阅读说明：本技术 一种电动汽车电能补充方法 (Electric energy supplementing method for electric automobile ) 是由 辛涛 张海军 张宇 张宁 安振佳 黄建民 刘强 于 2021-05-17 设计创作，主要内容包括：一种电动汽车电能补充方法,其利用统计方式计算电动汽车利用当前剩余能量行驶至各个可充电或换电区域的概率大小,能够较为快速地规划出行驶至充换电设施的行车路径,保证车辆即使在电量较低的情况下仍可正常到达。考虑了不同环境温度、载荷以及交通路况等条件下所得到的概率密度差异,使方法提供了更强的自适应性。基于统计的方法所需的计算成本相对较低,且具有较高的精度。在路径规划中充分利用制动能量回收功能,进一步保障车辆顺利到达,并可维持车上其他用电器的正常使用。(The method for supplementing the electric energy of the electric automobile calculates the probability of the electric automobile driving to each chargeable or battery-replacing area by using the current residual energy in a statistical mode, can relatively quickly plan the driving path from the electric automobile to the charging and battery-replacing facility, and ensures that the electric automobile can still normally arrive even if the electric quantity is low. The probability density difference obtained under the conditions of different environmental temperatures, loads, traffic road conditions and the like is considered, so that the method provides stronger adaptivity. Statistical-based methods require relatively low computational costs and have high accuracy. The braking energy recovery function is fully utilized in the path planning, the smooth arrival of the vehicle is further ensured, and the normal use of other electric appliances on the vehicle can be maintained.)

1. An electric energy supplementing method for an electric automobile is characterized in that: the method specifically comprises the following steps:

s1, detecting the residual capacity in real time in the driving process of the electric automobile, and determining whether the electric automobile has an electric energy supplement requirement;

s2, when the electric energy supplement demand is found, calculating the distance range in which the residual electric quantity can travel, and determining an alternative sub-area for setting a charging or battery replacement facility; the determination process of the candidate sub-region comprises the following steps:

for each sub-area divided based on a charging or battery replacement facility within a certain distance range, calculating the probability that the vehicle can normally run to each sub-area according to the current residual energy, and taking the corresponding sub-area with the probability greater than a probability threshold value as an alternative sub-area;

the probability is obtained by counting in advance the probability density function of the energy consumed by the similar vehicle type when the vehicle type runs from the current place to each subarea and integrating the probability density function according to the current residual energy;

and S3, finally determining one of the selected candidate sub-areas as a target sub-area and planning a path so that the vehicle can drive to a charging or battery replacement facility based on the path.

2. The method of claim 1, wherein: the probability density function for counting the energy consumed by the same type of vehicle driving from a specific place to each subarea comprises the step of respectively calculating the probability density functions according to the energy consumed under different environmental temperature conditions and/or different vehicle load conditions and/or different time period traffic road conditions.

3. The method of claim 1, wherein: the method for counting energy consumed by similar vehicle types running from the current place to each subarea in advance specifically comprises the following steps:

and respectively counting the energy consumed by the similar vehicle type driving in a plurality of subsections forming a complete route from the current position to a certain sub-area, and accumulating to obtain the energy consumed on the complete route.

4. The method of claim 1, wherein: step S3 specifically includes: after the candidate sub-area is selected, the sub-area with the largest difference between the average altitude and the current position is determined as the target sub-area.

5. The method of claim 1, wherein: after the target sub-area is determined in step S3, all possible paths of the vehicles from the current location to the charging or battery replacement facilities in the target sub-area are analyzed, and the path with the most downhill sections is selected as the final planned path.

6. The method of claim 1, wherein: and the probability density function and the probability threshold value are used for collecting energy consumption data of the same type of vehicle through a cloud server or a central server and calculating the energy consumption data.

7. The method of claim 5, wherein: the step of selecting the path with the largest downhill section as the final planned path comprises the steps of collecting running condition information of different vehicles by using a cloud server or a central server, determining the average gradient and/or the distance of the downhill section of all possible paths in a target self-region, and selecting the section with the largest average gradient and/or the section with the longest distance of the downhill section.

Technical Field

The invention belongs to the technical field of operation scheduling and charging of electric automobiles, and particularly relates to a method for automatically planning driving to a charging and battery replacing device and supplementing electric power under the condition of insufficient electric power of an electric automobile.

Background

With the gradual development and popularization of electric automobiles, the endurance mileage of the electric automobiles is greatly different from that of the traditional fuel oil vehicles, and the ubiquitous mileage anxiety of users cannot be eliminated at present, so that the application of the electric automobiles in certain long-distance and high-energy-consumption scenes such as commercial transportation, engineering operation and the like is also restricted to a certain extent. At the present stage, the construction of the charging pile, the battery replacement station and other infrastructure of the electric automobile is relatively lagged, and the vehicle can not be charged or the battery can not be replaced in time under the condition of insufficient electric power. The quantity of the infrastructure is small, the distribution is uneven, the charging process generally consumes a long time, and the fuel filling speed of the fuel vehicle cannot be reached even if the charging is carried out quickly, so that the peak of waiting for charging in line is easily caused in certain areas or time periods, and the inconvenience of users is increased. Therefore, how to enable the electric vehicle to reach an appropriate charging and battery replacing facility as soon as possible when the electric vehicle has a charging demand, and reduce unnecessary waiting is a technical problem to be solved in the art.

