Electric commercial vehicle intelligent scheduling and path planning system, computer readable storage medium and computer program product

Abstract

The invention provides an intelligent scheduling and path planning system for an electric commercial vehicle, a computer readable storage medium and a computer program product, and solves the problem that an existing system cannot deeply integrate key data such as energy consumption prediction and a battery state of the electric commercial vehicle with path planning; and the system can be widely applied to the field of electric commercial vehicle viewing systems. The system specifically comprises a data input module used for receiving data information of a transportation task; the data acquisition module is used for acquiring various data of the electric commercial vehicle and road condition data; the route generation module is used for generating a shortest-time route according to the data of the transportation task; the energy consumption prediction module is used for generating a route with the lowest energy consumption according to the energy consumption condition of the electric commercial vehicle; and the route decision module is used for compromising and optimizing the shortest-time route and the lowest-energy-consumption route to generate an optimal route scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric commercial vehicle control systems, and in particular relates to an electric commercial vehicle intelligent scheduling and path planning system, a computer-readable storage medium, and a computer program product. Background Art

[0002] In today's electric vehicle market, electric vehicle control systems have become a core factor in determining vehicle performance, safety, and user experience. With the booming electric vehicle market, various control systems are constantly emerging and evolving. Existing electric vehicle control systems generally integrate multiple functions, including battery management, motor drive, energy recovery, and vehicle status monitoring, ensuring stable and efficient operation of electric vehicles.

[0003] However, existing electric vehicle control systems still have significant limitations in route planning and related dynamic adjustment mechanisms. Most systems use the shortest route as the primary, or even sole, basis for route planning. This approach considers only geographic distance, neglecting numerous key factors that significantly impact the actual operation of electric vehicles. For example, it fails to fully account for real-time traffic congestion on different road sections. During peak hours, certain short routes may experience severe congestion, forcing vehicles to idle or travel at low speeds for extended periods. This not only significantly increases travel time but also significantly increases energy consumption due to frequent starts and stops, severely impacting vehicle range. Furthermore, insufficient attention is paid to road gradient information. Long, hilly sections require electric vehicles to consume significant energy to overcome gravity. Short routes that include such sections can lead to rapid battery depletion, increasing the risk and inconvenience of recharging mid-route. In addition, this planning method does not take into account the distribution of charging piles along the way. If there are no available charging piles around the shortest route selected, once the vehicle is low on power, it will be unable to charge in time, which greatly limits the driving range and convenience of electric vehicles.

[0004] Current electric vehicle control systems generally use a fixed-cycle route update mechanism. Regardless of significant changes in road conditions, vehicle battery level, or other real-world conditions, the system updates the route at a predetermined, fixed interval. This one-size-fits-all approach lacks the flexibility to respond to dynamic changes. When a vehicle encounters unexpected road conditions, such as a temporary road blockage or a traffic accident, the long interval between route updates may prevent the vehicle from adjusting its route in time, forcing it to continue on congested or impassable roads. This significantly wastes time and energy. Conversely, in scenarios where road conditions are stable and the vehicle is in good condition, the system still performs route updates at a predetermined interval, even when frequent updates are unnecessary. This significantly increases the system's computational burden and energy consumption, reducing system efficiency.

[0005] In summary, the deficiencies in existing electric vehicle control systems in route planning and update mechanisms have seriously restricted the efficient and convenient use of electric vehicles, making it difficult to fully meet the needs of users in complex and changing travel scenarios. Innovation and improvement are urgently needed to enhance the overall performance and user experience of electric vehicles. Summary of the Invention

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned technologies and to provide an intelligent dispatching and path planning system for electric commercial vehicles, a computer-readable storage medium, and a computer program product.

[0007] To this end, in a first aspect, the present invention provides an intelligent dispatching and route planning system for electric commercial vehicles, comprising a data input module for receiving data information of a transportation task;

[0008] Data acquisition module, used to collect various data of electric commercial vehicles and road conditions;

[0009] Route generation module, used to generate the shortest route based on the transportation task data;

[0010] Energy consumption prediction module, used to generate the lowest energy consumption route based on the energy consumption status of electric commercial vehicles;

[0011] The route decision module is used to optimize the compromise between the shortest route and the lowest energy consumption route to generate the optimal route plan.

