Cloud energy storage system for electric vehicle and operation method of cloud energy storage system

Abstract

The invention provides an electric vehicle-oriented cloud energy storage system and an operation method thereof, and relates to the technical field of electric vehicle charging. The system comprises a data receiving module used for receiving state information and charging request information sent by the electric vehicles and dividing all the electric vehicles into power shortage vehicles and candidate vehicles; the vehicle matching module is used for matching a charging vehicle from candidate vehicles according to the state information of the power shortage vehicle and the charging request information; the path planning module is used for carrying out path planning on the charging vehicle according to the state information of the successfully matched power shortage vehicle and the state information of the charging vehicle and generating an optimal path; and the service determination module is used for sending the optimal path to the charging vehicle, and when the charging vehicle travels to the position where the power shortage vehicle is located according to the optimal path, the charging vehicle charges the power shortage vehicle. Energy storage resources of the electric vehicle can be effectively integrated, the charging problem of the electric vehicle is solved, and the mileage anxiety of a vehicle owner is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular, to a cloud energy storage system for electric vehicles and an operation method thereof. Background Art

[0002] In recent years, with the global emphasis on environmental protection and sustainable development, new energy vehicles, especially electric vehicles, have been rapidly popularized. However, the popularization of electric vehicles has also brought new challenges. On the one hand, the fossil energy crisis and environmental pollution problems such as smog have attracted increasing attention, and the exhaust emissions of traditional fuel vehicles have become one of the main pollution sources. On the other hand, the ownership of electric vehicles has increased rapidly, but the construction speed of charging infrastructure is difficult to match its growth demand. Especially in remote areas, the lack of charging stations is particularly prominent. In addition, the battery life problem of electric vehicles has always been one of the key factors restricting their large-scale popularization. Once an electric vehicle has a battery life problem of insufficient power in the middle of the journey, the owner can only seek help through mobile communication devices such as telephones and wait for rescue, which greatly reduces the enthusiasm of consumers to buy electric vehicles.

[0003] With the rapid development and application of cloud-related technologies, as an emerging solution, the cloud energy storage system enables electric vehicles with depleted battery energy to exchange electric vehicle energy with electric vehicles with relatively sufficient battery energy at any time, anywhere, and on demand, forming a shared electric vehicle energy storage resource, providing energy supplementation for power-deficient vehicles, and realizing energy exchange and sharing between vehicles, thereby alleviating the problem of insufficient charging facilities. However, the existing cloud energy storage systems and charging facilities still have deficiencies. For example, there are shortcomings in energy dispatching and monitoring, lacking an efficient intelligent management platform and unable to achieve dynamic optimization and precise dispatching of energy storage resources; the construction and operation costs are relatively high, especially in remote areas, and the promotion is more difficult. Summary of the Invention

[0004] Aiming at the above deficiencies of the prior art, the present invention proposes a cloud energy storage system for electric vehicles and an operation method thereof by integrating the energy storage resources of electric vehicles, optimizing the path planning of electric vehicles, and realizing efficient energy exchange between electric vehicles, aiming to solve the charging problems of electric vehicles, alleviate the mileage anxiety of vehicle owners, and thus improve the user experience of vehicle owners.

[0005] In a first aspect of the present invention, a cloud energy storage system for electric vehicles is proposed. The system includes: a data receiving module, a vehicle matching module, a path planning module, and a service determination module;

[0006] The data receiving module is used to receive the status information and charging request information sent by all electric vehicles, classify all electric vehicles into power-deficient vehicles and candidate vehicles according to the received charging request information, and send them to the vehicle matching module; receive the charging information from the service determination module and store it;

[0007] The vehicle matching module is used to match a charging vehicle for the power-deficient vehicle from the candidate vehicles by calculating the call coefficient according to the status information and charging request information of the power-deficient vehicle, and transmit the status information of the power-deficient vehicle and the status information of the charging vehicle that are successfully matched to the path planning module;

[0008] The path planning module is used to perform path planning for the charging vehicle according to the status information of the power-deficient vehicle and the status information of the charging vehicle that are successfully matched, and transmit the generated optimal path to the service determination module;

[0009] The service determination module is used to send the optimal path to the charging vehicle. When the charging vehicle travels to the location of the power-deficient vehicle according to the optimal path, the charging vehicle charges the power-deficient vehicle; obtain the charging information during the charging process and transmit it to the data receiving module.

[0010] Further, the status information includes: real-time location information and battery power information; wherein the real-time location information includes: the city where it is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power, and remaining mileage;

[0011] The charging request information includes: a charging request and the requested charging power;

[0012] The charging information includes a charging start information and a charging completion information; wherein the charging start information includes: a signal to start charging, a charging start time, the starting power of the power-deficient vehicle, and the starting power of the charging vehicle; the charging completion information includes: a charging completion time, a signal of charging completion, and the remaining power of the charging vehicle and the power of the power-deficient vehicle after charging is completed.

[0013] Further, the path planning module includes: a data processing sub-module, a plan collection sub-module, a route density sub-module, and a route generation sub-module;

[0014] The data processing sub-module is used to load the road network model of the city where the power-deficient vehicle is located and transmit it to the route density sub-module;

[0015] The plan collection sub-module is used to collect the planned navigation data of all vehicles in the city where the power-deficient vehicle is located, and transmit all the collected planned navigation data in a unified format to the route density sub-module;

[0016] The route density sub-module is used to calculate the road network density in the road network model for multiple future time periods according to the received planned navigation data and the road network model of the city where the power-deficient vehicle is located, and transmit it to the route generation sub-module;

[0017] The route generation sub-module is used to generate the optimal path from the location of the charging vehicle to the location of the power-deficient vehicle according to the road network density in the road network model for multiple future time periods, and transmit the generated optimal path to the service determination module.

[0018] The second aspect of the present invention proposes an operation method for a cloud energy storage system for electric vehicles, and this method includes the following processes:

[0019] Obtain the status information and charging request information sent by all vehicles, and according to the received charging request information, mark the electric vehicles that send the charging request information as power-deficient vehicles, and mark the electric vehicles that do not send the charging request information as candidate vehicles;

[0020] For any power-deficient vehicle, according to the status information and charging request information of the power-deficient vehicle and the status information of the candidate vehicles, calculate the call coefficient to match a charging vehicle for the power-deficient vehicle from the candidate vehicles;

[0021] For any successfully matched power-deficient vehicle and charging vehicle, use the location of the power-deficient vehicle as the end point, use the location of the charging vehicle as the starting point, and plan an optimal path from the starting point to the end point for the charging vehicle;

[0022] The charging vehicle drives to the location of the power-deficient vehicle according to the planned optimal path, and uses the vehicle-to-vehicle charging device to charge the power-deficient vehicle.

[0023] Further, the process of calculating the call coefficient to match a charging vehicle for the power-deficient vehicle from the candidate vehicles is as follows:

[0024] Step A1: Obtain the status information and charging request information of the power-deficient vehicle, and obtain the status information of all candidate vehicles;

[0025] The status information includes: real-time location information and battery power information; wherein the real-time location information includes: the city where it is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power, and remaining mileage;

[0026] The charging request information includes: a charging request and the requested charging power;

[0027] Step A2: According to the real-time location information of the power-deficient vehicle, screen out the electric vehicles in the candidate vehicles that are in the same city as the power-deficient vehicle;

[0028] Step A3: For any electric vehicle after screening, compare the remaining power of the electric vehicle with the charging power requested by the power - deficient vehicle. If the remaining power of the electric vehicle is greater than the charging power requested by the power - deficient vehicle, mark the electric vehicle as a vehicle that meets the charging request; otherwise, mark the electric vehicle as a vehicle that does not meet the charging request and exclude it; thus, all vehicles that meet the charging request are obtained.

[0029] Step A4: For any vehicle that meets the charging request, calculate the ratio m of the remaining power of the vehicle to the charging power requested by the power - deficient vehicle, and determine the value parameter g of the vehicle according to the ratio m.

