Electric vehicle path and charging scheduling joint optimization method, system and device and storage medium

Abstract

The invention discloses an electric vehicle path and charging scheduling joint optimization method, system and device and a storage medium, and the method comprises the steps: dividing a nonlinear charging process of an electric vehicle into a plurality of stages, carrying out the approximation of the plurality of stages through employing a piecewise linear function, and obtaining piecewise linear charging data; a state-space-time network is constructed; calculating road section energy consumption between the physical nodes based on the vehicle parameters of the electric vehicle and an energy consumption model; establishing a mapping relation between charging station selection and charging amount decision; generating a feasible state-space-time arc in the state-space-time network, the feasible state-space-time arc connecting different nodes in the state-space-time network; and by taking minimization of the total time as an optimization target, searching a target path from the starting node to the terminal node in a state-space-time network formed by the feasible state-space-time arc, and obtaining an electric vehicle driving route and a charging scheduling scheme corresponding to the target path.

Description

Technical Field

[0001] This invention belongs to Intelligent Transportation This field relates to a method, system, device, and storage medium for joint optimization of electric vehicle routing and charging scheduling. Background Technology

[0002] In recent years, electric vehicles have experienced rapid development due to their economic, environmental, and social benefits. However, despite significant technological and market advancements, electric vehicles still face numerous challenges in long-distance travel scenarios. Limited driving range, insufficient charging station availability, and excessively long charging times are key factors hindering long-distance electric vehicle travel. These issues not only reduce the user experience but also impede the widespread adoption of electric vehicles.

[0003] Lithium-ion batteries are widely used in electric vehicles due to their high energy density. Despite continuous advancements in engineering technology to improve charging speed, excessively long charging times remain a major obstacle to user acceptance. Research indicates that the charging speed of lithium-ion batteries is not constant and is influenced by factors such as voltage, current, temperature, and battery protection mechanisms, typically decreasing over time. Therefore, accurately representing the electric vehicle charging process using a single deterministic function is challenging.

[0004] Existing research on electric vehicle path planning has the following main shortcomings: 1. Early research simplified the charging process by assuming that electric vehicles are fully charged at each charging station, which is quite different from reality.

[0005] 2. Some studies use linear charging functions to model charging behavior, which is closer to reality than the full charging assumption, but may still lead to inaccurate or overly optimistic results.

[0006] 3. In studies considering nonlinear charging processes, most research employs heuristic algorithms, which can yield approximate solutions but cannot guarantee optimality.

[0007] 4. A few studies that use precise algorithms have low computational efficiency and are difficult to support real-time planning and replanning requirements. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and storage medium for joint optimization of electric vehicle route and charging scheduling, which takes into account the nonlinear charging process, optimizes route and charging scheduling at the same time, and supports real-time replanning.

[0009] To achieve the above objectives, the present invention employs the following technical solution: A joint optimization method for electric vehicle routing and charging scheduling includes the following steps: The charging characteristic data of electric vehicles are obtained, the nonlinear charging process of electric vehicles is divided into multiple stages, and piecewise linear functions are used to approximate multiple stages to obtain piecewise linear charging data. Construct a state-space-time network that includes physical nodes, discrete time points, and discrete battery charge states. The physical nodes include charging station nodes and road intersection nodes. Based on the vehicle parameters and energy consumption model of electric vehicles, the energy consumption of road segments between physical nodes is calculated. Establish a mapping relationship between charging station selection and charging quantity decision. This mapping relationship is based on the optimal charging scheduling characteristics and sets the battery charge of the electric vehicle when it arrives at the selected charging station to be equal to a preset safe charge threshold. Based on the segmented linear charging data, road segment energy consumption and mapping relationship, a feasible state-space-time arc is generated in the state-space-time network, which connects different nodes in the state-space-time network. With minimizing the total time as the optimization objective, a target path from the starting node to the ending node is searched in a state-space-time network consisting of feasible state-space-time arcs. The total time includes travel time, charging time, and operation time; thus, the electric vehicle driving route and charging scheduling scheme corresponding to the target path are obtained.

[0010] A joint optimization system for electric vehicle routing and charging scheduling includes: The charging data acquisition module is used to acquire charging characteristic data of electric vehicles, divide the nonlinear charging process of electric vehicles into multiple stages, and approximate multiple stages with piecewise linear functions to obtain piecewise linear charging data. The State-Space-Time Network Building Module is used to construct a state-space-time network that includes physical nodes, discrete time points, and discrete battery power states. The physical nodes include charging station nodes and road intersection nodes. The road segment energy consumption calculation module is used to calculate the road segment energy consumption between physical nodes based on the vehicle parameters and energy consumption model of electric vehicles. The mapping relationship establishment module is used to establish a mapping relationship between charging station selection and charging quantity decision. This mapping relationship is based on the optimal charging scheduling characteristics and sets the battery charge of the electric vehicle when it arrives at the selected charging station to be equal to a preset safe charge threshold. The node connection module is used to generate a feasible state-space-time arc in the state-space-time network based on segmented linear charging data, road segment energy consumption and mapping relationship. This feasible state-space-time arc connects different nodes in the state-space-time network. The scheduling scheme generation module is used to search for the target path from the starting node to the ending node in a state-space-time network consisting of feasible state-space-time arcs with the optimization objective of minimizing the total time. The total time includes driving time, charging time and operation time; and obtains the electric vehicle driving route and charging scheduling scheme corresponding to the target path.