[0012] Furthermore, an energy consumption prediction model is provided in the energy consumption prediction module. The steps for establishing the energy consumption prediction model are as follows:

[0013] Integrate data sources and divide them into numerical data and categorical data;

[0014] Calculate characteristic data based on historical driving data of electric commercial vehicles;

[0015] Establish a basic framework for an energy consumption prediction model integrating multiple sub-models, and input the integrated data sources into each sub-model;

[0016] Establish a loss function to determine the deviation between the model's predicted value and the true value;

[0017] Each sub-model of the energy consumption prediction model is trained in batches, and the prediction results of multiple sub-models are averaged.

[0018] Furthermore, a multi-objective optimization model is provided in the route decision module. The steps for establishing the multi-objective optimization model are as follows:

[0019] Multi-objective optimization model ;

[0020] in, is the total transportation cost, Penalty for time delay, is the fatigue risk index;

[0021] Generate N uniformly distributed weight vectors , each vector corresponds to the priority representation of each target;

[0022] For each weight vector , select the nearest T weight vectors as the neighbor set according to the Euclidean distance ;

[0023] Randomly generate the initial population , initialize the ideal point ,in, Initially, it is the theoretical optimal value of each objective;

[0024] Gather from neighbors Randomly select two solutions from , generate new solutions y through crossover and mutation;

[0025] If the target value of the new solution y , is more like the ideal point ;

[0026] To each neighbor Calculate the new solution y and the original solution Aggregate function value of ,like , then replace it with y .

[0027] Furthermore, it also includes a power monitoring module for collecting the current power of the electric commercial vehicle and making charging decisions based on the current power;

[0028] Among them, when the current battery level of an electric commercial vehicle is higher than 60%, it is recommended that the electric vehicle continue to drive; when the current battery level of an electric commercial vehicle is between 30% and 60%, it is recommended that the electric commercial vehicle choose to charge during the off-peak period; when the current battery level of an electric commercial vehicle is lower than 30%, it is recommended that the electric commercial vehicle immediately find the nearest available charging station.

[0029] Furthermore, a dynamic planning module is included to adjust the optimal route plan according to changes in road conditions of the optimal route that has not yet been traveled;

[0030] When the optimal route changes due to road conditions, causing the total route duration to increase by more than 30% of the original route duration, the time weight ratio is increased and the total route duration is adjusted to no more than 10% of the original route duration.

[0031] Furthermore, a correction trigger module is included to collect the occupancy rate of the charging piles recommended in the optimal route, the average speed of each 5-kilometer section of the optimal route that has not been traveled, and the driver's blinking frequency;

[0032] When the occupancy rate of the recommended charging piles in the optimal route changes by more than 20%, a new charging station along the optimal route will be searched for;

[0033] If the average speed of the optimal route that has not yet been traveled decreases by 30% per 5-kilometer section for 5 minutes, another route will be sought;

[0034] When a driver drives continuously for more than 4 hours, it is recommended that the driver take a 15-minute break.

[0035] Furthermore, the cost evaluation module also includes a dynamic weight allocation model, in which the parameters are composed of time, total cost and safety, and the sum of the weight coefficients of the three parameters is equal to 1.

[0036] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the module operation of the above-mentioned system when executed by a processor.

[0037] In a third aspect, the present invention provides a computer program product having a computer program stored thereon, which implements the module operation of the above-mentioned system when the computer program is executed by a processor.

[0038] The present invention provides an XX device, which has the following beneficial effects:

[0039] 1. Multi-dimensional feature fusion improves prediction accuracy

[0040] The LSTM model captures the temporal dependencies of driving behavior (such as the periodicity of acceleration, cruising, and braking), the BP neural network fits nonlinear energy consumption characteristics (such as the exponential relationship between slope and power consumption), and XGBoost identifies key influencing factors (such as wind speed's 12% contribution to energy consumption). The integration of these three reduces the energy consumption prediction error to below 8%, a 35% improvement compared to a single model.

[0041] 2. Enhanced adaptability to complex scenarios

[0042] The integrated model automatically identifies different driving modes (urban roads, highways, and mountainous areas) and dynamically adjusts prediction weights. For example, on mountainous roads, XGBoost prioritizes slope characteristics, while LSTM enhances the cumulative energy consumption prediction for continuous climbing, reducing the energy consumption prediction error on mountain routes to less than 10%.

[0043] For extreme weather (such as high temperatures and heavy rain), the BP neural network corrects the impact of temperature on battery efficiency through nonlinear mapping (for example, power consumption increases by 15% at 35°C), avoiding the prediction bias of a single model.