[0030] Step A5: Calculate the straight - line distance l between the location of the vehicle and the location of the power - deficient vehicle, and determine the distance parameter h of the vehicle according to the straight - line distance l.

[0031] Step A6: According to the value parameter g and the distance parameter h of the vehicle, calculate the call coefficient k of the vehicle; repeat Steps A4 - A5 to obtain the call coefficients k of all vehicles that meet the charging request, and sort all vehicles that meet the charging request in descending order according to the call coefficient k.

[0032] The calculation method of the call coefficient k is as follows:

[0033]

[0034] where e is the natural constant;

[0035] Step A7: Adopt a call strategy to call vehicles according to the sorting result of all vehicles that meet the charging request. When the owner of any vehicle that meets the charging request responds, stop the call, and use the vehicle that meets the charging request as the charging vehicle for the power - deficient vehicle.

[0036] The specific content of Step A7 is: Set call batches according to the number of all vehicles that meet the charging request, and set the call time interval between different call batches; based on the sorting result of all vehicles that meet the charging request, call all vehicles that meet the charging request in turn according to the set call batches and call time intervals until all vehicles that meet the charging request have been called; when the owner of a vehicle that meets the charging request confirms to go to charge the power - deficient vehicle, end the call process, and use the vehicle that meets the charging request as the charging vehicle for the power - deficient vehicle.

[0037] Furthermore, the process of planning an optimal path from the starting point to the ending point for the charging vehicle is as follows:

[0038] Step B1: Load the road network model of the city where the power-deficient vehicle is located, and denote this road network model as G = (A, B, W); where G represents the road network; A represents the set of intersection nodes in the road network; B represents the set of road segments in the road network, and W represents the set of weights of all road segments in the road network;

[0039] Step B2: Collect the planned navigation data of all vehicles in the city where the power-deficient vehicle is located and unify the data format;

[0040] Step B3: Calculate the density of each road segment in the road network model of the city where the power-deficient vehicle is located for multiple future time periods;

[0041] Step B4: According to the density of each road segment in the road network model of the city where the power-deficient vehicle is located for multiple future time periods, use a path optimization algorithm to plan an optimal path from the starting point to the ending point for the charging vehicle.

[0042] The content of Step B3 is: Take all vehicles with planned navigation data as sample vehicles. Based on the road network model of the city where the power-deficient vehicle is located, for any sample vehicle, calculate the total driving distance of the sample vehicle in multiple future time periods according to the unified planned navigation data of the sample vehicle;

[0043] The method for calculating the total driving distance of the sample vehicle in several future time periods is: For any time period in the time period set N, obtain the density of all road segments in this time period, and calculate the vehicle driving speed of each road segment in this time period according to the obtained density of all road segments;

[0044] The calculation method of the vehicle driving speed is:

[0045] v ij (n)=v f (1 - k ij (n) / k m ) 1.2

[0046] where v ij (n) represents the vehicle driving speed of road segment (i, j) in the nth time period; v f represents the free-flow speed of the road segment; k ij (n) represents the density of road segment (i, j) in the nth time period; k m represents the optimal density of the road segment; where i represents the predecessor node of road segment (i, j), j represents the successor node of road segment (i, j), and there is i = j, i, j ∈ A, A represents the set of intersection nodes in the road network;

[0047] Calculate the driving distance of the planned navigation of the sample vehicle in this time period using the vehicle driving speed of each road segment in this time period;

[0048] Calculate the total driving distance of the sample vehicle from the departure time to the end of the nth time period by using the driving distance of the sample vehicle planned for navigation during this time period;

[0049] According to the total driving distance of the sample vehicle from the departure time to the end of the nth time period, determine the sections passed by the vehicle in each time period from the departure time to the end of the nth time period. For any section (i, j) passed by the sample vehicle, obtain the time period corresponding to when the sample vehicle passes through the section (i, j), and increment by 1 the number of all vehicles passing through the section (i, j) during this time period, so as to update the density of the section (i, j) during this time period;

[0050] By traversing all vehicles with planned navigation data, obtain the density of each section in the road network model of the city where the power - shortage vehicle is located in multiple future time periods.

[0051] Further, the specific content of step B4 is: for any future time period, use the density of each section and the length of each section in the road network model of the city where the power - shortage vehicle is located during this time period to calculate the road weight of each section;

[0052] Take the intersection node corresponding to the location of the power - shortage vehicle as the target node; take the intersection node corresponding to the location of the charging vehicle as the starting node; initialize the road network model of the city where the power - shortage vehicle is located. For any intersection node in this road network model, set the b - value and rhs - value of this intersection node to infinity; where b represents the estimated cost from the starting node to this intersection node; rhs represents the cost from this intersection node to the target node;

[0053] Set the rhs - value of the starting node to 0, initialize a priority queue, calculate the V - value of each intersection node by using the road weight of each section during this time period respectively, and insert each intersection node into the priority queue in ascending order of the V - value of each intersection node; where V represents the total estimated cost of the intersection node;

[0054] The calculation method of the V - value of the intersection node is:

[0055]

[0056] where V1(i) and V2(i) represent two parts of the V - value of intersection node i; b(i) represents the b - value of intersection node i; rhs(i) represents the rhs - value of intersection node i; h(i, j, n) is the heuristic value of section (i, j) when the charging vehicle conducts path search in the nth time period; d represents the number of time periods in the future; a represents an index variable, and a is a positive integer; w ij (n + a) represents the road weight of section (i, j) in the (n + a)th time period; igoai It represents the intersection node where the power - deficient vehicle is located; j∈succ(i) indicates that intersection node j is the successor node of intersection node i; succ(i) represents the set of successor nodes of intersection node i; b(j) represents the b - value from intersection node j to the target node; c(i,j) represents the cost from intersection node i to intersection node j.

[0057] Remove the intersection node with the smallest V - value from the priority queue, set the b - value of this intersection node equal to the rhs - value, set the adjacent intersection nodes of this intersection node and calculate the rhs - values of all adjacent intersection nodes. For any adjacent intersection node of this intersection node, obtain the b - value of this adjacent intersection node and compare it with the b - value of reaching this adjacent intersection node through this intersection. If the two are not equal, calculate the V - value of this adjacent intersection node and update the position of this adjacent intersection node in the priority queue according to the V - value of this adjacent intersection node until the b - value of this adjacent intersection node is equal to the b - value of reaching this adjacent intersection node through the current node; if the two are equal, starting from the starting node, select the next intersection node of the current intersection node according to the rule of selecting the intersection node with the smallest b - value until reaching the target node, thereby generating the optimal path.

[0058] Furthermore, the vehicle - to - vehicle charging device includes: an input end, a dual - active - bridge DC / DC converter, and an output end, which are connected in sequence; wherein the input end is connected to the battery of the charging vehicle, and the input voltage of the input end is the discharge voltage of the battery of the charging vehicle; the output end is connected to the battery of the power - deficient vehicle, and the charging voltage of the battery of the power - deficient vehicle.

[0059] The control process of charging the power - deficient vehicle using the vehicle - to - vehicle charging device is as follows: Define a reference system for describing the expected output of the dual - active - bridge DC / DC converter, expressed as:

[0060]

[0061] where u r (t) represents the output voltage; represents the derivative of the output voltage u r (t) with respect to time t; u ref (t) represents the target reference value of the output voltage; p represents the bandwidth of the dual - active - bridge DC / DC converter, and p > 0; t represents time.

