Path planning method and system for new energy automobile

Abstract

The invention relates to the technical field of path planning, in particular to a path planning method and system for a new energy automobile. The method comprises the following steps: acquiring a shortest distance from a starting point to a terminal point and a rechargeable node set in a path through a vehicle-mounted panoramic map of the new energy vehicle, estimating a theoretical driving distance, and acquiring real-time road condition information; then, based on real-time road condition information, simulating and deducing invalid energy consumption output of a congestion state, quantifying an energy consumption increment output ratio of road geometrical characteristics, and evaluating an energy consumption loss composite index by combining the invalid energy consumption output with the energy consumption increment output ratio; finally, according to the energy consumption loss composite index and the theoretical driving distance, the limit driving radius is judged, the rechargeable node set is optimized based on the limit driving radius, and path planning data is output. According to the invention, the path planning technology is optimized, so that the path planning technology is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path planning, in particular to a path planning method and system for new energy vehicles. BACKGROUND

[0002] Compared with traditional fuel vehicles, new energy vehicles not only have significant advantages in reducing carbon emissions and reducing air pollution, but also perform well in energy utilization efficiency. However, the popularization of new energy vehicles still faces some challenges, one of which is the limitation of the cruising range. Although the cruising range of new energy vehicles is gradually improving with the continuous development of battery technology, due to the limitation of battery capacity and the imperfect layout of charging facilities, the energy consumption and charging problems in long-distance travel are still one of the key factors affecting consumers' car purchase decisions. Therefore, how to optimize the path planning of new energy vehicles and reasonably arrange the selection of charging stations has become an important issue to improve the user experience of new energy vehicles.

[0003] In summary, the traditional path planning method for new energy vehicles still relies on a single mileage to predict the driving range, without fully considering the actual road conditions and traffic congestion, resulting in low traffic efficiency of path planning. SUMMARY

[0004] Therefore, it is necessary to provide a path planning method and system for new energy vehicles to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a path planning method for new energy vehicles, the method comprising the following steps: Step S1: obtaining the shortest distance from the starting point to the ending point and the set of chargeable nodes in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle; estimating the theoretical driving distance of the new energy vehicle according to the shortest distance, and obtaining the real-time road condition information in the theoretical driving distance; Step S2: simulating and deducing the invalid energy consumption output of the congestion state according to the real-time road condition information; performing energy consumption increment output ratio quantization of road geometric characteristics based on the real-time road condition information, obtaining the energy consumption increment output ratio associated with road geometric characteristics; performing energy consumption loss composite index evaluation according to the invalid energy consumption output and the energy consumption increment output ratio, forming the energy consumption loss composite index; Step S3: judging the limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance; selecting the charging node based on the limit driving radius for the optimized decision of the set of chargeable nodes, and performing path planning output to obtain path planning data.

[0006] Preferably, the application also provides a path planning system for a new energy vehicle, which is used to execute the path planning method for the new energy vehicle as described above, and the path planning system for the new energy vehicle comprises: a road condition information acquisition module, which is used to acquire the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle, estimate the theoretical driving distance of the new energy vehicle according to the shortest distance, and acquire the real-time road condition information in the theoretical driving distance; an energy consumption loss analysis module, which is used to simulate and deduce the invalid energy consumption output in the congestion state according to the real-time road condition information, perform the energy consumption increment output ratio quantization of the road geometric characteristics based on the real-time road condition information, obtain the energy consumption increment output ratio associated with the road geometric characteristics, perform the energy consumption loss composite index evaluation according to the invalid energy consumption output and the energy consumption increment output ratio, and form the energy consumption loss composite index; a path planning output module, which is used to judge the limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance, perform the optimal decision charging node selection on the chargeable node set based on the limit driving radius, and perform the path planning output to obtain the path planning data.

[0007] The beneficial effects of the present application are that, by obtaining the shortest distance path and the set of chargeable nodes in the path planning initial stage, a complete basic information framework can be constructed, so that the system has a clear route structure and potential energy supply location from the beginning. At the same time, by estimating the distance and obtaining the real-time traffic information in the range through the shortest distance estimation theory, the planning process can no longer rely on static maps, but fully utilize dynamic traffic data to improve the adaptability of path planning to real road environment, avoid the deviation of the endurance estimation caused by traffic changes, and improve the accuracy and reliability of the overall path planning from the source. Through real-time traffic information, the system can truly reflect the additional energy consumption of the vehicle in non-ideal conditions such as low speed and congestion, thereby avoiding the over-idealized energy estimation in traditional planning. Further, by quantifying the incremental influence of road geometric characteristics (such as slope, curvature, speed limit, etc.) on energy consumption, a more refined energy consumption prediction model can be realized, so that energy consumption estimation is no longer dependent on speed and distance. The combined index of energy consumption loss formed by jointly evaluating the invalid energy consumption and the incremental energy consumption of geometric characteristics can comprehensively reflect the energy consumption risk of the vehicle in the actual road, effectively improve the accuracy of energy consumption prediction, and provide a more reliable basis for subsequent endurance judgment and charging node selection. According to the energy consumption loss composite index and the theoretical driving distance, the limit driving radius of the new energy vehicle can be determined, so that the system can evaluate the reachable space of the vehicle in a way closer to the real energy consumption performance, thereby avoiding the risk of insufficient power caused by insufficient energy estimation in traditional methods. Based on the limit driving radius, the set of chargeable nodes is optimized and decided, which can effectively filter out the most suitable charging nodes, avoid unnecessary detours and repeated charging, and ensure that the vehicle completes the trip planning within the safe endurance range. The finally generated path planning data can take into account the driving efficiency, energy consumption safety and charging convenience, and provide a more stable, intelligent and energy-saving travel solution for new energy vehicle users. Therefore, the present application optimizes the traditional path planning method for new energy vehicles, solves the problem that the traditional path planning method for new energy vehicles only relies on a single mileage to predict the driving mileage and does not fully consider the actual road conditions and traffic congestion, thereby improving the traffic efficiency of path planning. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 The flowchart of the steps of the path planning method for a new energy vehicle is shown in Figure 1. Figure 2 The detailed implementation step flowchart of step S2 in the method is shown in Figure 2. Figure 1 The detailed implementation step flowchart of step S3 in the method is shown in Figure 3. Figure 3 The detailed implementation step flowchart of step S3 in the method is shown in Figure 3. Figure 1 The detailed implementation step flowchart of step S3 in the method is shown in Figure 3. DETAILED DESCRIPTION

[0009] Please refer to Figure 1 A path planning method of an energy vehicle, the method comprising the following steps: Step S1: obtaining the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle; estimating the theoretical driving distance of the new energy vehicle according to the shortest distance, and obtaining the real-time road condition information in the theoretical driving distance; In the embodiment of the present application, firstly, the current residual capacity of the new energy vehicle is obtained, which is obtained by reading in real time through the vehicle battery management system. For example, if the total capacity of the battery of the current new energy vehicle is 60 kWh and the percentage of the residual capacity is 75%, then the current residual capacity is 45 kWh. Through the vehicle-mounted panoramic map of the new energy vehicle, the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path are obtained. The vehicle-mounted panoramic map uses Dijkstra algorithm or A* pathfinding algorithm to calculate the shortest distance path from the starting point to the ending point. For example, if the user sets the starting point as city A and the ending point as city B, the shortest distance is calculated to be 150 kilometers through the algorithm, and the position information of all charging stations along the path is extracted to form a chargeable node set, which contains three chargeable nodes, charging station A 30 kilometers away from the starting point, charging station B 70 kilometers away from the starting point, and charging station C 120 kilometers away from the starting point. It should be noted that the theoretical driving distance of the new energy vehicle is estimated based on the residual capacity of the new energy vehicle and the shortest distance. The calculation of the theoretical driving distance is based on the 100 km energy consumption calibration value of the vehicle. The specific calculation formula is: theoretical driving distance = residual capacity ÷ 100 km energy consumption calibration value × 100, wherein the residual capacity represents the available capacity of the current vehicle battery, and the unit is kilowatt-hour; the 100 km energy consumption calibration value represents the energy consumption of the vehicle in standard working conditions for driving 100 kilometers, and the unit is kilowatt-hour per 100 kilometers; for example, when the residual capacity is 45 kWh and the 100 km energy consumption calibration value is 15 kWh per 100 km, the theoretical driving distance is 45 divided by 15 multiplied by 100, which is 300 kilometers. The real-time road condition information in the theoretical driving distance is obtained through data interaction between the vehicle-mounted panoramic map and the traffic management cloud platform. The real-time road condition information includes traffic congestion state, road slope information, road bend information and intersection information of each road section.

