CN113390430B - Electric vehicle dynamic path planning and charging method for multi-warp stop point trip - Google Patents
Electric vehicle dynamic path planning and charging method for multi-warp stop point trip Download PDFInfo
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Abstract
The invention discloses a multi-warp stop point trip-oriented dynamic path planning and charging method for an electric vehicle, which comprises the following specific steps of: s1, obtaining vehicle type, residual electric quantity E and position and time data of a future journey stop node 123 \8230andN; s2, predicting road acceleration and speed data of each minute of one hour in the future; s3, dynamically calculating the lowest energy consumption of the road according to the energy consumption objective function according to the prediction data, and comparing the magnitude relation between the electric quantity after driving and the residual electric quantity threshold value; s4, executing different commands according to the comparison result, and finding out a pre-selected optimal charging center M which can be reached by the automobile; s5, determining a final charging center M or directly planning a path according to a judgment result; and S6, selecting the optimal charging pile to plan a path. According to the invention, intelligent judgment and decision can be provided for the user to go out and charge accurately by combining with dynamic traffic data, so that the mileage anxiety is effectively relieved, and the user trip experience is optimized.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to a multi-stop-point trip-oriented dynamic path planning and charging method for an electric automobile.
Background
The endurance mileage of the electric automobile is greatly influenced by factors such as weather and service time, so that the endurance mileage is seriously shrunk, the charging requirement is increased, an electric automobile owner is difficult to accurately grasp the relation between electric quantity and mileage during traveling and always falls into the dilemma of 'decision difficulty and mileage anxiety', the electric automobile owner usually estimates the required driving energy consumption according to historical experience, but the electric automobile owner is not an expert, is not scientific and accurate in judgment, often cannot reach a destination due to wrong decision, and is in charge of no power at half way, so that great trouble is brought. Meanwhile, economics predict that by the end of 2035, the total quantity of electric vehicles in the world will reach about 1 hundred million vehicles (6% of the total quantity of the vehicles in the world), and as the quantity of electric vehicles increases, the charging pressure also increases, and for electric vehicles, it becomes very necessary to make scientific and accurate decisions and plans for each trip and charging.
Through search, the Chinese special for publication number CN108106626A in 2018, 6 months and 1 days discloses a travel path planning method for an electric vehicle based on a driving condition, which comprises the following steps: step 1, determining a starting point and a terminal point, loading road network information, average speed of each road section, uphill information of each road section and road section congestion information, and setting default initial speed v0 of a vehicle; the system automatically detects the remaining total energy of the current electric automobile; step 2, planning a shortest path L0 by using a shortest path Dijkstra algorithm according to the starting point and the end point; step 3, performing energy consumption analysis on the L0, calculating total energy consumption and judging whether the total energy consumption can reach, wherein the energy consumption analysis comprises the following steps: energy consumption of 2 kilometers in the future, energy consumption from the rest road sections to the terminal point and extra energy consumption brought by ascending. Although the driving condition is considered in the path planning of the electric automobile, the long-distance travel path planning of multiple transit stop points is not considered, and the problem of electric quantity supplement under the planned path is not considered.
The Chinese special benefit of publication No. CN109596137A, 2019, 4, 9, discloses a dynamic search charging pile path planning method, which comprises the following steps: calculating the remaining driving range of the vehicle according to the current power state and the destination information of the vehicle; searching a charging pile according to the current first position information of the vehicle when the remaining driving mileage reaches a first preset mileage; when the charging pile is searched, path planning is carried out according to the type of the charging pile, and a path planning result is generated and displayed; and acquiring a first charging pile place selected by a user according to a path planning result, calculating a path planning according to the first charging pile place and the first position information, and navigating. The patent application can avoid the situation that the vehicle cannot supplement power before the power is exhausted, but does not plan the path according to the multi-warp stop points of the vehicle in the trip, and cannot ensure the electric energy supplement of the vehicle under the condition that the vehicle runs through the multi-warp stop points.
Therefore, it is necessary to provide a method for planning and charging a route of an electric vehicle for a trip at multiple stopping points, which not only meets the demand of planning the route at the multiple stopping points, but also ensures the demand of timely supplementing the electric energy of the vehicle.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a dynamic path planning and charging method for an electric vehicle going out at multiple stopping points, so as to solve the problems of path planning and electric energy supplement in the process for going out at multiple stopping points.