[0011] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electric vehicle routing and charging scheduling joint optimization method.

[0012] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the electric vehicle routing and charging scheduling joint optimization method.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention addresses the challenge of accurately modeling the nonlinear charging characteristics of electric vehicle lithium batteries, which is computationally complex. It innovatively employs a piecewise linear approximation strategy and constructs a state-space-time (SST) network that integrates physical nodes, discrete time, and battery charge state. Through in-depth analysis of optimal charging scheduling characteristics, this invention establishes a deterministic mapping relationship between charging station selection and charging quantity decisions, successfully transforming the originally complex mixed-integer programming problem into a more efficient 0-1 programming problem. This technological breakthrough not only achieves an accurate description of the actual charging behavior of electric vehicles, avoiding estimation errors caused by traditional linear charging assumptions or full-charge-discharge strategies, but also significantly reduces the computational dimensionality while ensuring solution accuracy, solving the pain point of existing accurate algorithms struggling to handle nonlinear charging constraints in a short time.

[0014] This invention overcomes the limitations of traditional navigation systems where path planning and charging decisions are isolated or optimized step-by-step. It achieves joint global optimization of driving routes and charging scheduling, outputting the solution with the shortest total time (including driving, charging, and operation time) within seconds. Thanks to its extremely high computational efficiency, this system possesses powerful real-time replanning capabilities. When the vehicle encounters sudden disturbances such as traffic congestion, increased energy consumption, or the unavailability of the target charging station during actual driving, it can quickly regenerate the optimal strategy from the current state. This high dynamic adaptability significantly improves the reliability and efficiency of long-distance electric vehicle travel, effectively alleviates users' range anxiety, and provides a highly available technical path for electric vehicle route guidance in intelligent transportation environments. Attached Figure Description

[0015] Figure 1This is a flowchart of the electric vehicle route and charging scheduling joint optimization method of the present invention; Figure 2 This is a schematic diagram of the SST network construction of the present invention; Figure 3 This is a schematic diagram of the circuit simulation scenario of the present invention; Figure 4 This is a schematic diagram of the routes for two charging scheduling schemes in the SST network of the present invention; Figure 5 A schematic diagram showing the charging station locations and corresponding SOC levels selected for each of the two strategies of the present invention; Figure 6 This is a schematic diagram illustrating the SOC variation trend over time for the two charging scheduling schemes of the present invention. Figure 7 This is a schematic diagram showing the starting point (Xi'an), the ending point (Shijiazhuang), and the distribution of charging stations of this invention; Figure 8 This is a schematic diagram illustrating the route differences between the optimized scheduling and the baseline scheduling of this invention; Figure 9 A schematic diagram comparing the SOC changes of two scheduling methods is provided to illustrate the spatial dimension (order of charging stations along the optimized path) of this invention. Figure 10 The diagram illustrates the SOC changes and total time differences between the two scheduling methods in the context of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] The present invention discloses a joint optimization method for electric vehicle path and charging scheduling that considers the nonlinear charging process. This method can simultaneously optimize the driving path and charging scheduling of electric vehicles based on the nonlinear charging characteristics of lithium batteries, providing accurate solutions and supporting real-time planning at the second level.

[0018] This invention solves the above problems through the following technical means: First, a piecewise linear function is used to approximate the nonlinear charging process; second, a mixed integer programming model is established based on the piecewise linear approximation to optimize charging scheduling on a fixed path; then, by analyzing the characteristics of the optimal charging scheduling, a mapping relationship between charging station selection and charging quantity decision is established; finally, a state-space-time (SST) network is constructed to integrate path planning and charging scheduling optimization into a problem of searching for the optimal three-dimensional path in the SST network.

[0019] like Figure 1 As shown, the specific process of the joint optimization method for electric vehicle routing and charging scheduling is as follows: S1, Piecewise linear approximation of the nonlinear charging process.

[0020] The charging process of lithium batteries in electric vehicles exhibits nonlinear characteristics. A typical charging process includes two stages: constant current charging and constant voltage charging. Constant current stage (0%-80% SOC): The battery voltage gradually increases, the current remains constant, and the charging speed is stable.