[0010] Step S2: simulating and deducing the invalid energy consumption output of the congestion state according to the real-time road condition information; quantifying the energy consumption increment output ratio of the road geometric characteristics based on the real-time road condition information to obtain the energy consumption increment output ratio associated with the road geometric characteristics; evaluating the energy consumption loss composite index according to the invalid energy consumption output and the energy consumption increment output ratio to form the energy consumption loss composite index; In the embodiment of the present application, after obtaining real-time traffic information, first, the road sections marked as low-speed traffic state and the traffic congestion key nodes in the path are extracted, the average start-stop frequency of the congestion section is analyzed by the time sequence analysis method with fixed sliding window width, the frequency is mapped with the rated torque and speed interval of the vehicle drive motor, the decline gradient of the motor energy conversion efficiency in the start-stop state is determined, the efficiency sample data within 30 minutes is recorded by time series, the actual trend of the motor energy conversion efficiency decreasing over time is obtained by the weighted difference method, the additional motor energy consumption in the road section is calculated according to the trend curve, then the road geometric feature data contained in the real-time traffic is extracted, the slope value, curvature radius and intersection density of each road section are sampled, the discrete weighted cumulative method is used to calculate the incremental contribution ratio of road geometric features to vehicle energy consumption, the weight coefficient of the slope section with a slope greater than 5 degrees is enhanced in the weight matrix, the incremental ratio is adjusted according to the steering frequency and centrifugal force component when the curvature radius is less than 7.2 m, after the calculation of all real-time energy consumption factors is completed, the invalid energy consumption output and the energy consumption increment output ratio of each road section are unified, the energy consumption loss composite index is calculated by calling the weighted clustering algorithm, the index is obtained by normalizing the weighted superposition of the statistical average invalid energy consumption and the geometric incremental energy consumption, which is used to describe the energy consumption comprehensive loss characteristics varying with time and space on the path, the energy consumption loss composite index data output in this step is used for the next step of limit driving radius judgment, for example, in a path with a total length of 100 km, the real-time traffic information shows that the 20 km to 35 km section is a high congestion section, the average speed is 12 km / h, the traffic flow is 650 vehicles / h, the average start-stop frequency of the congestion section is 5 times / min, the motor efficiency decline gradient is-0.15 / kWh by collecting the output efficiency sample data of the drive motor under the start-stop frequency of 5 times / min, the additional energy loss of this section is calculated by time series weighted regression as 2.4 kWh, the average slope is 4°, the range is 14°, the turning radius is 5 m, and the intersection density is 2 per km are extracted from the road geometric information, the weight coefficient of the road section with a slope higher than 10° is set to 1.3, and the weight coefficient of the road section with a turning radius less than 7.2 m is set to 1.2, the road geometric feature related energy consumption increment output ratio is calculated as 1.18 by energy consumption increment ratio quantification, and finally the congestion state additional energy loss and the geometric feature energy consumption increment output ratio are normalized by aggregation operation to calculate the energy consumption loss composite index as 0.92, which is used for subsequent limit driving radius judgment.

[0011] Step S3: determining the limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance; selecting the charging node based on the limit driving radius to make a decision on the charging node selection, and performing path planning output to obtain path planning data; In the embodiment of the present application, after obtaining the energy consumption loss composite index, first, the theoretical driving distance is corrected according to the index, and the correction coefficient is set as the reciprocal of the energy consumption loss composite index. The limit driving radius is obtained by multiplying the theoretical distance by the correction coefficient. The limit driving radius represents the maximum sustainable driving distance of the vehicle under the real-time traffic conditions and the road geometry constraints. Then, the radius is taken as the search area radius, and the spatial domain screening is performed in the previously generated chargeable node set. The spherical distance algorithm is used to calculate the geographic straight-line distance from each charging node to the current position of the vehicle. All nodes with a distance less than or equal to the limit driving radius are included in the secondary optimization set. Then, data interaction is performed with the charging station cloud management system through the vehicle network interface to receive the real-time state parameters of each charging node, including the current available charging pile number, single pile output power, connection compatible protocol, and estimated queuing waiting time. A multi-objective minimization optimization algorithm is used to calculate the comprehensive priority value with the shortest total time consumption and the maximum power matching degree as the objective function. All nodes are sorted and the node with the highest comprehensive priority value is selected as the target charging node. On this basis, the path planning subsystem is called again to redraw the path. The A* heuristic path algorithm is used to combine the corrected energy consumption weight to regenerate the complete path information from the current vehicle position to the target charging node and the end point. The path planning result is output in the form of geographic coordinate sequence and mileage interval cost table as a path planning data file. The data file is used for dynamic path instruction generation and actual driving path control of the vehicle on-board navigation system.

[0012] Step S1 includes the following steps: Step S11: Obtain the remaining power of the current new energy vehicle; Step S12: Obtain the shortest distance from the starting point to the end point and the chargeable node set in the shortest distance path through the on-board panoramic map of the new energy vehicle; Step S13: Estimate the theoretical driving distance of the new energy vehicle according to the remaining power of the new energy vehicle and the shortest distance; Step S14: Obtain the real-time road condition information in the theoretical driving distance through the on-board panoramic map.

[0013] In the embodiment of the present application, after the new energy vehicle is started, the battery management system outputs the current state of charge value of the battery by sampling the voltage, current and residual capacity signals, the instantaneous current is collected by the milliammeter unit and the energy change amount per unit time is integrated and counted, the residual capacity data in percentage form is obtained by comparing the cumulative output power and the rated capacity of the battery, the sampling period is set to 1s, when the nominal capacity of the battery is 75kWh, the current residual capacity is detected as 42%, the value is transmitted to the energy consumption calculation control unit, under the control instruction arrangement, the information including time stamp, battery temperature, voltage distribution curve and single cell balancing coefficient is stored in the memory identification area in sequence number and is converted into calculation basis value source, in the power collection and calculation process, the current sampling accuracy is not less than 0.05A, and the voltage measurement accuracy is not less than 0.01V to ensure the accuracy of subsequent theoretical driving distance evaluation.

[0014] The latitude and longitude data of the starting point and the ending point are read through the path analysis interface of the vehicle-mounted panoramic map system, and the path grid index is constructed in the high-precision map database. The Dijkstra shortest path search algorithm is used to take the inverse ratio of road mileage and average passing speed as single-side weight value, weighted minimum cost calculation is performed on each connected node, and after traversal is completed, the path with the minimum total distance is output. After the path is rechecked by the path rechecking mechanism, the weight consistency is determined as the shortest path. In the path scanning stage, the charging facility data interface is called to extract all charging pile nodes with direct current output power greater than 60kW in the path. The charging interface is filtered to meet the nodes of national standard GB / T20234 protocol to form a chargeable node set. The node set includes charging station name, latitude and longitude coordinates, charging power, output voltage range and real-time running state information in the form of structured data, which provides basic node coordinate information for subsequent path planning.

[0015] The residual capacity obtained in step S11 is input into the calculation process through the energy consumption calculation formula node, and is matched with the average energy consumption value 15.2kWh / 100km of the vehicle under the rated load 1.8t and the constant speed 90km / h environment. The theoretical driving distance is obtained by conversion. In the data processing process, the air temperature compensation factor and the battery attenuation correction factor are introduced. When the detection environment temperature is 25℃ and the battery attenuation rate is 4%, the theoretical driving distance is corrected according to the compensation parameters, and the calculation result is theoretical driving distance=75kWh×42% / (15.2kWh / 100km)×(1-0.04). In the programming processing process, each value is stored in floating point format and set as km to form the theoretical driving distance data output, which ensures that the output data can be directly read and called by the path planning main process.

[0016] The real-time traffic information interface of the vehicle-mounted panoramic map is called, road section covered by the theoretical driving distance is subjected to road condition data collection, the collection range is set as all networked road nodes within the theoretical distance of 115km, the system obtains real-time dynamic information including six dimensions of average vehicle speed, traffic density, weather category, road wetness coefficient, traffic light cycle and lane occupancy rate from the traffic flow data center, wherein the data update time interval is 5min, the network transmission rate is not less than 10Mbps, after receiving the data, the real-time vehicle speed proportion, missing vehicle passing rate, large vehicle proportion and other flow characteristic data of each road section are indexed by road ID to establish a weight array table, then the current road congestion degree is confirmed by combining the local congestion index collected by the radar sensor, the real-time road condition information of each road section is stored in the path analysis buffer area in the form of a matrix structure and is marked with a time mark, serving as an input variable set for subsequent energy consumption simulation derivation.

[0017] Reference Figure 2 The step S2 comprises the following steps: Step S21: extracting congestion road section information and road slope and intersection structure information in real-time road condition information; In the embodiment of the application, the traffic information data stream is called in the real-time road condition data synchronization period, data index retrieval is performed on all road sections of the target path, road sections with a vehicle speed lower than 20km / h and a duration longer than 180s are extracted as congestion road section recognition results, slope and road intersection structure data in the same geographical interval are extracted from the three-dimensional map terrain information, the slope is obtained by the ratio of the elevation difference and the horizontal projection distance of the two end road nodes, the calculation accuracy is kept at 0.1°, the road intersection structure data is extracted by the intersection topology relationship stored in the map vector layer, the road connection number, lane distribution, traffic light control cycle and flow distribution ratio of each intersection are recorded, the above data are uniformly included in the congestion road section information set and the road slope and intersection structure information set, each road section is established with a structured record with time stamp, latitude and longitude, slope value, intersection type, vehicle flow and average vehicle speed as fields, in the data processing process, a dynamic real-time information table is formed with a data update period of 5min, serving as an input basis for the subsequent energy consumption calculation stage.

