Patent Application
20130024112
The present disclosure relates to generating a recommended travel route within a vehicle having an electric powertrain.
Vehicle navigation systems are networked computer devices which use global positioning data to accurately determine the position of a device or of a vehicle on a geocoded map. A server or host machine typically calculates a recommended travel route through a road network using a shortest distance or quickest drive time algorithm, and using associated geospatial, topographical, and road network information. A navigation system may also provide turn-by-turn directions to a destination in the form of text and/or speech, with corresponding route traces displayed on a map. However, conventional navigation systems may perform in a less than optimal manner when used in conjunction with emerging battery electric and extended-range electric vehicle designs having an electric powertrain.
A navigation system and method of using the same are disclosed herein for use in an electric vehicle, e.g., a battery electric vehicle (BEV), an extended-range electric vehicle (EREV), or any other vehicle having an electric powertrain. The present navigation system calculates and displays a recommended electric vehicle (EV) travel route, i.e., a route travelled solely using electric power, in part by using a directed graph (digraph) approach. That is, a digraph is used to evaluate all of the possible ways of traveling to a destination within a road network. The recommended EV travel route passes through the road network to the destination, with automatic modification of the travel route to charging stations along the way, i.e., charging waypoints, when the vehicle cannot reach its destination on remaining battery power.
That is, the present navigation system creates the road network and populates the same with all known charging waypoints. These charging waypoints are identified as nodes within the road network, as are the various road intersections and other demarcated points of interest within the road network. The various possible ways of moving through the road network with one or more battery charging events are then generated as a list of or vertices or nodes. One node in the map is always the present location of the vehicle within the road network.
For each additional node on the map, a host machine of the navigation system generates a list of “next-possible” nodes, that is, nodes which are reachable by the vehicle under existing battery power from its present position node. Existing routing algorithms and databases may be reused to automatically insert more charging waypoints as needed as the vehicle travels through the road network. This may be accomplished by modifying the representation of the vertices/nodes of the map to represent the sequence of charging events as they occur as the vehicle negotiates its way along the travel route.
Additionally, a calculation is performed by the host machine for each evaluated node to determine the remaining EV range of the vehicle. The host machine restricts the list of next-considered node(s), i.e., the nodes evaluated in the next iteration, to only include those nodes which can be reached in the initial range, and with any scheduled charging stops as needed. The routes thus largely eliminate EV range anxiety, a term used to describe the concern of depleting the battery prior to arriving at a charging waypoint or the final trip destination.
If a recommended travel route is available which does not require a charging event, this route may be selected by the host machine using, for instance, existing shortest distance or fastest drive time-routing algorithms, even if charging waypoints happen to be available along the travel route. Thus, the opportunity cost of charging is always considered, with a charging event processed as a new range constraint. The map is thus “layered” and evolves as the vehicle moves through the road network, with memory retained by the host machine of all previous charging events and map layers, and with a newly generated map being associated with each new charging waypoint.
In particular, a navigation system is disclosed for a vehicle having a battery and a traction motor. The system includes a display device, as well as a host machine in communication with a map database. Road network information from the database includes nodes, with each node describing a point within the road network. At least some of the nodes describe charging waypoints.
The host machine is configured for recording a trip destination, determining a remaining state of charge (SOC) of the battery, and calculating a remaining electric vehicle (EV) range of the vehicle from each node using the remaining SOC. The host machine also generates a first recommended electric-only (EV) travel route to the destination using one of a shortest distance and a shortest travel time approach when the destination lies within the remaining EV range from a node describing the present location of the vehicle, and generates a second recommended EV travel route to the destination through one or more charging waypoints when the destination lies outside of the remaining EV range. The route is displayed via the display device.
A vehicle is also disclosed herein having a traction motor, battery, and the navigation system essentially as noted above.
A method for using the navigation system includes receiving a trip destination using a display device of the system, recording the trip destination using a host machine in communication with the display device, and determining a remaining state of charge (SOC) of the battery using the host machine. The method also includes calculating a remaining electric vehicle (EV) range of the vehicle as a function of the remaining SOC for every node of the plurality of nodes, and generating a first recommended electric-only (EV) travel route to the destination using one of a shortest distance and a shortest travel time approach when the destination lies within the remaining EV range from a node describing the present location of the vehicle. A second recommended EV travel route is generated to the destination through the charging waypoint(s) when the destination lies outside of the remaining EV range. The method also includes displaying one of the first and second recommended EV travel routes via the display device.
