Range prediction system, method and server for pure electric commercial vehicles

Abstract

The present invention provides a range prediction system, method, and server for pure electric commercial vehicles, relating to the technical field of electric vehicle intelligence. The system comprises a data acquisition layer, a data processing layer, a data fusion layer, and a self-learning layer. The data acquisition layer is used to collect vehicle status information, external environment information, and actual range information; the data processing layer is used to determine an estimated energy consumption value, a first range prediction result, and a second range prediction result; the data fusion layer is used to perform fuzzy logic fusion processing on the vehicle status information and external environment information, determine a weighted average weight coefficient, and perform weighted average fusion processing based on the weight coefficient to determine the target range prediction result at the current moment; and the self-learning layer is used to perform real-time analysis and processing of deviation values ​​to determine the target weight coefficient, so that the data fusion layer can predict the range of the next moment based on the target weight coefficient. The present invention can significantly improve the accuracy of range prediction for pure electric commercial vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent electric vehicles, and in particular to a range prediction system, method and server for pure electric commercial vehicles. Background Art

[0002] With the rapid development of new energy technologies and the increasing awareness of environmental protection, pure electric commercial vehicles have gradually become an important part of the global transportation field. However, the range limitation of pure electric commercial vehicles is still a key obstacle to their large-scale application, especially in urban distribution, long-distance transportation and complex traffic environments. Users' anxiety about range will significantly affect the market acceptance of electric commercial vehicles. At present, the range can be calculated by average energy consumption and remaining power, but this solution fails to fully consider the influence of multiple complex factors, resulting in low accuracy of the prediction results. Related technologies have proposed that the loss data model can also be used to calculate the influencing factors during vehicle driving to obtain the total energy consumption of the vehicle, and further determine the remaining range based on the total energy consumption of the vehicle and the total battery power. However, since the loss data model cannot accurately express all factors that affect the range, the prediction results still have large errors. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide a range prediction system, method and server for a pure electric commercial vehicle, which can significantly improve the accuracy of the range prediction of the pure electric commercial vehicle.

[0004] In a first aspect, an embodiment of the present invention provides a cruising range prediction system for a pure electric commercial vehicle, the system comprising: a data acquisition layer, a data processing layer, a data fusion layer and a self-learning layer, the data processing layer being connected to the data acquisition layer and the data fusion layer respectively, and the self-learning layer being connected to the data fusion layer and the data acquisition layer respectively; wherein the data acquisition layer is used to collect vehicle status information, external environment information and actual cruising range information through a preset sensor set, wherein the vehicle status information comprises: vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status and driving mode, and the external environment information comprises: ambient temperature information, road condition information; the data processing layer is used to perform energy consumption estimation processing on the vehicle status information and the external environment information, determine the energy consumption estimation value, and the first cruising range information corresponding to the energy consumption estimation value. The mileage prediction result is obtained by performing deep learning prediction processing on the vehicle status information and the external environment information to determine the second cruising range prediction result, wherein the energy consumption estimation value includes the energy consumption per unit time and the energy consumption per unit mileage; the data fusion layer is used to perform fuzzy logic fusion processing on the vehicle status information and the external environment information to determine the weight coefficient of weighted average, and based on the weight coefficient, perform weighted average fusion processing on the first cruising range prediction result and the second cruising range prediction result to determine the target cruising range prediction result at the current moment; the self-learning layer is used to perform real-time analysis and processing on the deviation value between the energy consumption change in the target cruising range prediction result and the energy consumption change in the actual cruising range information, update the weight coefficient according to the deviation analysis result, and determine the target weight coefficient, so that the data fusion layer can perform cruising range prediction at the next moment based on the target weight coefficient.

[0005] In one embodiment, the data acquisition layer also includes: a self-diagnosis unit; wherein the self-diagnosis unit is used to perform abnormality detection and processing on the collected vehicle status information and external environment information, and perform fault sensor switching and fault alarm when a sensor failure or abnormal data is detected.

[0006] In one embodiment, the data processing layer includes: an average energy consumption estimation module; wherein the average energy consumption estimation module is used to calculate the remaining battery power, driving resistance loss power, motor efficiency loss power, battery consumption power and energy recovery power according to vehicle status information and external environment information, so as to determine the energy consumption per unit time according to the driving resistance loss power, motor efficiency loss power, battery consumption power and energy recovery power, and determine the energy consumption per unit mileage according to the energy consumption per unit time.

[0007] In one embodiment, the data processing layer also includes: a deep learning prediction module, and the deep learning prediction module includes: a random forest model; wherein the random forest model is used to analyze the driving situation and, when it is determined that the driver is a frequent vehicle driver, to predict the long-term energy consumption change trend of the cruising range and determine a second cruising range prediction result.

[0008] In one embodiment, the deep learning prediction module also includes: a long short-term memory network model; wherein the long short-term memory network model is used to analyze the driving situation and determine that the driver is not an ordinary vehicle driver, perform short-term energy consumption change trend prediction processing on the cruising range, and determine a second cruising range prediction result.

[0009] In one embodiment, the data fusion layer includes: a fuzzy logic subsystem; wherein the fuzzy logic subsystem is used to fuzzify vehicle status information and external environment information, and use a preset inference engine to infer the fuzzified information to defuzzify the inference results and obtain a weighted average weight coefficient.

[0010] In one embodiment, the data fusion layer also includes: a weighted average fusion unit; wherein the weighted average fusion unit is used to determine the target range prediction result at the current moment as the sum of the products of the first range prediction result and the second range prediction result and the corresponding weight coefficients based on the weight coefficient and the preset fusion model.

[0011] In one embodiment, the self-learning layer includes: a data feedback and storage module; wherein the data feedback and storage module is used to collect the deviation value between the target endurance prediction result at each moment and the energy consumption change in the actual endurance information, as well as the vehicle status information and external environment information corresponding to each moment, and construct a historical data set based on the deviation value, vehicle status information and external environment information at each moment.

[0012] In one embodiment, the self-learning layer further includes: a model optimization module; wherein the self-learning layer is used to update the weight coefficient based on the deviation value through the incremental learning model to determine the target weight coefficient.

