Method and device for providing a navigation route with recommended loading

Abstract

System (1), comprising: a processor (3) designed to Discovering charging station locations within a predefined proximity of a route, which ultimately requires vehicle charging; Determining charging costs at each discovered station (203, 205), including at least battery damage costs resulting from charging; Presenting several charging stations (203, 205), including charging costs and an estimated total route time for each station (203, 205); Receiving a station selection; and Presenting a route that includes a stop at a station selected by a user (203, 205).

Description

[0001] The exemplary embodiments generally relate to a method and apparatus for providing a navigation route with recommended loading.

[0002] Owners and drivers of battery electric vehicles (BEVs) often have to deal with planning routes that exceed the range of energy stored in a traction battery at any given time, whether the battery is fully charged or not. Route planning for these owners is important because if the vehicle runs out of charge, the owner cannot simply carry back a "canister of charge" as might be done with a gasoline-powered vehicle. Charging stations typically come in two forms: alternating current (AC) long-duration charging stations and direct current (DC) fast charging stations. Although fast charging stations offer the advantage of reduced charging time, they typically cost more and tend to degrade the overall lifespan of a vehicle's battery.Replacing vehicle batteries can be incredibly costly, so vehicle owners are advised to use DC fast charging only occasionally to avoid costly vehicle maintenance.

[0003] US application US 2009 / 0 082 957 A1 generally relates to an electric vehicle having an electric motor that drives one or more wheels of the vehicle and is powered by a battery. The electric vehicle determines a status of a battery of the vehicle and a geographic location of the vehicle. The electric vehicle then identifies at least one battery charging station that the vehicle can reach based on the charge status of the battery of the vehicle and the geographic location of the vehicle. The electric vehicle displays the at least one battery charging station to a user of the vehicle.

[0004] DE 10 2011 075 872 A1, DE 10 2012 200 143 A1, DE 195 19 107 C1 and DE 10 2012 200 104 A1 disclose generic methods for operating a vehicle.

[0005] The objective technical problem to be solved can be seen as eliminating or at least mitigating the disadvantages of the prior art. This problem is solved by the subject matter of the independent patent claims.

[0006] In a first exemplary embodiment, a system includes a processor configured to discover charging station locations within a predefined proximity of a route requiring vehicle charging for completion. The processor is further configured to determine charging costs at each discovered station, including battery damage resulting from at least one charge. The processor is further configured to present multiple charging stations, including the charging costs and an estimated total route time for each station. The processor is also configured to receive a station selection and present a route including a stop at the selected station.

[0007] In a second exemplary embodiment, a computer-implemented method includes discovering charging station locations within a predefined proximity of a route requiring vehicle charging for completion. The method also includes determining charging costs at each discovered station, including at least battery damage resulting from charging. The method further includes presenting a plurality of charging stations, including the charging costs and an estimated total route time for each station. The method also includes receiving a station selection and presenting a route including a stop at the selected station.

[0008] In a third exemplary embodiment, a non-transitory computer-readable storage medium stores instructions that, when executed by a processor, cause the processor to perform a method that includes discovering charging station locations within a predefined proximity of a route requiring vehicle charging for completion. The method also includes determining a charging cost at each discovered station, including at least battery damage resulting from charging. The method further includes presenting a plurality of charging stations, including the charging cost and an estimated total route time for each station. The method also includes receiving a station selection and presenting a route that includes a stop at the selected station. Fig. 1 shows an exemplary vehicle data processing system; Fig. 2A and Fig. 2B shows illustrative examples of routes with charging stations; Fig. 3 shows an illustrative example of a vehicle interface; Fig. 4 shows an illustrative example of a charging station discovery process; and Fig. Figure 5 shows an example of a route optimization process.

[0009] As required, detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to scale; certain features may be exaggerated or minimized to show details of particular components. The specific structural and functional details disclosed herein are, therefore, not to be considered limiting, but merely as a representative basis for teaching those skilled in the art to variously employ the present invention.

[0010] Fig. 1 shows an exemplary block topology for a vehicle-based computing system 1 (VCS) for a vehicle 31. An example of such a vehicle-based computing system 1 is the SYNC system manufactured by THE FORD MOTOR COMPANY. A vehicle enabled with a vehicle-based computing system may include an in-vehicle visual front-end interface 4. The user may also be able to interact with the interface if it is equipped with a touchscreen, for example. In another exemplary embodiment, the interaction occurs through button presses, audible speech, and speech synthesis.

[0011] At the Fig. In the exemplary embodiment shown in FIG. 1, a processor 3 controls at least part of the operation of the vehicle-based computing system. The processor is provided in the vehicle and allows onboard processing of instructions and routines. Furthermore, the processor is connected to both non-persistent memory 5 and persistent memory 7. In this exemplary embodiment, the non-persistent memory is random access memory (RAM), and the persistent memory is a hard disk drive (HDD) or flash memory.