[0021] Constant voltage stage (80%-100% SOC): The voltage remains constant, the current decreases exponentially, and the charging speed gradually decreases.

[0022] This invention uses a piecewise linear function to approximate the nonlinear charging process, dividing the charging curve into three stages, each represented by a linear function. The charging time starts from the initial state of 0. The SOC can be calculated using the following formula:

[0023] In the formula: It is a mapping function from charging time to battery capacity; and These are the battery levels at the start of the second and third stages of the charging process, respectively. , and These are the end times for stages 1, 2, and 3 of the charging process, respectively.

[0024] In a typical embodiment of the present invention, the following parameter settings are adopted: Phase 1 (0-80% SOC): Charging time 50 minutes.

[0025] Phase 2 (80%-95% SOC): Charging time 25 minutes.

[0026] Stage 3 (95%-100% SOC): Charging time 25 minutes.

[0027] This piecewise linear function accurately reflects the characteristic that the SOC grows rapidly before reaching 80% during actual charging, and then the growth rate gradually decreases.

[0028] S2, Electric Vehicle Energy Consumption Model.

[0029] This invention adopts a steady-state driving energy consumption model, which decomposes energy consumption power into four parts: air resistance power, transmission system power, rolling resistance power, and auxiliary power.

[0030] 2.1 Calculation of each power component.

[0031] Air resistance power: .

[0032] Transmission system power: .

[0033] Rolling resistance power: .

[0034] Auxiliary power (audio, lighting, air conditioning, etc.): .

[0035] 2.2 Total energy consumption calculation.

[0036] Steady-state power consumption: .

[0037] Total energy consumption: .

[0038] In the formula: The average speed of the vehicle. For vehicle weight, , , , , , These are the coefficients for the energy consumption model.

[0039] S3 is a charging scheduling optimization model under a fixed path.

[0040] 3.1 Problem Description and Objective Function.

[0041] Given a fixed path, there are on the path There are [number] charging stations. The optimization objective is to minimize the total charging time so that the electric vehicle can reach its destination.

[0042] Objective function: .

[0043] In the formula: It is a 0-1 variable, indicating whether it is at a charging station. Charging (1 indicates charging, 0 indicates not charging); For charging stations Charging time; The typical value for the operation time for each charge (parking, plugging and unplugging the charging gun, etc.) is 5 minutes. This represents the total number of charging stations along the route.

[0044] 3.2 Constraints.

[0045] Charging time calculation: .

[0046] In the formula: To reach the charging station Battery level at the time; For charging stations The amount of charge (expressed as a percentage of battery capacity); It is the inverse function of the charging function; A collection of charging stations.

[0047] Battery safety threshold constraints: .

[0048] Charging limit constraint: .

[0049] Battery capacity recursion: .

[0050] In the formula: The safe power threshold (typically 15%). For charging stations and Energy consumption (expressed as a percentage of battery capacity).

[0051] Decision variable constraints:

[0052]

[0053] The model contains integer variables. and continuous variables This is a mixed integer programming (MIP) problem with high computational complexity.

[0054] S4, Optimal Charging Scheduling Characteristics Analysis.

[0055] To reduce computational complexity, this invention provides an in-depth analysis of the characteristics of optimal charging scheduling and proves two key lemmas.

[0056] 4.1 Lemma 1: The final charge is equal to the safety threshold.

[0057] Lemma 1: Under optimal charging scheduling, the battery charge of an electric vehicle when it arrives at the last charging station is equal to the safety threshold. .

[0058] Proof: Proof by contradiction. Assume an optimal schedule exists. , making .

[0059] set up For the last selected charging station:

[0060] In scheduling Below, charging station The charging time is:

[0061] Now construct a new scheduler ,in:

[0062] In scheduling Below, charging station The charging time is:

[0063] because ,have ,Right now .

[0064] according to Figure 2 Due to the characteristics of its charging curve, it takes longer to charge more power from the same initial capacity. Therefore:

[0065] The only difference between the two scheduling methods is the charging station. The charging amount, therefore the total charging time difference is:

[0066] This illustrates scheduling Compared to the so-called "optimal" scheduling It saves more time, which contradicts the assumption. Therefore, Lemma 1 is proved.

[0067] 4.2 Lemma 2: The amount of electricity reached at the intermediate charging station is equal to the safety threshold.

[0068] Lemma 2: Under optimal charging scheduling, electric vehicles arrive at each selected charging station. ( The battery level at which point the battery capacity equals the safety threshold is reached. ,in The set of charging stations selected for charging. The first charging station selected.

[0069] Proof: Assume that in the optimal scheduling Next, we arrived at the charging station. and The battery levels at each time were respectively and The charging amounts are respectively and ,and ,in .