[0013] In a second aspect, an embodiment of the present invention further provides a method for predicting the range of a pure electric commercial vehicle, which is applied to the range prediction system of the pure electric commercial vehicle of any one of the first aspects, and the method comprises: collecting vehicle status information, external environment information and actual range information, wherein the vehicle status information comprises: vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status and driving mode, and the external environment information comprises: ambient temperature information, road condition information; performing energy consumption estimation processing on the vehicle status information and the external environment information, determining an energy consumption estimation value, and a first range prediction result corresponding to the energy consumption estimation value, and performing in-depth processing on the vehicle status information and the external environment information. The method further comprises the following steps: performing a fuzzy logic fusion process on the vehicle status information and the external environment information to determine a weight coefficient for weighted average, and performing a weighted average fusion process on the first cruising range prediction result and the second cruising range prediction result based on the weight coefficient to determine a target cruising range prediction result at the current moment; performing a real-time analysis process on the deviation value between the energy consumption change in the target cruising range prediction result and the energy consumption change in the actual cruising range information, updating the weight coefficient according to the deviation analysis result, and determining a target weight coefficient, so that the data fusion layer can make a cruising range prediction for the next moment based on the target weight coefficient.

[0014] In a third aspect, an embodiment of the present invention further provides a server, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the second aspect.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the second aspect.

[0016] The embodiments of the present invention bring the following beneficial effects:

[0017] Embodiments of the present invention provide a range prediction system, method, and server for a pure electric commercial vehicle. The system can use a data acquisition layer to collect vehicle status information, external environment information, and actual range information, and use a data processing layer to perform energy consumption estimation processing on the vehicle status information and external environment information to determine an estimated energy consumption value and a first range prediction result corresponding to the estimated energy consumption value. Deep learning prediction processing is then performed on the vehicle status information and external environment information to determine a second range prediction result. Then, a data fusion layer performs fuzzy logic fusion processing on the vehicle status information and external environment information to determine a weighted average weight coefficient. Based on the weight coefficient, a weighted average fusion processing is performed on the first range prediction result and the second range prediction result to determine a target range prediction result at the current moment. Finally, a self-learning layer performs real-time analysis and processing on the deviation between the energy consumption change in the target range prediction result and the energy consumption change in the actual range information. The weight coefficient is updated based on the deviation analysis result to determine the target weight coefficient, so that the data fusion layer performs a range prediction for the next moment based on the target weight coefficient. Embodiments of the present invention can significantly improve the accuracy of range prediction for pure electric commercial vehicles.

[0018] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A schematic diagram of the structure of a range prediction system for a pure electric commercial vehicle provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the structure of an average energy consumption estimation module provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of the structure of a deep learning prediction module provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of the structure of a random forest model provided by an embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of a data fusion module provided in an embodiment of the present invention;

[0026] Figure 6 A schematic diagram of a flow chart of a method for predicting the cruising range of a pure electric commercial vehicle provided by an embodiment of the present invention;

[0027] Figure 7 A schematic diagram of the structure of a server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] At present, with the rapid development of new energy technologies and the increasing awareness of environmental protection, pure electric commercial vehicles have gradually become an important part of the global transportation field. As the number of electric commercial vehicles continues to increase, the driving range prediction function has become a key factor that users pay attention to when choosing commercial vehicles. Although most existing pure electric commercial vehicles are equipped with a driving range prediction function, since the calculation only relies on basic factors such as state of charge and average energy consumption, there is a large gap between the predicted driving range and actual usage, which affects the user experience.

[0030] Compared with traditional fuel commercial vehicles, pure electric commercial vehicles have significant advantages in reducing carbon emissions and environmental pollution. However, range limitations remain a key obstacle to their large-scale application, especially in urban distribution, long-distance transportation and complex traffic environments. Users' anxiety about range has affected the market acceptance of electric commercial vehicles. Traditional range prediction methods usually rely on simple calculations of average energy consumption and remaining power. Although this method can provide preliminary estimates, it fails to fully consider the impact of multiple complex factors, resulting in low accuracy of the prediction results.

[0031] Factors affecting the range of pure electric commercial vehicles include not only battery capacity and state of charge, but also driving behavior, road conditions, climate conditions, vehicle load, battery aging, and other factors. For example, the frequent starts and stops in urban delivery increase energy consumption, while energy consumption is relatively low during highway driving. Ambient temperature also has a significant impact on battery performance. In low-temperature environments, the battery's output capacity will significantly decrease, resulting in a reduction in available range. Battery aging also affects range. In particular, as the battery ages, even if it is fully charged, the actual range is far less than that of a new battery.

[0032] The impact of the driver's driving style on energy consumption cannot be ignored. Commercial vehicle drivers often face frequent driving behaviors such as starting, braking, and sudden acceleration. Especially in complex urban traffic, an aggressive driving style can significantly increase instantaneous energy consumption and reduce battery life. Road gradient and traffic conditions also directly affect energy consumption. When driving uphill, the vehicle requires more energy to overcome the effects of gravity, while when driving downhill, the energy recovery system can partially recover kinetic energy, thereby reducing overall energy consumption.

[0033] Current technical solutions fail to fully integrate these influencing factors, resulting in inaccurate range predictions and exacerbating "range anxiety"; especially in areas with insufficient long-distance transportation and charging facilities, users have doubts about whether the vehicle can successfully complete the task. In addition, existing electric commercial vehicle systems generally lack intelligent energy consumption management and dynamic adjustment capabilities, and fail to make accurate range predictions based on real-time environmental and driving conditions.

[0034] Given this background, the industry urgently needs a more intelligent and accurate range prediction method. This method should comprehensively consider multiple factors, including the vehicle's real-time status, historical driving data, driver habits, and environmental changes. This method should also use machine learning and deep learning algorithms to conduct in-depth data analysis to maximize the accuracy of range predictions, reduce user anxiety, and enhance the user experience. This demand has spurred the research and development of a new generation of intelligent range prediction systems, which aim to combine data from various sources and utilize advanced machine learning techniques to provide more accurate range estimates.

[0035] Existing technologies suggest that the total energy consumption of a vehicle can be calculated by determining factors such as driving resistance loss, motor loss, battery consumption, and energy recovery during vehicle driving, and the remaining range can be further determined based on the vehicle's total energy consumption and the total battery charge. However, this ignores factors in reality that affect the range but cannot be accurately expressed through an energy loss mathematical model.

