WO2019042297A1 - 一种电动汽车最优路径和驾驶方式的规划方法及系统 - Google Patents

一种电动汽车最优路径和驾驶方式的规划方法及系统 Download PDF

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WO2019042297A1
WO2019042297A1 PCT/CN2018/102813 CN2018102813W WO2019042297A1 WO 2019042297 A1 WO2019042297 A1 WO 2019042297A1 CN 2018102813 W CN2018102813 W CN 2018102813W WO 2019042297 A1 WO2019042297 A1 WO 2019042297A1
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path
driving
feasible
electric vehicle
optimal
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PCT/CN2018/102813
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English (en)
French (fr)
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罗国鹏
王敏
何涛
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广州小鹏汽车科技有限公司
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469Fuel consumption; Energy use; Emission aspects
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3407Route searching; Route guidance specially adapted for specific applications
    • G01C21/343Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3461Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3484Personalized, e.g. from learned user behaviour or user-defined profiles
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3453Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical

Definitions

  • the present application relates to the field of control of electric vehicles, and in particular to a method and system for planning an optimal path and driving mode of an electric vehicle.
  • Electric vehicles have limited driving range. For long distance driving or low battery running, it is necessary to consider the charging demand in the path planning to eliminate the user's mileage anxiety and also consider the influence of driving conditions.
  • the driving mode of the electric vehicle includes the driving mode and the air conditioning mode, and the driving mode affects the power consumption of the electric vehicle, thereby having an important influence on the driving range of the electric vehicle, and the appropriate driving mode planning can extend the driving range of the vehicle.
  • there is a method for planning a vehicle travel route according to the number of charging stations of a driving route of a car focus on meeting the charging requirements of the electric vehicle, and do not comprehensively consider the driving process, the path, and the driving.
  • the way of affecting the electric power consumption of electric vehicles is relatively simple, and it is impossible to make full use of various vehicle characteristics of electric vehicles, and the optimization result is not satisfactory.
  • an object of the present application is to provide a method for planning an optimal path and driving mode of an electric vehicle. Another object of the present application is to provide a planning system for an optimal path and driving mode of an electric vehicle.
  • a method for planning an optimal path and driving mode of an electric vehicle comprising the steps of:
  • the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set are obtained.
  • the user weight coefficient is a coefficient preset by the user or automatically acquired according to the driving habit of the user;
  • the driving mode refers to a combination of a driving mode and an air conditioning mode of the electric vehicle.
  • the step of obtaining the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set based on the reachability analysis includes:
  • connection path For each connection path, based on different driving modes, after the reachability analysis is performed, a feasible connection path is obtained as a feasible path;
  • the feasible path After expanding the path search range, the feasible path is searched again, and the new quality evaluation parameters are calculated. After the search end condition is satisfied, according to the feasible path obtained, the feasible path set and the charging mode set of the electric vehicle in different driving modes are obtained.
  • the set of charging modes consists of a charging position and a charging amount corresponding to each feasible path.
  • the step of obtaining a feasible connection path as a feasible path after performing the reachability analysis for each connection path according to different driving modes specifically includes:
  • connection path For each connection path, according to the road condition prediction and the road level, the typical working conditions of the different paths of the connection path are obtained;
  • step of estimating the driving energy consumption of each path of the electric vehicle under different driving modes is to estimate the driving energy consumption according to the following formula:
  • Mode drv represents the driving mode
  • Mode AC represents the air conditioning mode
  • E represents the driving energy consumption
  • d represents the total driving mileage of the electric vehicle
  • p i represents the probability of the typical working condition i
  • EC i represents the unit mileage of the typical driving i Energy consumption
  • v air represents wind speed
  • T ext represents ambient temperature
  • T int represents the internal temperature of the electric vehicle
  • x rain snow represents the rain and snow condition
  • P AC represents the average energy consumption of the air conditioner
  • v i represents the electric vehicle in the typical working condition i Average speed.
  • the step of determining, according to the SOC value of the end point of each connection path, whether it is a feasible path, and then obtaining a feasible connection path as a feasible path is specifically:
  • the SOC value of the end point of each connection path after obtaining the end point, the SOC value of the electric vehicle is enough to supply the connection path of the car to the nearest charging station as a feasible path.
  • the search end condition is that the new quality evaluation parameter and the original optimal quality evaluation parameter are within a certain threshold range, and the quality evaluation parameter is driving time, driving distance or running power consumption.
  • the step of calculating an evaluation index of each feasible path in different driving modes is specifically:
  • Index represents the evaluation index
  • k1, k2, k3, and k4 are user weight parameters.
  • the total time refers to the sum of the travel time, the charge waiting time, and the charging time of the feasible path from the start point to the end point
  • the total cost is the charge station reservation waiting fee, the charging fee, and the road pass from the start point to the end point of the feasible path.