[0012] The processor is also equipped with a number of different inputs that allow the user to connect to the processor. In this exemplary embodiment, a microphone 29, an auxiliary input 25 (for input 33), a USB input 23, a GPS input 24, and a BLUETOOTH input 15 are all provided. An input selector 51 is also provided to allow a user to switch between different inputs. Inputs to both the microphone and auxiliary connectors are converted from analog to digital by a converter 27 before being passed to the processor. Although not shown, numerous of the vehicle components and auxiliary components in communication with the VCS may utilize a vehicle network (such as, but not limited to, a CAN bus) to pass data to and from the VCS (or components thereof).

[0013] Outputs of the system may include, but are not limited to, a visual display 4 and a speaker 13 or stereo output. The speaker is connected to an amplifier 11 and receives its signal from the processor 3 through a digital-to-analog converter 9. Outputs may also be provided to a remote BLUETOOTH device, such as the PND 54, or a USB device, such as the vehicle navigation device 60, along the bidirectional data streams shown at 19 and 21, respectively.

[0014] In an exemplary embodiment, system 1 uses BLUETOOTH transceiver 15 to communicate 17 with a user's mobile device 53 (e.g., a mobile phone, smartphone, PDA, or any other device with connectivity to a wireless remote network). The mobile device can then be used to communicate 59 with a network 61 external to vehicle 31, for example, through communication 55 with a cellular tower 57. In certain embodiments, tower 57 may be a Wi-Fi access point.

[0015] Example communication between the nomadic device and the BLUETOOTH transmitter / receiver is represented by signal 14.

[0016] Pairing of a nomadic device 53 and the BLUETOOTH transceiver 15 can be commanded by a button 52 or similar input. Accordingly, the CPU is notified that the onboard BLUETOOTH transceiver is being paired with a BLUETOOTH transceiver in a nomadic device.

[0017] Data may be communicated between the CPU 3 and the network 61, for example, using a data plan, data over voice, or DTMF tones associated with the nomadic device 53. Alternatively, it may be desirable to provide an onboard modem 63 having an antenna 18 to communicate 16 data between the CPU 3 and the network 61 over the voice band. The nomadic device 53 may then be used to communicate 59 with a network 61 external to the vehicle 31, for example, through communication 55 with a cellular tower 57. In certain embodiments, the modem 63 may establish communication 20 with the tower 57 for communication with the network 61. As a non-limiting example, the modem 63 may be a USB cellular modem, and the communication 20 may be cellular communication.

[0018] In an exemplary embodiment, the processor is equipped with an operating system that includes an API for communicating with modem application software. The modem application software can access an embedded module or firmware on the BLUETOOTH transceiver to establish wireless communication with a remote BLUETOOTH transceiver (such as one found in a nomadic device). BLUETOOTH is a subset of the IEEE 802 PAN (Personal Area Network) protocols. The IEEE 802 LAN (Local Area Network) protocols include Wi-Fi and have considerable cross-functionality with IEEE 802 PAN. Both are suitable for wireless communications in a vehicle. Other communication means that can be used in this area include free-space optical communications (such as IrDA) and non-standardized consumer IR protocols.

[0019] In another embodiment, the nomadic device 53 includes a modem for voiceband or broadband data communication. In the data-over-voice embodiment, a technique known as frequency division multiplexing may be implemented when the owner of the nomadic device can speak over the device while data is transferred. At other times, when the owner is not using the device, data transfer may utilize the entire bandwidth (in one example, 300 Hz to 3.4 kHz). Although frequency division multiplexing may be common for analog cellular communication between the vehicle and the Internet and continues to be used, it has largely been replaced by hybrids of CDMA (Code Domain Multiple Access), TDMA (Time Domain Multiple Access), SDMA (Space-Domain Multiple Access) for digital cellular communication.These are all ITU IMT-2000 (3G) compliant standards and offer data rates of up to 2 mbs for stationary or walking users and 385 kbps for users in a moving vehicle. 3G standards are now being replaced by IMT-Advanced (4G), which offers 100 mbs for users in a vehicle and 1 gbs for stationary users. If the user has a data plan associated with the nomadic device, it is possible that the data plan enables broadband transmission and the system could use a much larger bandwidth (thus speeding up data transfer). In yet another embodiment, the nomadic device 53 is replaced by a cellular communication device (not shown) installed in the vehicle 31. In yet another embodiment, the ND 53 may be a wireless local area network (LAN) device, for example (and without limitation) via an 802.11g network (i.e.WiFi) or a WiMax network.