[0036] In addition, researchers and automakers are also exploring various possible solutions. For example, they use multiple sensors on the vehicle to collect driving data in real time, and process and analyze the data through cloud computing and artificial intelligence algorithms to generate more accurate range predictions. Some studies also focus on using neural networks to predict battery discharge characteristics to better predict the remaining power. However, these studies still face many challenges in practical applications, such as the real-time nature of the data, the generalization ability of the model, and how to achieve efficient model inference with low computing resources.

[0037] Based on this, the present invention implements the provided pure electric commercial vehicle range prediction system, method and server. First, the data acquisition module collects multi-dimensional data in real time, and the collected data is transmitted to the central processing unit and input into the average energy consumption estimation module and the deep learning prediction module at the same time; in the average energy consumption estimation module, the set mathematical model is used for calculation, and the result is output to the data fusion module; in the deep learning prediction module, the system makes an output decision based on the driving style, and finally outputs the selected result to the data fusion module; in the data fusion module, the system performs fuzzy logic reasoning based on the environmental status and the vehicle status, and imports the output weights of each module into the weighted average fusion algorithm. At the same time, the self-learning module performs deviation analysis and model optimization on the input data, and feeds back the optimized parameters to the deep learning module and the data fusion module. Finally, the data fusion module dynamically adjusts the weight value and finally outputs the final range prediction value, which can significantly improve the accuracy of the range prediction of pure electric commercial vehicles.

[0038] To facilitate understanding of this embodiment, a method for predicting the range of a pure electric commercial vehicle disclosed in an embodiment of the present invention is first described in detail. The method is applied to a range prediction system for a pure electric commercial vehicle. The system includes: a data acquisition layer, a data processing layer, a data fusion layer, and a self-learning layer. To facilitate understanding of the range prediction method for a pure electric commercial vehicle, an embodiment of the present invention provides a structural schematic diagram of a range prediction system for a pure electric commercial vehicle, as shown in FIG. Figure 1 As shown in the figure, the data processing layer is connected to the data acquisition layer and the data fusion layer respectively, and the self-learning layer is connected to the data fusion layer and the data acquisition layer respectively. The data acquisition layer is composed of a data acquisition module, the data processing layer is composed of an average energy consumption estimation module and a deep learning prediction module, the data fusion layer is composed of a data fusion module, and the self-learning layer is composed of a data feedback and storage module and a model optimization module.

[0039] The data acquisition layer is composed of data acquisition modules, which are used to collect vehicle status information, external environment information and actual endurance information through a preset sensor set, providing accurate data support for subsequent data analysis, model prediction and decision-making. The data acquisition module comprehensively monitors the vehicle's operating status and dynamic changes in the external environment by deploying a variety of sensors at key locations of the vehicle to ensure that the system can obtain high-quality real-time data and make intelligent predictions and control decisions based on the data. Among them, the data acquisition module can collect vehicle status information and external environment information in real time. Vehicle status information includes: vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status and driving mode. External environment information includes: ambient temperature information and road condition information. Specifically, data collection can be achieved by deploying the following sensors at key locations of pure electric commercial vehicles:

[0040] Vehicle speed sensor: Installed on a wheel bearing or transmission output shaft, it monitors the vehicle's speed in real time. The sensor outputs a pulse signal, which is transmitted via the vehicle bus to the central processing unit (CPU) for display and calculation in km / h.

[0041] Battery status sensors: These include voltage sensors, current sensors, and temperature sensors. These sensors collect battery charge and discharge parameters in real time through the battery management system (BMS).

[0042] Ambient temperature sensor: Located on the vehicle's exterior underbody to avoid interference from vehicle exhaust heat. The sensor uses an NTC thermistor with a measurement range of -30°C to 50°C.

[0043] Road Condition Sensor: LiDAR and an Inertial Navigation Unit (IMU) work together to obtain slope information. LiDAR detects road surface features, while the IMU records vehicle inclination data. The calculated results are filtered and used as model input.

[0044] Throttle and brake pedal sensor: Detects the driver's throttle opening and brake force through a displacement potentiometer, with an opening range of 0%-100%.

[0045] Accelerometer: Installed on the vehicle chassis, it records the vehicle's acceleration and deceleration behavior in real time. The data is used to identify driving style.

[0046] Steering angle sensor: integrated on the steering wheel shaft, records the vehicle's steering angle changes in real time.

[0047] In one embodiment, the vehicle status information and external environment information are specifically described in (1) to (7) below:

[0048] (1) Vehicle speed information is used to monitor the vehicle’s current speed in real time, providing dynamic basic data for range prediction and energy efficiency analysis.

[0049] (2) Battery status information includes multiple key parameters such as voltage, current, and temperature, which comprehensively monitor the battery's discharge status and health status.

[0050] (3) Ambient temperature information: Temperature changes in the external environment have a significant impact on battery performance and energy consumption, especially under extreme temperature conditions. Cold weather will reduce the battery's energy output capacity. Ambient temperature sensors can monitor external temperature changes in real time and assist in range prediction and adjustment.

[0051] (4) Road condition information: Combined with road condition sensors and slope sensors, the road slope, type and other characteristics are collected to provide dynamic data for energy efficiency optimization and endurance prediction.

[0052] (5) Vehicle acceleration and steering angle: The acceleration and deceleration of the vehicle are monitored by the acceleration sensor, and the driver's driving behavior is analyzed in combination with the steering angle data. This helps to evaluate the impact of driving style (such as sudden acceleration, sudden braking, etc.) on energy consumption, thereby providing a basis for optimizing driving behavior and extending battery life.

[0053] (6) The accelerator and brake pedal displacement is used to monitor the driver's control behavior, further analyze the impact of the driver's behavior on energy consumption, and provide data support for personalized energy efficiency optimization and driving recommendations.

[0054] (7) Motor status and driving mode are used to help determine the energy efficiency performance of the vehicle under different working conditions.

[0055] The collected vehicle and environmental information is transmitted in real time through an efficient and stable vehicle bus system and sent to the central processing unit (CPU) for unified processing and storage, ensuring the real-time, integrity and consistency of the data, and providing reliable support for subsequent data analysis, model prediction and decision-making.