  • the battery life decay is obtained by the following formula: k31 ⁇ depth discharge interval total discharge power + k32 ⁇ DC charge power; wherein k31 and k32 are preset weight parameters;
  • the driving ability after reaching the end point is based on multiple levels of the SOC of the electric vehicle at the end point, and the first-level representative has the strongest driving ability after reaching the end point, and is successively decreased.
  • step of performing optimal path and driving mode planning based on the calculated evaluation indicator comprises:
  • the feasible path with the smallest evaluation index for each driving mode is selected as the recommended path
  • a planning system for an optimal path and driving mode of an electric vehicle includes a processor and a storage device, the storage device storing a plurality of instructions loaded by the processor and performing the following steps:
  • the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set are obtained.
  • the user weight coefficient is a coefficient preset by the user or automatically acquired according to the driving habit of the user;
  • the driving mode refers to a combination of a driving mode and an air conditioning mode of the electric vehicle.
  • the step of obtaining the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set based on the reachability analysis includes:
  • connection path For each connection path, based on different driving modes, after the reachability analysis is performed, a feasible connection path is obtained as a feasible path;
  • the feasible path After expanding the path search range, the feasible path is searched again, and the new quality evaluation parameters are calculated. After the search end condition is satisfied, according to the feasible path obtained, the feasible path set and the charging mode set of the electric vehicle in different driving modes are obtained.
  • the set of charging modes consists of a charging position and a charging amount corresponding to each feasible path.
  • step of performing optimal path and driving mode planning based on the calculated evaluation indicator comprises:
  • the feasible path with the smallest evaluation index for each driving mode is selected as the recommended path
  • the beneficial effects of the present application are: a method for planning an optimal path and driving mode of an electric vehicle according to the present application, comprising the steps of: selecting a feasible path set of the electric vehicle in different driving modes and corresponding charging based on the reachability analysis; a mode set; after obtaining the user weight coefficient, calculating an evaluation index of each feasible path in different driving modes; the user weight coefficient is a coefficient preset by the user or automatically acquired according to the driving habit of the user; and based on the calculated evaluation index Optimal path and driving style planning.
  • the method can calculate the corresponding evaluation index according to the user's demand index, thereby screening and obtaining the optimal feasible path and the driving mode, that is, the combination of the driving mode and the air conditioning mode, which can fully utilize the vehicle characteristics of the electric vehicle and obtain the optimal path planning. And the driving mode planning results, the optimization results are good.
  • 1 is a flow chart of a method for planning an optimal path and driving mode of an electric vehicle according to the present application
  • FIG. 2 is a schematic block diagram of a detailed embodiment of the present application.
  • an embodiment of the present application provides a method for planning an optimal path and driving mode of an electric vehicle, including the following steps:
  • the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set are obtained.
  • the user weight coefficient is a coefficient preset by the user or automatically acquired according to the driving habit of the user;
  • the driving mode refers to a combination of a driving mode and an air conditioning mode of the electric vehicle.
  • the step of obtaining the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set based on the reachability analysis includes:
  • connection path For each connection path, based on different driving modes, after the reachability analysis is performed, a feasible connection path is obtained as a feasible path;
  • the feasible path After expanding the path search range, the feasible path is searched again, and the new quality evaluation parameters are calculated. After the search end condition is satisfied, according to the feasible path obtained, the feasible path set and the charging mode set of the electric vehicle in different driving modes are obtained.
  • the set of charging modes consists of a charging position and a charging amount corresponding to each feasible path.
  • the step of obtaining a feasible connection path as a feasible path after performing the reachability analysis for each connection path according to different driving modes includes:
  • connection path For each connection path, according to the road condition prediction and the road level, the typical working conditions of the different paths of the connection path are obtained;
  • the SOC value after charging is determined according to a preset charging strategy; whether the feasible path is obtained according to the SOC value of the end point of each connection path, and then a feasible connection path is obtained as a feasible path.
  • the battery energy model includes battery health, state of charge, temperature, environmental factors, etc.
  • the battery energy model can be used to estimate the total available energy of the battery and the available energy that does not enter the deep discharge interval.
  • the total available energy of the battery and the available energy that does not enter the deep discharge interval can be obtained from a predetermined MAP lookup table.
  • the SOC value of each path can be calculated.
  • the step of estimating the driving energy consumption of each path of the electric vehicle in different driving modes is to estimate the driving energy consumption according to the following formula:
  • Mode drv represents the driving mode
  • Mode AC represents the air conditioning mode
  • E represents the driving energy consumption
  • d represents the total driving mileage of the electric vehicle
  • p i represents the probability of the typical working condition i
  • EC i represents the unit mileage of the typical driving i Energy consumption
  • v air represents wind speed
  • T ext represents ambient temperature
  • T int represents the internal temperature of the electric vehicle
  • x rain snow represents the rain and snow condition
  • P AC represents the average energy consumption of the air conditioner
  • v i represents the electric vehicle in the typical working condition i Average speed, Indicates the total travel time.