[0018] Step S22: simulating and deriving additional energy consumption loss of congestion state according to congestion road section information; In the embodiment of the present application, after the congestion section information is extracted, the energy consumption loss of each congestion section is calculated according to the sampled congestion mileage, average speed and start-stop frequency. Assuming that the length of a certain section is 3.2 km, the average speed is 10 km / h, and the vehicle start-stop frequency is 5 times / min, the energy output efficiency reduction rate of the driving motor in this frequency interval is-0.14 / kWh, which is used as the basis for calculation. According to the time series statistics of speed fluctuation amplitude and motor output power change, the energy consumption increase value is calculated, and the auxiliary equipment power output state is collected. The air conditioning system output power is 1.2 kW, the car machine system is 0.3 kW, and the lighting system is 0.15 kW. Within the average congestion travel time of 19.2 min, the additional equipment energy output is 0.0256 kWh. The additional energy consumption loss in congestion state is obtained by adding the device energy consumption and driving energy loss. The data including road section number, energy loss, start-stop frequency and traffic density are recorded in the form of numerical table for the next non-linear energy consumption fitting processing.

[0019] Step S23: non-linear energy consumption loss fitting is performed on the additional energy consumption loss to obtain non-linear energy consumption loss fitting data; In the embodiment of the present application, after the calculation in S22 is completed, the energy loss of each congestion section is arranged in time sequence and the energy consumption loss curve is constructed. 600 sampling point data are formed with a sampling time interval of 1s. The second difference operation is performed on the sampling sequence to reflect the progressive non-linear change trend of energy consumption with time. The change gradient of energy consumption growth rate is calculated by the least square regression method. After the gradient is continuously time-smoothed, the smooth curve sequence is output. The curve is executed segmented exponential regression to identify the attenuation intensity of energy conversion efficiency in different time periods. The relative change rate of adjacent time from the attenuation sequence is extracted and the time sequence intensity average value is calculated. The non-linear energy consumption loss fitting data is output and the data field structure is established. The field includes energy consumption gradient, fitting accuracy, time window index and cumulative energy consumption loss, which is used for synchronous analysis with geometric feature increment ratio.

[0020] Step S24: based on the road slope and intersection structure information, the energy consumption increment output ratio of road geometric features is quantified to obtain the energy consumption increment output ratio related to road geometric features; In the embodiment of the present application, based on the road slope and intersection structure information extracted in S21, the slope and turning radius data measured for each slope section are quantitatively calculated, the slope interval is set to 0° to 12°, the turning radius interval is set to 5.8m to 7.2cm, the energy consumption increment of the section with a slope greater than 5° is corrected according to the linear weighting coefficient 1.2, the energy consumption increment of the section with a slope greater than 10° is corrected according to the weighting coefficient 1.5, the coefficient 1.1 is added to the curved road with a turning radius less than 7.2m, the waiting signal loss time is calculated according to the vehicle flow distribution ratio for the node with three or more connecting roads in the intersection structure, the additional energy consumption value is obtained by multiplying the vehicle idling power 0.9kW by the signal loss time, which is weighted into the total road section energy consumption as the intersection additional energy consumption part, and in the full path analysis, 100m is taken as the minimum section unit, the slope, curvature and intersection factor energy consumption of all road sections are superimposed and averaged to form the road geometric feature related energy consumption increment output ratio.

[0021] Step S25: Perform energy loss composite index evaluation according to the non-linear energy consumption loss fitting data and the energy consumption increment output ratio to form the energy consumption loss composite index.

[0022] In the embodiment of the present application, the non-linear energy consumption loss fitting data obtained in S23 and the road geometric feature related energy consumption increment output ratio obtained in S24 are matched one by one according to the time stamp and mileage coordinates, the data in the same mileage interval is normalized and then composed into a two-dimensional energy consumption matrix, the energy loss intensity is taken as the vertical axis and the geometric increment ratio is taken as the horizontal axis, the composite energy consumption value is calculated by a weighted aggregation algorithm, and the full path range is rolled and averaged, the energy consumption matrix average value is updated every 100s, and the energy consumption loss composite index is output, which is recorded as a dimensionless value, used to quantitatively represent the vehicle energy loss degree under real-time road conditions, and contains path number, time interval, average composite index and corresponding mileage section information in the record, providing input basis for subsequent limit driving radius evaluation and path optimization decision.

[0023] Step S22 includes the following steps: Step S221: Extract the congestion mileage, estimated travel time, traffic flow and average speed in the congestion section information; Step S222: Calculate the vehicle average start-stop frequency according to the congestion mileage, estimated travel time, traffic flow and average speed, and simulate and deduce the energy conversion efficiency decline gradient of the driving motor according to the vehicle average start-stop frequency, traffic flow and average speed; Step S223: Time series intensity regression fitting is performed on the energy conversion efficiency decline gradient to obtain efficiency loss intensity fitting data; Step S224: obtaining the energy consumption output state of the vehicle built-in device through the vehicle machine system; predicting the energy consumption output increment ratio of the vehicle device energy consumption output state according to the predicted travel time, to obtain the device energy consumption output increment ratio; wherein the vehicle built-in device includes the vehicle machine system, air conditioner and vehicle-mounted electrical appliances; Step S225: simulating and deriving the additional energy consumption loss of the congestion state according to the efficiency loss intensity fitting data and the device energy consumption output increment ratio.

[0024] In the embodiment of the application, after extracting real-time road condition data, the road segment numbering system is used to traverse all the traffic information fields of the road segments identified as congestion state and extract the start and end mileage coordinates to calculate the congestion mileage. In the implementation process, the total length of the congestion segment is 5.6 km, the average traffic volume of the road segment is 780 vehicles / h, the average speed is 14 km / h, and the predicted travel time is 24 min. In the data collection stage, each parameter is recorded by the traffic perception node at a sampling interval of 60 s and the speed abnormal value is removed by the filtering algorithm. The small fluctuation is less than 10%, and the weighted smooth average value is taken. All data is stored in the travel buffer in time sequence for subsequent driving energy efficiency calculation. The time accuracy is 1 s, and the speed accuracy is 0.1 km / h. After obtaining the above data, the average start-stop frequency of the vehicle is calculated. The average speed of the road segment is 14 km / h and the traffic volume is 780 vehicles / h. The average interval time period is estimated according to the vehicle travel interval of 25 m. The average acceleration and deceleration frequency per unit time is calculated. The start-stop frequency is 4.8 times / min according to the time sequence statistical result. Then, the start-stop frequency, traffic density and average speed are taken as input variables to analyze the output power and motor input power ratio through the motor efficiency characteristic curve table. The descending gradient of the driving motor energy conversion efficiency is identified. When the start-stop frequency is 4.8 times / min and the speed is lower than 15 km / h, the highest conversion efficiency of the driving motor decreases from 94% to 80%, and the descending gradient is calculated as -0.14 / kWh. This value is obtained by point-by-point linear interpolation calculation and forms the efficiency descending time sequence data set output. Based on the energy conversion efficiency descending gradient sequence calculated in step S222, a time sequence function set with a sampling interval of 1 s is constructed. The time sequence intensity analysis operation is performed on the data recorded for 300 s continuously. The weighted regression algorithm is used to calculate the mean and variance of the efficiency descending intensity in each minute. Further sliding window regression smoothing processing is performed with a window length of 60 s and a step of 10 s to reduce short-term fluctuation interference. Then, the exponential decay regression is performed to fit the efficiency descending gradient sequence. The time constant and decay index of the fitting curve are compared to obtain the efficiency loss intensity mean value of 0.13. The fitting accuracy and time scale index are recorded to finally generate the efficiency loss intensity fitting data result. This data set is stored in the form of time sequence number for energy consumption superposition calculation in the next stage.

[0025] The power sensor signals of various electrical systems in the vehicle are read through the vehicle bus during road driving to obtain the real-time power output states of the air conditioning system, the infotainment system and the vehicle electrical appliances. When the ambient temperature detected by the vehicle temperature sensor is 30℃ and the vehicle set temperature is 23℃, the air conditioning system compressor power is kept at 1.3kW, the air supply fan output current corresponding power is 0.15kW, the infotainment system power is 0.4kW, and the lighting system power is 0.2kW. Taking the travel time of 24min as the input parameter, the energy consumption output is calculated by power-time integration, and the air conditioning system energy consumption is 0.52kWh, the infotainment system energy consumption is 0.16kWh, and the lighting and auxiliary electrical appliances energy consumption is 0.08kWh. The total device energy output is 0.76kWh, which is compared with the vehicle normal steady-state operating power of 0.6kWh to form the energy consumption output increment ratio of 1.27. The value is marked as the device energy consumption output increment ratio.

[0026] The efficiency loss intensity fitting data obtained in step S223 is combined with the device energy consumption output increment ratio obtained in step S224 to perform point-to-point association calculation in a time stamp matching manner. The driving motor efficiency loss and the device additional output energy occurring simultaneously in each minute are subjected to energy consumption superposition operation. According to the weight distribution of the driving system proportion of 0.8 and the device system proportion of 0.2, when the efficiency loss intensity fitting data is 0.13 and the device energy consumption increase ratio is 1.27, the additional energy consumption ratio is 0.13×0.8+0.27×0.2, and the calculation result is 0.186. Multiply the ratio by the vehicle theoretical energy consumption value of 14.8kWh / 100km and combine the congestion mileage of 5.6km to calculate the additional energy consumption loss of 1.55kW / h. The additional energy consumption loss data output in the congestion state is written into the energy consumption database as the basis input for energy consumption loss composite analysis.

