start time, l i is the latest service start time, S i is the service time (s) of the customer at node i, and the maximum load that the vehicle can carry is given by Q, q i is the total weight of the goods required by customer node i (kg), the empty vehicle weight (kg) is represented by W, and in order to achieve the distribution of K vehicles in the warehouse, the power consumption of the road section (i, j) is calculated:

[0050]

[0051] In the formula, the binary decision variable x ij Indicates whether to drive from node i to node j, w ij Refers to the total weight of the cargo from node i to node j (kg). Taking into account the energy consumption of auxiliary equipment, the final total energy consumption and time are calculated as follows:

[0052]

[0053] By optimizing the following function, we can find the path with the goal of minimizing the total energy consumption and realize the distribution of warehouse vehicles.

[0054]

[0055] The relevant constraints are as follows:

[0056]

[0057] Where b i is the remaining battery capacity (Wh) when reaching node i, y ij is the time (s) from node i to node j.

[0058] The present invention also provides a device, comprising:

[0059] a memory having a computer program stored thereon;

[0060] The computer program, when executed by the controller, implements the above-mentioned economical routing method for the electric vehicle fleet.

[0061] The present invention also provides a vehicle, comprising the above device.

[0062] The present invention also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned economic routing method for an electric vehicle fleet is implemented.

[0063] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0064] 1. Propose an energy consumption model that takes into account the actual road section inclination, power system efficiency, and speed changes including acceleration and braking, converting energy consumption into a linear relationship of quality, with high energy consumption prediction accuracy;

[0065] 2. Energy consumption estimation is integrated into the electric vehicle routing problem to form an integrated management method;

[0066] 3. Solved the problem of negative edge weight in economic routing that cannot be solved by existing algorithms;

[0067] 4. This method is more in line with the actual situation of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a road section between any two connected intersections a and b in the embodiment of the present invention The schematic diagram of the vehicle's motion state is divided into an acceleration phase, a constant speed phase and a deceleration phase;

[0069] Figure 2 is a schematic diagram of a specific reference operating condition used to calculate the approximate efficiency of the entire power system according to an embodiment of the present invention;

[0070] Figure 3 The invention discloses a high-precision integrated electric vehicle fleet economic routing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The present invention will be further described below in conjunction with the accompanying drawings:

[0072] For electric vehicle fleets, Figure 3 The present invention is explained in detail with reference to the flow chart of FIG.

[0073] The first step is to select a city map. The map data can be obtained from relevant open source databases. The additional information of traffic lights and traffic signs comes from Open Street Maps software and is established as a directed graph. The nodes Indicates the intersections and customer locations of each road in the city The set of warehouses {0} and charging stations S, namely Arc Length Represents the road section between two intersections (two nodes).

[0074] Furthermore, taking into account the vehicle's acceleration, speed, terrain, and power system efficiency on all road sections, the vehicle longitudinal dynamics model is first established:

[0075]

[0076] Where m is the total mass of the vehicle (kg), a(t) is the instantaneous acceleration (m / s 2 ), F t (t) is the force generated by the powertrain or brake (N). When driving on a non-level road, F g (t) is the slope resistance (N), F r (t) is the rolling friction (N), F a (t) is the acceleration resistance (N), g is the acceleration due to gravity (9.81m / s 2 ), θ(t) is the instantaneous slope angle (°), C r is the rolling resistance coefficient. d is the drag coefficient, A is the front surface area of ​​the vehicle (m 2 ), ρ is the air density (kg / m 3 ), v(t) is the instantaneous velocity (m / s).

[0077] The instantaneous power p on the wheels when the vehicle is moving m (t)(W) can be calculated by the following formula:

[0078] p m (t)=mav(t)+mgv(t)sinθ(t)+mgv(t)C r cosθ(t)+0.5C d Aρv(t) 3 (2)

[0079] For electric vehicles, the power on the wheels comes from the motor, which consumes battery power and can recover energy during braking. m (t)≥0, the vehicle is in traction state, p m(t)<0, the vehicle is in the regenerative braking state. Considering the efficiency of the vehicle power system, the instantaneous power of the motor p e (t)(W) is calculated as follows:

[0080]

[0081] Where η + (t) is when p m (t)≥0, when the vehicle is in traction state, the efficiency of the vehicle power system and battery, η - (t) is p m (t)<0, when the vehicle is in regenerative braking state, the efficiency of the vehicle power system and battery, etc.