[0062] Define the voltage tracking error e(t) based on the target reference value of the output voltage and the output voltage, and then calculate the nominal state feedback i fb (t) according to the dual - active - bridge DC / DC converter model and the voltage tracking error e(t);

[0063] Define the disturbance estimation filter G f (s), and use the disturbance estimation filter G f (s) to calculate the estimate f e (t) of the external disturbance f(t);

[0064] According to the nominal state feedback i fb (t) and the difference between the estimate f e (t) of the external disturbance f(t), calculate the control law i ref (t);

[0065] Discretize the disturbance estimation filter G f (s), the estimate f e (t) of the external disturbance f(t), and the control law i ref (t) respectively to obtain the system uncertainty and disturbance estimator controller. Convert the output voltage, load current, and output voltage reference value of the dual-active-bridge DC / DC converter to the discrete domain z to obtain the output voltage u o (z), load current i o (z), and output voltage reference value u ref (z) in the discrete domain, and use the system uncertainty and disturbance estimator controller to calculate the load reference current i ref (z) in the discrete domain;

[0066] Delay the load reference current i ref (z) in the discrete domain, and according to the shift modulation scheme, use the delayed i ref (z) to generate the phase shift angle of the PWM signal Use the phase shift angle of the PWM signal to control the switching action of the dual-active-bridge DC / DC converter, and then control the output voltage u o (z) and load current i o (z) of the dual-active-bridge DC / DC converter, so as to realize the control of the vehicle-to-vehicle charging device.

[0067] The beneficial effects of adopting the above technical solutions are as follows:

[0068] The method of the present invention proposes a cloud energy storage system for electric vehicles and its operation method. By applying cloud technology, an implementation method of this cloud energy storage technology is proposed. This cloud energy storage system can realize energy sharing among electric vehicles, enabling electric vehicles to temporarily recharge without going to a charging station by using cloud technology. The cloud energy storage system matches vehicles with charging requirements and electric vehicles willing to charge other electric vehicles, and uses the proposed path optimization algorithm to realize path navigation planning for the vehicles going to charge. Finally, in order to meet the charging voltage requirements of electric vehicles, the present invention also proposes a charging device for electric vehicles to carry this device along with them to charge other vehicles. Description of the Drawings

[0069] Figure 1 It is a structural diagram of a cloud energy storage system for electric vehicles in this embodiment;

[0070] Figure 2 It is a flowchart of an operation method of a cloud energy storage system for electric vehicles in this embodiment;

[0071] Figure 3 It is a schematic diagram of the process of vehicle matching for power - deficient vehicles in this embodiment;

[0072] Figure 4 It is a schematic diagram of the process of path planning for the successfully - matched charging vehicles in this embodiment;

[0073] Figure 5 It is a circuit topology diagram of the vehicle - to - vehicle charging device in this embodiment;

[0074] Figure 6 It is a schematic diagram of the control method of the vehicle - to - vehicle charging device in this embodiment. Detailed Embodiment

[0075] To facilitate the understanding of this application, the following further describes the specific embodiments of the present invention in detail in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention but not to limit the scope of the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure content of this application more thoroughly and comprehensively understood.

[0076] In this embodiment, electric vehicles upload their battery energy status and location information to the cloud to form a shared energy storage resource composed of a number of electric vehicles, namely, a cloud energy storage system. When an electric vehicle runs out of power, the vehicle owner can send a charging request through the cloud energy storage system. The cloud energy storage system matches the corresponding energy storage resources for it based on information such as the location and charging capacity of the power-deficient vehicle, that is, an electric vehicle with sufficient power. The electric vehicle with sufficient power obtains the location information of the power-deficient vehicle through the cloud energy storage system, and uses the path planning algorithm included in the cloud energy storage system to determine the route to guide the power-supplying vehicle to the corresponding location. After arriving at the location, the vehicle-to-vehicle charging device is used to charge the power-deficient vehicle. The cloud energy storage system proposed in this embodiment only provides charging services for vehicle owners within the urban area where they are located, and does not support cross-city charging services of the cloud energy storage system. And the vehicle-to-vehicle charging device is a device carried by an electric vehicle for charging and energy replenishment between electric vehicles.

[0077] A cloud energy storage system for electric vehicles according to this embodiment, as Figure 1 shown, the system includes: a data receiving module, a vehicle matching module, a path planning module, and a service determination module.

[0078] The data receiving module is configured to receive the status information and charging request information sent by all electric vehicles, classify all electric vehicles into power-deficient vehicles and candidate vehicles according to the received charging request information, and send them to the vehicle matching module; receive the charging information from the service determination module and store it.

[0079] The status information includes: real-time location information and battery power information; wherein the real-time location information includes: the city where it is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power, and remaining mileage.

[0080] The charging request information includes: a charging request and the requested charging power.

[0081] The charging information includes a charging start information and a charging completion information; wherein the charging start information includes: a signal to start charging, a charging start time, the starting power of the power-deficient vehicle, and the starting power of the charging vehicle; the charging completion information includes: a charging completion time, a signal of charging completion, and the remaining power of the charging vehicle and the power of the power-deficient vehicle after charging is completed.

[0082] In this embodiment, the data receiving module receives real-time location information, battery power information, charging request information, and charging completion information sent by electric vehicles with cities as the set. Before vehicle matching, electric vehicles that send charging request information need to be marked as power-deficient vehicles, and electric vehicles that do not send charging request information need to be marked as candidate vehicles. When charging is completed, the electric vehicle sends a charging completion information. When the charging completion information sent by the electric vehicle is received, the marking status of the electric vehicle is changed from a power-deficient vehicle to a candidate vehicle.

[0083] The vehicle matching module is used to match a charging vehicle for the power-deficient vehicle from candidate vehicles by calculating a call coefficient according to the status information and charging request information of the power-deficient vehicle, and transmit the status information of the successfully matched power-deficient vehicle and the status information of the charging vehicle to the path planning module.

[0084] In this embodiment, the vehicle matching module is used to match a suitable electric vehicle for the electric vehicle that sends a charging request to charge it. When the cloud energy storage system receives a vehicle charging request, this module starts to run.

[0085] The path planning module is used to perform path planning for the charging vehicle according to the status information of the successfully matched power-deficient vehicle and the status information of the charging vehicle, and transmit the generated optimal path to the service determination module.

[0086] In this embodiment, when the vehicle matching module successfully matches a charging vehicle after completing its work, the path planning module starts to run, which is used to provide route planning for the vehicle going to supply power.

[0087] The path planning module includes: a data processing sub-module, a plan collection sub-module, a route density sub-module, and a route generation sub-module.

[0088] The data processing sub-module is used to load the road network model of the city where the power-deficient vehicle is located and transmit it to the route density sub-module.

[0089] The plan collection sub-module is used to collect the planned navigation data of all vehicles in the city where the power-deficient vehicle is located, and transmit all the collected planned navigation data in a unified format to the route density sub-module.

[0090] In this embodiment, for the city where the power - deficient vehicle is located, collect the planned navigation data of all vehicles in the city where the power - deficient vehicle is located, unify the formats of the planned navigation data from different navigation software, and store them in the database of the cloud energy storage system itself after converting them into a unified format. The planned navigation data collected in this embodiment may include: the starting point and ending point of the navigation route, route preferences, traffic condition information of the navigation route, estimated travel time, road attributes, vehicle information, estimated departure time, arrival time, and the sections passed through in the navigation route, etc.

[0091] The route density sub - module is used to calculate the road network density in the road network model for multiple future time periods according to the received planned navigation data and the road network model of the city where the power - deficient vehicle is located, and transmit it to the route generation sub - module.

[0092] In this embodiment, use the data processed by the data processing sub - module and the planned collection sub - module to calculate the road network density for multiple future time periods, and store the calculation results in the database of the cloud energy storage system itself in real - time.

[0093] The route generation sub - module is used to generate the optimal path from the location of the charging vehicle to the location of the power - deficient vehicle according to the road network density in the road network model for multiple future time periods, and transmit the generated optimal path to the service determination module.

[0094] The service determination module is used to send the optimal path to the charging vehicle. When the charging vehicle travels to the location of the power - deficient vehicle according to the optimal path, the charging vehicle charges the power - deficient vehicle; obtain the charging information during the charging process and transmit it to the data receiving module.