[0056] In one embodiment, the data acquisition layer also includes: a self-diagnosis unit; wherein the self-diagnosis unit is used to perform abnormality detection and processing on the collected vehicle status information and external environment information, and perform fault sensor switching and fault alarm when a sensor failure or abnormal data is detected. That is to say, in order to ensure the high quality of data and the stability of the system, the data acquisition module also adopts a redundant design and self-diagnosis mechanism, which can automatically perform sensor switching adjustment and alarm in the event of sensor failure or data abnormality, thereby minimizing the risk of data loss and erroneous data, and ensuring the high reliability and continuous operation of the system in harsh environments.

[0057] Furthermore, to achieve comprehensive awareness of the vehicle's status and external environment, the data acquisition module includes the following sensors: vehicle speed sensor, battery status sensor, ambient temperature sensor, acceleration sensor, road condition sensor, motor status sensor, driving mode sensor, and accelerator and brake pedal position sensors. Through the collaborative operation and data fusion of these multiple sensor types, comprehensive monitoring of vehicle status and environmental information is achieved, providing a high-quality data foundation for range prediction, energy efficiency analysis, and intelligent control.

[0058] The data processing layer is used to perform energy consumption estimation processing on the vehicle status information and external environment information, determine the energy consumption estimation value, and the first cruising range prediction result corresponding to the energy consumption estimation value, and perform deep learning prediction processing on the vehicle status information and external environment information to determine the second cruising range prediction result, wherein the energy consumption estimation value includes energy consumption per unit time and energy consumption per unit mileage.

[0059] In one embodiment, see Figure 2 The structure diagram of an average energy consumption estimation module shown in the figure, the data processing layer includes: an average energy consumption estimation module; wherein the average energy consumption estimation module is used to calculate the remaining battery power, driving resistance loss power, motor efficiency loss power, battery consumption power and energy recovery power according to vehicle status information and external environment information, so as to determine the energy consumption per unit time according to the driving resistance loss power, motor efficiency loss power, battery consumption power and energy recovery power, and determine the energy consumption per unit mileage according to the energy consumption per unit time. That is to say, the average energy consumption estimation module mainly estimates energy consumption based on the current vehicle status and driving status. Through the battery current, battery voltage, vehicle speed, acceleration, road rolling resistance coefficient, road slope angle, and motor speed measured by sensors, the system can quickly calculate the energy consumption per unit time and the energy consumption per unit mileage. Specifically, the calculation process includes the following: battery SOC value calculation, driving resistance loss power calculation, motor efficiency loss power calculation, battery consumption power calculation, energy recovery power calculation, total energy consumption per unit time calculation, and energy consumption per unit mileage calculation.

[0060] In another embodiment, see Figure 3 The diagram shows the structure of a deep learning prediction module. The data processing layer also includes a deep learning prediction module, which includes a long short-term memory network (LSTM) and a random forest model. These two algorithms are combined to conduct in-depth analysis and modeling of the vehicle's historical driving data, driving behavior characteristics, and environmental conditions to achieve accurate range prediction.

[0061] Before model training, data preprocessing is required. This aims to improve the quality and consistency of input data, thereby enhancing the model's generalization and prediction accuracy. Specific preprocessing steps include: data cleaning, which involves cleaning the collected raw data. Using statistical methods and the triple standard deviation principle, outliers are detected, identified, and removed to ensure the reliability and consistency of the input data. After data cleaning, data normalization is performed to convert the data to a consistent scale to improve training efficiency and accuracy, preventing differences in the order of magnitude of different features from adversely affecting model training. These preprocessing steps ensure higher quality and consistency of the input data, ensuring that the deep learning model can be effectively trained and produce highly accurate predictions.

[0062] For further information, see Figure 4 The diagram shows the structure of a random forest model. The random forest model is used to analyze driving conditions and predict the long-term energy consumption trend of the cruising range when it is determined that the driver is a frequent vehicle driver, and to determine the second cruising range prediction result. Specifically, random forest model training is an integrated learning method to improve the model's predictive ability and generalization performance. By generating multiple independent decision trees and integrating their prediction results, it can effectively handle complex nonlinear relationships and is particularly suitable for capturing the impact of environmental conditions and driving behavior on cruising range. The training process of the random forest model includes generating multiple random feature subsets and constructing multiple decision trees based on these subsets. Each decision tree independently predicts the cruising range, and its final output is the weighted average of the prediction results of all decision trees. Specifically, let fᵢ(X) be the prediction result of the i-th decision tree, and the final output F(X) of the random forest be the weighted average of the outputs of all decision trees, that is:

[0063]

[0064] Here, N is the total number of decision trees. The random forest model can not only effectively reduce the overfitting risk of a single decision tree, but also improve the stability and robustness of the prediction by integrating the results of multiple trees.

[0065] Furthermore, a long-short-term memory (LSTM) network model is used to analyze driving conditions and identify non-occupant drivers. This model then predicts short-term energy consumption trends within the driving range and determines a second range prediction. Specifically, the long-short-term memory (LSTM) network model is a key component of the deep learning prediction module for processing time series data. It effectively captures the temporal dependencies between vehicle status and energy consumption. Through its unique memory gating mechanism, the LSTM network retains long-term dependency information and selectively forgets unnecessary information during training, effectively simulating the dynamic behavior of the vehicle over time. During model training, vehicle status data is fed into the LSTM network as an input sequence. By learning the relationship between this status data and range, the LSTM model captures trends in energy consumption over time.

[0066] Because LSTM networks focus on capturing time series features in vehicle status data, they are suitable for predicting short-term energy consumption trends. Random forests, on the other hand, can handle complex nonlinear relationships between input features, offering significant advantages in analyzing the impact of environmental conditions and driving behavior on range, making them suitable for predicting long-term energy consumption trends. To combine the strengths of random forest and LSTM models for accurate range prediction, an output decision is incorporated into the system's deep learning prediction module. Specifically, the system uses the driving conditions of pure electric commercial vehicles to determine whether the driver is a regular user of the vehicle. If so, the system outputs the random forest prediction result. If not, the system outputs the short-term prediction of the vehicle's energy consumption trend using the LSTM model. This prediction process can be dynamically adjusted to output the optimal range prediction based on real-time conditions.

[0067] By integrating the advantages of the random forest model and the LSTM model, the deep learning prediction module of the system of the present invention can provide high-precision range estimation, has strong adaptability and robustness, and can provide stable and reliable range estimation in complex road environments and under different drivers.