  • the step of determining, according to the SOC value of the end point of each connection path, whether it is a feasible path, and then obtaining a feasible connection path as a feasible path is specifically:
  • the SOC value of the end point of each connection path after obtaining the end point, the SOC value of the electric vehicle is enough to supply the connection path of the car to the nearest charging station as a feasible path.
  • the search end condition is that the new quality assessment parameter and the original optimal quality assessment parameter are within a certain threshold range, and the quality assessment parameter is a driving time, a driving distance, or a driving power consumption.
  • the step of calculating an evaluation index of each feasible path in different driving modes is specifically:
  • Index represents the evaluation index
  • k1, k2, k3, and k4 are user weight parameters.
  • the total time refers to the sum of the travel time, the charge waiting time, and the charging time of the feasible path from the start point to the end point
  • the total cost is a charging station reservation waiting fee from the start point to the end point of the feasible path, The sum of the charging cost and the road toll cost minus the value of the remaining electricity at the end point;
  • the battery life decay is obtained by the following formula: k31 ⁇ depth discharge interval total discharge power + k32 ⁇ DC charge power; wherein k31 and k32 are preset weight parameters;
  • the driving ability after reaching the end point is based on multiple levels of the SOC of the electric vehicle at the end point, and the first-level representative has the strongest driving ability after reaching the end point, and is successively decreased.
  • the step of performing optimal path and driving mode planning based on the calculated evaluation indicator includes:
  • the feasible path with the smallest evaluation index for each driving mode is selected as the recommended path
  • a planning system for an optimal path and driving mode of an electric vehicle includes a processor and a storage device, the storage device storing a plurality of instructions loaded by the processor and performing the following steps:
  • the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set are obtained.
  • the user weight coefficient is a coefficient preset by the user or automatically acquired according to the driving habit of the user;
  • the driving mode refers to a combination of a driving mode and an air conditioning mode of the electric vehicle.
  • the step of obtaining the feasible path set of the electric vehicle in different driving modes and the corresponding charging mode set based on the reachability analysis includes:
  • connection path For each connection path, based on different driving modes, after the reachability analysis is performed, a feasible connection path is obtained as a feasible path;
  • the feasible path After expanding the path search range, the feasible path is searched again, and the new quality evaluation parameters are calculated. After the search end condition is satisfied, according to the feasible path obtained, the feasible path set and the charging mode set of the electric vehicle in different driving modes are obtained.
  • the set of charging modes consists of a charging position and a charging amount corresponding to each feasible path.
  • the step of performing optimal path and driving mode planning based on the calculated evaluation indicator includes:
  • the feasible path with the smallest evaluation index for each driving mode is selected as the recommended path
  • the electric vehicle of the present embodiment has three driving modes including a normal mode, an economy mode, a sport mode, and the like.
  • the air conditioning system has a comfort mode, a power saving mode, and a minimum energy consumption mode.
  • the combination of driving styles is shown in Table 1 below:
  • the typical working conditions of the road can be graded according to the combination of road grade and congestion level, as shown in Table 2 below:
  • the average running speed is less than 20km/h.
  • the average driving speed is less than 20 ⁇ 40km/h.
  • the average driving speed is less than 40 ⁇ 60km/h.
  • the average running speed is 60 ⁇ 90km/h.
  • High-speed working conditions The average driving speed is greater than 90km/h.
  • traffic data over a period of time can be obtained to predict the typical conditions for each path.
  • the charging station interactively indicates that the charging station information obtained from the charging station includes charging station available information, reservation information, fee information, and the like.
  • the reservation information may be reserved by the electric car navigation system and the charging station.
  • the first step feasible program planning
  • connection path can be expressed as "starting point - charging station 1 - charging station 2 ... charging station n - end point - nearest charging station in different driving directions".
  • step 2 For the path of step 1, based on different driving modes, that is, based on the combination of different driving modes and air conditioning modes, the reachability analysis is performed, and a feasible connection path is obtained as a feasible path.
  • the detailed process of reachability analysis is as follows:
  • connection path for each connection path, according to the road condition prediction and the road level, obtain the typical working conditions of the different paths of the connection path;
  • step 2.2 After obtaining the environmental parameters, analyze the driving energy consumption of each path under the given driving mode and air conditioning mode (driving mode) according to the typical working conditions obtained in step 2.1);
  • Steps 2.3.1) to 2.3.3) embodies the charging strategy preset in this method.
  • the path is feasible. After the arrival of the end point, the SOC value of the electric vehicle is sufficient to supply the connection path of the car to the nearest charging station as a feasible path.