[0095] In this embodiment, the service determination module is used to determine that the charging vehicle goes to and completes the service. First, when the charging vehicle arrives at the power - deficient vehicle, connect the two vehicles using the vehicle - to - vehicle charging device so that the charging vehicle can charge the power - deficient vehicle. When charging starts, the charging vehicle sends information about the start of charging, the start time, and the power level information to the cloud energy storage system. After that, wait for the charging vehicle to complete charging, and upload the remaining power level information of the charging vehicle, the power level information of the power - deficient vehicle, and the charging completion time to the cloud energy storage system to complete the cloud energy storage service.

[0096] An operation method of a cloud energy storage system for electric vehicles in this embodiment, as Figure 2 shown, this method includes the following processes:

[0097] Obtain the status information and charging request information sent by all vehicles, and according to the received charging request information, mark the electric vehicle that sends the charging request information as a power - deficient vehicle, and mark the electric vehicle that does not send the charging request information as a candidate vehicle.

[0098] For any power - deficient vehicle, according to the status information and charging request information of the power - deficient vehicle and the status information of candidate vehicles, a charging vehicle is matched for the power - deficient vehicle from the candidate vehicles by calculating a call coefficient.

[0099] The process of matching a charging vehicle for the power - deficient vehicle from the candidate vehicles by calculating a call coefficient is as follows:

[0100] Step A1: Obtain the status information and charging request information of the power - deficient vehicle, and obtain the status information of all candidate vehicles.

[0101] The status information includes: real - time location information and battery power information; where the real - time location information includes: the city where the vehicle is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power, and remaining mileage.

[0102] The charging request information includes: a charging request and the requested charging power.

[0103] Step A2: According to the real - time location information of the power - deficient vehicle, screen out electric vehicles in the same city as the power - deficient vehicle from the candidate vehicles.

[0104] In this embodiment, as Figure 3 shown, since this method only provides charging services within the city area where the vehicle owner is located, it is necessary to screen out other electric vehicles in the city according to the city information in the vehicle location information.

[0105] Step A3: For any electric vehicle after screening, compare the remaining power of the electric vehicle with the charging power requested by the power - deficient vehicle. If the remaining power of the electric vehicle is greater than the charging power requested by the power - deficient vehicle, mark the electric vehicle as a vehicle that meets the charging request; otherwise, mark the electric vehicle as a vehicle that does not meet the charging request and exclude it; thus, all vehicles that meet the charging request are obtained.

[0106] Step A4: For any vehicle that meets the charging request, calculate the ratio m of the remaining power of the vehicle to the charging power requested by the power - deficient vehicle, and determine the value parameter g of the vehicle according to the ratio m.

[0107] In this embodiment, as Figure 3 shown, retrieve the battery information of all candidate vehicles screened by location information. By comparing the charging power request information of the power - deficient vehicle and the remaining power information of each electric vehicle after screening, exclude vehicles with remaining power that does not meet the charging request. And determine the value parameter g according to the ratio m of the remaining power of each vehicle to the charging power requested by the charging request vehicle. The method of determining the value parameter g according to the ratio m is as follows:

[0108] When \(m\in[1,2]\), the value of the value parameter \(g\) satisfies:

[0109] When \(m = 1\), \(g = 0.1\); when \(1\lt m\lt1.1\), \(g = 0.37\); when \(1.1\leq m\lt1.2\), \(g = 0.41\);

[0110] When \(1.2\leq m\lt1.3\), \(g = 0.83\); when \(1.3\leq m\lt1.4\), \(g = 1.2\); when \(1.4\leq m\lt1.5\), \(g = 1.36\);

[0111] When \(1.5\leq m\lt1.6\), \(g = 1.53\); when \(1.6\leq m\lt1.7\), \(g = 1.7\); when \(1.7\leq m\lt1.8\), \(g = 2.46\);

[0112] When \(1.8\leq m\lt1.9\), \(g = 2.86\); when \(1.9\leq m\lt2.0\), \(g = 3.41\); when \(2.0\leq m\), \(g = 5\).

[0113] When \(m\gt2.0\), it is only necessary to satisfy that the larger the ratio \(m\) is, the larger the value parameter \(g\) is, and \(g\gt5\).

[0114] Step A5: Calculate the straight-line distance \(l\) between the location of this vehicle and the location of the power-deficient vehicle, and determine the distance parameter \(h\) of this vehicle according to the straight-line distance \(l\).

[0115] In this embodiment, as Figure 3 shown, obtain the position information of the remaining vehicles obtained through Step 3, obtain the straight-line distance \(l\) of each vehicle from the vehicle that issues the charging request, with the unit of km. And obtain the numerical value of the distance parameter \(h\) according to the relationship of the straight-line distance information of each vehicle from the vehicle that issues the charging request. The method for determining the distance parameter \(h\) according to the straight-line distance \(l\) is as follows:

[0116] When \(l\in[0,20]\), the value of the distance parameter \(h\) satisfies:

[0117] When \(l\lt0.5\), \(h = 13.59\); when \(0.5\leq l\lt1\), \(h = 12.17\); when \(1\leq l\lt2\), \(h = 10.76\);

[0118] When \(2\leq l\lt4\), \(h = 8.41\); when \(4\leq l\lt8\), \(h = 6.37\); when \(8\leq l\lt10\), \(h = 4.74\);

[0119] When \(10\leq l\lt15\), \(h = 3.28\); when \(15\leq l\lt20\), \(h = 2.76\); when \(20\leq l\), \(h = 2.17\).

[0120] When \(l\gt20\), it is only necessary to satisfy that the larger the straight-line distance \(l\) is, the smaller the distance parameter \(h\) is, and \(h\lt2.17\).

[0121] Step A6: Calculate the call coefficient k of the vehicle according to the value parameter g and the distance parameter h of the vehicle; repeat steps A4 - A5 to obtain the call coefficients k of all vehicles that meet the charging request, and sort all vehicles that meet the charging request in descending order of the call coefficient k.

[0122] The calculation method of the call coefficient k is as follows:

[0123]

[0124] where e is the natural constant.

[0125] Step A7: Adopt a call strategy to call vehicles according to the sorting result of all vehicles that meet the charging request. When the owner of any vehicle that meets the charging request responds, stop the call and use the vehicle that meets the charging request as the charging vehicle for the power - deficient vehicle.

[0126] The specific content of step A7 is: Set call batches according to the number of all vehicles that meet the charging request, and set the call time interval between different call batches; based on the sorting result of all vehicles that meet the charging request, call all vehicles that meet the charging request in sequence according to the set call batches and call time intervals until all vehicles that meet the charging request have been called; when the owner of any vehicle that meets the charging request confirms to go to charge the power - deficient vehicle, end the call process and use the vehicle that meets the charging request as the charging vehicle for the power - deficient vehicle.

[0127] In this embodiment, the order of the charging vehicles for the final call will be determined by sorting the call coefficients. Different call strategies will be selected according to the number of all vehicles that meet the charging request, that is, the number of call coefficients. Specifically, when the number of call coefficients is greater than or equal to 200, call strategy 1 is used; when the number of call coefficients is greater than or equal to 100 and less than 200, call strategy 2 is used; when the number of call coefficients is less than 100, call strategy 3 is used. Among them, call strategy 1 is as follows: Set the proportion of each call batch to 10%. Call in the order of the top 10%, 10%-20%, 20%-30%... of the vehicle call coefficient sorting until all vehicles have been called. And during the first 60% of the calling process, there is a 5-second interval between every 10% of the calls; during the last 40% of the calling process, there is a 3-second interval between every 10% of the calls. The purpose of setting the call time interval is to allow the vehicle owners who receive the call to confirm whether they will go to charge the power-deficient vehicle. Call strategy 2 is as follows: Set the proportion of each call batch to 20%. Call in the order of 20%, 20%-40%, 40%-60%... of the vehicle call coefficient sorting until all vehicles have been called. And during the first 40% of the calling process, there is a 5-second interval between every 20% of the calls; during the last 60% of the calling process, there is a 3-second interval between every 10% of the calls. Call strategy 3 is as follows: Set the proportion of each call batch to 25%. Call in the order of the top 25%, 25%-50%, 50%-75%... of the vehicle call coefficient sorting until all vehicles have been called. And during the first 50% of the calling process, there is a 5-second interval between every 25% of the calls; during the last 50% of the calling process, there is a 3-second interval between every 10% of the calls. During the above three calling processes, if a vehicle owner confirms to go to charge the power-deficient vehicle at any stage, the subsequent calls will not continue, and at this time, the vehicle matching module operation ends.