[0068] The data fusion layer is used to perform fuzzy logic fusion processing on vehicle status information and external environment information, determine the weight coefficient of weighted average, and based on the weight coefficient, perform weighted average fusion processing on the first cruising range prediction result and the second cruising range prediction result to determine the target cruising range prediction result at the current moment. The data fusion layer is composed of a data fusion module. The data fusion module generates a more accurate and reliable final cruising range prediction value by comprehensively analyzing the cruising range prediction results from different sources. In order to achieve this goal, a weighted average fusion unit and a fuzzy logic subsystem are adopted, which are used to effectively fuse the outputs from different sources and dynamically adjust the weights, so as to cope with complex and changeable driving environments and vehicle conditions.

[0069] In one embodiment, see Figure 5 The data fusion module shown in the figure is a schematic diagram. The data fusion layer includes a fuzzy logic subsystem. The fuzzy logic subsystem is used to fuzzify vehicle status information and external environment information. It then uses a preset inference engine to infer the fuzzified information, defuzzifying the inference results to obtain weight coefficients for the weighted average. In other words, the fuzzy logic subsystem can dynamically adjust the weights in the weighted average to adapt to different driving environments and vehicle status changes. The core of fuzzy logic fusion lies in processing uncertainty and ambiguity to transform driving environment and vehicle status information into a basis for model weight adjustment. Specifically, the fuzzy logic system consists of three parts: fuzzification, inference engine, and defuzzification.

[0070] Fuzzification: First, the actual collected environmental information and vehicle status are input into the fuzzy logic system, and these precise data are converted into membership in the fuzzy set.

[0071] Inference Engine: The fuzzified input is then inferred using a pre-defined fuzzy rule base. This rule base contains a series of "if-then" rules that dynamically adjust the weights of different prediction models based on the driving environment and vehicle status.

[0072] Defuzzification: The fuzzy output obtained by the inference engine is finally converted into precise values, which are the adjusted weights w1 and w2.

[0073] In another embodiment, the data fusion layer further includes: a weighted average fusion unit; wherein the weighted average fusion unit is used to determine the target range prediction result at the current moment by summing the products of the first range prediction result and the second range prediction result and the corresponding weight coefficients based on the weight coefficient and the preset fusion model. That is, the weighted average fusion is used to comprehensively process the range prediction results from different models to obtain an overall range prediction value. Specifically, this method performs weighted averaging on the results from the average energy consumption estimation model and the deep learning prediction model according to the weights. The fusion formula is as follows:

[0074]

[0075] in Indicates the final fused endurance prediction value, is the output value of the average energy consumption estimation module, is the predicted value output by the deep learning prediction module, and are the weight coefficients of the two prediction models, satisfying + =1, these weight coefficients are not fixed, but are dynamically adjusted according to the current state of the vehicle and external environmental conditions. The specific adjustment values ​​are given by the fuzzy logic system.

[0076] By combining weighted average fusion and fuzzy logic systems, we can effectively cope with the changing external environment and unstable driving conditions during vehicle operation. For example, in the low temperature environment of winter, battery performance tends to decline. At this time, the fuzzy logic system will reduce its reliance on average energy consumption estimation and rely more on deep learning models to provide more targeted range predictions. In the more stable highway driving scenario, weighted average fusion will focus on the average energy consumption estimation model to improve the accuracy of the range.

[0077] The self-learning layer is used to perform real-time analysis and processing on the deviation between the energy consumption change in the target range prediction result and the energy consumption change in the actual range information. Based on the deviation analysis results, the weight coefficient is updated to determine the target weight coefficient, so that the data fusion layer can use the target weight coefficient to predict the range at the next moment. In one embodiment, the self-learning layer includes a data feedback and storage module. The data feedback and storage module is used to collect the deviation between the target range prediction result and the energy consumption change in the actual range information at each moment, as well as the vehicle status information and external environment information corresponding to each moment. Based on the deviation, vehicle status information, and external environment information at each moment, the module constructs a historical data set. In other words, the data feedback and storage module is responsible for collecting, in real time, the deviation data between the range prediction value output by the data fusion module and the vehicle's actual range value during actual driving, as well as the corresponding environmental conditions and vehicle status information. This data is then stored in the vehicle's onboard memory and cloud server to form a complete historical data set to facilitate subsequent optimization processes.

[0078] In another embodiment, the self-learning layer also includes a model optimization module. This module uses an incremental learning model to update weight coefficients based on deviations and determine target weight coefficients. Specifically, the model optimization module uses online learning to incrementally update the weight parameters of the average energy consumption estimation module and deep learning prediction module in the data fusion layer based on collected data deviations, ensuring the system can quickly adapt to the latest environmental and driving conditions. Furthermore, a reinforcement learning approach is used to establish a reward mechanism between model predictions and actual performance, enabling adaptive system adjustments and dynamically adjusting the weight allocation strategy in the data fusion module.

[0079] See also Figure 6 FIG. 1 is a flow chart of a method for predicting the cruising range of a pure electric commercial vehicle, which mainly includes the following steps S602 to S608:

[0080] Step S602 collects vehicle status information, external environment information, and actual endurance information. The vehicle status information includes vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status, and driving mode. The external environment information includes ambient temperature information and road condition information. In one embodiment, data transmission and storage are performed by transmitting data to a central processing unit (CPU) via the vehicle-mounted CAN bus. The data is stored in the vehicle-mounted memory with a timestamp as the index. To ensure the reliability of data collection, a dual redundant design can be adopted: each group of key sensors is equipped with a backup sensor, which automatically switches to the backup sensor when the main sensor fails. In addition, the system uses a self-diagnostic algorithm to regularly detect whether the sensor output exceeds a preset range. If abnormal data is found, an alarm is triggered.

[0081] Step S604: perform energy consumption estimation processing on the vehicle status information and external environment information to determine an energy consumption estimation value and a first cruising range prediction result corresponding to the energy consumption estimation value, and perform deep learning prediction processing on the vehicle status information and external environment information to determine a second cruising range prediction result, wherein the energy consumption estimation value includes energy consumption per unit time and energy consumption per unit mileage.

[0082] In one embodiment, the energy consumption per unit time and per unit mileage can be calculated based on real-time data in a step-by-step manner. The specific steps are as follows:

[0083] SOC value estimation:

[0084]

[0085]

[0086] in, The busbar power of the batteries connected in series; is the rated capacity of the battery, Ah; is the initial SOC value of the battery; For battery efficiency.