  • the calculated travel time is used as the quality evaluation parameter to evaluate the quality of the search range; the travel distance or the running power consumption can also be calculated as the quality evaluation parameter.
  • the feasible path is re-searched, and if the searched new feasible path quality evaluation parameter is within a certain threshold range from the previously evaluated optimal feasible path evaluation parameter, the search is stopped. For example, calculating the travel time for newly acquiring all feasible paths, if the travel time of the best feasible path of the previous search range differs within a certain threshold (for example, 5%), the search ends.
  • a certain threshold for example, 5%
  • the first step results : a set of feasible solutions under different driving modes ⁇ Route ⁇ ij and charging mode set, where i represents the driving mode (1 is the sport mode, 2 is the normal mode, 3 is the economic mode), and j represents the air conditioning mode (1) It is comfortable mode, 2 is energy saving mode, 3 is the lowest energy consumption mode), including multiple feasible paths.
  • the charging mode set includes information such as a charging position and a charging amount corresponding to each feasible path.
  • Step 2 Establish evaluation indicators
  • K1, k2, k3, and k4 are user weight parameters, which may be set according to data input by the user, or may be self-learned according to historical driving data of the user.
  • the total time refers to the time from the start point to the end point, that is, the sum of the travel time, the charge waiting time, and the charging time;
  • Battery life decay calculation formula k31 ⁇ deep discharge interval total discharge power + k32 ⁇ DC charge; where k31 and k32 are preset weight parameters;
  • the driving ability after reaching the end point is based on multiple levels of the SOC of the electric vehicle at the end point. Divided into the following levels: Level 4: able to reach the nearest charging station, Level 3: the nearest charging station that can reach all directions, Level 2: The nearest charging station that can reach all directions within the SOC health interval, ie the discharge volume in the deep discharge interval is 0; Level 1: The SOC set by the user is reached. If the SOC of the user is lower than the SOC corresponding to the second level, the SOC corresponding to the second level is adjusted by a certain amount as the SOC target value of the first level.
  • the user can also set a certain indicator as a constraint that must be reached. For example, the driving ability after reaching the end point must reach one level.
  • the third step the best solution recommendation
  • the present application comprehensively considers the road working conditions and the vehicle characteristics of the electric vehicle, and can calculate the corresponding evaluation index according to the user's demand index, such as the shortest total time and the lowest power consumption of the battery, thereby screening and obtaining the optimal feasible path and