The invention is realized by the following technical scheme:
the electric vehicle dynamic path planning and charging method for multi-warp stop point traveling comprises the following steps:
obtaining the vehicle type, the residual electric quantity E and the position and time data of a future journey stop node 123 \8230Jand N;
predicting road acceleration and speed data of each minute of one hour in the future;
according to the predicted road acceleration and speed data, dynamically calculating the lowest energy consumption of the road passing through each stopping node based on an energy consumption objective function, and comparing the electric quantity after driving with the threshold value of the residual electric quantity;
if the electric quantity after driving is larger than the residual electric quantity threshold value and the current stop node is equal to N, directly planning a path; if the electric quantity after driving is larger than the residual electric quantity but the current stay node is not equal to N, acquiring charging pile data based on the residual electric quantity of the current stay node by taking the current stay node as a center;
and recommending the optimal charging pile based on the obtained charging pile data, and planning a path.
According to the technical scheme, the charging amount required by the vehicle is obtained through the acquired vehicle type and the acquired residual electric quantity, and the staying time and the residual electric quantity at each target node are calculated through the position data and the time data of the staying node in the future journey; predicting road acceleration and speed data of each minute of the future one hour according to road toxin and speed data of each minute of the past one hour, then dynamically calculating the lowest energy consumption of a road of a vehicle passing each node through an energy consumption target function according to the predicted data, and comparing the residual electric quantity of the vehicle after the vehicle passes the target node with a residual electric quantity threshold value; when the electric quantity after driving is larger than the residual electric quantity threshold value and the current stop node is the destination node, the electric quantity of the vehicle is sufficient, and path planning can be directly carried out; when the electric quantity after driving is smaller than the residual electric quantity threshold value, the vehicle needs to be charged midway, at the moment, nearby charging pile data are acquired by taking the current target node as the center, the optimal charging pile is recommended according to the acquired charging pile data, and the optimal charging pile is added to the journey as a new node position and is subjected to path planning. The technical scheme solves the problems of route planning and electric energy supplement in the way for the trip at multiple stopping points.
As a further technical scheme, if the electric quantity after driving is less than the residual electric quantity threshold value, the current stay node is taken as the node with the longest stay time except the selected node, the stay time at each target node is taken as a weight, node assignment is carried out from large to small, the energy consumption numerical value of the path with the lowest energy consumption when the vehicle drives from the current position to the target node is calculated again every time the node assignment is carried out, until the electric quantity after driving is greater than the residual electric quantity threshold value, and the target node at the moment is a preselected optimal charging center.
As a further technical scheme, if the charging pile data is not successfully acquired, the current stay node is taken as the node with the longest stay time except the selected node, the stay time at each target node is taken as the weight, node assignment is carried out from large to small, the energy consumption value of the path from the current position to the lowest energy consumption of the target node is calculated again after each assignment, and the charging pile data is acquired based on the calculated residual energy of the target node by taking the target node as the center until the electric quantity after driving is greater than the residual energy threshold value.
As a further technical scheme, position data of a plurality of stay nodes in a future journey and time data of arrival of the stay nodes are obtained, a final destination is dynamically assigned, and stay time and residual capacity at each target node are calculated.
As a further technical solution, predicting road acceleration and speed data every minute for one hour in the future further comprises: the long and short term memory network LSTM is combined with the STGCN, the STGCN consists of two layers of time sequence convolution layers and one layer of space convolution layer, and the acceleration and speed data of the road in each minute of the next hour is predicted according to the acceleration and speed data of the road in each minute of the past hour.
As a further technical scheme, the energy consumption y for vehicle running is determined according to the acceleration and speed of the road, the rotating speed of a motor, the output torque, the final ratio, the air resistance coefficient, the rolling resistance coefficient and the road slope angle i (t) performing a calculation with the energy consumption objective function expressed as:
energy i (t)＝P i (t)/η M (t)
wherein, P i (t) representing an energy consumption value of the vehicle, obtained according to the objective function (a); eta M (t) the efficiency of the motor at the moment t is represented and obtained through a motor efficiency equation;
P i (t)＝n i (t)*T i (t)/9550 (a)
wherein n is i (t) represents the driving motor speed of the electric vehicle, obtained by the vehicle power transmission function (b); t is a unit of i (t) represents motor output torque, obtained by a balance equation (c) of driving force and driving resistance of the vehicle:
wherein, T i (t) watchIndicating the output torque of the motor; i all right angle 0 Representing the final ratio of the electric vehicle; c D A represents the frontal area air resistance coefficient of the vehicle i; f represents a rolling resistance coefficient; eta r Represents transmission efficiency; m represents the mass of the vehicle; g represents the gravitational acceleration; δ represents a rotational mass coefficient; r represents the radius of the wheel; alpha represents a road slope angle.