[0027] Step S223 comprises: Drawing a conversion efficiency decline curve based on the energy conversion efficiency decline gradient; extracting a monotonically decreasing curve in the conversion efficiency decline curve; Calculating the decreasing relative numerical variance under time sequence advancement based on the monotonically decreasing curve; Exponential rate attenuation fitting is performed on the monotonically decreasing curve to obtain an attenuation rate index; Point process accumulation identification is performed according to the decreasing relative numerical variance and the attenuation rate index to obtain conversion efficiency attenuation point process data; Time sequence intensity regression fitting is performed based on the conversion efficiency attenuation point process data to obtain efficiency loss intensity fitting data.

[0028] In the embodiment of the present application, based on the driving motor energy conversion efficiency decline gradient data obtained in step S222, first, the efficiency decline curve is drawn on the time axis according to the sampling interval 1s, the efficiency value of each sampling point is sequentially arranged with time as the horizontal coordinate and the efficiency decline gradient as the vertical coordinate, 300 groups of record points are formed within the 300s sampling period, linear interpolation is used to perform smoothing processing between adjacent sampling points, the abnormal rising value caused by the motor starting transient peak in the time interval is deleted, the overall trend of the curve is ensured to be continuous, then the monotonicity of the curve is detected, the positive and negative change intervals are determined by the derivative sign, all continuous negative derivative intervals are extracted as the main body of the monotone decreasing curve, the number of monotone decreasing curve segments is averaged to be 5 segments, the average length of each segment is about 60s, the efficiency value data of the monotone decreasing curve is re-indexed to form a time sequence matrix as the input basis for subsequent calculation.

[0029] Based on the extracted monotone decreasing curve, the relative numerical variance of the decrease in each time segment under the time sequence advancement is calculated, in this process, the efficiency change value of adjacent 5s is subjected to sliding difference operation, the variance mean value is taken and the relative change amplitude is recorded, the relative numerical variance data set of the decrease is obtained, in order to reduce the influence of short-time shock, the weight coefficient is set in the variance calculation, the weight coefficient of the difference value within the time interval of 2s to 10s is set to 0.8, the weight coefficient of the interval above 10s is set to 0.4, the average value of the relative numerical variance of the decrease in each segment is output after weight normalization, the average value is controlled between 0.002 and 0.006, which represents the stable rate interval of the efficiency attenuation.

[0030] The exponential rate attenuation fitting is performed on the above-mentioned monotone decreasing curve sequence to obtain the attenuation rate index, in the fitting process, the stepwise regression method is used to calculate the curve decline rate segment by segment, the initial efficiency value and the tail efficiency value of each curve segment are updated as the input sample of exponential regression, which is converted into paired data of time and efficiency loss intensity, the attenuation rate index result is output after solving by the least square method, the test data shows that the attenuation rate index range is 0.012 to 0.021 under normal congestion conditions, the index is as high as 0.025 for a long time stagnation road section, the calculation result is stored in the attenuation curve index table in time sequence for subsequent pointer access.

[0031] Subsequently, the point process accumulation recognition operation is performed according to the combination of the relative numerical variance of the decrease and the attenuation rate index, the time axis is divided into 10s detection windows, the efficiency attenuation mutation points are identified in each window, the attenuation event points are determined when the 10s variance value is higher than 1.5 times of the average variance of the whole area and the instantaneous value of the attenuation rate is higher than 1.3 times of the average rate index, the index number and time record of all event points are accumulated to form the conversion efficiency attenuation point process data, the data field includes event number, time stamp, local variance value, instantaneous value of attenuation rate and position index.

[0032] After obtaining the above conversion efficiency decay point process data, the data sequence is input to perform time series intensity regression calculation of the number of event points in each 60s window, the event density function in unit time is calculated and sliding regression fitting is performed, the abnormal density is smoothed by weighted moving average, the event point density of each window is matched with the decay rate change amplitude to calculate the efficiency loss intensity average value, and the normalized output efficiency loss intensity fitting data average value is 0.13, the unit is kWh, the output format includes time sequence index, window number, event count and fitting intensity value, and this data is used as the core input of the subsequent congestion energy consumption derivation stage.

[0033] Step S24 includes the following steps: Step S241: determining a slope turning structure road section based on the road slope and intersection structure information; and calculating the slope difference of the road slope; Step S242: determining the additional slope resistance according to the slope difference, and evaluating the energy consumption additional output ratio associated with the slope difference; Step S243: performing slope turning energy consumption growth ratio regression analysis according to the slope turning structure road section, to obtain the energy consumption growth regression ratio between slope turns; Step S244: performing road geometric feature energy consumption increment output ratio quantification based on the energy consumption additional output ratio and the energy consumption growth regression ratio, to obtain the road geometric feature associated energy consumption increment output ratio.

[0034] In the embodiment of the application, after obtaining the path information through the high-precision road geographic data interface, the road is sampled in steps of 100m for slope and curvature, the road section with a continuous length of more than 300m and a non-zero slope is marked as a slope section, and the road network node combination with a turning radius of less than 5m in the adjacent section is extracted to form a slope turning structure road section set. A total of 46 sections containing slope and turning features are detected in the road sample, the average slope is 4.2°, the maximum slope is 12.8°, and the minimum slope is 0.3°. The slope difference is calculated by calculating the maximum and minimum slope difference of each section, which reflects the energy change amplitude of the vehicle during climbing and descending. In data processing, the segmented cumulative method is used to avoid local calculation errors caused by terrain mutations. Finally, the slope difference range is calculated to be 3° to 12.5°, and the average slope, difference and corresponding turning radius of each section are recorded in the road geometric feature data table, providing a basis for subsequent energy consumption characteristic quantification.

[0035] After obtaining the slope range data, the additional slope resistance of each section is calculated, including the gravity component projection and the rolling resistance deviation. The vehicle curb weight is 1800 kg, the rolling resistance coefficient is 0.01, the air resistance parameter is 0.32, and the windward area is 2.2 m². When the slope range is 10°, the additional gravity projection component increases by 0.173 due to the slope change, and the rolling resistance increases by 0.006. The total additional slope resistance is equivalent to 1.179 times the theoretical driving resistance of the vehicle. The additional energy consumption output ratio is determined based on this resistance ratio. The motor conversion efficiency and vehicle transmission efficiency are considered in the calculation of the additional energy consumption output ratio. When the vehicle climbing output power is stable at 45kW with an efficiency of 0.9, the power demand increases to 52.3kW due to the slope range, and the efficiency decreases to 0.84. After conversion, the additional energy consumption output ratio is 1.162. The results of each slope section are output in table form, including the average slope, slope range, additional resistance ratio, and additional energy consumption output ratio for subsequent energy consumption growth regression analysis.

[0036] According to the slope turning structure section determined in step S241, the average turning radius of each turning section and the vehicle speed are the main variables for energy consumption growth regression analysis. It is assumed that the vehicle turning speed range is 20km / h to 45km / h, and the turning radius range is 6m to 8m. Each set of data is sampled 30 times to calculate the change of lateral acceleration, centrifugal force and tire side slip angle of the vehicle when turning. The measured vehicle driving power and the power data when driving straight are compared to calculate the additional energy consumption increase ratio per turning. The correlation coefficient obtained by multiple least squares regression is 0.91, and the energy consumption growth ratio interval is between 1.05 and 1.21. Considering the slope effect, when the slope is more than 5° and the turning radius is less than 7.2m, the slope resistance factor is 0.08 to reflect the coupling effect of gravity and centrifugal force. Finally, the energy consumption growth ratio of each slope turning section is calculated, and the numerical value is numbered and stored in the energy consumption growth database. The record fields include turning radius, slope, vehicle speed, additional energy consumption ratio, and regression residual for subsequent quantitative comprehensive processing.

[0037] The energy consumption additional output ratio obtained in step S242 is matched and integrated with the slope turning energy consumption increase ratio obtained in step S243 according to the road number. A weighting factor is calculated according to the weighted relationship of the two types of ratios per 100m section. When the length proportion of the slope section is higher than 70%, the weight distribution is 0.7 for the energy consumption additional output ratio and 0.3 for the turning increase ratio. When the turning section proportion is greater than 50%, the weight distribution is the opposite. After weighted summation, the composite energy consumption increase output ratio data is formed. The time continuity of the data is maintained in the operation by using a sliding window with a 5s moving step of 1s to update the average ratio, avoiding numerical jump processing results. The results are normalized to the interval of 1.00 to 1.25 after standardization. The output results are recorded as the road geometry feature related energy consumption increase output ratio, and stored as a list data structure. The column fields include time index, road number, composite energy consumption increase value, current slope, current turning radius, and slope range. The composite energy consumption increase output ratio is used for energy consumption loss composite index calculation.