Disclosure of Invention

In view of this, the present invention provides an electric vehicle electric energy supplementing method by finding an infrastructure where a vehicle can travel when having a battery charging and replacing requirement, and combining an optimized path selection and planning means, the method specifically includes the following steps:

s1, detecting the residual capacity in real time in the driving process of the electric automobile, and determining whether the electric automobile has an electric energy supplement requirement;

s2, when the electric energy supplement demand is found, calculating the distance range in which the residual electric quantity can travel, and determining an alternative sub-area for setting a charging or battery replacement facility; the determination process of the candidate sub-region comprises the following steps:

for each sub-area divided based on a charging or battery replacement facility in a certain region range, calculating the probability that the vehicle can normally run to each sub-area according to the current residual energy, and taking the corresponding sub-area with the probability greater than a probability threshold value as an alternative sub-area;

the probability is obtained by counting in advance the probability density function of the energy consumed by the similar vehicle type when the vehicle type runs from the current place to each subarea and integrating the probability density function according to the current residual energy;

and S3, finally determining one of the selected candidate sub-areas as a target sub-area and planning a path so that the vehicle can drive to a charging or battery replacement facility based on the path.

Further, the calculating the probability density function of the energy consumed by the similar vehicle type driving from the specific location to each sub-area comprises respectively calculating the probability density function according to the consumed energy under different environmental temperature conditions and/or different vehicle load conditions and/or different time period traffic road conditions.

Further, the step of counting energy consumed by similar vehicle types running from the current location to each sub-area in advance specifically comprises the following steps:

and respectively counting the energy consumed by the similar vehicle type driving in a plurality of subsections forming a complete route from the current position to a certain sub-area, and accumulating to obtain the energy consumed on the complete route.

Further, step S3 specifically includes: after the candidate sub-area is selected, the sub-area with the largest difference between the average altitude and the current position is determined as the target sub-area.

Further, after the target sub-area is determined in step S3, all possible paths of the vehicle from the current location to each charging or battery replacement facility in the target sub-area are analyzed, and the path with the most downhill sections is selected as the final planned path.

Further, the probability density function and the probability threshold value are used for collecting energy consumption data of the same type of vehicle through a cloud server or a central server and calculating the energy consumption data.

Further, selecting the path with the largest downhill section as the final planned path comprises collecting driving condition information of different vehicles by using a cloud server or a central server, determining the average gradient and/or the distance of the downhill section of all possible paths in the target self-region, and selecting the section with the largest average gradient and/or the section with the longest distance of the downhill section.

The method provided by the invention can calculate the probability of the electric automobile driving to each chargeable or battery replacement area by using the current residual energy in a statistical mode, can relatively quickly plan the driving path driving to the chargeable or battery replacement facility, and ensures that the automobile can still normally arrive even under the condition of low electric quantity. The probability density difference obtained under the conditions of different environmental temperatures, loads, traffic road conditions and the like is considered, so that the method provides stronger adaptivity. Statistical-based methods require relatively low computational costs and have high accuracy. The braking energy recovery function is fully utilized in the path planning, the smooth arrival of the vehicle is further ensured, and the normal use of other electric appliances on the vehicle can be maintained.

Drawings

FIG. 1 is a schematic flow chart of the method of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The electric vehicle electric energy supplementing method provided by the invention, as shown in fig. 1, specifically comprises the following steps:

s1, detecting the residual capacity in real time in the driving process of the electric automobile, and determining whether the electric automobile has an electric energy supplement requirement;

s2, when the electric energy supplement demand is found, calculating the distance range in which the residual electric quantity can travel, and determining an alternative sub-area for setting a charging or battery replacement facility; as shown in the dotted line in fig. 1, the process of determining the alternative sub-region includes:

for each sub-area divided based on a charging or battery replacement facility in a certain region range, calculating the probability that the vehicle can normally run to each sub-area according to the current residual energy, and taking the corresponding sub-area with the probability greater than a probability threshold value as an alternative sub-area;

the probability P is a probability density function f (E) for counting the energy E consumed by similar vehicle types to travel from the current place to each subarea in advance, and the current residual energy E is aimed at_τIntegrating the probability density function to obtain:

the statistics can utilize an electric vehicle big data platform to obtain the running condition information of the same type of vehicle types, and the energy consumption of running to some sub-areas in the current place is obtained by combining with high-precision map data.