[0087] Calculation of power loss due to driving resistance:

[0088]

[0089]

[0090] in, is the vehicle's driving resistance, N; v is the vehicle's speed, m / s; m is the vehicle's mass, kg; f is the rolling resistance coefficient; α is the road slope angle, (°); is the drag coefficient; A is the frontal area of ​​the car; δ is the rotational inertia coefficient of the transmission system; a is the vehicle acceleration, ; is the power loss due to vehicle running resistance, W.

[0091] Motor efficiency loss power calculation:

[0092]

[0093]

[0094] in: is the motor efficiency loss power, W; P is the motor theoretical output power, W; is the actual output power of the motor, W; T is the required torque of the vehicle, Nm; n is the motor speed, r / min; η is the motor efficiency at the current torque and speed.

[0095] Battery power consumption:

[0096]

[0097] in: is the battery power consumption, W; is the battery loss voltage, V.

[0098] Energy recovery power:

[0099]

[0100] in: is the energy recovery power, W; I is the battery bus current, A; is the battery terminal voltage, V.

[0101] Calculation of energy consumption per unit time:

[0102]

[0103] in: is the energy consumption per unit time, J.

[0104] Calculation of energy consumption per unit mileage:

[0105]

[0106] in: is the energy consumption per unit mile, J.

[0107] In one embodiment, the calculation results can be updated based on the data collected in real time by the data acquisition module and transmitted to the data fusion module. Deep learning prediction processing is performed on the vehicle status information and external environment information. When determining the second cruising range prediction result, if the deep learning prediction module is used, the data set collected by the data acquisition module is first divided into a training set and a test set. First, the training set data is randomly sampled to obtain i training set samples {D1(X), D2(X), D3(X), ..., Di(X)} and i test set samples. A decision tree model is established and the decision tree is randomly split to establish i decision trees. The test set data is brought into the trained decision tree model. Through model calculation, i results {C1(X), C2(X), C3(X), ..., Ci(X)} can be obtained, and the average value of {Ci, i=1, 2, 3, ..., i} can be used to obtain the predicted value of the cruising range.

[0108] In addition, the LSTM neural network model can also be used for deep learning prediction processing to determine the second range prediction result. The LSTM neural network model is an improved variant based on the recurrent neural network (RNN). By introducing three "gate" structures (forget gate, input gate, and output gate) into the RNN, the LSTM neural network can selectively retain the input sequence information, effectively solving the problems of gradient explosion and gradient vanishing in the RNN long sequence training process, making the LSTM neural network very effective in processing long sequence information. The steps of building a pure electric commercial vehicle range prediction model based on the LSTM neural network are as follows:

[0109] According to the data collected by the data acquisition module, the input and output of the LSTM neural network model at time t are defined as:

[0110]

[0111] in, is the input at time t; is the output at time t.

[0112] The forget gate is processed by the Sigmoid function and After the information is received, a vector between 0 and 1 is output to determine the degree of information retention, 0 means all is discarded, and 1 means all is retained. The formula is described as:

[0113]

[0114] in, and is the weight matrix and bias of the forgetting layer, σ is the Sigmoid nonlinear activation function, The input gate determines what important information is input to the current cell state. and The information is passed into the Sigmoid function and the tanh function respectively. The Sigmoid function determines which information to update, and the tanh function generates a new candidate value vector. Finally, the output value of the Sigmoid function is multiplied by the output value of the tanh function, and the forget gate is combined to select and forget part of the old cell information to obtain the new cell information. The formula is described as follows:

[0115]

[0116] in, , , , are the weight matrices and biases of the two layers, tanh is the nonlinear activation function, is the input gate.

[0117] The output gate is used to determine the value of the next hidden layer state. First, the Sigmoid function is used to obtain the state characteristics of the output cell. Then, the tanh activation function is used to obtain the state value of the cell. The state value is then multiplied by the output value of the Sigmoid function. The specific formula is described as follows:

[0118]

[0119] Where, and are the weight matrix and bias respectively, is the output gate.

[0120] Implementation of data fusion module:

[0121] In the data fusion module, the input variables in the fuzzy logic rule base are defined as follows:

[0122] Ambient temperature:

[0123] Fuzzy sets: low temperature (LT), moderate temperature (MD), high temperature (HT).

[0124] Domain: [-30℃, 10℃], [10℃, 25℃], [25℃, 50℃].

[0125] Road slope:

[0126] Fuzzy sets: flat (FL), moderate slope (MD), steep slope (ST).

[0127] Domain: [0%, 5%], [5%, 10%], [10%, 20%].

[0128] Vehicle speed:

[0129] Fuzzy sets: low speed (LS), medium speed (MS), high speed (HS).

[0130] Domain: [0km / h, 40km / h], [40km / h, 80 km / h], [80km / h, 120km / h].

[0131] Battery SOC (State of Charge):

[0132] Fuzzy sets: low (LOW), medium (MED), high (HIGH).

[0133] Domain: [0%, 20%], [20%, 60%], [60%, 100%].

[0134] In the data fusion module, the output variables in the fuzzy logic rule base are defined as follows:

[0135] Weight w1 (weight of the average energy consumption estimation module output value):

[0136] Fuzzy sets: low (LOW), medium (MED), high (HIGH).

[0137] Domain: 0.2, 0.5, 0.8.

[0138] Weight w2 (weight of the output value of the deep learning prediction module):

[0139] Fuzzy sets: low (LOW), medium (MED), high (HIGH).

[0140] Domain: 0.2, 0.5, 0.8.

[0141] Step S606: Perform fuzzy logic fusion processing on the vehicle state information and the external environment information to determine a weight coefficient for weighted averaging. Based on the weight coefficient, perform weighted averaging fusion processing on the first cruising range prediction result and the second cruising range prediction result to determine the target cruising range prediction result at the current moment. In one embodiment, the fuzzy logic system consists of three parts: fuzzification, inference engine, and defuzzification. The inference engine uses a set fuzzy rule base to infer the fuzzified input. The fuzzy logic rule base can be seen in Table 1 below:

[0142] Table 1 Fuzzy logic rule base

[0143]

[0144] Furthermore, the rules of the fuzzy logic rule base used by the inference engine are specifically explained as follows (1) to (10):

[0145] (1) When the input variables after fuzzification are low ambient temperature (LT), flat road slope (FL), low vehicle speed (LS), and low battery SOC (LOW), due to the complexity of the environment and battery status, the weights are biased towards the deep learning model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to LOW, and the output value weight of the deep learning prediction module is set to HIGH.