[0038] Step S243 includes the following steps: The average turning radius and slope inclination are calculated based on the slope turning structure road section. The vehicle minimum passing speed is derived based on the average turning radius and slope inclination. The gravity and centrifugal force vectors of lateral turning are simulated and derived based on the vehicle minimum passing speed, average turning radius and slope inclination. The tilt dynamic resultant force sector is obtained by identifying the tilt dynamic resultant force sector of the gravity and centrifugal force vectors. The additional yaw moment imbalance data is obtained by additional yaw moment imbalance analysis based on the tilt dynamic resultant force sector. The vector space dynamic deviation of gravity resistance and lateral resistance is obtained by vector space dynamic deviation analysis of gravity resistance and lateral resistance based on the additional yaw moment imbalance data. The dynamic counteracting numerical torque of the power driving output is analyzed based on the vector space dynamic deviation, and the dynamic counteracting numerical torque is obtained. The energy consumption increase regression ratio between slope turns is obtained by slope turning energy consumption increase ratio regression analysis based on the dynamic counteracting numerical torque.

[0039] In the embodiment of the present application, the road space geometry is analyzed in detail based on the ramp turning structure road segment sample data extracted in step S241, equidistant sampling points are established along the section, the sampling interval is 10 m, the tangent direction change rate Δθ between adjacent sampling points is calculated by the difference of the geometric coordinates of the road center line, the average turning angle change rate is calculated according to the arc length accumulation, the average turning radius is calculated by inversely calculating the angle change per unit arc length, and the average turning radius measurement value of the typical urban uphill turning road section is 6.8 m. The elevation coordinates of the sampling points are extracted by the digital elevation data in the same area, the elevation change to horizontal distance ratio is counted, the slope angle of the road section is calculated as 5.7°, and the slope interval of the other similar structure road section data obtained by sampling is 3° to 9°. Thus, the basic data set containing the average turning radius, the slope angle, the turning angle rate and the sampling time index is generated.

[0040] According to the average turning radius 6.8 m and the slope angle 5.7°, the vehicle stability calculation interval is established, the vehicle kerb mass is 1800 kg, the wheelbase is 1.62 m, the center of gravity height is 0.55 m, and the tire friction coefficient is 0.82. According to the balance limit constraint that the rollover and sideslip critical conditions are met at the same time, the decomposition projection of the gravity and centrifugal force in the slope plane is calculated, and the lowest passing speed of the vehicle on the road section is 21 km / h, which is calculated from the balance condition. If the passing speed is insufficient, the lateral adhesion demand will be higher than the tire limit, and if the passing speed exceeds, the centrifugal acceleration will be greater than the gravity component balance interval. The lowest passing speed calculation result is controlled in the safety interval by the stability boundary algorithm and recorded as the interval speed threshold parameter at the passing speed of 21 km / h. The subsequent analysis takes this speed value as the input variable.

[0041] After obtaining the lowest passing speed, the lateral force and gravity vector decomposition derivation is carried out in combination with the average turning radius and the slope angle. The vehicle force is divided into two parts, i.e. the three-dimensional gravity vector and the horizontal centrifugal force vector. The total amount of gravity is 17.64 kN, which is obtained by multiplying the vehicle kerb mass by the gravity acceleration. Under the condition of slope angle 5.7°, the gravity component along the slope surface is 1.75 kN and the tangential component is about 17.56 kN. The centrifugal force is 2.52 kN, which is calculated according to the vehicle speed 21 km / h=5.83 m / s and the radius 6.8 m. Then, in the slope plane coordinate system, the resultant force vector is constructed, the centrifugal force and the gravity component are added to obtain the resultant force modulus of about 18.4 kN, and the direction of the resultant force modulus is inclined at an angle of about 7.6° relative to the vertical axis. Then, by the vector scanning method, 360° sectors are divided into 120 regions with an angle step of 3°. The force vector projection intensity is calculated region by region, and the region with force projection greater than 1.15 times the average force intensity of the whole region is classified as the main inclined dynamic resultant force sector. The force value modulus, direction angle and time index information of each sector are output to form the inclined dynamic resultant force sector data.

[0042] Subsequently, the yaw moment imbalance is calculated based on the inclined dynamic resultant force sector data, the normal support force of the front and rear axles of the vehicle is distinguished, the normal force of the front axle is corrected by the resultant force direction, the normal force of the front axle is reduced to 8.1 kN and the normal force of the rear axle is increased to 9.2 kN, the force difference between the two axles is 1.1 kN, and the wheel tread 1.62 m is taken as the length of the force arm to calculate the yaw moment as 0.891 kN·m, the moment acts in the inward inclined direction to form the tendency of the vehicle rotating around the vertical axis, and the instantaneous yaw angular acceleration is 0.25 rad / s², the instantaneous yaw angular velocity is about 0.1 rad / s within the 0.4 s integration time window, thereby obtaining the additional yaw moment imbalance data and recording the time index, the horizontal offset angle, the yaw angular acceleration and the front-rear axle force ratio parameters. The data set is used as the input basis for subsequent slope turning energy consumption growth regression analysis.

[0043] Based on the obtained additional yaw moment imbalance data, first, the dynamic deviation analysis of the gravity resistance and lateral resistance vector space is performed, the instantaneous force data of the vehicle during turning is taken, the coordinate system where the center of gravity of the vehicle body is located is taken as the analysis reference, the gravity resistance vector is defined along the longitudinal direction of the slope, and the lateral resistance vector is defined along the centrifugal direction of turning. In a typical sample, the curb weight of the vehicle is 1800 kg, the vehicle speed is 21 km / h, which is equivalent to 5.83 m / s, the slope inclination is 5.7°, the turning radius is 6.8 m, the instantaneous centrifugal force is 2.52 kN, and the gravity component is 17.64 kN. According to the real-time sampling step of 0.01 s, the direction change angle and the modulus change rate of the two vectors in unit time are calculated respectively, the average direction deviation angle is calculated by five-order difference smoothing to be 7.3°, the maximum deviation angle is 9.8°, and the instantaneous vector space dynamic deviation amount is 1.54 kN. The deviation represents that the direction of the gravity resistance is dynamically deviated due to the invasion of the lateral inertial force of the vehicle at the moment of turning. Subsequently, the deviation amount at all times in the whole road section is executed interval integration statistics, the average dynamic deviation value and the peak value of each sampling window are output, and the corresponding time index and position coordinates are recorded to form the dynamic deviation of the gravity resistance and lateral resistance vector space.

[0044] Based on the above vector space dynamic deviation data, the dynamic counteracting numerical torque analysis of the power driven output is performed, the vehicle driving torque time sequence is matched with the vector deviation angle sequence point by point, the instantaneous force of the driving motor output torque is calculated in the sampling window with 10ms as the time step width, the asymmetric driving force caused by the deviation angle is quantified as the torque loss component, and the effective driving torque after the angle correction is 320×cos7.3°=317.5N·m under the comprehensive working condition of the slope of 5.7° and the turning radius of 6.8m, the nominal output torque of the motor is 320N·m, the speed is 2700r / min, the counteracting ratio is 0.0078, and the energy loss rate is 0.6%. In the driving state of continuous sampling for 60s, 21 dynamic counteracting peak values are recorded, each peak value lasts for no more than 0.36s, the total energy consumption of the dynamic counteracting in the whole process is calculated by the integral method, and the total energy consumption is 0.064kWh, and the dynamic counteracting numerical torque sequence data record field includes time sequence index, angle deviation, instantaneous torque value, correction coefficient and loss power.

[0045] According to the dynamic counteracting numerical torque data, the slope turning energy consumption growth ratio regression analysis is performed, in the analysis process, the driving power correction amount and the vehicle real-time running speed at each time are used to construct a multivariate regression data table, the energy consumption growth rate is used as the dependent variable, the dynamic counteracting torque, the slope angle and the turning radius are used as the independent variables, the multivariate least square method is used for regression analysis, 120 groups of different turning condition data are fitted, the fitting determination coefficient R² is 0.89, the regression function slope corresponds to the energy consumption growth ratio interval of 1.06 to 1.22, the additional regression residual of the high slope and small radius section is executed with a weighted correction factor of 0.05 to eliminate the calculation deviation caused by the low-speed torque jitter, and the calculation conclusion output is the slope turning energy consumption growth regression ratio data generation result set. The column fields include average slope, average radius, vehicle speed, dynamic counteracting torque, average energy loss and regression ratio value, and the data timestamp is recorded as the input basis of the road geometry energy consumption increment output ratio calculation. According to the calculation process, it is updated once per second to realize the continuous quantification of the dynamic growth trend of the slope turning energy consumption.

[0046] Reference Figure 3 The step S3 includes the following steps: Step S31: performing composite energy consumption loss characteristic analysis based on the energy consumption loss composite index to obtain the composite energy consumption loss characteristic; In the embodiment of the present application, the composite energy loss characteristic analysis is performed based on the composite energy loss index. Firstly, the time sequence segmentation processing is performed on the composite energy loss index. The entire driving path is divided into analysis units each having a length of 5 kilometers. The composite energy loss index values corresponding to each analysis unit are extracted to form a composite energy loss index sequence. For example, a 150-kilometer driving path is divided into 30 analysis units, and each analysis unit corresponds to a composite energy loss index value. The statistical characteristic extraction is performed on the composite energy loss index sequence. The energy loss mean value, energy loss variance, energy loss peak value, and energy loss distribution skewness are calculated. The calculation formula of the energy loss mean value is equal to the sum of the composite energy loss indices of all analysis units divided by the total number of analysis units. The calculation formula of the energy loss variance is equal to the sum of the squares of the differences between the composite energy loss indices of each analysis unit and the mean value divided by the total number of analysis units. The energy loss peak value is the maximum composite energy loss index value in the sequence. The energy loss distribution skewness is used to measure the symmetry of the energy loss distribution on the path.