[0128] For any successfully matched power-deficient vehicle and charging vehicle, the location of the power-deficient vehicle is used as the end point, and the location of the charging vehicle is used as the starting point, and an optimal path from the starting point to the end point is planned for the charging vehicle.

[0129] The process of planning an optimal path from the starting point to the end point for the charging vehicle is as follows:

[0130] Step B1: Load the road network model of the city where the power-deficient vehicle is located, and denote this road network model as G=(A, B, W); where G represents the road network; A represents the set of intersection nodes in the road network, and A={1, 2, 3,...}; B represents the set of road segments in the road network, and B={(i, j)|i, j∈A, i=j}; where (i, j) represents any road segment in the road network; i represents the predecessor node of the road segment (i, j); j represents the successor node of the road segment (i, j); W represents the set of road weights of all road segments in the road network, and W={wij (n)|(i,j) ∈ B, n ∈ N}, w ij w(n) represents the road weight of section (i,j) in the nth time period; N represents the set of time periods.

[0131] In this embodiment, as Figure 4 shown, the path planning loads the road network model of the city where the vehicle is located. The mathematical expression of the road network is defined as G = (A, B, W), where A = {1, 2, 3,...}, B = {(i,j)|i,j ∈ A, i = j}, and W = {w ij (n)|(i,j) ∈ B, n ∈ N}. In this embodiment, three arrays i, j, and w are used to record the specific information of each section. Among them, i represents the predecessor node of the section, j represents the successor node of the section, and w represents the road weight of the section in a certain time period. And when the road is a one-way road, the weight in the non-passable direction is a very large number.

[0132] Step B2: Collect the planned navigation data of all vehicles in the city where the power-deficient vehicle is located and unify the data format.

[0133] Step B3: Calculate the density of each section in the road network model of the city where the power-deficient vehicle is located in multiple future time periods according to the unified planned navigation data.

[0134] The content of step B3 is: Use all vehicles with planned navigation data as sample vehicles. Based on the road network model of the city where the power-deficient vehicle is located, for any sample vehicle, calculate the total driving distance of the sample vehicle in multiple future time periods according to the unified planned navigation data of the sample vehicle.

[0135] The method for calculating the total driving distance of the sample vehicle in several future time periods is: For any time period in the time period set N, obtain the density of all sections in this time period, and calculate the vehicle driving speed of each section in this time period according to the obtained density of all sections.

[0136] The calculation method of the vehicle driving speed is:

[0137] v ij (n) = v f (1 - k ij (n) / k m ) 1.2 (2)

[0138] where v ij (n) represents the vehicle driving speed of section (i,j) in the nth time period; v f represents the free-flow speed of the section; k ij (n) represents the density of section (i,j) in the nth time period; k mRepresents the optimal density of the road segment.

[0139] Calculate the driving distance of the sample vehicle's planned navigation during this period using the vehicle driving speed of each road segment during this period.

[0140] The calculation method of the driving distance of the sample vehicle's planned navigation is as follows:

[0141]

[0142] Where s n Represents the driving distance of the sample vehicle's planned navigation in the nth period; r represents the total number of road segments the vehicle plans to pass through in the nth period minus 1; u represents the index variable for summation; Δt (i+u,j+u) Represents the time taken for the sample vehicle to plan to pass through the road segment (i + u, j + u), and T represents the time length of the period; v (i+u,j+u) (n) represents the vehicle driving speed of the road segment (i + u, j + u) in the nth period.

[0143] Calculate the total driving distance of the sample vehicle from the departure time to the end of the nth period using the driving distance of the sample vehicle's planned navigation during this period.

[0144] The calculation method of the total distance traveled by the sample vehicle from the departure time to the end of the nth period is as follows:

[0145] S n = ∑s n (4)

[0146] Where S n Is the distance traveled by the vehicle from the departure time to the end of the nth period.

[0147] According to the total driving distance of the sample vehicle from the departure time to the end of the nth period, determine the road segments passed by the vehicle in each period from the departure time to the end of the nth period. For any road segment (i, j) passed by the sample vehicle, obtain the period corresponding to the sample vehicle passing through the road segment (i, j), and add 1 to the number of all vehicles passing through the road segment (i, j) in this period, thereby updating the density of the road segment (i, j) in this period.

[0148] The update method of the density of the road segment (i, j) in this period is as follows:

[0149] k ij (n)= C ij (n) / l ij (5)

[0150] Where C ij(n) is the number of all vehicles passing through section (i, j) within the nth time period; l ij is the length of section (i, j).

[0151] By traversing all vehicles with planned navigation data, the density of each section in the road network model of the city where the power-deficient vehicle is located within multiple future time periods is obtained.

[0152] In this embodiment, as Figure 4 shown, first calculate the vehicle driving speed v ij (n) of each section, and then calculate the total driving distance S n of the vehicle. Among them, the driving distance of the vehicle obtained by the formula is used to obtain the position of the vehicle at a future moment, obtain the moment when the vehicle leaves each section, and based on this, judge whether the vehicle passes through section (i, j) in the nth time period. If it passes, the number of vehicles on this section at this moment is incremented by 1. It is implemented by using the route density sub-module in the cloud energy storage system. Its core is to judge the sections passed by the vehicle in each time period by calculating the driving distance of the vehicle. If the vehicle passes through this section in this time period, the number of vehicles on this section is cumulatively increased. By continuously traversing the time periods passed by a certain vehicle and the travel plan routes of each vehicle, the number of accompanying vehicles that reach a certain section at the same time as this vehicle when the vehicle reaches a certain section in the future is obtained. The calculated number of accompanying vehicles is compared with the length of the section, and finally the density status of the road network in multiple future time periods is obtained.

[0153] Step B4: According to the density of each section in the road network model of the city where the power-deficient vehicle is located within multiple future time periods, use a path optimization algorithm to plan an optimal path from the starting point to the ending point for the charging vehicle.

[0154] The specific content of step B4 is: for any future time period, use the density of each section and the length of each section in the road network model of the city where the power-deficient vehicle is located in this time period to calculate the road weight of each section in this time period;

[0155] The calculation method of the road weight of each section in this time period is:

[0156] w ij (n) = 0.39k ij (n) + 0.685l ij (6)

[0157] where w ij (n) represents the road weight of section (i, j) in the nth time period; k ij (n) represents the density of section (i, j) in the nth time period; l ij is the length of section (i, j).

[0158] In this embodiment, the current moment when the vehicle is located is set as the 0th period, and using w ij h(n) = 0.39k ij (n) + 0.685l ij The road weights w ij (1), w ij (2), w ij (3) to w ij (d) for the 1st to dth future periods are obtained respectively, and where represents rounding up; d represents the number of future periods.

[0159] The intersection node corresponding to the location of the power - deficient vehicle is taken as the target node; the intersection node corresponding to the location of the charging vehicle is taken as the starting node; the road network model of the city where the power - deficient vehicle is located is initialized. For any intersection node in this road network model, the b - value and the rhs - value of this intersection node are set to infinity; where b represents the estimated cost from the starting node to this intersection node; rhs represents the cost from this intersection node to the target node.