The above features and advantages and other features and advantages of the present invention are readily apparent from the following detailed description of the best modes for carrying out the invention when taken in connection with the accompanying drawings.
Referring to the drawings, wherein like reference numbers correspond to like or similar components throughout the several figures, an example vehicle 10 is shown schematically in
The navigation system 50 automatically generates and displays a geocoded map with a recommended EV travel route using the present method 100, which may be embodied as a set of process instructions or computer code recorded in tangible/non-transitory memory 25. The navigation system 50 executes method 100 from memory 25 to generate the recommended EV travel route from a point of origin to a point of destination through a road network on the map, as explained below with reference to
The navigation system 50 may be embodied as a host machine, whether fixed or portable, as noted above. For example, the navigation system 50 may include one or multiple digital computers or data processing devices, each having one or more microprocessors or central processing units (CPU), read only memory (ROM), random access memory (RAM), electrically-erasable programmable read only memory (EEPROM), a high-speed clock, analog-to-digital (A/D) circuitry, digital-to-analog (D/A) circuitry, and any required input/output (I/O) circuitry and devices, as well as signal conditioning and buffering electronics. While shown as a single device in
In the non-limiting illustrative embodiment shown in
A high-voltage DC bus may be electrically connected between the PIM 18 and the battery 22. A DC-DC power converter (not shown) may also be used as needed to increase or decrease the level of DC power to a level suitable for use by various DC-powered vehicle systems. When it is alternatively configured as an EREV, the vehicle 10 would include an internal combustion engine (not shown), which selectively generates engine torque to charge the battery 22. The traction motor 16 is connected to the transmission 14, e.g., one or more gear sets, clutches, etc., and to a set of drive wheels 32 via an output shaft 31. In other embodiments, the traction motor 16 or multiple traction motors 16 may be directly connected to one or more of the drive wheels 32.
Still referring to
The navigation system 50 displays a recommended EV travel route to a user via a display screen 52. The display screen 52 may graphically or visually display the recommended EV travel route via a graphical route/map trace and/or text-based driving directions, and/or may be further configured with an audio speaker 54 that broadcasts turn-by-turn driving directions as audible speech. Additional input data (arrow 15) to the navigation system 50 may include a detected or entered route origin and a recorded route destination, which may be entered prior to commencing the trip via the display screen 52 when the display screen 52 is configured as an optional touch screen device, or using any other suitable input device.
The navigation system 50 uses a directed graph (digraph) to generate the map shown in
Referring to
In
At step 102 of
At step 104, the navigation system 50 of
At step 106, the navigation system 50 calculates, for each node in the graph (G) having at least one charging point (C), as represented by point 41 in
At step 108, the navigation system 50 determines whether the vehicle 10 can reach the destination (point D) in EV mode. If so, step 110 is executed. If not, step 112 is executed.
At step 110, the navigation system 50 of
At step 112, the navigation system 50 of
A new map is thus generated with knowledge of all prior charging event(s) in memory, with previously searched nodes and additional newly searched nodes displayed as map information. In this manner a new map is associated with each charging waypoint, and the sequence of maps changes depending on how a user travels through the road network with respect to the charging stations.
That is, the navigation system 50 keeps track of all prior charging/refueling events and modifies the travel route as needed with each event. The loop of steps 106, 108, and 112 in
Referring to
geoGOAL=physical location of the goal for the route;
START=the location of the starting point and an empty list which represents available charging stations in a road network;
C, F=empty sets;
O=a set containing START;
cSCORE, gSCORE, hSCORE, fSCORE=mappings from a corresponding geographic location and a list of charging locations to a real number;
At step 202, cSCORE[START] is set equal to 0, as is gSCORE[START]. hSCORE is a heuristic estimate of the distance or cost to arrive at the location geoGOAL. Additionally, fSCORE[START] is set equal to hSCORE[START]. Let came from be a mapping from a geographic location and list of charging locations to another geographic location and list of charging locations. Once all variables have been set in this manner, the method 200 proceeds to step 204.
At step 204, the system 50 of
At step 206, the system 50 returns an indication that no path exists.
At step 208, a variable x is defined as the element in which set O minimizes the function fSCORE[X].
At step 210, the system 50 determines if the geographic location of (x) is the same as that of geoGOAL. If so, the method 100 proceeds to step 212. Otherwise, the method 100 proceeds to step 214.
At step 212, the system 50 returns a path from (x) to the start using came_from (see step 202) to construct the path.