[0047] In one implementation manner of the embodiment of the present application, the trend characteristic analysis is performed on the composite energy loss index sequence to identify the change trend of the energy loss on the driving path, including the energy loss increasing trend segment, the energy loss decreasing trend segment, and the energy loss stable trend segment. The moving average method is used to perform the smoothing processing on the composite energy loss index sequence. The moving window size is set to 3 analysis units. The calculation formula is that the smoothed energy loss value is equal to the sum of the composite energy loss indices of the current analysis unit and the adjacent analysis units divided by 3. The energy loss change rate between adjacent analysis units is calculated based on the smoothed energy loss sequence. The calculation formula is that the energy loss change rate is equal to the energy loss value of the next analysis unit minus the energy loss value of the previous analysis unit divided by the energy loss value of the previous analysis unit. When the energy loss change rate is greater than 0.05, it is determined as the increasing trend segment. When the energy loss change rate is less than -0.05, it is determined as the decreasing trend segment. When the energy loss change rate is between -0.05 and 0.05, it is determined as the stable trend segment. It should be noted that the composite energy loss characteristic is formed by comprehensively combining the energy loss mean value, the energy loss variance, the energy loss peak value, the energy loss distribution skewness, and the distribution proportion of each trend segment. The composite energy loss characteristic is used for the subsequent energy loss optimization analysis.

[0048] Step S32: Gradient descent optimization of the energy loss is performed according to the composite energy loss characteristic to obtain energy loss optimization data. In the embodiment of the present application, the energy consumption loss target function is constructed according to the composite energy consumption loss characteristics, which comprehensively considers the influence of the energy consumption loss mean value, the energy consumption loss variance and the energy consumption loss peak value on the overall energy consumption, and the mathematical expression of the target function is that the energy consumption loss target function is equal to the energy consumption loss mean value multiplied by the mean value weight coefficient, plus the energy consumption loss variance multiplied by the variance weight coefficient, plus the energy consumption loss peak value multiplied by the peak value weight coefficient, wherein the mean value weight coefficient represents the influence degree of the energy consumption loss mean value on the overall energy consumption and takes the value of 0.5, the variance weight coefficient represents the influence degree of the energy consumption loss volatility on the overall energy consumption and takes the value of 0.3, the peak value weight coefficient represents the influence degree of the extreme energy consumption loss on the overall energy consumption and takes the value of 0.2, the sum of the three weight coefficients is 1 and is obtained by calibrating the historical driving data; the gradient descent optimization parameters are initialized, the learning rate is set to 0.01, the maximum iteration number is set to 8 times, the convergence threshold is set to 0.0001, and the initial energy consumption loss optimization parameter vector includes the driving speed adjustment factor (including the influence of road conditions and road structure state), the energy recovery intensity factor and the air conditioner power adjustment factor.

[0049] In one implementation manner of the embodiment of the present application, the gradient descent iteration optimization process is performed, the partial derivative of the energy consumption loss target function to each optimization parameter is calculated to form a gradient vector in each iteration, the gradient vector is calculated by using the numerical differentiation method, and the calculation formula is that the partial derivative of the current parameter (i.e. the initial energy consumption loss optimization parameter vector) is equal to the function value of the target function when the parameter increases by a small increment minus the function value of the target function when the parameter decreases by a small increment, and then divided by twice the small increment, wherein the small increment takes the value of 0.001; the optimization parameter vector is updated according to the gradient vector, and the update formula is that the new parameter value is equal to the original parameter value minus the learning rate multiplied by the gradient value corresponding to the parameter, for example, when the initial value of the driving speed adjustment factor is 1.0 and the corresponding gradient value is 0.5, the updated driving speed adjustment factor is 1.0 minus 0.01 multiplied by 0.5, which is equal to 0.995; it should be noted that the convergence condition is judged in the iteration process, the iteration is stopped when the absolute value of the difference between the target function values of the adjacent two iterations is less than the convergence threshold 0.0001 or the maximum iteration number 8 times is reached, the final optimization parameter vector is output, the energy consumption loss optimization coefficient is calculated according to the final optimization parameter vector, and the calculation formula is that the energy consumption loss optimization coefficient is equal to 1 minus the optimized target function value divided by the initial target function value. The energy consumption loss optimization coefficient and the optimized energy consumption loss mean value, the energy consumption loss variance and the energy consumption loss peak value together constitute the energy consumption loss optimization data.

[0050] In another embodiment, the average gradient direction of the composite index is determined, i.e. the change trend of the energy consumption loss along the path with the distance, and then the minimum energy consumption loss direction is calculated by a numerical gradient descent algorithm, the optimization algorithm performs iterative correction on the difference between the energy consumption value of each continuous road section and the target minimum energy consumption value, and the energy consumption gradient weighting coefficient is updated in the iteration process, so that the energy consumption distribution gradually tends to be flat. The convergence threshold is set to control the adjustment range of the energy consumption of each road section, and when the gradient change is less than the convergence threshold, the optimization process stops. The final output energy consumption loss optimization data includes the optimized energy consumption value of each road section, the energy consumption gradient vector and the total correction ratio of the path. The data maintains the consistency of the original path data index in structure, so as to realize the accurate determination of the limit driving radius by subsequent joint calculation with the theoretical driving distance.

[0051] Step S33: judging the limit driving radius of the new energy vehicle according to the energy consumption loss optimization data and the theoretical driving distance; In the embodiment of the application, the energy consumption loss optimization coefficient and the optimized average energy consumption loss are extracted according to the energy consumption loss optimization data, and the actual drivable distance is calculated in combination with the theoretical driving distance, and the calculation formula is that the actual drivable distance is equal to the theoretical driving distance multiplied by 1 minus the difference of the optimized average energy consumption loss, wherein the theoretical driving distance represents the maximum driving distance supported by the remaining power of the vehicle under ideal working conditions, and the optimized average energy consumption loss represents the average energy consumption loss ratio after gradient descent optimization. For example, when the theoretical driving distance is 300 kilometers and the optimized average energy consumption loss is 0.12, the actual drivable distance is 300 multiplied by 1 minus 0.12, which is equal to 264 kilometers. It should be noted that, in order to ensure driving safety and reserve sufficient charging buffer margin, the actual drivable distance needs to be corrected with safety margin, and the safety margin coefficient is dynamically adjusted according to the energy consumption loss variance. When the energy consumption loss variance is large, it means that the energy consumption fluctuation is large and more safety margin needs to be reserved. The calculation formula of the safety margin coefficient is that the safety margin coefficient is equal to the basic safety coefficient minus the energy consumption loss variance multiplied by the variance adjustment factor, wherein the basic safety coefficient is 0.95, and the variance adjustment factor is 0.1. For example, when the energy consumption loss variance is 0.3, the safety margin coefficient is 0.95 minus 0.3 multiplied by 0.1, which is equal to 0.92.

[0052] In one implementation manner of the embodiment of the present application, the limit driving radius of the new energy vehicle is calculated according to the actual driving distance and the safety margin coefficient, and the calculation formula is that the limit driving radius is equal to the actual driving distance multiplied by the safety margin coefficient. For example, when the actual driving distance is 264 kilometers and the safety margin coefficient is 0.92, the limit driving radius is 264 multiplied by 0.92, which is equal to 242.88 kilometers, and the limit driving radius is 242 kilometers after rounding. It should be noted that the influence of the energy consumption loss peak value on the limit driving radius also needs to be considered. When the optimized energy consumption loss peak value exceeds the preset peak threshold value 0.25, the limit driving radius needs to be additionally corrected. The correction formula is that the corrected limit driving radius is equal to the limit driving radius multiplied by 1 minus half of the difference between the energy consumption loss peak value and the peak threshold value. This correction ensures that the vehicle still has sufficient power to reach the charging node when passing through a high energy consumption section. For example, when the energy consumption loss peak value is 0.35, the corrected limit driving radius is 242 multiplied by 1 minus half of the difference between 0.35 and 0.25, which is equal to 242 multiplied by 0.95, which is equal to 229.9 kilometers. The corrected limit driving radius is 229 kilometers after rounding. Finally, the corrected limit driving radius is output as the limit driving radius of the new energy vehicle.

[0053] Step S34: Based on the limit driving radius, the charging node selection is made for the set of chargeable nodes, and the path planning is output to obtain the path planning data.