As a further technical scheme, according to predicted dynamic real-time road data, a road network information adjacency matrix is input, the energy consumption required by each road is calculated by using the energy consumption objective function, and according to an ant colony algorithm, the lowest energy consumption route from the current position to the objective node and the predicted total energy consumption are obtained by taking the energy consumption as a weight.
As a further technical scheme, the obtained charging pile data are calculated according to the required time, price, distance and queuing time, the charging pile position data are inserted into 123 \8230M \8230Nafter the optimal scheme is determined, and multi-stop-point travel path planning is carried out, wherein M represents a position node where the charging pile corresponding to the optimal scheme is located, and N represents a destination node.
As a further technical scheme, for a certain target node, if the electric quantity after driving is greater than the residual electric quantity threshold value, path planning is carried out by taking the fastest time and the minimum energy consumption as weights; and if the electric quantity after driving is less than the residual electric quantity threshold value, searching an optimal charging position.
And as a further technical scheme, the obtained stopping time of the stopping nodes of the future journey is sequenced, the passing points are selected from large to small, each passing point is selected, the residual energy consumption of the vehicle from the current position to the passing point is calculated, and the charging pile data which can be reached by the residual energy consumption near the passing point is obtained by taking the residual energy consumption as a reference.
Compared with the prior art, the invention has the beneficial effects that:
(1) The method comprises the steps of obtaining the charging amount required by the vehicle through the acquired vehicle type and the acquired residual electric quantity of the vehicle, and calculating the staying time and the residual electric quantity at each target node through the position data and the time data of the staying node in the future journey; predicting road acceleration and speed data of each minute of the next hour according to the road toxin and speed data of each minute of the past hour, then dynamically calculating the lowest energy consumption of the road of the vehicle passing through each node according to the predicted data and the energy consumption target function, and comparing the residual electric quantity of the vehicle after the vehicle passes through the target node with the residual electric quantity threshold value; when the electric quantity after driving is larger than the residual electric quantity threshold value and the current stop node is the destination node, the electric quantity of the vehicle is sufficient, and path planning can be directly carried out; when the electric quantity after driving is smaller than the residual electric quantity threshold value, the vehicle needs to be charged midway, at the moment, nearby charging pile data needs to be obtained by taking the current target node as the center, an optimal charging pile is recommended according to the obtained charging pile data, the optimal charging pile is used as a new node position to be added into a journey and subjected to path planning, and therefore the problems of path planning and midway electric energy supplement for the trip at multiple stopping points are solved.
(2) According to the method, when the multi-pass stop point-oriented path planning is carried out, the path planning is carried out according to dynamic road acceleration, speed data and real-time electric quantity of the vehicle, for a target node which cannot meet the requirement of residual electric quantity of the vehicle, the charging pile is searched, the searched optimal charging pile is used as a node to be supplemented to a journey, and then the updated multi-pass stop point is used for carrying out the path planning, so that the electric automobile can accurately master the electric quantity and mileage when going out, mileage anxiety is effectively relieved, and user traveling experience is optimized.
Drawings
Fig. 1 is a schematic flow chart of a method for planning a dynamic path of an electric vehicle for traveling at multiple stop points according to an embodiment of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a multi-stop-point trip-oriented dynamic path planning and charging method for an electric vehicle, which comprises the following steps of:
s1, obtaining the position and time data of a vehicle type, the residual electric quantity E and a future journey stop node 123 \8230n.
In step S1, position data of a plurality of target nodes 123 \8230nand time data of reaching the target nodes in the travel route are obtained, a final destination N is assigned to M, the residence time and the residual electric quantity at each target node are calculated, and route and charging planning can be carried out on the long-distance travel route with multiple transit stop points.
And step S2, predicting road acceleration and speed data of each minute in one hour in the future.