[0082] When a certain approximate acceleration / deceleration is given Initial velocity v i , the total mechanical energy e of a vehicle traveling on a certain length d section of road m (Wh) can be calculated as follows:

[0083]

[0084]

[0085] Where, t f is the time consumed by the entire section (S), v f is the final speed. Based on this, the total amount of battery consumption in the vehicle, e(Wh), can be calculated:

[0086]

[0087] Since the motor mode is transient, It is also an approximate value of the overall power system efficiency. At the same time, because the length of the given section is d, there must be changes in acceleration during the section, and it is impossible to always be a constant value. The speed changes at t f It is also difficult to linearly change from v i Change to v f , in order to be closer to reality and achieve high-precision calculation of path cost, the overall power system efficiency, acceleration and speed are refined. The approximate efficiency of the entire power system depends on several parameters, the most relevant of which are wheel speed, torque, power and gear. Here, a specific reference driving condition of the vehicle is used to simulate ( Figure 1 ) is used to establish an efficiency function model based on the regression analysis of the vehicle's current approximate speed. As input, the model is more robust, the function is simpler and the accuracy is higher.

[0088]

[0089] Where λ1, λ2 and λ3 are regression coefficients, λ1 = 4.5 × 10 -4 ,λ2=1.02×10 -3 , λ3=0.5.

[0090] To refine the impact of acceleration, the model takes into account stops at traffic lights and intersections as well as speed changes. The vehicle's motion state can be divided into acceleration phase, constant speed phase and deceleration phase. Figure 2 As shown, the acceleration during the acceleration and deceleration phases is specified. At a given initial velocity v i , constant speed in constant speed phase (usually the maximum speed limit of the vehicle) and the terminal speed v at point b f , the battery energy consumption e of the road section (a, b) during the acceleration phase, constant speed phase and deceleration phase can be calculated ↑ 、e → and e ↓ .

[0091]

[0092]

[0093] In the formula, ↑, → and ↓ represent the acceleration phase, constant speed phase and deceleration phase of motion. is the instantaneous slope angle between (a, b). The vehicle travel distance d during the acceleration and deceleration phases ↑ and d ↓ The calculation is as follows:

[0094]

[0095] So far, the total battery energy consumption between the road section (a, b) is e ab for:

[0096] e ab =e ↑ +e → +e ↓ (9)

[0097] In actual situations, the distance between the road section (a, b) and the distance between the road section (a, b) and the distance between the road section (b ...b) and the distance between the road section (a, b) and the ab If the distance is shorter, the vehicle cannot complete the above three stages. The above method still applies, and the energy consumption of the unfinished stage is zero.

[0098] From the above energy consumption calculation formula are all input variables, but mass m is the only decision variable that affects energy consumption and is linear, which mainly depends on the vehicle load and the order of visiting customer nodes. The energy cost coefficient α is introduced hereab and β ab , the above energy consumption cost calculation process is linearly transformed as follows.

[0099] e ab =α ab m+β ab (10)

[0100] The energy cost coefficient α ab and β ab The calculation of is as follows:

[0101]

[0102] At the same time, in order to ensure the time requirement for reaching the customer's location, the travel time t of the road section (a, b) is calculated. ab , the calculation formula is as follows:

[0103]

[0104]

[0105] Where, t p The time the vehicle is stopped.

[0106] Based on the above formula, the energy costs of all road sections in the directed graph in step 1 can be calculated to generate a path cost set.

[0107] The third step is to calculate the energy consumption cost between the customer, charging station and warehouse by taking into account the energy consumption of vehicle auxiliary equipment (such as air conditioners, cabin heaters, refrigerators, etc.). Based on step 1, for any starting point i to destination j, there must be a series of continuous paths between (i, j). Here, based on the path cost set in step 2, the power of the vehicle auxiliary equipment is set to p aux (W), calculate the cost of all paths between (i, j) and find the path with the lowest energy consumption

[0108]

[0109] The relevant parameter constraints are as follows:

[0110]

[0111] In the formula, the binary decision variable x ab Indicates whether the road segment (a, b) is part of the path. Based on the above formula, the Bellman-Ford method can easily solve the path with the minimum energy consumption. The final energy of the path with the minimum energy consumption is e ij The calculation is as follows:

[0112]

[0113] In the formula, once the path initial point is determined, the vehicle mass m will no longer change, then the total energy cost coefficient α of the road section (a, b) is ij , β ij It can be calculated by the above formula. In these coefficients, it embeds all the details of the road, such as the slope of the road section, the efficiency of the power system, and the speed changes including acceleration and braking, which greatly improves the accuracy of the vehicle energy consumption calculation, so that the customer demand library reaching all customer locations can be determined.