[0054] In the embodiment of the present application, the set of chargeable nodes is screened based on the limit driving radius. All charging nodes in the set of chargeable nodes are traversed, the path distance between each charging node and the starting point is calculated, and the charging nodes with a path distance less than the limit driving radius are screened out to form the distance-adapted set of chargeable nodes. For example, when the limit driving radius is 229 kilometers, the set of chargeable nodes includes charging station A 30 kilometers away from the starting point, charging station B 70 kilometers away from the starting point, charging station C 120 kilometers away from the starting point, charging station D 180 kilometers away from the starting point, and charging station E 250 kilometers away from the starting point, a total of 5 charging nodes. The distance-adapted set of chargeable nodes includes charging station A, charging station B, charging station C, and charging station D, a total of 4 charging nodes. The data connection is established between the vehicle system and the charging station cloud platform to obtain the running state information of all charging nodes in the distance-adapted set of chargeable nodes in real time. The available number of charging piles, the maximum charging power, and the charging compatibility data in the charging node state are extracted. For example, charging station A has 4 available charging piles, the maximum charging power is 60 kW, and it supports national standard fast charging. Charging station B has 2 available charging piles, the maximum charging power is 120 kW, and it supports national standard fast charging. Charging station C has 6 available charging piles, the maximum charging power is 180 kW, and it supports national standard fast charging and super fast charging. Charging station D has 3 available charging piles, the maximum charging power is 90 kW, and it supports national standard fast charging.

[0055] In one implementation form of the embodiment of the application, the charging node comprehensive evaluation model is constructed to make an optimal decision on the distance-adapted chargeable node set. The comprehensive evaluation model adopts a multi-attribute decision method, and the evaluation indexes include the available number of charging piles, charging power, charging compatibility, and distance adaptation. First, the evaluation indexes are normalized. The available number of charging piles is normalized by dividing the available number of the charging node by the maximum available number of all candidate nodes. The charging power is normalized by dividing the charging power of the charging node by the maximum charging power of all candidate nodes. The charging compatibility is quantified according to the number of compatible interface types, and two interface types are assigned a value of 1 and one interface type is assigned a value of 0.6. The distance adaptation is equal to 1 minus the distance of the charging node divided by the limit driving radius. The calculation formula of the comprehensive evaluation score is: wherein S represents the comprehensive evaluation score; N represents the available number of charging piles of the charging node; represents the maximum available number of all candidate nodes; P represents the charging power of the charging node; represents the maximum charging power of all candidate nodes; C represents the charging compatibility quantization value; and D represents the distance of the charging node from the starting point. represents the limit driving radius; represents the available number weight and takes a value of 0.2; represents the charging power weight and takes a value of 0.35; represents the compatibility weight and takes a value of 0.15; and δ represents the distance weight and takes a value of 0.3. It should be noted that the charging node with the highest comprehensive evaluation score is selected as the optimal charging node, and complete path planning data from the starting point to the ending point via the optimal charging node is generated. The path planning data includes segmented navigation path information, the expected driving time of each segment, the expected remaining battery percentage when reaching the optimal charging node, the recommended charging duration, and the expected range after charging. Finally, the path planning data is visually output through the navigation interface of the vehicle system and voice broadcast prompts are provided.

[0056] It needs to be explained that: between step S31 and step S34, firstly, based on the comprehensive consideration of the composite index of congestion energy consumption and road geometric feature energy consumption, the characteristics of energy consumption loss in different scenarios are analyzed in depth, and the core influencing factors and variation trend of energy consumption loss are determined; then gradient descent optimization is carried out for these characteristics, invalid energy consumption is removed, and energy consumption estimation deviation is corrected to obtain more accurate energy consumption optimization data; then, combined with the optimization data and the theoretical driving distance of the vehicle, the actual maximum driving range of the vehicle under the current power and road condition is scientifically defined to avoid the range anxiety caused by energy consumption misjudgment; finally, the charging node is selected based on the limit range, the key information such as the available condition of the charging pile of the node, the charging efficiency and the compatibility is obtained through networking, and the optimal selection of the charging node is completed by comprehensively considering these practical indexes, and the final path is planned. The whole process takes energy consumption characteristic analysis as the starting point, realizes the accurate judgment of the range through optimization and correction, and then completes the path closed loop planning combined with the actual state of the charging node, which is layer by layer progressive in logic and data is mutually supported, which not only guarantees the range safety of the path planning, but also improves the energy supplement efficiency by optimizing the charging node, effectively solves the path planning and energy supplement decision problem of the new energy vehicle in long distance driving.

[0057] Step S34 comprises the following steps: Step S341: selecting distance-adapted chargeable node set based on the limit driving radius; Step S342: obtaining the charging node state of all distance-adapted chargeable node set through networking between the vehicle machine system and the charging station cloud; Step S343: extracting the available number of charging piles, charging power and charging compatibility in the charging node state; Step S344: selecting the charging node by optimizing the limit driving radius according to the available number of charging piles, charging power and charging compatibility, and planning the path to output to obtain the path planning data.

[0058] In the embodiment of the application, the distance of the chargeable node set is screened based on the limit driving radius. First, the position coordinate information of all charging nodes in the chargeable node set is obtained, and the actual path distance between each charging node and the current starting point is calculated through the vehicle-mounted panoramic map. The actual path distance is calculated by the shortest path algorithm of the road network, not the straight-line distance. For example, the chargeable node set includes charging station A, charging station B, charging station C, charging station D and charging station E, a total of 5 charging nodes. The path calculation obtains that the distance from charging station A to the starting point is 30 kilometers, the distance from charging station B to the starting point is 70 kilometers, the distance from charging station C to the starting point is 120 kilometers, the distance from charging station D to the starting point is 180 kilometers, and the distance from charging station E to the starting point is 250 kilometers. The path distance of each charging node is compared with the limit driving radius to determine the distance screening condition, that is, the path distance of the charging node is less than or equal to the limit driving radius.

[0059] The wireless communication module of the car machine system establishes a data connection with the charging station operator cloud server, the wireless communication module supports 4G and 5G cellular network communication protocols, the car machine system sends a charging node state query request to the cloud server, the request data packet carries the unique identification code of the distance adaptive chargeable node set all charging nodes, the cloud server retrieves the real-time running state data of the corresponding charging station according to the identification code and returns it to the car machine system; it should be noted that, in order to ensure that the charging node state data obtained has timeliness, the car machine system sets the data refresh period to 60 seconds, continuously updates the charging node state information during navigation, and when the network connection is interrupted, the system will use the last cached charging node state data and mark the data update time on the interface.

[0060] In an implementation manner of the embodiment of the application, the charging node state data returned by the cloud server adopts a structured data format, and the state data of each charging node includes the charging station name, the charging station address, the total number of charging piles, the available number of charging piles, the occupied number of charging piles, the fault number of charging piles, the rated charging power of each charging pile, the supported charging interface type, the current number of vehicles waiting in line and the estimated average waiting time; for example, the state data of charging station A shows that the total number of charging piles is 6, the available number is 4, the occupied number is 1, the fault number is 1, the rated charging power is 60 kW, the supported national standard fast charging interface, the current number of vehicles waiting in line is 0, and the estimated average waiting time is 0 minutes; after the car machine system receives the state data of all distance adaptive chargeable nodes, it is locally cached, and the data integrity is checked, if the state data of a charging node is missing or abnormal, the state of the node is marked as unknown and its priority is reduced in subsequent evaluation.

[0061] The available number of charging piles is extracted from the charging node state data, which represents the number of charging piles that are currently in an idle standby state and can be normally used, and the available number of charging piles directly affects the waiting time of the user after arriving at the charging station, and the more the available number is, the more sufficient the charging resources are; the charging power field is extracted, which represents the maximum rated charging power of the charging pile configured by the charging station, and the unit is kilowatt, the charging power determines the charging speed of the vehicle, and the higher the power is, the shorter the charging time is, and the common charging power levels include 60 kW, 120 kW, 180 kW and 250 kW; the charging compatibility field is extracted, which represents the charging interface type and charging protocol standard supported by the charging station, including national standard fast charging interface, national standard slow charging interface, super fast charging interface and the like.

[0062] In one implementation form of the embodiment of the application, the extracted charging compatibility data is quantitatively processed to facilitate subsequent comprehensive evaluation calculation, and the charging compatibility quantitative rules are as follows: when the charging station simultaneously supports the national standard fast charging interface and the super fast charging interface, the charging compatibility quantitative value is 1.0; when the charging station only supports the national standard fast charging interface, the charging compatibility quantitative value is 0.7; when the charging station only supports the national standard slow charging interface, the charging compatibility quantitative value is 0.3; when the charging interface type is incompatible with the vehicle, the charging compatibility quantitative value is 0; it should be noted that the vehicle machine system automatically matches the compatible charging station according to the charging interface configuration of the vehicle, and filters out the charging nodes with a charging compatibility quantitative value of 0; for example, after data extraction, it is obtained that the available number of charging station A is 4, the charging power is 60 kilowatts, and the compatibility quantitative value is 0.7; the available number of charging station B is 2, the charging power is 120 kilowatts, and the compatibility quantitative value is 0.7; the available number of charging station C is 6, the charging power is 180 kilowatts, and the compatibility quantitative value is 1.0; the available number of charging station D is 3, the charging power is 90 kilowatts, and the compatibility quantitative value is 0.7.

[0063] The charging node comprehensive evaluation model is constructed to make an optimal decision on the distance-adapted chargeable node set, the comprehensive evaluation model adopts a weighted comprehensive scoring method, the evaluation indexes include four dimensions of charging pile available number, charging power, charging compatibility and distance adaptation degree, first, the normalization processing is performed on each evaluation index to eliminate the dimensional difference, the calculation formula of the comprehensive evaluation score is the formula in the embodiment of the above step S34, the comprehensive evaluation scores of all candidate charging nodes are compared, the charging station with the highest score is selected as the optimal charging node, and the complete path planning data from the starting point to the charging station to the end point is generated, the path planning data includes the first navigation path from the starting point to the charging station, and finally the path planning data is visually presented through the navigation interface of the vehicle machine system and the voice navigation broadcast is started.