[0070] Construct a new schedule Adjust the charging station and Charge amount:

[0071]

[0072] The charging time difference between the two schedules is:

[0073] The first item indicates that it is at a charging station. Charge Time required, initial charging capacity: The second item indicates at the charging station. Charge Time required, initial charging capacity: .

[0074] The two items have the same charging capacity, but different initial charging capacities:

[0075] Based on the characteristics of the charging curve ( Figure 2 The higher the initial battery level, the longer it takes to charge the same amount of battery. Therefore, the first term is greater than the second term, i.e. .

[0076] This illustrates scheduling Comparison of scheduling More time-saving, and This contradicts the assumption of optimal scheduling. Therefore, Lemma 2 is proved.

[0077] Note: The first selected charging station The battery level at arrival depends on the initial battery level and the distance traveled, and is not necessarily equal to... .

[0078] S5, the mapping relationship between charging station selection and charging amount.

[0079] Based on the two lemmas mentioned above, a charging station selection mechanism can be established. With charge amount Mapping relationship between them:

[0080]

[0081] In the formula: The set of charging stations selected for charging; and For set The first and second charging stations in the middle; Battery capacity; For charging stations arrive Energy consumption between them.

[0082] Mapping relationship explanation: The first charging station ( The charging amount needs to be sufficient to ensure the vehicle reaches the second charging station. Hourly power consumption ,therefore: .

[0083] Other selected charging stations ( According to Lemma 2, the vehicle's charge level when it arrives at these stations is... The amount of charge is equal to the energy required to reach the next station: .

[0084] Unselected charging station: Charging amount is 0.

[0085] This mapping relationship will connect continuous variables With 0-1 variables A definite functional relationship was established, which simplified the mixed integer programming problem into a pure 0-1 programming problem, greatly reducing the computational complexity.

[0086] Part Two: Joint Optimization Model and Solution Method.

[0087] S6, State-Space-Time (SST) Network Construction.

[0088] To achieve joint optimization of path planning and charging scheduling, this invention constructs a state-space-time network that includes three dimensions: space, time, and battery charge (SOC).

[0089] 6.1 Physical road network construction.

[0090] Define the road network structure and set... Represents a transportation network consisting of a finite number of nodes, where: A set of physical nodes (including charging stations and road intersections); For directed road segments; Represents a physical node in the network; Indicates from node To the node A directional section of road.

[0091] 6.2 SST node set construction.

[0092] Construct SST node set The nodes of the SST network consist of triples. It means that, among them: Spatial location (physical node); For time (the set of discrete time points); For SOC value (discrete set of electrical states); , SST node Add to collection .

[0093] Discretization explanation: Time dimension: Discretization is performed using a 1-hour time step.

[0094] SOC dimension: Discretization is performed using a 5% power step size.

[0095] The discretization precision can be adjusted according to actual needs; the higher the precision, the greater the computational load.

[0096] 6.3 SST arc construction.

[0097] Construct the SST arc set For road segments in the physical road network ,set up: The average vehicle speed on the road segment; and Given two nodes in an SST network, the SST arc can be represented as follows: Its endpoint time and energy satisfy: Due to time constraints: .

[0098] In the formula: For the node Charging time; Whether or not at the node The 0-1 variable for charging; Charging operation time (5 minutes); For road section The length.

[0099] Battery life relationship: .

[0100] In the formula: For the node The amount of charge; For road section Energy consumption.

[0101] By iterating through all possible... and Combining different charging station selection strategies ( (with different values), construct all feasible SST arcs and add them to the set. .

[0102] Feasible SST arc constraint: Based on the mapping relationship between charging station selection and charging amount established in Section 5, charging time and charging amount The value is selected by the charging station set. Uniquely determined. Therefore, by considering Different combinations of values ​​can yield a feasible set of SST arcs. .

[0103] Meanwhile, the endpoints of all feasible SST arcs constitute a set of feasible SST nodes. These constraints significantly reduce the number of SST nodes and arcs that need to be considered, thus improving computational efficiency.

[0104] like Figure 2 As shown, the construction process of the SST network includes: (a) a directed physical road network; (b) a physical node-time node network; and (c) a three-dimensional network of physical nodes-time nodes-SOC nodes.

[0105] S7, a joint optimization model for path and charging scheduling.

[0106] Based on the constructed SST network, the joint optimization problem of path planning and charging scheduling can be transformed into searching within the SST network from the starting node. To the destination node The optimal three-dimensional path.

[0107] 7.1 Objective function.

[0108]

[0109] In the formula: A 0-1 variable, representing the SST arc. Whether it is selected (1 indicates selected, 0 indicates not selected); the first item is the total charging time (including charging time and operation time); the second item is the total driving time.

[0110] 7.2 Constraints.

[0111] Flow conservation constraint:

[0112] This constraint ensures that electric vehicles form a continuous SST path from the start point to the end point by connecting multiple SST arcs.