[0020] In one embodiment, incoming data can be routed through the nomadic device via Data-over-Voice or data plan, through the onboard BLUETOOTH transceiver, and into the vehicle's internal processor 3. In the case of certain temporary data, the data can be stored, for example, on the HDD or other storage medium 7 until the data is no longer needed.

[0021] Additional sources that may be connected to the vehicle include a personal navigation device 54 having, for example, a USB connection 56 and / or an antenna 58, a vehicle navigation device 60 having a USB 62 or other connection, an onboard GPS device 24, or a remote navigation system (not shown) having connectivity to the network 61. USB is one of a class of serial networking protocols. IEEE 1394 (Firewire), EIA (Electronics Industry Association) serial protocols, IEEE 1284 (Centronics Port), S / PDIF (Sony / Philips Digital Interconnect Format), and USB-IF (USB Implementers Forum) form the backbone of device-to-device serial standards. Most of the protocols can be implemented for either electrical or optical communication.

[0022] Furthermore, the CPU could be in communication with a variety of other auxiliary devices 65. These devices may be connected by a wireless 67 or wired 69 connection. The auxiliary device 65 may include, but is not limited to, personal media players, wireless healthcare devices, portable computers, and the like.

[0023] Additionally, or alternatively, the CPU could be connected to a vehicle-based wireless router 73, for example, using a WiFi transceiver 71. This would allow the CPU to connect to remote networks within range of the local router 73.

[0024] In addition to example processes being performed by a vehicle computing system located in a vehicle, in certain embodiments, the example processes may be performed by a computing system in communication with a vehicle computing system. Such a system would be, for example, but not limited to, a cellular phone or other wireless device, or a server or other remote computing system connected by the wireless device. Collectively, such systems may be referred to as a vehicle-associated computing system (VACS). In certain embodiments, depending on the particular implementation of the system, certain components of the VACS may perform certain portions of a process. E.g.and without limitation, if a process includes a step of sending or receiving information with a paired wireless device, then the wireless device is likely not performing the process, as the wireless device would not "send and receive" information with itself. Those of ordinary skill in the art will understand when it is not appropriate to apply a specific VACS to a given solution. All solutions contemplate that at least the vehicle computing system (VCS) located within the vehicle itself is capable of performing the example processes.

[0025] As previously mentioned, fast charging can be detrimental to a vehicle battery. In the exemplary embodiments, the charging route algorithm accordingly takes into account the value differences between AC charging stations and DC fast charging stations. Furthermore, DC charging stations can typically only deliver up to 80% full charge, so if more charge is needed, AC charging may be recommended.

[0026] Fig. 2A and Fig. 2B show illustrative examples of routes with charging stations. In this example, the system has solved for optimal charging routes, considering charging stations as waypoints. These solutions are then subjected to possible weighting rules to determine, for example, a route optimized in terms of cost or time.

[0027] Several routes can reach the destination 207 from the origin 201, which have identical travel times. An example of such a route is shown in Fig. 2A with the solid line. In this example, all possible routes are too long to reach the destination with a single charge, so the routing system will calculate a stop at one of the charging stations between the origin and the destination. In this example, there are two stations: Station 1, an AC charging station 203, and Station 2, which is a DC charging station 205.

[0028] In this example, the routing algorithm solves all routes with the same distance and selects a route that, in this case, runs adjacent to AC station 203. This route is shown as route 211. If DC charging were desired, route 213 could be selected; although in this example, this route is longer than route 211. However, since DC charging is faster, route 213 could be faster overall. In contrast, AC route 211 could be longer but cost less or be better for battery life, so this route can be selected.

[0029] The process may need to discover stations within a predefined proximity of a route if it determines that charging is necessary. Charging stations are not as common as gas stations, and the proximity may need to be relatively large. Furthermore, the process can expand the proximity until at least two stations, or at least one station of each type, are discovered. A maximum expansion can also be applied, so that if the nearest DC charging station is, for example, 50 miles away in the wrong direction, it is not presented as a choice.

[0030] MIT recently discovered algorithms for discovering an optimal route when the total route time is considered a stochastic variable. Other stochastic variables include battery damage, the energy cost of a route, etc. One such algorithm, developed as part of a Ford-MIT Alliance project for energy-efficient routing, is called Robust Routing. Other suitable route-finding methods can also be used.

[0031] The exemplary embodiments can be used for any number of problems where there are multiple acceptable routes based on travel time and energy consumption. Energy consumption can be calculated using physics-based black-box models with learned parameters. Data such as, but not limited to, predicted temperature and other factors affecting battery charge can be considered along the route.

[0032] Fig. 3 shows an illustrative example of a vehicle interface depicting route presentation. This example interface 301 could, for example, be presented as an in-vehicle display. After a user specifies a destination, the routing process could determine whether a vehicle with a current charge can complete the trip (one-way or round-trip). This can be determined using any known suitable algorithm. If the vehicle can complete the trip with a single current charge, the process can simply present a route, or it can present a map and a choice to find an alternative route with charging (if the user has more time and wishes to charge the vehicle).