[0146] (2) When the input variables after fuzzification are low ambient temperature (LT), steep road slope (ST), medium vehicle speed (MS), and medium battery SOC (MED), due to the complexity of the environment and battery status, the weights are biased towards the deep learning model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to w1 = LOW, and the output value weight of the deep learning prediction module is set to w2 = HIGH.

[0147] (3) When the input variables after fuzzification are medium ambient temperature (MD), flat road slope (FL), medium vehicle speed (MS), and high battery SOC (HIGH), since the environment and battery status are not complicated, the weights tend to be in the average energy consumption model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to HIGH, and the output value weight of the deep learning prediction module is set to LOW.

[0148] (4) When the input variables after fuzzification are medium ambient temperature (MD), medium road slope (MD), medium vehicle speed (MS), and medium battery SOC (MED), since the environment and battery status are medium, the weights are not biased. Therefore, the output value weight of the output variable average energy consumption estimation module is set to w1 = MED, and the output value weight of the deep learning prediction module is set to w2 = MED.

[0149] (5) When the input variables after fuzzification are high ambient temperature (HT), flat road slope (FL), high vehicle speed (HS), and high battery SOC (HIGH), since the environment and battery status are not complicated, the weights tend to be in the average energy consumption model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to HIGH, and the output value weight of the deep learning prediction module is set to LOW.

[0150] (6) When the input variables after fuzzification are low ambient temperature (LT), steep road slope (ST), low vehicle speed (LS), and low battery SOC (LOW), due to the complexity of the environment and battery status, the weights are biased towards the deep learning model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to LOW, and the output value weight of the deep learning prediction module is set to HIGH.

[0151] (7) When the input variables after fuzzification are medium ambient temperature (MD), medium road slope (MD), high vehicle speed (HS), and low battery SOC (LOW), due to the complexity of the environment and battery status, the weights are biased towards the deep learning model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to LOW, and the output value weight of the deep learning prediction module is set to HIGH.

[0152] (8) When the input variables after fuzzification are high ambient temperature (HT), steep road slope (ST), high vehicle speed (HS), and medium battery SOC (MED), due to the complexity of the environment and battery status, the weights are biased towards the deep learning model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to LOW, and the output value weight of the deep learning prediction module is set to HIGH.

[0153] (9) When the input variables after fuzzification are low ambient temperature (LT), medium road slope (MD), low vehicle speed (LS), and medium battery SOC (MED), since the environment and battery status are medium, the weights are not biased. Therefore, the output value weight of the output variable average energy consumption estimation module is set to w1 = MED, and the output value weight of the deep learning prediction module is set to w2 = MED.

[0154] (10) When the input variables after fuzzification are medium ambient temperature (MD), flat road slope (FL), low vehicle speed (LS), and high battery SOC (HIGH), since the environment and battery status are not complex, the weights tend to be in the average energy consumption model. Therefore, the output value weight of the output variable average energy consumption estimation module is set to HIGH, and the output value weight of the deep learning prediction module is set to LOW.

[0155] Step S608 performs real-time analysis on the deviation between the energy consumption change in the target range prediction result and the energy consumption change in the actual range information. The weight coefficient is updated based on the deviation analysis result to determine the target weight coefficient, so that the data fusion layer can make a range prediction for the next moment based on the target weight coefficient. In one embodiment, by acquiring real-time operating data, the data stream can be shared with the existing average energy consumption estimation module and deep learning prediction module. After each trip, the system automatically evaluates the accuracy of the prediction value. When the deviation exceeds the set threshold, the self-learning module is triggered to optimize the model. In particular, regarding the allocation of dynamic weights, the optimization results of the self-learning module are dynamically updated in real time through the fuzzy logic system to ensure that the fusion model can provide the best prediction under the new operating conditions.

[0156] Furthermore, the operation process of the self-learning module is as follows: first, the data acquisition module obtains the vehicle operation data, and then passes the data to the deep learning prediction module, the average energy consumption estimation module and the self-learning module. The deep learning prediction module and the average energy consumption estimation module output preliminary prediction values ​​according to the existing algorithm. Then, the self-learning module performs deviation analysis and model optimization on the input data, and feeds back the optimized parameters to the deep learning module and the data fusion module. Finally, the data fusion module dynamically adjusts the weight value and outputs the final endurance prediction value.

[0157] In summary, by combining traditional average energy consumption estimation with historical data analysis based on deep learning, the present invention can more accurately predict the range in various complex environments and driving conditions. Specifically, the present invention uses a long short-term memory network (LSTM) to capture energy consumption trends in time series, combines it with a random forest algorithm for feature importance analysis, and uses fuzzy logic to perform weighted fusion of the results of different prediction models. Finally, using a self-learning layer, through continuous model optimization and data feedback mechanism, the system's robustness and prediction accuracy in diverse scenarios are improved.

[0158] By predicting mileage using the aforementioned integrated approach, pure electric commercial vehicles can provide users with reliable range information under various driving conditions, reduce range anxiety, and further improve user acceptance and satisfaction with pure electric commercial vehicles. This innovative solution not only provides real-time range predictions during normal vehicle driving, but also continuously optimizes the model through deep learning, allowing its prediction accuracy to gradually improve over time.

[0159] An embodiment of the present invention provides a server. Specifically, the server includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned embodiments.

[0160] Figure 7 A structural diagram of a server provided in an embodiment of the present invention, wherein the server 100 includes: a processor 70, a memory 71, a bus 72 and a communication interface 73, wherein the processor 70, the communication interface 73 and the memory 71 are connected via the bus 72; the processor 70 is used to execute an executable module stored in the memory 71, such as a computer program.

[0161] Memory 71 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. Communication between the system network element and at least one other network element is achieved through at least one communication interface 73 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0162] The bus 72 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 7 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0163] Among them, the memory 71 is used to store programs, and the processor 70 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 70 or implemented by the processor 70.