[0113] Endpoint constraints:

[0114] In the formula, Let this be the destination node. According to Lemma 1, the terminal energy should equal the safety threshold. .

[0115] Decision variable constraints:

[0116]

[0117] Constraints related to charging station selection and route:

[0118] This constraint ensures that only when the SST arc... When on the selected path ( Only then can it be done at the node. Charge( ).

[0119] Charging cycles constraint:

[0120] Lower bound: The number of charging cycles is no less than the minimum number of charging cycles required to complete the path (based on total energy consumption estimation).

[0121] Upper bound: The number of charging times does not exceed the number of SST arcs on the path (i.e., the case where charging is done at every charging station).

[0122] This constraint further narrows the feasible solution space and improves solution efficiency by estimating the number of charging cycles.

[0123] S8, Model Solving and Algorithm Implementation.

[0124] 8.1 Model characteristics.

[0125] By establishing a mapping relationship between charging station selection and charging volume, this invention successfully transforms the original Mixed Integer Programming (MIP) problem into a 0-1 programming problem. Compared with the traditional MIP problem: Simplified variable types: All decision variables are 0-1 variables, eliminating the need to handle continuous variables. .

[0126] Reduced computational complexity: The branch and bound search space of 0-1 programming is much smaller than that of the MIP problem.

[0127] Improved solution speed: Experimental results show that the solution time has been reduced from several minutes or even hours to seconds.

[0128] 8.2 Solution method.

[0129] The model can be solved using mature mixed-integer programming solvers, such as: commercial solvers: CPLEX, Gurobi; open-source solvers: GLPK, CBC.

[0130] In an embodiment of the present invention, the CPLEX solver is called using the Yalmip toolbox of MATLAB, with the following configuration: Processor: Intel i7 CPU; Memory: 16 GB RAM.

[0131] Solution time: The optimal solution is typically obtained within 1-2 seconds.

[0132] 8.3 Real-time replanning.

[0133] In case of unexpected situations (such as traffic congestion leading to additional energy consumption, or charging stations being unavailable), you can use your current location, time, and battery level as a new starting point. Then, the above model is called again to solve the problem, and a new optimal path and charging strategy are quickly obtained.

[0134] Since the model solves in seconds, it fully meets the requirements of real-time replanning, making this invention highly practical and robust.

[0135] S9, Parameter Settings and Implementation Details. 9.1 Charging-related parameters: In a typical embodiment, the following charging parameters are set:

[0136] 9.2 Energy consumption model parameters: The energy consumption model parameters are adopted from the study by Van Haaren (2011):

[0137] 9.3 Vehicle parameters.

[0138]

[0139] 9.4 Discretization parameters.

[0140]

[0141] The above parameters can be adjusted according to the actual application scenario, vehicle characteristics, and accuracy requirements.

[0142] Example 1: Line Simulation To verify the effectiveness of the "Charging Scheduling Optimization Method Based on State-Space-Time (SST) Network" in a fixed route scenario, the Beijing-Shenzhen Expressway, with a total length of approximately 2353 kilometers, was selected as the test environment (see schematic diagram of the route scenario). Figure 3 As shown in the figure, 16 charging stations are distributed along the route, which can fully simulate the charging scheduling needs of electric vehicles traveling long distances. The test object is a single electric vehicle, and the parameters are set as follows: initial SOC 80%, average vehicle speed 80km / h, maximum battery capacity 60kWh, safe SOC threshold 15%, and piecewise linearization of the charging function (SOC [0,80%] charging time 50 minutes, [80%,95%] 25 minutes, [95%,100%] 25 minutes). The core of the test is to compare the total charging time difference between the optimized charging scheduling strategy of this invention and the traditional full charging strategy, and to verify the advantages of the method in "charging on demand and improving efficiency". The optimized charging scheduling strategy of this invention selects charging stations 2, 4, 6, 8, 10, 12, and 14 for on-demand charging through SST network analysis: during the vehicle's journey, the charging amount and charging time of each charging station are precisely controlled according to the energy consumption of subsequent road segments and the safe SOC threshold. For example, charging 35.04 kWh at charging station 2 (taking 41.5 minutes) increases the SOC from 22% to 75% after driving; charging 42.36 kWh at charging station 4 (taking 52.63 minutes) increases the SOC from 18% to 80%, and so on, with a final total charging time of only 306.5 minutes.

[0143] In contrast, the traditional full-charging strategy (baseline scheduling) selects charging stations 2, 5, 7, and 9, following the rule of "charging to full capacity at each charging station": charging 43.20 kWh at charging station 2 (100 minutes), and charging 49.20 kWh at charging station 5 (93.75 minutes), for a total charging time of 526 minutes. The optimized strategy, through a "high-frequency, short-duration, on-demand charging" mode, avoids the slow charging phase after SOC > 80%, ultimately achieving a time saving of 219.5 minutes, a saving rate of 41.7%.