[0064] The application further provides a path planning system of a new energy vehicle, which is used for executing the path planning method of the new energy vehicle as described above, and comprises: A road condition information acquisition module is configured to acquire the shortest distance from the starting point to the end point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle, estimate the theoretical driving distance of the new energy vehicle according to the shortest distance, and acquire the real-time road condition information in the theoretical driving distance. An energy consumption loss analysis module is configured to simulate and deduce the invalid energy consumption output in a congestion state according to the real-time road condition information, perform the energy consumption increment output ratio quantization of the road geometric characteristics based on the real-time road condition information to obtain the energy consumption increment output ratio associated with the road geometric characteristics, and perform the energy consumption loss composite index evaluation according to the invalid energy consumption output and the energy consumption increment output ratio to form the energy consumption loss composite index. The path planning output module is configured to determine a limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance; to make an optimal decision on the chargeable node set based on the limit driving radius to select a charging node, and to output path planning to obtain path planning data.

[0065] The above description is merely that of the preferred embodiments of the application, and modifications of these embodiments will occur to those skilled in the art. All such modifications that do not depart from the spirit of the application are intended to be within the scope of the claims. The language used in the specification should not be used to limit the scope of the claims.

Claims

1. A path planning method for a new energy vehicle, characterized in that, The method comprises the following steps: Step S1: obtaining the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle; estimating the theoretical driving distance of the new energy vehicle according to the shortest distance, and obtaining the real-time road condition information in the theoretical driving distance; Step S2: simulating and deducing the invalid energy consumption output of the congestion state according to the real-time road condition information; performing energy consumption increment output ratio quantization of the road geometric feature based on the real-time road condition information to obtain the energy consumption increment output ratio related to the road geometric feature; performing energy consumption loss composite index evaluation according to the invalid energy consumption output and the energy consumption increment output ratio to form the energy consumption loss composite index; Step S3: judging the limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance; performing optimal decision charging node selection on the chargeable node set based on the limit driving radius, and performing path planning output to obtain path planning data; Step S2 comprises the following steps: Step S21: extracting congestion section information and road slope and intersection structure information in the real-time road condition information; Step S22: simulating and deducing the additional energy consumption loss of the congestion state according to the congestion section information; Step S23: performing nonlinear energy consumption loss fitting on the additional energy consumption loss to obtain nonlinear energy consumption loss fitting data; Step S24: performing energy consumption increment output ratio quantization of the road geometric feature based on the road slope and intersection structure information to obtain the energy consumption increment output ratio related to the road geometric feature; Step S25: performing energy consumption loss composite index evaluation according to the nonlinear energy consumption loss fitting data and the energy consumption increment output ratio to form the energy consumption loss composite index. 2.The method according to claim 1, wherein, Step S1 comprises the following steps: Step S11: obtaining the remaining power of the current new energy vehicle; Step S12: obtaining the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle; Step S13: estimating the theoretical driving distance of the new energy vehicle according to the remaining power of the new energy vehicle and the shortest distance; Step S14: obtaining the real-time road condition information in the theoretical driving distance through the vehicle-mounted panoramic map.

3. The path planning method of a new energy vehicle according to claim 1, wherein, Step S22 comprises the following steps: Step S221: extracting the congestion mileage, estimated passing time, traffic flow and average speed in the congestion section information; Step S222: calculating the vehicle average start-stop frequency according to the congestion mileage, estimated passing time, traffic flow and average speed; simulating and deducing the energy conversion efficiency decline gradient of the drive motor according to the vehicle average start-stop frequency, traffic flow and average speed; Step S223: performing time series intensity regression fitting on the energy conversion efficiency decline gradient to obtain efficiency loss intensity fitting data; Step S224: obtaining the energy consumption output state of the vehicle built-in device through the vehicle machine system; performing energy consumption output increment ratio prediction on the energy consumption output state of the vehicle device according to the estimated passing time to obtain the device energy consumption output increment ratio; wherein the vehicle built-in device comprises the vehicle machine system, air conditioner and vehicle-mounted electrical appliances; Step S225: simulate the additional energy consumption loss of the congestion state according to the efficiency loss intensity fitting data and the device energy consumption output increment ratio.

4. The path planning method of the new energy vehicle according to claim 3, characterized in that, Step S223 includes: Draw a conversion efficiency decline curve based on the energy conversion efficiency decline gradient; extract the monotonic decreasing curve in the conversion efficiency decline curve; Calculate the decreasing relative numerical variance under time sequence advancement based on the monotonic decreasing curve; Exponential rate attenuation fitting is performed on the monotonic decreasing curve to obtain the attenuation rate index; Point process accumulation identification is performed according to the decreasing relative numerical variance and the attenuation rate index to obtain conversion efficiency attenuation point process data; Time sequence intensity regression fitting is performed based on the conversion efficiency attenuation point process data to obtain efficiency loss intensity fitting data.

5. The path planning method of a new energy vehicle according to claim 1, wherein, Step S24 includes the following steps: Step S241: determine the slope turning structure road section based on the road slope and intersection structure information; calculate the slope range of the road slope; Step S242: determine the additional slope resistance according to the slope range, and evaluate the energy consumption additional output ratio related to the slope difference; Step S243: perform slope turning energy consumption growth ratio regression analysis on the slope turning structure road section to obtain the energy consumption growth regression ratio between slope turns; Step S244: based on the energy consumption additional output ratio and the energy consumption growth regression ratio, the energy consumption increment output ratio of the road geometric feature is quantified to obtain the energy consumption increment output ratio related to the road geometric feature.

6. The path planning method of a new energy vehicle according to claim 5, characterized in that, Step S243 includes the following steps: Calculate the average turning radius and slope inclination based on the slope turning structure road section; and derive the minimum passing speed of the vehicle according to the average turning radius and slope inclination; Simulate the gravitational and centrifugal force vectors of lateral turning according to the minimum passing speed of the vehicle, the average turning radius and the slope inclination; perform inclination dynamic resultant force sector identification on the gravitational and centrifugal force vectors to obtain the inclination dynamic resultant force sector; Perform additional yaw moment imbalance analysis based on the inclination dynamic resultant force sector to obtain additional yaw moment imbalance data; Perform gravitational resistance and lateral resistance vector space dynamic deviation analysis according to the additional yaw moment imbalance data to obtain the vector space dynamic deviation of gravitational resistance and lateral resistance; Based on the vector space dynamic deviation, perform dynamic cancellation numerical torque analysis of the power driving output to obtain the dynamic cancellation numerical torque; Perform slope turning energy consumption growth ratio regression analysis according to the dynamic cancellation numerical torque to obtain the energy consumption growth regression ratio between slope turns.

7. The path planning method of a new energy vehicle according to claim 1, wherein, Step S3 includes the following steps: Step S31: perform composite energy consumption loss characteristic analysis based on the energy consumption loss composite index to obtain the composite energy consumption loss characteristic; Step S32: perform gradient descent optimization of energy consumption loss according to the composite energy consumption loss characteristic to obtain energy consumption loss optimization data; Step S33: determine the limit driving radius of the new energy vehicle according to the energy consumption loss optimization data and the theoretical driving distance; Step S34: based on the limit driving radius, select the optimal charging node from the set of chargeable nodes, and perform path planning output to obtain path planning data.

8. The path planning method of the new energy vehicle according to claim 7, characterized in that, Step S34 includes the following steps: Step S341: selecting distance-adapted chargeable node set based on the limit driving radius for the chargeable node set; Step S342: obtaining the state of all distance-adapted chargeable node sets through networking of the vehicle system and the charging station cloud; Step S343: extracting the available number of charging piles, charging power and charging compatibility in the state of the charging piles; Step S344: making an optimal decision on the charging node selection based on the available number of charging piles, charging power and charging compatibility in the limit driving radius, and performing path planning output to obtain path planning data.

9. A path planning system for a new energy vehicle, characterized in that, The new energy vehicle path planning system comprises: a road condition information acquisition module for acquiring the shortest distance from the starting point to the ending point and the chargeable node set in the shortest distance path through the vehicle-mounted panoramic map of the new energy vehicle, estimating the theoretical driving distance of the new energy vehicle according to the shortest distance, and acquiring real-time road condition information in the theoretical driving distance; an energy consumption loss analysis module for simulating and deducing invalid energy consumption output in a congestion state according to the real-time road condition information, performing energy consumption increment output ratio quantization based on the real-time road condition information to obtain energy consumption increment output ratio associated with road geometric characteristics, and performing energy consumption loss composite index evaluation according to the invalid energy consumption output and the energy consumption increment output ratio to form the energy consumption loss composite index; a path planning output module for judging the limit driving radius of the new energy vehicle according to the energy consumption loss composite index and the theoretical driving distance, making an optimal decision on the charging node selection based on the limit driving radius for the chargeable node set, and performing path planning output to obtain path planning data.

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