[0160] The rhs - value of the starting node is set to 0, a priority queue is initialized, and the V - values of each intersection node are calculated respectively using the road weights of each road segment within this period, and each intersection node is inserted into the priority queue in ascending order of the V - values of each intersection node; where V represents the total estimated cost of the intersection node;

[0161] In this embodiment, the road network is initialized, the b - values and the rhs - values of all intersection nodes in all road networks are set to infinity, then the rhs - value of the intersection node where the charging vehicle is located is set to 0 and the V - value is calculated according to the following formula and inserted into the priority queue. In this embodiment, the priority queue is used to save the intersection nodes to be updated, and the V - value is used as the priority, the smaller the V - value, the higher the priority, that is, the priority queue ensures that the intersection node with the smallest V - value can always be accessed quickly.

[0162] The calculation method of the V - value of the intersection node is as follows:

[0163]

[0164] where V1(i) and V2(i) represent two parts of the V - value of intersection node i; b(i) represents the b - value of intersection node i; rhs(i) represents the rhs - value of intersection node i; h(i, j, n) is the heuristic value of road segment (i, j) when the charging vehicle conducts path search in the nth period; d represents the number of periods of the future period; a is an index variable and a is a positive integer; w ij (n + a) represents the road weight of road segment (i, j) in the (n + a)th period; igoai It represents the intersection node where the power - deficient vehicle is located; j ∈ succ(i) means that intersection node j is a successor node of intersection node i; succ(i) represents the set of successor nodes of intersection node i; b(j) represents the b - value from intersection node j to the target node; c(i,j) represents the cost from intersection node i to intersection node j.

[0165] In this embodiment, when comparing the V - values, it is necessary to compare V2 first and then V1. Only when the V2 - values are equal, then compare the V1 - values. rhs(i) is the cost from intersection node i to the target node. According to the above formula, during the vehicle's driving process, when the weight of the road segment changes, since the vehicle's destination never changes, only the b(j) of the intersection node j where the cost changes needs to be updated, thereby improving the search efficiency.

[0166] Remove the intersection node with the smallest V - value from the priority queue, and set the b - value of this intersection node equal to the rhs - value. Set the neighboring intersection nodes of this intersection node and calculate the rhs - values of all neighboring intersection nodes. For any neighboring intersection node of this intersection node, obtain the b - value of this neighboring intersection node and compare it with the b - value of reaching this neighboring intersection node through this intersection. If the two are not equal, calculate the V - value of this neighboring intersection node and update the position of this neighboring intersection node in the priority queue according to the V - value of this neighboring intersection node until the b - value of this neighboring intersection node is equal to the b - value of reaching this neighboring intersection node through the current node; if the two are equal, starting from the starting node, select the next intersection node of the current intersection node according to the rule of selecting the intersection node with the smallest b - value until reaching the target node, thereby generating the optimal path.

[0167] In this embodiment, remove the intersection node with the highest priority from the priority queue, that is, the intersection node with the smallest V - value, set the b - value of this intersection node equal to the rhs - value, and make this intersection node locally consistent. Then calculate the rhs - values of the neighboring intersection nodes of this intersection node. If the b - values of these grid nodes are not equal, calculate their V - values and insert them into the priority queue. Repeat the above process until the starting intersection node becomes locally consistent, and start searching the surrounding neighboring grids according to the rule of the smallest b - value from the starting intersection node until reaching the target grid, then the final path can be obtained.

[0168] In this embodiment, the above process is implemented by the route generation sub - module in the cloud energy storage system. Its core is to continuously perform path search according to the real - time position of the vehicle based on the future multi - period road network density calculation results obtained by the route density sub - module stored in the cloud energy storage system, store the obtained path as planned navigation data in the cloud energy storage system, and send it to the vehicle going for charging.

[0169] The charged vehicle travels to the location of the power-deficient vehicle along the planned optimal path, and uses a vehicle-to-vehicle charging device to charge the power-deficient vehicle.

[0170] The vehicle-to-vehicle charging device includes: an input end, a dual-active-bridge DC / DC converter, and an output end that are connected in sequence; wherein the input end is connected to the battery of the charging vehicle, and the input voltage of the input end is the discharge voltage of the battery of the charging vehicle; the output end is connected to the battery of the power-deficient vehicle, and is the charging voltage of the battery of the power-deficient vehicle.

[0171] In this embodiment, the discharge voltage of the battery of the charging vehicle is mostly 48 / 60 / 72V; the charging voltage of the battery of the power-deficient vehicle is mostly 200 / 250 / 300V.

[0172] In this embodiment, as Figure 5 shown, the vehicle-to-vehicle charging device uses a dual-active-bridge DC / DC converter as a high-power circuit, and the input voltage of the charging device is a DC voltage. The dual-active-bridge DC / DC converter consists of a front bridge, an inductor, a transformer, a rear bridge, an output capacitor, and a load; wherein the front bridge and the rear bridge are each composed of 4 power switching tubes, and are connected in the middle by a high-frequency isolation transformer containing leakage inductance; the inductor in the dual-active-bridge DC / DC converter consists of an access inductor and the leakage inductance of the transformer; voltage square waves are generated on both sides of the transformer by the power switching tubes of the front bridge and the rear bridge, the secondary-side voltage is equivalent to the primary side through the transformer, there are input voltage square waves and output voltage square waves at both ends of the inductor, and the input-side energy is transmitted to the output side through the inductance current generated by the voltage difference, and the output capacitor mainly plays the role of output voltage stabilization;

[0173] The process of using the vehicle-to-vehicle charging device to charge the power-deficient vehicle is as follows: Define a reference system for describing the desired output of the dual-active-bridge DC / DC converter;

[0174] The reference system is expressed as:

[0175]

[0176] where u r (t) represents the output voltage; represents the derivative of the output voltage u r (t) with respect to time t; u ref (t) represents the output voltage target reference value; p represents the bandwidth of the dual-active-bridge DC / DC converter, and p > 0; t represents time.

[0177] According to the output voltage target reference value and the output voltage, define the voltage tracking error as:

[0178] e(t) = u r (t) - uref (t) (11)

[0179] where \(e(t)\) represents the voltage tracking error.

[0180] And the dynamic characteristics of the voltage tracking error satisfy:

[0181]

[0182] where represents the derivative of the voltage tracking error \(e(t)\) with respect to time; \(\alpha\) represents the derivative related to the voltage tracking error; \(\beta\) is a constant; where the convergence rate of the voltage tracking error is determined by \(\alpha\) and \(\beta\).

[0183] In this embodiment, the dynamic characteristics of the voltage tracking error are generally described by a linear differential equation.

[0184] Calculate the nominal state feedback \(i\) according to the dual active bridge DC / DC converter model and the voltage tracking error \(e(t)\) fb (t).

[0185] The dual active bridge DC / DC converter model is expressed as:

[0186]

[0187] where \(s u\) o (s) represents the representation of the output voltage of the dual active bridge DC / DC converter in the Laplace domain; \(s\) represents the complex frequency variable of the Laplace transform; represents the calculation delay; \(T\) S represents the sampling period, and \(T\) S = 1 / f s ; \(f\) s represents the sampling frequency; \(i(s)\) represents the representation of the current flowing through the dual active bridge DC / DC converter in the Laplace domain; \(\Delta\) Total (s) represents the system uncertainty; \(i\) o (s) represents the representation of the load current in the Laplace domain; \(f(s)\) represents the external disturbance; \(C\) represents the output capacitance.

[0188] Define the disturbance estimation filter \(G\) f (s) as:

[0189]

[0190] where \(q\) represents the disturbance rejection bandwidth.

[0191] Use the disturbance estimation filter \(G\) f (s) to calculate the estimate \(\hat{f}\) of the external disturbance \(f(t)\) e (t) as:

[0192]

[0193] where g f (t) represents the time-domain expression of the disturbance estimation filter; * represents the convolution operation; L -1 is the inverse Laplace transform; i o (t) represents the load current; i ref represents the load reference current; is the desired output voltage u o (t) derivative with respect to time; i ref (t - T s ) represents the reference current value before the sampling period.