And S3, finally determining one of the selected candidate sub-areas as a target sub-area and planning a path so that the vehicle can drive to a charging or battery replacement facility based on the path.

Those skilled in the art will appreciate that the route that can be selected from traveling from the current location to each of the alternative sub-areas can be limited to a reasonable number by conditions including, but not limited to, distance, road conditions, number of traffic lights, etc.

Because the power battery of the electric automobile is greatly influenced by environmental conditions, particularly temperature, the energy consumption performance of the automobile at different room temperatures and seasons has obvious difference, and the energy consumed by the same automobile to finish the same route in spring and autumn can not meet the requirements in summer and winter. Meanwhile, the load condition of the vehicle, whether other electric appliances such as a refrigerating and heating device are used, and whether the traffic road condition is smooth or congested can also cause different energy consumptions of the vehicle, so that the energy consumptions of similar vehicle types under different conditions need to be respectively counted, and the accuracy of the method is further improved. In a preferred embodiment of the present invention, the calculating the probability density function of the energy consumed by the similar vehicle type driving from the specific location to each sub-area includes, but is not limited to, calculating the probability density function for the consumed energy under different environmental temperature conditions and/or different vehicle load conditions and/or different time periods of traffic conditions respectively.

The covered mileage range can be specifically determined according to actual requirements at the current location, the total energy consumption of the vehicle running in each segment is calculated by carrying out segmented historical energy consumption statistics on the running route and the like, and the energy consumed by the same type of vehicle in a plurality of subsections forming a complete route running from the current location to a certain sub-area is respectively consumed. Taking Beijing as an example, suppose that a loop main road is preferentially considered as an optimal route in a complete route from a western straight gate to a sub-area near a sunward gate at a current place, and complex statistical work of other routes such as a statistical auxiliary road and the like is eliminated. The energy consumption of different vehicles of a specific vehicle type on different sub-sections such as 'sunward door-east straight door', 'east straight door-stable door', 'stable door-degress door', 'degress door-west straight door' is counted, and the energy consumption of the whole section can be obtained by averaging, accumulating and summing or other known methods. The method avoids repeatedly acquiring the energy consumption of a single-time running complete route of the single vehicle, only needs to respectively calculate the energy consumption of different vehicles on each subsection in different periods, obtains the total energy consumption through accumulation, and simplifies the operation amount by utilizing the sampling idea. By adjusting the number of samples, the accuracy of the method can be adjusted according to actual needs, for example, for some commercial freight vehicles, the working route and the loading condition have higher uniformity, so that the number of samples can be controlled to a lower degree.

For vehicles of the same type, the conditions such as working conditions and environments running on the same road section are not greatly different, so that the problems of overlarge sample number and calculation overhead are further avoided, and the accuracy is ensured. Particularly, for commercial operation vehicles, the normal running route is relatively fixed, so that each corresponding sub-area and route can be determined in advance more accurately when charging or battery replacement is needed, and the statistical calculation amount can be further reduced.

When the method is implemented, the braking energy recovery function is utilized, so that the sufficient endurance mileage can be further ensured when the residual electric quantity is low. Especially for passenger transport or freight transport operation vehicles, it is usually impossible to change the route to the charging and replacing facility at any time when a predetermined working route is not completed, and power consumption is inevitably increased in some situations, such as passenger transport situations requiring air-conditioning cooling or heating or cold chain transportation. In order to save energy consumption and smoothly reach the charging and replacing place under the condition of low electric quantity, the energy consumption in the way needs to be saved. Therefore, in a preferred embodiment of the present invention, step S3 specifically includes: after the candidate sub-area is selected, the sub-area with the largest difference between the average altitude and the current position is determined as the target sub-area. And after the target sub-area is determined in step S3, all possible paths of the vehicle from the current location to each charging or battery replacement facility in the target sub-area are analyzed, and the path with the most downhill sections is selected as the final planned path. The means ensures that the vehicle can more utilize braking energy to recover on the way of going to charge and exchange the battery, ensures that the vehicle can still successfully arrive under the condition of low electric quantity, and can maintain the normal use of other necessary power utilization functions on the vehicle to a certain extent.

Along with the establishment of a big data platform of a national new energy vehicle and the like, the probability density function and the probability threshold value can realize the energy consumption data collection and calculation of the same type of vehicle models through a cloud server, distributed calculation and the like. For the commercial vehicles, the vehicle types, the driving routes and the number of selectable sub-areas are greatly reduced, and even if a traditional central server is adopted, too high system load cannot be caused.

In a preferred embodiment of the present invention, selecting the path with the largest downhill section as the final planned path includes collecting driving condition information of different vehicles by using a cloud server or a central server, determining an average gradient and/or a distance of the downhill section of all possible paths in the target sub-area, and selecting a section with the largest average gradient and/or a section with the longest distance of the downhill section.

It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.