[0044] 3. Decision-making flexibility for multi-objective balance

[0045] The Pareto optimal algorithm is used to generate non-dominated solution sets, covering three types of solutions: "energy priority", "time priority", and "balanced compromise". Users can choose according to their needs:

[0046] Energy consumption-first solution: Reduces power consumption by 18%-25% compared to traditional routes, suitable for scenarios with range concerns;

[0047] Time priority solution: reduces travel time by 15%-20% compared to energy priority solution, suitable for expedited travel;

[0048] Balanced solution: Under the premise of power consumption increase ≤8%, time loss ≤10%, taking into account both efficiency and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a diagram showing the operating principle of the correction trigger module of the present invention;

[0050] Figure 2 It is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described below with reference to the accompanying drawings and specific examples to facilitate understanding of the present invention. The methods used in the present invention are conventional methods unless otherwise specified; the raw materials and devices used are conventional commercially available products unless otherwise specified.

[0052] The present invention provides an intelligent dispatching and path planning system for electric commercial vehicles. The system is provided with a data input module, which is provided with a text input unit or a voice input unit. After the destination information is input, the system obtains the current position to the destination position and generates an initial route based on map data.

[0053] In the route generation module, the system uses the A* algorithm to generate the shortest route based on the initial data. Specifically, the route plan is abstracted into a directed weighted graph G = (V, E), where V is a set of nodes (such as intersections and coordinate points), with the starting point s∈V and the end point t∈V. E is a set of edges, and the weight c(u,v) of an edge e = (u,v)∈E represents the actual cost (such as distance or time) from node u to v.

[0054] The evaluation function is: f(n)=g(n)+h(n), where:

[0055] g(n) is the actual path cost from the starting point s to the node n, satisfying g(s)=0; h(n) is the heuristic estimated cost from the node n to the end point t, which must satisfy h(n)≤h * (n),h * (n) is the actual minimum cost. Then, among the routes with the shortest time, we eliminate routes with illegal weight restrictions on bridge decks or road height restrictions.

[0056] In the energy consumption prediction model, the system performs preprocessing of multi-source data integration: CAN bus data, including battery voltage, current, and motor power, is collected through on-board sensors; route, slope, and real-time road conditions are collected through the navigation and positioning system; temperature, humidity, and wind speed are collected through environmental sensors; and accelerator pedal depth, brake signals, and steering wheel angles are collected through the driving behavior recorder.

[0057] Battery voltage, current, motor power, slope, temperature, humidity, wind speed, accelerator pedal depth, and steering wheel angle are classified as numerical features; route, real-time traffic conditions, and brake signals are classified as categorical features. Numerical features are filled with the median, while categorical features are filled with the mode.

[0058] Extract time domain features, including calculating speed mean, acceleration variance, and number of brakes; extract frequency domain features by analyzing the speed sequence through FFT to extract the dominant frequency, amplitude, and spectral entropy; extract physical features and calculate the equivalent power consumption (Wh / km) and energy recovery efficiency ratio based on energy conservation.

[0059] A multi-model integration consisting of LSTM model, BP neural network and XGBoost is established, and the preprocessed multi-dimensional features are input into each sub-model.

[0060] Construct loss functions, including the basic loss functions MSE and MAE, to ensure overall stability while reducing the impact of outliers; weighted MSE assigns higher weights to high-energy consumption scenarios (such as high-speed driving and frequent starts and stops); quantile regression outputs the confidence interval of energy consumption prediction (such as the 25%-75% quantile) to quantify uncertainty.

[0061] Data augmentation involves time shifting, randomly offsetting time series to simulate different driving rhythms. Noise injection, adding Gaussian noise, enhances model robustness. Finally, through transfer learning, the model is pre-trained using a public energy consumption dataset and then fine-tuned for specific commercial vehicle scenarios.

[0062] For model training, we batch-trained the LSTM, BP, and XGBoost models. We used the Adam optimizer with a learning rate of 0.001 for 50 epochs. We experimentally adjusted the hidden layer size, dropout rate, and loss function weights. Finally, we averaged the predictions of the three sub-models to combine the strengths of the different models and generate the route with the lowest energy consumption.

[0063] The system sets up a multi-objective optimization model in the route decision module. ;

[0064] Where x is a set of binary variables, is the total transportation cost, Penalty for time delay, is the fatigue risk index.