  • the driving mode is the combination of driving mode and air conditioning mode. Considering the influence of road conditions and driving style on the electric power consumption of electric vehicles, the vehicle characteristics of electric vehicles can be fully utilized, and the optimal path planning and driving mode planning results are obtained, and the optimization results are good. .

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Abstract

提供了一种电动汽车最优路径和驾驶方式的规划方法及系统,其中该方法包括步骤:基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;该用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;基于计算的评估指标,进行最优路径和驾驶方式规划。该方法可以根据用户的需求指标,计算对应偏重的评估指标,从而筛选获得最优的可行路径以及驾驶方式即驾驶模式和空调模式的组合,可以充分利用电动汽车的车辆特性,得到最优路径规划和驾驶方式规划结果,优化结果好,可广泛应用于电动汽车的控制领域中。

Description

一种电动汽车最优路径和驾驶方式的规划方法及系统
相关申请的交叉引用
本申请要求于2017年08月29日提交中国专利局的申请号为CN201710754825.6、名称为“一种电动汽车最优路径和驾驶方式的规划方法及系统”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
技术领域
本申请涉及电动汽车的控制领域,特别是涉及一种电动汽车最优路径和驾驶方式的规划方法及系统。
背景技术
电动汽车续驶里程有限,对于长距离行驶或低电量时继续行驶,需要在路径规划时考虑充电需求,以消除用户的里程焦虑,同时也要考虑行驶路况的影响。另外,电动汽车的驾驶方式包括驾驶模式和空调模式,驾驶方式会影响电动汽车耗电量,从而对电动汽车续驶里程有重要影响,合适的驾驶方式规划可以延长车辆续驶里程。现有技术中,已经一部分有根据汽车的行驶路径的充电站数量来进行汽车行驶路径规划的方法,但是这些方法,重点在于满足电动汽车的充电要求,并没有综合考虑行驶过程中,路径、驾驶方式对电动汽车的用电影响,较为单一,无法充分利用电动汽车的各种车辆特性,优化结果不理想。
发明内容
为了解决上述的技术问题,本申请的目的是提供一种电动汽车最优路径和驾驶方式的规划方法,本申请的另一目的是提供一种电动汽车最优路径和驾驶方式的规划系统。
本申请解决其技术问题所采用的技术方案是:
一种电动汽车最优路径和驾驶方式的规划方法,包括步骤:
基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
基于计算的评估指标,进行最优路径和驾驶方式规划;
所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
进一步,所述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接 路径;
针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
计算所有可行路径的质量评估参数;
扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
进一步,所述针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径的步骤,具体包括:
针对每个连接路径,根据路况预测和道路等级,获得连接路径的不同路径的典型工况;
估算获得电动汽车在不同驾驶方式下的每个路径的行驶能耗;
基于电动汽车的电池能量模型,预测每个路径终点的SOC值;
针对经过充电站的路径,按照预设的充电策略确定充电后的SOC值;
根据每个连接路径终点的SOC值判断是否是可行路径,进而获取可行的连接路径作为可行路径。
进一步,所述估算获得电动汽车在不同驾驶方式下的每个路径的行驶能耗的步骤,是根据下式估算获得行驶能耗的:
Figure PCTCN2018102813-appb-000001
上式中,Mode drv表示驾驶模式,Mode AC表示空调模式,E表示行驶能耗,d表示电动汽车的总行驶里程,p i表示典型工况i的概率,EC i表示典型行驶i的单位里程能耗,v air表示风速,T ext表示环境温度,T int表示电动汽车的内部温度,x rain,snow表示雨雪状况,P AC表示空调平均能耗,v i表示电动汽车在典型工况i的平均车速。
进一步,所述根据每个连接路径终点的SOC值判断是否是可行路径,进而获取可行的连接路径作为可行路径的步骤,其具体为:
根据每个连接路径终点的SOC值,获取在到达终点后,电动汽车的SOC值足以供电动汽车到达最近的充电站的连接路径作为可行路径。
进一步,所述搜索结束条件为新的质量评估参数与原最优质量评估参数相差在一定阈 值范围内,所述质量评估参数为行驶时间、行驶距离或行驶用电量。
进一步,所述获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标的步骤,其具体为:
获取用户权重系数后,根据下式,计算不同驾驶方式下的每个可行路径的评估指标:
Index=k1×总时间+k2×总费用+k3×电池寿命衰减+k4×到达终点后的行驶能力
上式中,Index表示评估指标,k1、k2、k3、k4均为用户权重参数。
进一步,所述总时间是指可行路径从起点到终点的行驶时间、充电等待时间和充电时间之和,所述总费用为可行路径从起点到终点的充电站预约等待费用、充电费用和道路通行费用之和再减去到达终点时剩余电量的价值后所得到的费用;
所述电池寿命衰减通过下式计算获得:k31×深度放电区间总放电电量+k32×直流充电电量;其中,k31和k32为预设的权重参数;