[0114] The fourth step is to ensure that all vehicles have sufficient power during the mission and meet customer time requirements, select the optimal path, and achieve the goal of minimizing the final total energy cost of distributing vehicles from the warehouse. Here, the total power of the vehicle battery is represented by B (Wh), and the minimum power of a single battery L (Wh) ≥ 20% B. Taking into account the actual situation of vehicle charging, the vehicle has a specific charging rate r at each charging station. i (W), and assume that all vehicles leave the charging station with fully charged batteries. To represent the time requirements of customers and charging stations, f i is the earliest service start time, l i is the latest service start time, S i is the service time (s) of the customer at node i, and the maximum load that the vehicle can carry is given by Q, q i is the total weight of the goods required by customer node i (kg), the empty vehicle weight (kg) is represented by W, and in order to achieve the distribution of K vehicles in the warehouse, the power consumption of the road section (i, j) is calculated:

[0115]

[0116] In the formula, the binary decision variable x ij Indicates whether to drive from node i to node j, w ij Refers to the total weight of the cargo from node i to node j (kg). Taking into account the energy consumption of auxiliary equipment, the final total energy consumption and time are calculated as follows:

[0117]

[0118] By optimizing the following function, we can find the path with the goal of minimizing the total energy consumption and realize the distribution of warehouse vehicles.

[0119]

[0120] The relevant constraints are as follows:

[0121]

[0122]

[0123] Where b iis the remaining battery capacity (Wh) when reaching node i, y ij is the time (s) from node i to node j.

[0124] The embodiment of the present invention further provides a device, including:

[0125] a memory having a computer program stored thereon;

[0126] The computer program is executed by the controller to implement the economical routing method for the electric vehicle fleet of the above embodiment.

[0127] An embodiment of the present invention further provides a vehicle, which, in addition to the battery, electric motor, suspension and steering system etc. included in the vehicle, also includes the device of the above embodiment, through which the path of the vehicle is selected and controlled.

[0128] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the above-mentioned economical routing method for an electric vehicle fleet is implemented.

Claims

1. An economical routing method for an electric vehicle fleet, characterized by: The following steps are involved: Step 1: Build a map model including charging stations, warehouses, and customer locations to realize the intersection and road section division of the map; Step 2: Add constraints to the map model established in step 1 to generate a path cost set; the constraints include the acceleration, speed, terrain and power system efficiency of the vehicle on all road sections; Step 3: Taking into account the energy consumption of vehicle auxiliary equipment, calculate the energy consumption cost between the customer site, charging station and warehouse, find the path with the minimum energy consumption between each pair, and build a customer demand database; Step 4: Ensure that all vehicles have sufficient power during the mission and meet customer time requirements, select the optimal path, and minimize the final total energy cost of distributing vehicles from the warehouse; In step 2, constraints are added to the map model established in step 1 to generate a path cost set, including: Establishing the vehicle longitudinal dynamics model: if(t)=F t (t)-(F g (t)+F r (t)+F a (t)) =F t (t)-(mg sinθ(t)+mgC r cosθ(t)+0.5C d Aρv(t) 2 ) Where m is the total mass of the vehicle, a(t) is the instantaneous acceleration, and F t (t) is the force generated by the powertrain or brakes; when driving on a non-level road, F g (t) is the slope resistance, F r (t) is the rolling friction, F a (t) is the acceleration resistance, g is the acceleration due to gravity, θ(t) is the instantaneous slope angle, C r is the rolling resistance coefficient; C d is the drag coefficient, A is the front surface area of ​​the vehicle, ρ is the air density, and v(t) is the instantaneous velocity; Calculate the instantaneous power p on the wheels when the vehicle is moving m (t): p m (t)=mav(t)+mgv(t)sinθ(t)+mgv(t)C r cosθ(t)+0.5C d Aρv(t) 3 Calculate the instantaneous power p of the motor e (t): Where η + (t) is when p m (t)≥0, when the vehicle is in traction state, the efficiency of the vehicle power system and battery, η - (t) is p m (t)<0, when the vehicle is in regenerative braking state, the efficiency of the vehicle power system and battery; Give an approximate acceleration / deceleration and initial velocity v i , calculate the total mechanical energy e of a vehicle traveling on a certain length d section of road m : In the formula, v f is the terminal velocity; Calculate the total amount of battery consumption e of the vehicle: Where λ1, λ2 and λ3 are regression coefficients, λ1 = 4.5 × 10 -4 ,λ2=1.02×10 -3 ,λ3=0.5; At a given initial velocity v i , constant speed in constant speed phase and the terminal velocity v at point b f , the battery energy consumption e of the road section (a, b) during the acceleration phase, constant speed phase and deceleration phase can be calculated → 、e → and e ↓ : In the formula, ↑, → and ↓ represent the acceleration phase, constant speed phase and deceleration phase of motion. is the instantaneous slope angle between (a, b); the vehicle travel distance d during the acceleration and deceleration phases ↑ and d ↓ The calculation is as follows: The total battery energy consumption between the road sections (a, b) is e ab for: And ab =and ↑ +e → +e ↓ Total battery energy consumption ab The calculation process performs the following linear transformation: e ab =a ab m+b ab Among them, α ab and β ab is the energy cost coefficient, α ab and β ab The calculation of is as follows: α ab ＝α ↑ +α → +α ↓ β ab ＝β ↑ +β→+β ↓ Calculate the travel time t of road segment (a, b) ab : t ab =t ↑ +t → +t ↓ +t p Where, t p The time the vehicle is stopped; Travel time t based on road segment (a, b) ab The calculation method calculates the energy costs of all road sections in the directed graph in step 1 and generates a path cost set.