In step S2, a long-short term memory network LSTM is combined with an STGCN, where the STGCN is composed of two time sequence convolutional layers and one space convolutional layer, and the road acceleration and speed data of each minute in the next hour is predicted according to the road acceleration and speed data of each minute in the past hour, the two time sequence convolutional layers in the STGCN can accelerate the training speed of the LSTM on the time sequence data, and the space convolutional layer can analyze the influence relationship between the road segments in the road network, so as to improve the prediction accuracy.
And S3, dynamically calculating the lowest energy consumption of the road according to the energy consumption objective function according to the prediction data, and comparing the size relation between the electric quantity after driving and the residual electric quantity threshold value.
In step S3, firstly, based on the data of S1 and S2, obtaining the most energy-saving path from the current location to the destination M and the predicted total energy consumption E ', comparing the remaining energy consumption E-E' with the remaining energy threshold | E |, and then proceeding to step S4; the residual electric quantity threshold | E | is the preset electric quantity which needs to be charged, so that the vehicle is ensured to have residual electric quantity to deal with emergency even when the vehicle is about to arrive at the destination, and the fault tolerance rate and the driving safety of the planning are improved.
In S3, the energy consumption y for vehicle running is determined according to the following objective function according to a plurality of parameters such as road acceleration and speed, motor rotating speed, output torque, final ratio, air resistance coefficient, rolling resistance coefficient and the like i (t) performing a calculation with the energy consumption objective function expressed as:
energy i (t)＝P i (t)/η M (t)
wherein, P i (t) representing an energy consumption value of the vehicle, obtained according to the objective function (a); eta M (t) representing the efficiency of the motor at the moment t, and obtaining the efficiency through a motor efficiency equation;
P i (t)＝n i (t)*T i (t)/9550 (a)
wherein n is i (t) represents the driving motor speed of the electric vehicle, obtained by the vehicle power transmission function (b); t is a unit of i (t) represents motor output torque, obtained by a balance equation (c) of driving force and driving resistance of the vehicle:
wherein, T i (t) represents motor output torque; i all right angle 0 Representing the final ratio of the electric vehicle; c D A represents the frontal area air resistance coefficient of the vehicle i; f represents a rolling resistance coefficient; eta r Represents transmission efficiency; m represents the mass of the vehicle; g represents the gravitational acceleration; δ represents a rotational mass coefficient; r represents the radius of the wheel; alpha represents a road grade angle.
In S3, according to predicted dynamic real-time road data, inputting a road network information adjacency matrix G, calculating energy consumption required by each road by using the energy consumption objective function, solving the lowest energy consumption route from the current position to the M point and the predicted total energy consumption by taking the energy consumption as weight according to an ant colony algorithm, and calculating the energy consumption and planning the route by combining the dynamic traffic data, thereby greatly improving the calculation accuracy and reducing the lowest energy consumption of driving.
And S4, executing different commands according to the comparison result, and finding out the pre-selected optimal charging center M which can be reached by the automobile.
In S4, if E-E < | E |, entering S5, if E-E < | E |, selecting the stay time at each target node as a weight, assigning the nodes to M one by one, repeating the S3 step once every assignment until E-E' > | E |, finding a preselected optimal charging center M at the moment, and entering S5.
And S5, determining a final charging center M or directly planning a path according to the judgment result.
In S5, judging whether M and N are the same node, and if M = = N, directly planning a multi-transit stop point travel path from the current position to the final destination for the vehicle; and if M is not equal to N, taking the point M as a center, acquiring the reachable charging pile data based on the calculated residual electric quantity at the point M, judging whether the data is successfully acquired, if so, entering S6, if not, repeating the step S3, returning to the step S5 for judgment, until a final charging center capable of acquiring the charging pile data is found and assigned to M, and entering the step S6.
And S6, selecting the optimal charging pile and planning a path.
In S6, the obtained charging pile data are calculated according to factors such as required time, price, distance and queuing time, the charging pile position data are inserted into 123 \8230M \8230Nafter the optimal scheme is determined, and multi-channel stop point travel path planning is carried out.