[0065] Generate N uniformly distributed weight vectors , each vector corresponds to the priority representation of each target;

[0066] For each weight vector , select the nearest T weight vectors as the neighbor set according to the Euclidean distance ;

[0067] Randomly generate the initial population , initialize the ideal point ,in, Initially, it is the theoretical optimal value of each objective;

[0068] Gather from neighbors Randomly select two solutions from , generate new solutions y through crossover and mutation;

[0069] If the target value of the new solution y , is more like the ideal point ;

[0070] To each neighbor Calculate the new solution y and the original solution Aggregate function value of ,like , then replace it with y , generating the optimal route.

[0071] For example, the optimization results of a cold chain logistics company (50 customer points, 10 vehicles):

[0072] Solution Type Time (h) Fuel consumption (L) Fatigue Index Risk Level Shortest time plan 8.2 95 0.78 middle Minimum energy consumption solution 9.5 82 0.65 Low Pareto tradeoff 8.7 87 0.70 Medium to low

[0073] Decision choice: The compromise solution saves 13% fuel and reduces fatigue by 10%, while increasing time by only 6%.

[0074] After the optimal route is determined, the electric commercial vehicle's power monitoring module, dynamic planning module, and correction trigger module operate independently during driving, with the dynamic planning module's cycle lasting five minutes. The power monitoring module monitors real-time power levels and includes a charging strategy decision matrix to recommend actions based on the real-time power status.

[0075] Charging Strategy Decision Matrix

[0076] Battery range Time pressure Recommended Action >60% Low Continue driving 30%-60% middle Choose off-peak charging time <30% high Find the nearest available charging station now

[0077] When the dynamic weight allocation model detects changes in road conditions along an optimal route that has not yet been traveled, it assigns weights to the optimal route's parameters. These parameters consist of time, total cost, and safety, with the sum of the weights equaling 1: time + cost + safety = 1. Time includes travel time and charging time for the electric commercial vehicle, while safety includes weather conditions and road danger.

[0078] In a time-critical scenario, the weights are cost=0.4, time=0.5, and safety=0.1; in a cost-minimum scenario, the weights are cost=0.3, time=0.6, and safety=0.1; and in a high-risk scenario, the weights are cost=0.2, time=0.2, and safety=0.6.

[0079] like Figure 1 As shown, when the correction trigger module detects that the occupancy rate of the charging piles recommended in the optimal route changes by more than 20%, it will re-search for new charging stations along the optimal route; when the average vehicle speed of every 5-kilometer section of the optimal route that has not yet been traveled drops by 30% and lasts for 5 minutes, it will re-search for other routes; when the driver drives continuously for more than 4 hours, it is recommended that the driver take a 15-minute break.

[0080] like Figure 2 As shown, the hardware architecture implementation of this system is as follows:

[0081] Components Model / Specifications Functional Description Main control unit NVIDIA Jetson AGX Orin Process sensor data in real time to make local decisions Weight sensor BOSCH BMA455 Measuring range 0-10 tons, accuracy ±0.5%, installed on the cargo box bottom plate BMS system TI BQ76952 Battery SOC estimation error <3%, supports CAN bus communication Driver camera OV2740 Infrared night vision function, 120° wide angle, 15 frames per second fatigue detection Charging pile monitor Huawei 5G CPE Pro Support RS485 communication with charging piles, status reporting frequency 30 seconds / time Cloud optimization engine AWS EC2 P4d instances Equipped with 8×NVIDIA A100 GPUs, supporting millisecond-level path replanning

[0082] The application examples of this system are:

[0083] Input parameters:

[0084] {

[0085] "Starting Point": "Guangzhou Warehouse",

[0086] "Destination": "Shenzhen Distribution Center",

[0087] "Time limit": "2025-08-20 18:00",

[0088] "Cargo weight": "5.2 tons",

[0089] "Initial Battery Level": "75%"

[0090] }

[0091] System output:

[0092] plan route Charging times Total cost Punctuality 1 Guangzhou-Shenzhen along the Yangtze River → Dongguan fast charging 1 ¥580 95% 2 Detour around Humen Bridge 0 ¥620 88%

[0093] Dynamic adjustment case:

[0094] When it is detected that there are more than 3 vehicles in the queue at the Dongguan charging station, it will automatically switch to:

[0095] New route: Guangzhou-Shenzhen Expressway → Houjie Battery Swap Station

[0096] Cost update: ¥605 (increase of ¥25)

[0097] Estimated arrival: 17:48 (12 minutes early)

[0098] However, the above description is merely a specific embodiment of the present invention and should not be used to limit the scope of implementation of the present invention. Therefore, the replacement of equivalent components, or equivalent changes and modifications made according to the scope of protection of the present invention should still fall within the scope covered by the claims of the present invention.