2. The economical routing method for electric vehicle fleet according to claim 1, characterized in that: In step 3, the method for finding the path with the minimum energy consumption between two nodes is based on the Bellman-Ford method.

3. The economical routing method for electric vehicle fleet according to claim 1, characterized in that: In step 1, a method for constructing a map model including charging stations, warehouses, and customer locations includes: Get map data including traffic lights and traffic signs; According to the map data, a directed graph is established: in, Indicates the intersections and customer locations of each road in the city The set of warehouses {0} and charging stations S, namely n represents the set The number of elements of is the arc length, Represents the road section between two intersections.

4. The economical routing method for electric vehicle fleet according to claim 3, characterized in that: In step 3, the energy consumption of vehicle auxiliary equipment is taken into account and the energy consumption cost between the customer site, charging station and warehouse is calculated, including: Based on step 1, for any starting point i to destination j, find the path with the lowest energy consumption In the formula, p aux is the power of vehicle auxiliary equipment, x is a binary variable on the road segment (ab); The relevant parameter constraints are as follows: In the formula, the binary decision variable x ab Indicates whether the segment (a, b) is part of the path; The final energy of the path with the lowest energy consumption is e ij The calculation is as follows: e ij =a ij m+b ij In the formula, α ij , β ij is the total energy cost coefficient of road section (i, j).

5. The economical routing method for electric vehicle fleet according to claim 4, characterized in that: In step 4, ensure that all vehicles have sufficient power during the mission and meet customer time requirements, and select the optimal path, including: Calculate the power consumption of section (i, j): In the formula, the binary decision variable x ij Indicates whether to drive from node i to node j; w ij Refers to the total weight of cargo from node i to node j; W represents the empty vehicle weight; Taking into account the energy consumption of auxiliary equipment, calculate the final total energy consumption and time: In the formula, y i0 is the travel time from the warehouse to node i; By optimizing the following function, we can find the path with the goal of minimizing total energy consumption and realize warehouse vehicle distribution: Where x, w, y, and b represent binary variables, vehicle mass, time, and battery power constraints, respectively; The relevant constraints are as follows: Where b i is the remaining battery capacity when reaching node i; y ij is the time from node i to node j; f i The earliest service start time; i The latest service start time; S i is the service time of the customer at node i; Q is the maximum load that the vehicle can carry; q i is the total weight of the goods required by customer node i; B is the total battery power of the vehicle, L is the minimum battery power of a single battery; K is the number of vehicles in the warehouse; r i The charging rate for each charging station.

6. The economical routing method for electric vehicle fleet according to claim 5, characterized in that: The minimum charge of a single cell is L ≥ 20% B.

7. A device, characterized in that: include: a memory having a computer program stored thereon; A controller, wherein when the computer program is executed by the controller, the method according to any one of claims 1 to 6 is implemented.

8. A vehicle, characterized in that: Comprising the device as claimed in claim 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 6 is implemented.

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