[0144] Comparison of SOC changes from multiple dimensions (e.g.) Figure 4-6 As shown in the figure, the efficiency advantage of the optimization strategy can be intuitively demonstrated: Figure 4 (Schematic diagram of two charging scheduling routes in SST network) shows the path difference between optimized scheduling and baseline scheduling in the three-dimensional network. Optimized scheduling routes are denser but charging time is shorter. Figure 5 (Charging station locations and corresponding SOC levels selected by the two strategies) clarified that the charging stations selected by the optimization strategy are more in line with the node where "SOC is close to the safe threshold"; Figure 6The trend of SOC change over time for the two charging scheduling schemes shows that the SOC of the optimized strategy is always maintained in the high-efficiency charging range of 15%-80%, while the baseline strategy often shows that the SOC stagnates in the high range for a long time during slow charging.

[0145] Example 2: Network Simulation To verify the effectiveness of the "Joint Optimization Method Based on State-Space-Time (SST) Network" of this invention in a real-world scenario, the Xi'an-Shijiazhuang Expressway network containing 48 charging stations was selected. Figure 7 The test environment is designed to simulate the actual road network node connections, road segment distances, and charging station distribution, which can simulate the collaborative needs of electric vehicles in "route selection-charging scheduling".

[0146] The test subject was a single electric vehicle traveling from Xi'an to Shijiazhuang. Parameters were set as follows: maximum battery capacity 60kWh, safe SOC threshold 15%, average vehicle speed 80km / h, and piecewise linearized charging function (50 minutes for SOC [0,80%], 25 minutes for [80%,95%], and 25 minutes for [95%,100%]). It was also assumed that there were no queues at charging stations. The core of the test was to compare the total charging time of the optimized strategy of this invention with that of the traditional fully charged strategy, and to verify the adaptability and computational efficiency of the method to different initial SOC and OD pairs.

[0147] like Figure 8 The optimization strategy shown uses the SST network to fuse 3D information, and the output path is "charging station 7→49→29→48→47→26→27→40". Charging is only done on demand at stations 7, 48, and 26: the initial SOC is 50%, which drops to 22% at station 7, and charges 19.92kWh (25.75 minutes) to 55%; at station 48, it drops to 18%, and charges 33.12kWh (39.5 minutes) to 70%; at station 26, it drops to 16%, and charges 38.52kWh (45.13 minutes) to 79%, and finally arrives safely, with a total time of 657 minutes (542 minutes of driving and 115 minutes of charging).

[0148] The traditional full-charge strategy, following the same path but charging at stations 7, 47, and 27, takes 84.5 minutes to fully charge at station 7, 81.5 minutes at station 47, and 19.38 minutes at station 27, for a total of 732 minutes (190 minutes of charging). The optimized strategy, by avoiding high SOC and low-speed charging, saves 75 minutes, a time saving of 10.2%.

[0149] From the SOC change graph ( Figure 9-10As can be seen, the optimized strategy consistently maintains a SOC efficiency range of 15%-80%, with precise matching between charging nodes and battery capacity; the baseline strategy suffers from charging redundancy and low time efficiency. In terms of computational efficiency, the method can solve all 6 initial SOCs and 6 OD pairs in the second range (<2 seconds, average 0.72 seconds), far exceeding the traditional CPLEX solver (average 426 seconds), thus meeting the requirements for real-time replanning.

[0150] Symbol Explanation Table: variable:

[0151] index:

[0152] parameter:

[0153] gather:

[0154] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not omitted in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0155] In another embodiment of the present invention, a joint optimization system for electric vehicle routing and charging scheduling is provided. This joint optimization system can be used to implement the above-mentioned joint optimization method for electric vehicle routing and charging scheduling. Specifically, the joint optimization system includes a charging data acquisition module, a state-space-time network construction module, a road segment energy consumption calculation module, a mapping relationship establishment module, a node connection module, and a scheduling scheme generation module.

[0156] The charging data acquisition module is used to acquire charging characteristic data of electric vehicles. It divides the nonlinear charging process of electric vehicles into multiple stages and uses piecewise linear functions to approximate multiple stages to obtain piecewise linear charging data.

[0157] The State-Space-Time Network Building Module is used to build a state-space-time network that includes physical nodes, discrete time points, and discrete battery power states. The physical nodes include charging station nodes and road intersection nodes.

[0158] The road segment energy consumption calculation module is used to calculate the road segment energy consumption between physical nodes based on the vehicle parameters and energy consumption model of electric vehicles.