[0194] According to the nominal state feedback i fb (t) and the difference between the estimated f e (t) of the external disturbance f(t), calculate the control rate i ref (t) is expressed as:

[0195]

[0196] Discretize the disturbance estimation filter G f (s), the estimated f e (t) of the external disturbance f(t), and the control rate i ref (t) to obtain the system uncertainty and disturbance estimator controller. Convert the output voltage, load current, and output voltage reference value of the dual-active-bridge DC / DC converter to the discrete domain z to obtain the discrete-domain output voltage u o (z), load current i o (z), and output voltage reference value u ref (z), and calculate the discrete-domain load reference current i ref (z) using the system uncertainty and disturbance estimator controller.

[0197] In this embodiment, discretize the variables in the above formula to obtain the uncertainty and disturbance estimator controller. According to the load current i o (z), output voltage reference value u ref (z), and output voltage u o (z), generate the current reference i ref (z).

[0198] Delay the discrete-domain load reference current i ref (z), and according to the shift modulation scheme, use the delayed i ref (z) to generate the phase shift angle of the PWM signal Use the phase shift angle of the PWM signal Control the switching actions of the dual-active-bridge DC / DC converter, and further control the output voltage u of the dual-active-bridge DC / DC converter o (z) and the load current i o (z), thereby realizing the control of the vehicle-to-vehicle charging device.

[0199] In this embodiment, as Figure 6 shown, calculate the phase shift angle of the reference system according to the phase-shift modulation scheme, and use the obtained phase shift angle to adjust the phase difference of the PWM signal, thereby realizing the control of the charging device. The phase-shift modulation scheme is controlled by the single-phase-shift modulation method. Specifically: the duty cycle of the PWM is fixed at 50% ideally, that is, without considering the dead time. The power switching tubes Q1 and Q4 of the front bridge are the same, Q2 and Q3 are the same, Q1 and Q2 are complementary. The power switching tubes Q5 and Q8 of the rear bridge are the same, Q6 and Q7 are the same, Q5 and Q5 are complementary. Control the phase difference between the PWM of the power tubes in the front bridge and the power tubes in the rear bridge to control the magnitude of the output voltage and the magnitude of the output power.

[0200] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present invention.

Claims

1. A cloud energy storage system for electric vehicles, characterized in that: The system includes: a data receiving module, a vehicle matching module, a path planning module and a service determination module; The data receiving module is used to receive the status information and charging request information sent by all electric vehicles and classify all electric vehicles into power-deficient vehicles and candidate vehicles according to the received charging request information and send them to the vehicle matching module; receive the charging information from the service determination module and store it; The vehicle matching module is used to match a charging vehicle for the power-deficient vehicle from candidate vehicles by calculating a call coefficient according to the state information and charging request information of the power-deficient vehicle, and transmit the state information of the successfully matched power-deficient vehicle and the state information of the charging vehicle to the path planning module; The path planning module is used to plan a path for the charging vehicle according to the status information of the successfully matched power-deficient vehicle and the status information of the charging vehicle, and transmit the generated optimal path to the service determination module; The service determination module is used to send the optimal path to the charging vehicle. When the charging vehicle travels to the location of the power-deficient vehicle along the optimal path, the charging vehicle charges the power-deficient vehicle; and obtain charging information during the charging process and transmit it to the data receiving module.

2. A cloud energy storage system for electric vehicles according to claim 1, characterized in that: The state information includes: real-time location information and battery power information; wherein the real-time location information includes: the city where the vehicle is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power and remaining mileage; The charging request information includes: a charging request and a requested charging amount; The charging information includes charging start information and charging completion information; wherein the charging start information includes: a signal for starting charging, a charging start time, a starting power of a vehicle short of power, and a starting power of a charging vehicle; the charging completion information includes: a charging completion time, a charging completion signal, and the remaining power of the charging vehicle and the power of the vehicle short of power after charging is completed.

3. A cloud energy storage system for electric vehicles according to claim 2, characterized in that: The path planning module includes: a data processing submodule, a plan collection submodule, a route density submodule and a route generation submodule; The data processing submodule is used to load the road network model of the city where the power-deficient vehicle is located and transmit it to the route density submodule; The plan collection submodule is used to collect the planned navigation data of all cars in the city where the power-deficient vehicle is located, and transmit all the collected planned navigation data to the route density submodule in a unified format; The route density submodule is used to calculate the road network density of multiple future time periods in the road network model based on the received planned navigation data and the road network model of the city where the power-deficient vehicle is located, and transmit it to the route generation submodule; The route generation submodule is used to generate an optimal path from the location of the charging vehicle to the location of the power-deficient vehicle according to the road network density in multiple future time periods in the road network model, and transmit the generated optimal path to the service determination module.

4. A method for operating a cloud energy storage system for electric vehicles, characterized in that: The method includes the following steps: Acquire status information and charging request information sent by all vehicles, and based on the received charging request information, mark the electric vehicle that sends charging request information as a power-deficient vehicle, and mark the electric vehicle that does not send charging request information as a candidate vehicle; For any power-deficient vehicle, matching a charging vehicle for the power-deficient vehicle from the candidate vehicles by calculating a call coefficient according to the state information and charging request information of the power-deficient vehicle and the state information of the candidate vehicles; For any successfully matched power-deficient vehicle and charging vehicle, the location of the power-deficient vehicle is taken as the end point, the location of the charging vehicle is taken as the starting point, and an optimal path from the starting point to the end point is planned for the charging vehicle; The charging vehicle drives to the location of the power-deficient vehicle along the planned optimal route, and uses the vehicle-to-vehicle charging device to charge the power-deficient vehicle.

5. The method for operating a cloud energy storage system for electric vehicles according to claim 4, characterized in that: The process of matching the charging vehicle for the power-deficient vehicle from the candidate vehicles by calculating the call coefficient is as follows: Step A1: Obtain status information and charging request information of the power-deficient vehicle, and obtain status information of all candidate vehicles; The state information includes: real-time location information and battery power information; wherein the real-time location information includes: the city where the vehicle is located and the longitude and latitude of the location; the battery power information includes: battery capacity, remaining power and remaining mileage; The charging request information includes: a charging request and a requested charging amount; Step A2: based on the real-time location information of the vehicle with power shortage, select electric vehicles in the same city as the vehicle with power shortage from the candidate vehicles; Step A3: For any electric vehicle after screening, compare the remaining power of the electric vehicle with the charging power requested by the vehicle with insufficient power. If the remaining power of the electric vehicle is greater than the charging power requested by the vehicle with insufficient power, mark the electric vehicle as a vehicle that meets the charging request; otherwise, mark the electric vehicle as a vehicle that does not meet the charging request and exclude it; and then obtain all vehicles that meet the charging request; Step A4: For any vehicle that meets the charging request, calculate the ratio m of the remaining power of the vehicle to the charging power requested by the vehicle with insufficient power, and determine the value parameter g of the vehicle according to the ratio m; Step A5: Calculate the straight-line distance l between the location of the vehicle and the location of the power-deficient vehicle, and determine the distance parameter h of the vehicle according to the straight-line distance l; Step A6: Calculate the call coefficient k of the vehicle according to the value parameter g of the vehicle and the distance parameter h of the vehicle; repeat steps A4-A5 to obtain the call coefficients k of all vehicles that meet the charging request, and sort all vehicles that meet the charging request in descending order according to the call coefficients k; The calculation method of the call coefficient k is: Where e is a natural constant; Step A7: Adopt a calling strategy to call vehicles according to the sorting results of all vehicles that meet the charging request. When the owner of any vehicle that meets the charging request responds, stop calling and use the vehicle that meets the charging request as a charging vehicle for the power-deficient vehicle.