Example 1
The embodiment provides a multi-warp stop point trip-oriented dynamic path planning and charging method for an electric vehicle, which comprises the following specific steps: the method comprises the steps that S1, position and time data of a vehicle model, a residual electric quantity E and a future journey stop node 123 \8230areobtained, the position and time data of N are obtained, the purpose is to obtain the overall journey arrangement of a vehicle, the N is assigned to the M and then enters S2, the direct influence of an upstream road section on the flow of a downstream road section is considered, each road section in a road network forms a non-European space structure, the step abstracts the road network into an adjacent matrix by adopting a graph structure to represent the space relation among the road sections, a long-short-term memory network LSTM is combined with an STGCN, the STGCN comprises two time sequence convolutional layers and a space convolutional layer, an input time sequence is subjected to preliminary analysis through the time sequence convolutional layers to accelerate the training speed of the LSTM on the time sequence data, the influence relation among the road sections in the road network is analyzed through the space convolutional layers, the road acceleration and the speed data of each minute in the past are put into a model according to the road acceleration and speed data of each minute in the past, and then the step S3 is carried out after the prediction is finished; and inputting road network information adjacency matrixes based on dynamic real-time road data predicted in the S2, calculating energy consumption required by each road by using the energy consumption objective function, solving the lowest energy consumption route from the current position to the M point and the predicted total energy consumption by taking the energy consumption as weight according to an ant colony algorithm, and calculating the energy consumption and planning the route by combining the dynamic traffic data, thereby greatly improving the accuracy of energy consumption calculation and the acquisition of the lowest energy consumption route.
Further, the lowest energy consumption is compared with the automobile residual energy consumption, namely the relation between E-E 'and | E |, if E-E' > | E |, the automobile residual energy consumption can complete the driving task, and path planning is carried out by taking the fastest time, the minimum energy consumption and the like as weights; if E-E' < | E |, the instruction vehicle cannot complete the driving task, an optimal charging position needs to be searched for charging, the number of the stopping points of the vehicles obtained by S1 is sorted, the stopping points are selected from large to small, each stopping point is selected, the residual energy consumption of the vehicle from the current position to the point is calculated, charging pile data which can be reached by the residual energy consumption near the stopping points is obtained by taking the residual energy consumption as reference, whether the charging pile data are obtained successfully or not is further judged, if the obtaining is successful, the obtained charging pile parameters such as price, distance and time are optimally selected through a correlation function, after the selection, the stopping points are assigned to M, the charging pile position data are inserted into 123 \\8230, M \8230, N, a path from the current position to M through each stopping point, then from M to N, and finally to N points is planned; if the acquisition fails, selecting the next passing stop point in the sequence, repeating the acquisition judgment, and after the judgment is successful, performing the corresponding steps to finally complete the charging and path planning.
Example 2
Different from the embodiment 1, in the embodiment, in the step S1, the position and time data of the future journey stop node 123 \ 8230n of the vehicle are not obtained, and only the vehicle type, the residual electric quantity and the current position data are obtained, that is, the vehicle does not have a journey but needs to be charged, at this time, the step S5 is directly entered, the current position is assigned to the position M, the charging pile data which can be reached by the residual energy consumption is obtained, and after the obtaining is successful, the step S6 is directly entered to perform charging pile optimization and path planning.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (8)
1. The electric vehicle dynamic path planning and charging method for multi-warp stop point traveling is characterized by comprising the following steps of:
s1, obtaining vehicle models and residual electric quantity E, obtaining position data of a plurality of target nodes 123 \8230ina travel route and time data of reaching the target nodes, assigning a final destination N to M, and calculating the retention time and residual electric quantity at each target node;
s2, predicting road acceleration and speed data of each minute of one hour in the future;
s3, dynamically calculating the lowest energy consumption of the road according to the energy consumption objective function according to the prediction data, and comparing the size relation between the electric quantity after driving and the residual electric quantity threshold value;
in step S3, firstly, based on the data in steps S1 and S2, obtaining the most energy-saving path from the current location to the destination M and the predicted total energy consumption E ', and comparing the remaining energy consumption E-E' with the remaining energy threshold | E |, where the remaining energy threshold | E | is a preset amount of energy that must be charged;
s4, executing different commands according to the comparison result, and finding out a pre-selected optimal charging center M which can be reached by the automobile;
in step S4, if E-E ' is more than or equal to | E |, entering step S5, if E-E ' < | E |, selecting the stay time at each target node as a weight, assigning the nodes to M by the size of each target node, repeating the step S3 once for each assignment until E-E ' > | E |, finding a pre-selected optimal charging center M at this moment, and entering step S5;
s5, determining a final charging center M or directly planning a path according to a judgment result;
in step S5, it is determined whether M and N are the same node, and if M = = N, the multi-transit stop point trip path planning from the current position to the final destination is directly performed on the vehicle; if M is not equal to N, taking the point M as a center, acquiring the data of each reachable charging pile based on the calculated residual electric quantity at the point M, judging whether the data is successfully acquired, and if yes, entering a step S6; if not, repeating the step S3, returning to the step S5 for judgment until a final charging center capable of obtaining the charging pile data is found and assigned to the M, and entering the step S6;
and S6, selecting the optimal charging pile and planning the path.