[0159] The mapping relationship establishment module is used to establish a mapping relationship between charging station selection and charging quantity decision. This mapping relationship is based on the optimal charging scheduling characteristics and sets the battery charge of the electric vehicle when it arrives at the selected charging station to be equal to a preset safe charge threshold.

[0160] The node connection module is used to generate feasible state-space-time arcs in the state-space-time network based on segmented linear charging data, road segment energy consumption and mapping relationships. These feasible state-space-time arcs connect different nodes in the state-space-time network.

[0161] The scheduling scheme generation module is used to search for the target path from the starting node to the ending node in a state-space-time network consisting of feasible state-space-time arcs with the optimization objective of minimizing the total time. The total time includes driving time, charging time and operation time; and obtains the electric vehicle driving route and charging scheduling scheme corresponding to the target path.

[0162] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, the computer program including program instructions, and the processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., are the computing and control core of the terminal. They are suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the invention can be used for the operation of the joint optimization method of electric vehicle path and charging scheduling, including: acquiring charging characteristic data of electric vehicles, dividing the nonlinear charging process of electric vehicles into multiple stages, approximating multiple stages with piecewise linear functions to obtain piecewise linear charging data; constructing a state-space-time network containing physical nodes, discrete time points, and discrete battery charge states, where physical nodes include charging station nodes and road intersection nodes; based on electric vehicles The system uses vehicle parameters and energy consumption models to calculate road segment energy consumption between physical nodes; it establishes a mapping relationship between charging station selection and charging quantity decision-making, based on optimal charging scheduling characteristics, setting the battery charge level of the electric vehicle when it arrives at the selected charging station to be equal to a preset safe charge threshold; based on piecewise linear charging data, road segment energy consumption, and the mapping relationship, it generates feasible state-space-time arcs in the state-space-time network, which connect different nodes in the state-space-time network; with minimizing the total time as the optimization objective, it searches for the target path from the starting node to the ending node in the state-space-time network composed of feasible state-space-time arcs, where the total time includes travel time, charging time, and operation time; and obtains the electric vehicle driving route and charging scheduling scheme corresponding to the target path.

[0163] In another embodiment, the present invention also provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here may include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here may be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0164] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the joint optimization method for electric vehicle path and charging scheduling in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: acquiring charging characteristic data of electric vehicles, dividing the nonlinear charging process of electric vehicles into multiple stages, approximating multiple stages with piecewise linear functions to obtain piecewise linear charging data; constructing a state-space-time network including physical nodes, discrete time points, and discrete battery charge states, where physical nodes include charging station nodes and road intersection nodes; calculating the road segment energy consumption between physical nodes based on the vehicle parameters and energy consumption model of electric vehicles; building A mapping relationship between charging station selection and charging quantity decision is established. This mapping relationship is based on optimal charging scheduling characteristics, setting the battery level of the electric vehicle when it arrives at the selected charging station to be equal to a preset safe charging threshold. Based on piecewise linear charging data, road segment energy consumption, and the mapping relationship, feasible state-space-time arcs are generated in the state-space-time network, which connect different nodes in the state-space-time network. With minimizing the total time as the optimization objective, a target path from the starting node to the ending node is searched in the state-space-time network composed of feasible state-space-time arcs. The total time includes driving time, charging time, and operation time. The electric vehicle driving route and charging scheduling scheme corresponding to the target path are obtained.