At step 214, the system 50 removes (x) from set O, and adds (x) to set C, then proceeds to step 216.
At step 216, N is established as the set of neighbor nodes in the network which can be reached from (x) given the value of cSCORE(x). After N is established, the method 200 proceeds to step 218.
At step 218, the system 50 of
At step 220, an element (y) of set N is removed from set N.
At step 222, the system 50 determines if (y) is in set C. If so, step 218 is repeated. If not, the method 200 proceeds to step 224.
At step 224, the system sets a variable tentative gSCORE equal to gSCORE(x) plus the cost of travel from (x) to (y) plus a cost of charging, if charging is needed.
At step 226, the system 50 determines if (y) is in set O. If so, the method 100 proceeds to step 228. Otherwise, step 218 is repeated.
At step 228, (y) is added to set O. The method 100 then proceeds to step 232.
At step 230, the system 50 of
At step 232, the value of came from (y) is set equal to (x). The method 100 then proceeds to step 234.
At step 234, the value of gSCORE(y) is set equal to tentative gSCORE. hSCORE is set as a heuristic estimate of the cost of travel from (y) to the goal, and fSCORE(y) is set equal to the sum of gSCORE(y) and hSCORE(y).
At step 236, the system 50 determines if the transition from (x) to (y) involved charging. If so, the method 200 proceeds to step 238. Otherwise, the method 200 proceeds to step 240.
At step 238, cSCORE(y) is set to 0, and the method 200 repeats step 218.
At step 240, the value of cSCORE(y) is set to the sum of the cost of traveling from (x) to (y) and the value of cCOST(x). The method 200 then repeats step 218
Referring to
At step 304, the system 50 of
At step 306, an element (y) in set A is removed from A, and the method 300 proceeds to step 308.
At step 308, the system 50 determines if cSCORE(x) plus the cost of traveling from the location of (x) to the location of (y) is less than a range threshold. If so, the method 300 proceeds to step 310. Otherwise, the method 300 repeats step 304.
At step 310, a variable full_y is set as the combination of the physical location of (y) and the history of charging prior to arriving at (y).
At step 312, full_y is added to set N, and the method 300 repeats step 304.
At step 314, the system 50 of
At step 316, a variable new_charging_history includes the history of charging locations, with the location of (x) added to the list at this step.
At step 318, new_y is set as the combination of the location of (x) and new_charging_history (see step 316).
At step 320, the system 50 adds new_y to set N, and proceeds to step 322.
At step 322, the system 50 returns set N, which can be displayed as nodes on the route.
While the method described above can generate the routes, such a method can be improved on by only searching a portion of the map for feasible routes which satisfy range requirements between charge events. Such a method can be created starting from an algorithm like the A* algorithm which is commonly used to find the shortest path between two locations. As understood in the art, A* uses a best-first search and finds the least-cost path from a given initial node to one goal node out of one or more possible goals. A* does uses a distance-plus-cost heuristic function, ƒ(x), to determine the order in which the search visits nodes. The distance-plus-cost heuristic is a sum the path-cost function, i.e., the cost from the starting node to the current node g(x), and a “heuristic estimate” of the distance to the goal, h(x).
In modifying the A* algorithm, each point in the graph and travel represents not only the physical location of the node, but also the history of charging locations which preceded arrival at that particular node. Furthermore, because charging incurs a cost, some positive cost is assigned to each charging event when calculating the route. An illustrative flowchart for calculation of the route is shown in
A further complication in route generation for a problem like this is determining when a neighboring node, which is physically connected, cannot be reached because of a restriction like range or energy. This problem is solved by tracking the amount of range or energy which is consumed since the last charging event and using this information when selecting neighbors where travel can be happen. A flow chart for accomplishing this is illustrated in
Existing routing algorithms and databases may be reused to automatically insert charging waypoints as needed by modifying the representation of vertices (V) in the digraph to represent the physical location of the vehicle 10, plus the sequence of charging events which have occurred along the recommended travel route. A calculation is added for each evaluated node, wherein the remaining range of the vehicle 10 is determined. The navigation system 50 can restrict the next point considered to only include points which can be reached based on the initial range and charging stops. In this manner, range anxiety can be eliminated relative to conventional methods such as searching for a charging station along a best distance/best time travel route, or within a calibrated range thereof.
While the best modes for carrying out the invention have been described in detail, those familiar with the art to which this invention relates will recognize various alternative designs and embodiments for practicing the invention within the scope of the appended claims.