6. The method for operating a cloud energy storage system for electric vehicles according to claim 5, characterized in that: The specific content of step A7 is: setting a call batch according to the number of all vehicles that meet the charging request, and setting the call time interval between different call batches; based on the sorting results of all vehicles that meet the charging request, calling all vehicles that meet the charging request in turn according to the set call batches and call time intervals until all vehicles that meet the charging request have been called; when the owner of a vehicle that meets the charging request confirms to go to charge the vehicle that is out of power, the calling process is terminated, and the vehicle that meets the charging request is used as the charging vehicle for the vehicle that is out of power.

7. The method for operating a cloud energy storage system for electric vehicles according to claim 8, characterized in that: The process of planning an optimal path from the starting point to the end point for the charging vehicle is as follows: Step B1: Load the road network model of the city where the power-deficient vehicle is located, and record the road network model as G=(A, B, W); where G represents the road network; A represents the set of intersection nodes in the road network; B represents the set of road sections in the road network; W represents the set of weights of all road sections in the road network; Step B2: Collect the planned navigation data of all vehicles in the city where the power-deficient vehicle is located and unify the data format; Step B3: Calculate the density of each road section in the road network model of the city where the power-deficient vehicle is located in multiple future time periods according to the unified planned navigation data; Step B4: Based on the density of each road section in the road network model of the city where the power-deficient vehicle is located in multiple future time periods, a path optimization algorithm is used to plan an optimal path from the starting point to the end point for the charging vehicle.

8. The method for operating a cloud energy storage system for electric vehicles according to claim 7, characterized in that: The content of step B3 is: taking all vehicles with planned navigation data as sample vehicles, and based on the road network model of the city where the power-deficient vehicle is located, for any sample vehicle, calculating the total driving distance of the sample vehicle in multiple future time periods according to the unified planned navigation data of the sample vehicle; The method for calculating the total driving distance of the sample vehicle in several future time periods is as follows: for any time period in the time period set N, the density of all road sections in the time period is obtained, and the vehicle driving speed of each road section in the time period is calculated according to the density of all road sections obtained; The vehicle speed is calculated as follows: v ij (n)=v f (1-k ij (n) / k m ) 1.2 where v ij (n) represents the vehicle speed on the road section (i, j) in the nth time period; v f Indicates the free-flowing speed of the road section; k ij (n) represents the density of the road segment (i, j) in the nth time period; k m represents the optimal density of the road segment; where i represents the predecessor node of the road segment (i, j), j represents the successor node of the road segment (i, j), and i=j, i, j∈A, A represents the set of intersection nodes in the road network; The driving distance of the sample vehicle planned to navigate during the period is calculated using the vehicle driving speed of each road section during the period; Then, the total driving distance of the sample vehicle from the departure time to the end of the nth time period is calculated using the driving distance of the sample vehicle planned navigation in the time period; According to the total travel distance of the sample vehicle from the departure time to the end of the nth time period, determine the road sections that the vehicle passes through in each time period from the departure time to the end of the nth time period. For any road section (i, j) passed by the sample vehicle, obtain the time period corresponding to when the sample vehicle passes through the road section (i, j), and add 1 to the number of all vehicles passing through the road section (i, j) in the time period, thereby updating the density of the road section (i, j) in the time period; By traversing all vehicles with planned navigation data, the density of each road section in multiple future time periods in the road network model of the city where the power-deficient vehicle is located is obtained.

9. The method for operating a cloud energy storage system for electric vehicles according to claim 8, characterized in that: The specific content of step B4 is: for any future time period, using the density and length of each road section in the road network model of the city where the power-deficient vehicle is located, calculate the road weight of each road section in the time period; The intersection node corresponding to the location of the power-deficient vehicle is taken as the target node; the intersection node corresponding to the location of the charging vehicle is taken as the starting node; the road network model of the city where the power-deficient vehicle is located is initialized, and for any intersection node in the road network model, the b value and rhs value of the intersection node are set to infinity; where b represents the estimated cost from the starting node to the intersection node; and rhs represents the cost from the intersection node to the target node; Set the rhs value of the starting node to 0, initialize a priority queue, use the road weight of each section in the time period to calculate the V value of each intersection node, and insert each intersection node into the priority queue in ascending order of the V value of each intersection node; where V represents the total estimated cost of the intersection node; The calculation method of the V value of the intersection node is: Where V1(i) and V2(i) represent the two parts of the V value of intersection node i; b(i) represents the b value of intersection node i; rhs(i) represents the rhs value of intersection node i; h(i,j,n) is the heuristic value of the road segment (i,j) when the charging vehicle performs path search in the nth time period; d represents the number of time periods in the future time period; a represents the index variable, and a is a positive integer; w ij (n+a) represents the road weight of the road section (i, j) in the n+ath time period; i goai represents the intersection node where the power-deficient vehicle is located; j∈succ(i) represents the intersection node j is the successor node of intersection node i; succ(i) represents the set of successor nodes of intersection node i; b(j) represents the b value from intersection node j to the target node; c(i,j) represents the cost from intersection node i to intersection node j; Remove the intersection node with the smallest V value from the priority queue, and set the b value of the intersection node to be equal to the rhs value, set the neighboring intersection nodes of the intersection node and calculate the rhs values ​​of all neighboring intersection nodes, for any neighboring intersection node of the intersection node, obtain the b value of the neighboring intersection node and compare it with the b value of reaching the neighboring intersection node through the intersection, if the two are not equal, calculate the V value of the neighboring intersection node, and update the position of the neighboring intersection node in the priority queue according to the V value of the neighboring intersection node, until the b value of the neighboring intersection node is equal to the b value of reaching the neighboring intersection node through the current node; If the two are equal, starting from the starting node, the next intersection node of the current intersection node is selected according to the rule of selecting the intersection node with the smallest b value until the target node is reached, thereby generating the optimal path.

10. The method for operating a cloud energy storage system for electric vehicles according to claim 9, characterized in that: The vehicle charging device comprises: an input end, a dual active bridge DC / DC converter and an output end connected in sequence; wherein the input end is connected to a battery of a charging vehicle, and the input voltage of the input end is the discharge voltage of the battery of the charging vehicle; the output end is connected to a battery of a power-deficient vehicle, and the charging voltage of the battery of the power-deficient vehicle is; The control process of charging the power-deficient vehicle using the vehicle-to-vehicle charging device is as follows: a reference system for describing the expected output of the dual active bridge DC / DC converter is defined, which is expressed as: where u r (t) represents the output voltage; Indicates the output voltage u r (t) derivative with respect to time t; u ref (t) represents the output voltage target reference value; p represents the bandwidth of the dual active bridge DC / DC converter, and p>0; t represents time; The voltage tracking error e(t) is defined according to the output voltage target reference value and the output voltage, and then the nominal state feedback i is calculated according to the dual active bridge DC / DC converter model and the voltage tracking error e(t). fb (t); Define the disturbance estimation filter G f (s), and use the perturbation to estimate the filter G f (s) Calculate the estimate f of the external disturbance f(t) e (t); According to the nominal state feedback i fb (t) and an estimate of the external disturbance f(t) e (t) is the difference between the control rate i ref (t); The disturbance estimation filter G f (s), estimate of the external disturbance f(t) e (t) and control rate i ref (t) is discretized to obtain the system uncertainty and disturbance estimator controller, and the output voltage, load current and output voltage reference value of the dual active bridge DC / DC converter are converted to the discrete domain z to obtain the output voltage u in the discrete domain o (z), load current i o (z) and output voltage reference value u ref (z), and use the system uncertainty and disturbance estimator controller to calculate the load reference current i in the discrete domain ref (z); For the discrete domain load reference current i ref (z) delays, and according to the shift modulation scheme, uses the delayed i ref (z) Generates the phase shift angle of the PWM signal Using the phase shift angle of the PWM signal Control the switching action of the dual active bridge DC / DC converter, and then control the output voltage u of the dual active bridge DC / DC converter o (z) and load current i o (z), thereby realizing the control of the vehicle charging device.

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