所述到达终点后的行驶能力是根据电动汽车在终点的SOC划分的多个级别,一级代表到达终点后的行驶能力最强,依次递减。
进一步,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
本申请解决其技术问题所采用的另一技术方案是:
一种电动汽车最优路径和驾驶方式的规划系统,包括处理器和存储设备,所述存储设备存储有多条指令,所述指令由处理器加载并执行以下步骤:
基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
基于计算的评估指标,进行最优路径和驾驶方式规划;
所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
进一步,所述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接路径;
针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
计算所有可行路径的质量评估参数;
扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
进一步,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
本申请的有益效果是:本申请的一种电动汽车最优路径和驾驶方式的规划方法,包括步骤:基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;基于计算的评估指标,进行最优路径和驾驶方式规划。本方法可以根据用户的需求指标,计算对应偏重的评估指标,从而筛选获得最优的可行路径以及驾驶方式即驾驶模式和空调模式的组合,可以充分利用电动汽车的车辆特性,得到最优路径规划和驾驶方式规划结果,优化结果好。
附图说明
图1是本申请的电动汽车最优路径和驾驶方式的规划方法的流程图;
图2是本申请详细实施例中的原理框图。
具体实施方式
参照图1,本申请实施例提供了一种电动汽车最优路径和驾驶方式的规划方法,包括步骤:
基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权 重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
基于计算的评估指标,进行最优路径和驾驶方式规划;
所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
进一步作为优选的实施方式,所述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接路径;
针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
计算所有可行路径的质量评估参数;
扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
进一步作为优选的实施方式,所述针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径的步骤,具体包括:
针对每个连接路径,根据路况预测和道路等级,获得连接路径的不同路径的典型工况;
根据典型工况、环境参数,估算获得电动汽车在不同驾驶方式下的每个路径的行驶能耗;
基于电动汽车的电池能量模型,结合每个路径的行驶能耗,预测每个路径终点的SOC值;
针对经过充电站的路径,按照预设的充电策略确定充电后的SOC值;根据每个连接路径终点的SOC值判断是否是可行路径,进而获取可行的连接路径作为可行路径。
电池能量模型包括电池健康状态、荷电状态、温度、环境因素等,通过电池能量模型可以估算电池的总可用能量、不进入深度放电区间的可用能量。电池的总可用能量和不进入深度放电区间的可用能量可以根据事先确定的MAP查表得到。根据电池能量模型,结合每个路径的行驶能耗,可以计算每个路径的SOC值。
进一步作为优选的实施方式,所述估算获得电动汽车在不同驾驶方式下的每个路径的行驶能耗的步骤,是根据下式估算获得行驶能耗的:
Figure PCTCN2018102813-appb-000002
上式中,Mode drv表示驾驶模式,Mode AC表示空调模式,E表示行驶能耗,d表示电动汽车的总行驶里程,p i表示典型工况i的概率,EC i表示典型行驶i的单位里程能耗,v air表示风速,T ext表示环境温度,T int表示电动汽车的内部温度,x rain,snow表示雨雪状况,P AC表示空调平均能耗,v i表示电动汽车在典型工况i的平均车速,
Figure PCTCN2018102813-appb-000003
表示总行驶时间。
进一步作为优选的实施方式,所述根据每个连接路径终点的SOC值判断是否是可行路径,进而获取可行的连接路径作为可行路径的步骤,其具体为:
根据每个连接路径终点的SOC值,获取在到达终点后,电动汽车的SOC值足以供电动汽车到达最近的充电站的连接路径作为可行路径。
进一步作为优选的实施方式,所述搜索结束条件为新的质量评估参数与原最优质量评估参数相差在一定阈值范围内,所述质量评估参数为行驶时间、行驶距离或行驶用电量。
进一步作为优选的实施方式,所述获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标的步骤,其具体为:
获取用户权重系数后,根据下式,计算不同驾驶方式下的每个可行路径的评估指标:
Index=k1×总时间+k2×总费用+k3×电池寿命衰减+k4×到达终点后的行驶能力
上式中,Index表示评估指标,k1、k2、k3、k4均为用户权重参数。
进一步作为优选的实施方式,所述总时间是指可行路径从起点到终点的行驶时间、充电等待时间和充电时间之和,所述总费用为可行路径从起点到终点的充电站预约等待费用、充电费用和道路通行费用之和再减去到达终点时剩余电量的价值后所得到的费用;
所述电池寿命衰减通过下式计算获得:k31×深度放电区间总放电电量+k32×直流充电电量;其中,k31和k32为预设的权重参数;
所述到达终点后的行驶能力是根据电动汽车在终点的SOC划分的多个级别,一级代表到达终点后的行驶能力最强,依次递减。
进一步作为优选的实施方式,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶 模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
本申请解决其技术问题所采用的另一技术方案是:
一种电动汽车最优路径和驾驶方式的规划系统,包括处理器和存储设备,所述存储设备存储有多条指令,所述指令由处理器加载并执行以下步骤:
基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