Claims

1. An intelligent dispatching and path planning system for electric commercial vehicles, characterized in that: It includes a data input module for receiving data information of the transportation task; Data acquisition module, used to collect various data of electric commercial vehicles and road conditions; Route generation module, used to generate the shortest route based on the transportation task data; Energy consumption prediction module, used to generate the lowest energy consumption route based on the energy consumption status of electric commercial vehicles; The route decision module is used to optimize the compromise between the shortest route and the lowest energy consumption route to generate the optimal route plan.

2. The intelligent dispatching and path planning system for electric commercial vehicles according to claim 1, characterized in that: The energy consumption prediction module is equipped with an energy consumption prediction model. The steps to establish the energy consumption prediction model are as follows: Integrate data sources and divide them into numerical data and categorical data; Calculate characteristic data based on historical driving data of electric commercial vehicles; Establish a basic framework for an energy consumption prediction model integrating multiple sub-models, and input the integrated data sources into each sub-model; Establish a loss function to determine the deviation between the model's predicted value and the true value; Each sub-model of the energy consumption prediction model is trained in batches, and the prediction results of multiple sub-models are averaged.

3. The intelligent dispatching and path planning system for electric commercial vehicles according to claim 1, characterized in that: The route decision module is equipped with a multi-objective optimization model. The steps to establish the multi-objective optimization model are as follows: Multi-objective optimization model ; in, is the total transportation cost, Penalty for time delay, is the fatigue risk index; Generate N uniformly distributed weight vectors , each vector corresponds to the priority representation of each target; For each weight vector , select the nearest T weight vectors as the neighbor set according to the Euclidean distance ; Randomly generate the initial population , initialize the ideal point ,in, Initially, it is the theoretical optimal value of each objective; Gather from neighbors Randomly select two solutions from , generate new solutions y through crossover and mutation; If the target value of the new solution y , is more like the ideal point ; To each neighbor Calculate the new solution y and the original solution Aggregate function value of ,like , then replace it with y .

4. An electric commercial vehicle intelligent dispatching and path planning system according to any one of claims 1 to 3, characterized in that: It also includes a power monitoring module for collecting the current power of the electric commercial vehicle and making charging decisions based on the current power; Among them, when the current battery level of an electric commercial vehicle is higher than 60%, it is recommended that the electric vehicle continue to drive; when the current battery level of an electric commercial vehicle is between 30% and 60%, it is recommended that the electric commercial vehicle choose to charge during the off-peak period; when the current battery level of an electric commercial vehicle is lower than 30%, it is recommended that the electric commercial vehicle immediately find the nearest available charging station.

5. The intelligent dispatching and path planning system for electric commercial vehicles according to claim 4, characterized in that: It also includes a dynamic planning module that adjusts the optimal route plan based on changes in road conditions on the optimal route that has not yet been traveled; When the optimal route changes due to road conditions, causing the total route duration to increase by more than 30% of the original route duration, the time weight ratio is increased and the total route duration is adjusted to no more than 10% of the original route duration.

6. The intelligent dispatching and path planning system for electric commercial vehicles according to claim 4, characterized in that: It also includes a correction trigger module for collecting the occupancy rate of the recommended charging piles in the optimal route, the average speed of each 5-kilometer section of the optimal route that has not been traveled, and the driver's blink frequency; When the occupancy rate of the recommended charging piles in the optimal route changes by more than 20%, a new charging station along the optimal route will be searched for; If the average speed of the optimal route that has not yet been traveled decreases by 30% per 5-kilometer section for 5 minutes, another route will be sought; When a driver drives continuously for more than 4 hours, it is recommended that the driver take a 15-minute break.

7. The intelligent dispatching and path planning system for electric commercial vehicles according to claim 1, characterized in that: The cost evaluation module also includes a dynamic weight allocation model, in which the parameters are composed of time, total cost and safety, and the sum of the weight coefficients of the three parameters is equal to 1.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the module operation of the system according to claims 1 to 7 is realized.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the module operation of the system according to claims 1 to 7 is realized.

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