[0164] The processor 70 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above-described method may be performed by hardware integrated logic circuits or software instructions within the processor 70. The processor 70 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present invention may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 71 , and the processor 70 reads the information in the memory 71 and completes the steps of the above method in combination with its hardware.

[0165] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be referred to the previous method embodiment and will not be repeated here.

[0166] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0167] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A range prediction system for a pure electric commercial vehicle, characterized in that: The system includes: a data acquisition layer, a data processing layer, a data fusion layer and a self-learning layer, wherein the data processing layer is connected to the data acquisition layer and the data fusion layer respectively, and the self-learning layer is connected to the data fusion layer and the data acquisition layer respectively; wherein, The data acquisition layer is used to collect vehicle status information, external environment information and actual endurance information through a preset sensor set, wherein the vehicle status information includes: vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status and driving mode; the external environment information includes: ambient temperature information and road condition information; The data processing layer is configured to perform energy consumption estimation processing on the vehicle state information and the external environment information to determine an estimated energy consumption value and a first cruising range prediction result corresponding to the estimated energy consumption value, and perform deep learning prediction processing on the vehicle state information and the external environment information to determine a second cruising range prediction result, wherein the estimated energy consumption value includes energy consumption per unit time and energy consumption per unit mileage; The data fusion layer is used to perform fuzzy logic fusion processing on the vehicle state information and the external environment information to determine a weight coefficient for weighted averaging, and based on the weight coefficient, perform weighted averaging fusion processing on the first cruising range prediction result and the second cruising range prediction result to determine a target cruising range prediction result at a current moment; The self-learning layer is used to perform real-time analysis and processing on the deviation value between the energy consumption change in the target endurance prediction result and the energy consumption change in the actual endurance information, update the weight coefficient according to the deviation analysis result, and determine the target weight coefficient so that the data fusion layer can make an endurance mileage prediction at the next moment based on the target weight coefficient.

2. The cruising range prediction system for pure electric commercial vehicles according to claim 1, characterized in that: The data acquisition layer also includes: a self-diagnosis unit; wherein, The self-diagnosis unit is used to perform abnormality detection processing on the collected vehicle status information and the external environment information, and to switch the fault sensor and issue a fault alarm when a sensor fault or abnormal data is detected.

3. The cruising range prediction system for pure electric commercial vehicles according to claim 1, characterized in that: The data processing layer includes: an average energy consumption estimation module; wherein, The average energy consumption estimation module is used to calculate the remaining battery power, driving resistance loss power, motor efficiency loss power, battery consumption power and energy recovery power according to the vehicle status information and the external environment information, so as to determine the energy consumption per unit time according to the driving resistance loss power, the motor efficiency loss power, the battery consumption power and the energy recovery power, and determine the energy consumption per unit mileage according to the energy consumption per unit time.

4. The cruising range prediction system for a pure electric commercial vehicle according to claim 3, characterized in that: The data processing layer further includes: a deep learning prediction module, and the deep learning prediction module includes: a random forest model; wherein, The random forest model is used to analyze the driving situation and determine that the driver is a frequent vehicle driver, to perform long-term energy consumption change trend prediction processing on the cruising range and determine the second cruising range prediction result.

5. The cruising range prediction system for a pure electric commercial vehicle according to claim 4, characterized in that: The deep learning prediction module also includes: a long short-term memory network model; wherein, The long short-term memory network model is used to analyze the driving situation and determine that the driver is an uncommon vehicle driver, to perform short-term energy consumption change trend prediction processing on the cruising range and determine the second cruising range prediction result.

6. The cruising range prediction system for a pure electric commercial vehicle according to claim 1, characterized in that: The data fusion layer includes: a fuzzy logic subsystem; wherein, The fuzzy logic subsystem is used to fuzzify the vehicle state information and the external environment information, and use a preset inference engine to infer the fuzzified information to defuzzify the inference result and obtain the weight coefficient of the weighted average.

7. The cruising range prediction system for a pure electric commercial vehicle according to claim 6, characterized in that: The data fusion layer further includes: a weighted average fusion unit; wherein, The weighted average fusion unit is used to determine the target cruising range prediction result at the current moment by summing the products of the first cruising range prediction result and the second cruising range prediction result and the corresponding weight coefficients based on the weight coefficient and the preset fusion model.

8. The cruising range prediction system for a pure electric commercial vehicle according to claim 1, characterized in that: The self-learning layer includes: a data feedback and storage module; wherein, The data feedback and storage module is used to collect the deviation value between the target endurance prediction result at each moment and the energy consumption change in the actual endurance information, as well as the vehicle status information and the external environment information corresponding to each moment, and construct a historical data set based on the deviation value, the vehicle status information and the external environment information at each moment.

9. The cruising range prediction system for a pure electric commercial vehicle according to claim 8, characterized in that: The self-learning layer also includes: a model optimization module; wherein, The self-learning layer is used to update the weight coefficient based on the deviation value through an incremental learning model to determine the target weight coefficient.

10. A method for predicting the cruising range of a pure electric commercial vehicle, characterized in that: The method is applied to the cruising range prediction system of a pure electric commercial vehicle according to any one of claims 1 to 9, and the method comprises: Collect vehicle status information, external environment information, and actual driving range information. The vehicle status information includes: vehicle speed information, battery status information, vehicle acceleration and steering angle, throttle and brake pedal displacement, motor status and driving mode. The external environment information includes: ambient temperature information and road condition information. performing energy consumption estimation processing on the vehicle state information and the external environment information to determine an estimated energy consumption value and a first cruising range prediction result corresponding to the estimated energy consumption value, and performing deep learning prediction processing on the vehicle state information and the external environment information to determine a second cruising range prediction result, wherein the estimated energy consumption value includes energy consumption per unit time and energy consumption per unit mileage; performing fuzzy logic fusion processing on the vehicle state information and the external environment information to determine a weight coefficient for weighted averaging, and performing weighted averaging fusion processing on the first cruising range prediction result and the second cruising range prediction result based on the weight coefficient to determine a target cruising range prediction result at the current moment; The deviation value between the energy consumption change in the target endurance prediction result and the energy consumption change in the actual endurance information is analyzed and processed in real time, the weight coefficient is updated according to the deviation analysis result, and the target weight coefficient is determined so that the data fusion layer predicts the endurance mileage at the next moment based on the target weight coefficient.

11. A server, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method of claim 10.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the method of claim 10 .

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