基于计算的评估指标,进行最优路径和驾驶方式规划;
所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
进一步作为优选的实施方式,所述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接路径;
针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
计算所有可行路径的质量评估参数;
扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
进一步作为优选的实施方式,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
以下结合一详细实施例对本申请做进一步说明。
本实施例的电动汽车具有三种驾驶模式,包括正常模式、经济模式、运动模式等模式。空调系统具有舒适模式、节能模式、最小能耗模式。驾驶方式的组合如下表1所示:
表1电动汽车的驾驶方式
Figure PCTCN2018102813-appb-000004
道路的典型工况可根据道路等级和拥堵程度的组合进行等级划分,划分如下表2所示:
表2典型工况等级划分表格
道路等级\拥堵程序 拥堵 一般 畅通
高速公路 中速工况 中高速工况 高速工况
快速路 中低速工况 中速工况 中高速工况
一般主干路 低速工况 中低速工况 中速工况
支路 低速工况 中低速工况 中低速工况
各个等级的工况的速度范围如下:
低速工况:平均行驶车速小于20km/h。
中低速工况:平均行驶车速小于20~40km/h。
中速工况:平均行驶车速小于40~60km/h。
中高速工况:平均行驶车速60~90km/h。
高速工况:平均行驶车速大于90km/h。
对于每个路径,可以获取其在一段时间内的交通数据,从而预测获得每个路径的典型工况。
图2是本实施例的原理框图,图2中,充电站交互表示,从充电桩获取的充电站信息,包括充电站可用信息、预约信息、费用信息等。预约信息可以是通过电动汽车导航系统与充电站预约。
本申请具体执行步骤如下:
第一步:可行方案规划
1)获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围。根据道路拓扑结构,寻找路径搜索范围内,起点到达终点的方式。在一般导航路径规划的基础上,特别考虑经过 充电站的路径,搜索获得从驾驶起点到达驾驶终点的多个连接路径。连接路径可以表示为“起点-充电站1-充电站2……充电站n-终点-不同行驶方向上的最近充电站”。
2)针对步骤1的路径,基于不同驾驶方式,即基于不同驾驶模式和空调模式的组合进行可达性分析,获得可行的连接路径作为可行路径。可达性分析的详细过程如下:
2.1)针对每个连接路径,根据路况预测和道路等级,获得连接路径的不同路径的典型工况;
2.2)获取环境参数后,根据步骤2.1)获得的典型工况,分析给定驾驶模式和空调模式(驾驶方式)下的每一路径的行驶能耗;
2.3)根据电动汽车的电池能量模型,基于当前SOC值和每一路径的行驶能耗,预测每一路径的SOC值;并根据以下步骤,获得该连接路径对应的充电位置和充电量组成,即获得该连接路径的充电模式:
2.3.1)遇到充电站时,如果当前SOC较高(如大于80%)且足以到达规划路径终点或下一个充电站,则不充电;
2.3.2)非连接终点之前的最后一个充电站,进行充电时的操作为:快充则充到自动结束;慢充可以选择充到恒流阶段结束或充满;
2.3.3)到达终点之前的最后一个终点站:根据达到终点后达到设定目标SOC为目标进行充电。步骤2.3.1)~2.3.3),体现了本方法中预设的充电策略。
2.4)如果可以达到终点且剩余电能足以到达最近的充电站,则路径可行。获取在到达终点后,电动汽车的SOC值足以供电动汽车到达最近的充电站的连接路径作为可行路径。
3)计算的行驶时间作为质量评估参数,用于评价搜索范围的质量;也可以计算行驶距离或行驶用电量作为质量评估参数。
4)扩大搜索范围。在扩大的范围内,重新搜索可行路径,若搜索到的新可行路径的质量评估参数与之前已搜索到的最优可行路径的评估参数相差在一定阈值范围内则停止搜索。例如,计算新获取所有可行路径的行驶时间,如果与前一搜索范围的最优可行路径的行驶时间相差在一定阈值内(例如5%),则搜索结束。
第一步结果:不同驾驶方式下的可行方案集合{Route}ij和充电模式集,其中i代表驾驶模式(1为运动模式,2是正常模式,3是经济模式),j代表空调模式(1是舒适模式,2是节能模式,3是最低能耗模式),包括多个可行路径。充电模式集包括每个可行路径对应的充电位置和充电量等信息。
第二步:建立评估指标
Index=k1×总时间+k2×总费用+k3×电池寿命衰减+k4×到达终点后的行驶能力
k1、k2、k3、k4均为用户权重参数,可以是根据用户输入的数据设定的,也可以是根据用户的历史驾驶数据自学习得到的。
总时间指:从起点到终点的时间,即行驶时间、充电等待时间、充电时间之和;
总费用指:从起点到终点的费用=从起点到终点的充电站预约等待费用+充电费用(由充电电量、充电时间计算获得)-到达终点剩余电量的价值(根据剩余电量与电费计算获得,目的地是家庭或特殊充电优惠单位,则此价值较低)+道路通行费用;
电池寿命衰减计算公式:k31×深度放电区间总放电电量+k32×直流充电电量;其中,k31和k32为预设的权重参数;
到达终点后的行驶能力是根据电动汽车在终点的SOC划分的多个级别。分为以下级别:四级:能到达最近充电站,三级:能到达所有方向的最近充电站,二级:能在SOC健康区间内到达所有方向的最近充电站,即深度放电区间放电量为0;一级:达到用户设定的SOC,如果用户设定SOC比二级对应的SOC更低,则将二级对应的SOC上调一定幅度作为一级的SOC目标值。
此外,在进行可到达性分析时,用户也可将某一指标设为必须达到的约束条件,如到达终点后的行驶能力必须达到一级。
第三步,最优方案推荐
1)获取不同驾驶方式下的可行路径集以及对应的充电模式集;
2)基于1)的方案集合,分别计算每一种可行路径的评估指标。
3)获取每种驾驶方式下的评估指标最小的出行方案,作为对应驾驶模式下的推荐路径;
4)比较不同驾驶模式下的推荐路径,获得评估指标最小的驾驶模式、空调模式和出行方案组合。
5)推荐两种结果供用户选取:第一种是当前驾驶方式(驾驶模式和空调模式组合)下的推荐方案;第二种即步骤4)的结果,输出该最优结果最为最优的可行路径,并输出对应的驾驶方式,建议用户更换驾驶模式和空调模式。
6)按照用户确定的驾驶模式和空调模式下的推荐方案提供导航服务。
本申请综合考虑道路工况和电动汽车的车辆特性,可以根据用户的需求指标,例如总时间最短、电池的用电量最低等,计算对应偏重的评估指标,从而筛选获得最优的可行路径以及驾驶方式即驾驶模式和空调模式的组合,综合考虑道路工况、驾驶方式对电动汽车的用电影响,可以充分利用电动汽车的车辆特性,得到最优路径规划和驾驶方式规划结果,优化结果好。