Claims

1. A method for electric vehicle path and charging scheduling joint optimization, characterized in that, The method comprises the following steps: obtaining charging characteristic data of the electric vehicle, dividing a nonlinear charging process of the electric vehicle into multiple stages, and using a piecewise linear function to approximate the multiple stages to obtain piecewise linear charging data; constructing a state-space-time network comprising physical nodes, discrete time points and discrete battery state of charge, wherein the physical nodes comprise charging station nodes and road intersection nodes; calculating the road section energy consumption between the physical nodes based on the vehicle parameters and energy consumption model of the electric vehicle; establishing a mapping relationship between the charging station selection and the charging amount decision, wherein the mapping relationship is based on the optimal charging scheduling characteristics, and the battery state of charge of the electric vehicle when arriving at the selected charging station is set to be equal to the preset safe state of charge threshold; generating a feasible state-space-time arc in the state-space-time network according to the piecewise linear charging data, the road section energy consumption and the mapping relationship, wherein the feasible state-space-time arc connects different nodes in the state-space-time network; searching for a target path from a starting node to an end node in the state-space-time network composed of the feasible state-space-time arcs, wherein the optimization objective is to minimize the total time, and the total time comprises driving time, charging time and operation time; and obtaining an electric vehicle driving route and a charging scheduling scheme corresponding to the target path. 2.The method of claim 1, wherein, The step of dividing the nonlinear charging process of the electric vehicle into multiple stages comprises: dividing the charging process into a first stage of constant current charging, a second stage of constant voltage and decaying current, and a third stage of further reduced charging speed; determining the starting battery state of charge, the ending battery state of charge and the corresponding charging duration for each stage; constructing a piecewise linear function reflecting the mapping relationship between the charging time and the battery state of charge by using the determined starting battery state of charge, the ending battery state of charge and the charging duration. 3.The method of claim 1, wherein, The step of establishing a mapping relationship between the charging station selection and the charging amount decision comprises: analyzing the optimal charging scheduling characteristics to determine that, under the optimal charging scheduling, the battery state of charge of the electric vehicle when arriving at the selected charging station other than the first selected charging station is equal to the safe state of charge threshold; calculating the charging amount of the first selected charging station so that the battery state of charge of the electric vehicle when arriving at the second selected charging station is equal to the safe state of charge threshold; calculating the charging amount of the other selected charging stations so that the battery state of charge of the electric vehicle when arriving at the next selected charging station or the end node is equal to the safe state of charge threshold; setting the charging amount of the unselected charging stations to zero. 4.The method of claim 1, wherein, The step of generating a feasible state-space-time arc comprises: traversing the directed road sections in the physical road network to determine the starting node and the ending node in the state-space-time network; calculating the driving energy consumption of the vehicle on the directed road section by using the energy consumption model, and calculating the battery state of charge of the ending node based on the battery state of charge of the starting node, the charging amount at the node and the driving energy consumption; calculating the charging time at the node by using the piecewise linear charging data, and calculating the time of the ending node by combining the time of the starting node, the charging time, the preset operation time and the driving time on the directed road section. determining whether the calculated battery level and time of the end node are within the discrete range of the state-space-time network, and if so, generating a feasible state-space-time arc connecting the start node and the end node. 5.The method of claim 4, wherein, The step of calculating the charging time at the node specifically includes: determining the target battery level at the end of charging according to the charging amount determined by the mapping relationship and the battery level of the start node; using the inverse function of the piecewise linear charging data to calculate the total time required to reach the target battery level and the total time required to reach the battery level of the start node, respectively; subtracting the total time required to reach the battery level of the start node from the total time required to reach the target battery level to obtain the actual charging time at the node. 6.The method of claim 1, wherein, The step of searching for a target path from the start node to the end node specifically includes: establishing an optimization model containing flow conservation constraints, battery safety threshold constraints, charging upper limit constraints, and charging frequency constraints; the flow conservation constraint ensures that the electric vehicle forms a continuous path by connecting multiple feasible state-space-time arcs; the battery safety threshold constraint requires that the amount of electricity arriving at any charging station be no less than the safety threshold; the charging frequency constraint sets a lower bound and an upper bound for the number of charges, with the lower bound estimated based on the minimum energy consumption required to complete the path, and the upper bound not exceeding the total number of charging stations on the path; solving the optimization model to identify a series of feasible state-space-time arcs with selected states as state variables, which form the target path. 7.The method of claim 1, wherein, The method further includes a real-time re-planning step: monitoring the current location, time, and battery level during the electric vehicle's journey; when detecting traffic congestion leading to additional energy consumption or charging stations being unavailable, using the current location, time, and battery level as a new start node; re-executing the steps of constructing the state-space-time network, generating feasible state-space-time arcs, and searching for a target path to generate a new driving route and charging schedule.

8. An electric vehicle path and charging scheduling joint optimization system, characterized in that, includes: a charging data acquisition module for acquiring the charging characteristics data of the electric vehicle, dividing the nonlinear charging process of the electric vehicle into multiple stages, and using piecewise linear functions to approximate the multiple stages to obtain piecewise linear charging data; a state-space-time network construction module for constructing a state-space-time network containing physical nodes, discrete time points, and discrete battery level states, with the physical nodes including charging station nodes and road intersection nodes; a road segment energy consumption calculation module for calculating the road segment energy consumption between physical nodes based on the vehicle parameters and energy consumption model of the electric vehicle; a mapping relationship establishment module for establishing a mapping relationship between charging station selection and charging amount decision, which is based on optimal charging scheduling characteristics and sets the battery level of the electric vehicle when it arrives at the selected charging station to be equal to a predetermined safety threshold; a node connection module for generating feasible state-space-time arcs in the state-space-time network according to the piecewise linear charging data, road segment energy consumption, and mapping relationship, which connects different nodes in the state-space-time network; The scheduling scheme generation module is configured to search a target path from a start node to an end node in a state-space-time network composed of feasible state-space-time arcs, with the optimization objective being to minimize a total time including a travel time, a charging time and an operation time, and obtain an electric vehicle travel route and a charging scheduling scheme corresponding to the target path.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the electric vehicle path and charging scheduling joint optimization method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by the processor, implements the steps of the electric vehicle path and charging scheduling joint optimization method according to any one of claims 1 to 7.

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