2. The multi-stop-point-trip-oriented dynamic path planning and charging method for the electric vehicle according to claim 1, wherein position data of a plurality of stop nodes and time data of reaching the plurality of stop nodes in a future trip are acquired, a final destination is dynamically assigned, and the stop time and the residual capacity at each target node are calculated.
3. The method for planning and charging a dynamic path of an electric vehicle for a trip at multiple stopping points according to claim 1, wherein road acceleration and speed data of each minute for one hour in the future are predicted, and the method further comprises the following steps: the long and short term memory network LSTM is combined with the STGCN, the STGCN consists of two layers of time sequence convolution layers and one layer of space convolution layer, and the acceleration and speed data of the road in each minute of the next hour is predicted according to the acceleration and speed data of the road in each minute of the past hour.
4. The method for planning and charging the dynamic path of the electric vehicle for traveling at multiple stopping points according to claim 1, wherein the energy consumption y for vehicle driving is determined according to the road acceleration and speed, the motor rotation speed, the output torque, the final ratio, the air resistance coefficient, the rolling resistance coefficient and the road slope angle i (t) performing a calculation with an energy consumption objective function expressed as:
energy i (t)＝P i (t)/η M (t)
wherein, P i (t) representing an energy consumption value of the vehicle, obtained according to the objective function (a); eta M (t) represents the efficiency of the motor at the moment t and is obtained through a motor efficiency equation;
P i (t)＝n i (t)*T i (t)/9550 (a)
wherein n is i (t) represents the driving motor speed of the electric vehicle, obtained by the vehicle power transmission function (b); t is a unit of i (t) represents motor output torque, obtained by a balance equation (c) of driving force and driving resistance of the vehicle:
wherein, T i (t) represents motor output torque; i.e. i 0 Representing the final ratio of the electric vehicle; c D A represents the frontal area air resistance coefficient of vehicle i; f represents a rolling resistance coefficient; eta r Represents transmission efficiency; m represents the mass of the vehicle; g represents the gravitational acceleration; δ represents a rotational mass coefficient; r represents the radius of the wheel; alpha represents a road grade angle.
5. The dynamic path planning and charging method for the electric vehicle traveling at multiple stop points according to claim 1, wherein a road network information adjacency matrix is input according to predicted dynamic real-time road data, the energy consumption objective function is utilized to calculate the energy consumption required by each road, and the lowest energy consumption route from the current position to the target node and the predicted total energy consumption are obtained according to an ant colony algorithm by taking the energy consumption as a weight.
6. The dynamic path planning and charging method for the electric vehicle traveling towards the multiple stop points according to claim 1 is characterized in that the acquired charging pile data are calculated according to required time, price, distance and queuing time, the charging pile position data are inserted into 123 \8230, M \8230, N after an optimal scheme is determined, and the path planning of the traveling of the multiple stop points is performed, wherein M represents a position node where the charging pile corresponding to the optimal scheme is located, and N represents a destination node.
7. The dynamic path planning and charging method for the electric vehicle traveling at multiple stopping points according to claim 1, wherein for a certain target node, if the electric quantity after driving is greater than a residual electric quantity threshold value, path planning is performed by taking the fastest time and the minimum energy consumption as weights; and if the electric quantity after driving is smaller than the residual electric quantity threshold value, searching an optimal charging position.
8. The dynamic path planning and charging method for the electric vehicle traveling towards multiple stop points according to claim 6, wherein the stop points are selected from large to small according to the obtained sequence of the stop time of the stop nodes of the future journey, the remaining energy consumption of the vehicle from the current position to the stop points is calculated when one stop point is selected, and charging pile data which can be reached by the remaining energy consumption near the stop points is obtained by taking the remaining energy consumption as a reference.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202110648824.XA CN113390430B (en) | 2021-06-10 | 2021-06-10 | Electric vehicle dynamic path planning and charging method for multi-warp stop point trip |
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