以上是对本申请的较佳实施进行了具体说明,但本申请创造并不限于所述实施例,熟悉本领域的技术人员在不违背本申请精神的前提下还可做出种种的等同变形或替换,这些等同的变型或替换均包含在本申请权利要求所限定的范围内。

Claims (10)

  1. 一种电动汽车最优路径和驾驶方式的规划方法,其特征在于,包括步骤:
    基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
    获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
    基于计算的评估指标,进行最优路径和驾驶方式规划;
    所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
  2. 根据权利要求1所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
    获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
    获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接路径;
    针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
    计算所有可行路径的质量评估参数;
    扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
    所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
  3. 根据权利要求2所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所述针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径的步骤,具体包括:
    针对每个连接路径,根据路况预测和道路等级,获得连接路径的不同路径的典型工况;
    估算获得电动汽车在不同驾驶方式下的每个路径的行驶能耗;
    基于电动汽车的电池能量模型,预测每个路径终点的SOC值;
    针对经过充电站的路径,按照预设的充电策略确定充电后的SOC值;
    根据每个连接路径终点的SOC值判断是否是可行路径,进而获取可行的连接路径作为可行路径。
  4. 根据权利要求2所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所 述搜索结束条件为新的质量评估参数与原最优质量评估参数相差在一定阈值范围内,所述质量评估参数为行驶时间、行驶距离或行驶用电量。
  5. 根据权利要求1所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所述获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标的步骤,其具体为:
    获取用户权重系数后,根据下式,计算不同驾驶方式下的每个可行路径的评估指标:
    Index=k1×总时间+k2×总费用+k3×电池寿命衰减+k4×到达终点后的行驶能力
    上式中,Index表示评估指标,k1、k2、k3、k4均为用户权重参数。
  6. 根据权利要求5所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所述总时间是指可行路径从起点到终点的行驶时间、充电等待时间和充电时间之和,所述总费用为可行路径从起点到终点的充电站预约等待费用、充电费用和道路通行费用之和再减去到达终点时剩余电量的价值后所得到的费用;
    所述电池寿命衰减通过下式计算获得:k31×深度放电区间总放电电量+k32×直流充电电量;其中,k31和k32为预设的权重参数;
    所述到达终点后的行驶能力是根据电动汽车在终点的SOC划分的多个级别,一级代表到达终点后的行驶能力最强,依次递减。
  7. 根据权利要求1所述的电动汽车最优路径和驾驶方式的规划方法,其特征在于,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
    基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
    获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
  8. 一种电动汽车最优路径和驾驶方式的规划系统,其特征在于,包括处理器和存储设备,所述存储设备存储有多条指令,所述指令由处理器加载并执行以下步骤:
    基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集;
    获取用户权重系数后,计算不同驾驶方式下的每个可行路径的评估指标;所述用户权重系数为用户预设的或者根据用户驾驶习惯自动获取的系数;
    基于计算的评估指标,进行最优路径和驾驶方式规划;
    所述驾驶方式指电动汽车的驾驶模式和空调模式的组合。
  9. 根据权利要求8所述的电动汽车最优路径和驾驶方式的规划系统,其特征在于,所 述基于可到达性分析,筛选获得电动汽车在不同驾驶方式下的可行路径集以及对应的充电模式集的步骤,具体包括:
    获取电动汽车的驾驶起点和驾驶终点,确定路径搜索范围;
    获取路径搜索范围内的道路拓扑结构,搜索获得从驾驶起点到达驾驶终点的多个连接路径;
    针对每个连接路径,基于不同驾驶方式,进行可到达性分析后,获得可行的连接路径作为可行路径;
    计算所有可行路径的质量评估参数;
    扩大路径搜索范围后,重新搜索可行路径,最后计算新的质量评估参数,直到满足搜索结束条件后,根据获得的可行路径,得到电动汽车在不同驾驶方式下的可行路径集以及充电模式集;
    所述充电模式集由与每个可行路径对应的充电位置和充电量组成。
  10. 根据权利要求8所述的电动汽车最优路径和驾驶方式的规划系统,其特征在于,所述基于计算的评估指标,进行最优路径和驾驶方式规划的步骤,具体包括:
    基于计算的评估指标,筛选获得每种驾驶方式下的评估指标最小的可行路径作为推荐路径;
    获取电动汽车当前驾驶方式下的推荐路径作为初选的最优路径;同时,比对所有驾驶模式下的推荐路径,获得评估指标最小的推荐路径作为最优的可行路径后,向用户推荐该最优的可行路径和对应的驾驶方式。
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