[0033] However, if charging is necessary, the process can Fig. 3. A notification may be presented 303 indicating that charging is necessary. This could, for example, also indicate charging optional, charging not necessary, etc. In another example, this could show an estimated remaining charge for a route over which charging is not necessary, or show the probability of reaching a destination.

[0034] In this example, the interface shows options for both AC and DC charging. This is because both stations are within a reasonable distance and / or on a planned route. In another example, the user could have the process show only AC charging, reserving DC charging for situations when time is critical (it could be available on demand). Or, in another example, the process could present DC options if the system has determined that DC charging has not been used too frequently. Both route options 305 are shown here for user review and selection.

[0035] With AC charging option 307, the process demonstrates the impact on battery life 311, which in this case is low. Although DC charging has a greater impact on battery life, manufacturers agree that limited DC charging is acceptable. For this reason, DC charging option 309 may also demonstrate a "low" impact on battery life 313 until the DC charging process is used too frequently. Then, the impact state may change to "high" until an appropriate amount of time has passed. DC charging and AC charging have different impacts on battery integrity. Both can be detrimental to battery life. There may be an optimal balance of DC and AC charging. Because a battery also naturally degrades due to other factors, a battery can only be assigned a certain average lifetime.Accordingly, it may be acceptable to use DC charging a certain number of times over the battery's lifetime. DC charging doesn't actually reduce the battery's impact if it is used only a limited number of times, but since the battery naturally degrades anyway, limited DC charging may not have a noticeable effect, and thus the impact can be classified as "minor."

[0036] The AC and DC routes may also show the estimated time to charge the battery 315. In this example, the process shows the time for a full charge 317 and a minimum charge 319. The minimum charge may be the limited amount of charge needed to complete the route. In this example, AC charging takes 185 minutes to full charge, and the limited amount of charge takes 60 minutes to complete 319. Similarly, full DC charging takes 30 minutes to complete 321, and the minimum DC charging takes 10 minutes to complete 323. Other example display items would be the estimated arrival time. The minimum charge may be based on a user-defined threshold (e.g., how close to "empty" the user is willing to go).

[0037] It can be seen that the time factor of DC charging can be significant compared to AC charging. This time can also include travel time from the route, and since DC charging takes much less time, the driver may be willing to drive farther / longer to obtain faster DC charging. Conversely, AC charging can be cheaper than DC charging. The cost section of the display 325 shows the cost of a full charge 327 and a minimum charge 329. In the DC section, the cost is fixed 331, since DC charging often has a fixed charge, as charging is much faster.

[0038] In addition, the user is presented with a choice to select which charging type is desired 333. The charging selection leads to the display of a route that is assigned to the specific charging type selected.

[0039] In addition to time and cost, the display can present the user with a list of points of interest (POIs) at the charging location to help them decide how to spend their time while charging. POIs could include restaurants, shopping, entertainment, etc., but can also be based on user-defined POI preferences. The user can be presented with a manageable number of choices optimized for different purposes, depending on a user profile and / or settings.

[0040] Fig. Figure 4 shows an illustrative example of a charging station discovery process. This process can lead to the presentation of both DC and AC charging options or only one of the options. The process can also lead to the selection of an optimal route / station, in conjunction with the parameters discussed for rule-based route selection as described with reference to Fig. 5 shown.

[0041] In this illustrative example, the process determines a route to the destination 401. This could be a standard navigation process, discovering an optimal route based on distance, time, or any other suitable parameter. After discovering the route, the process determines whether charging will be necessary to complete the route 405. This determination could be whether charging will be necessary for a one-way trip or whether charging will be necessary for a round-trip trip. If charging is not necessary, the process simply presents the route 403. This presentation may also include an option to view charging stations, if available. If charging stations are necessary to complete the trip, the process may find AC and / or DC stations along the route 407.

[0042] Typical fuel station discovery applications usually find fuel stations that are physically located along a route or within a very short distance of the route. Since charging stations are currently much less common, the station discovery algorithm can discover stations farther from the route. Since charging also typically takes much longer than refueling, the process may be less concerned with finding a station that's only 1-2 minutes from the route, as the time spent driving to the station may represent only a very small fraction of the total charging time.

[0043] In this example, the process also calculates the cost associated with each charging station 409. This cost may be based on known or available cost data for each station. Costs related to battery damage may also be included in this cost equation, as well as any other long-term costs to the vehicle (additional distance traveled, etc.). It also determines the total time 411, which may include charging time and time to / from the station (i.e., off-route time), which may also have a cost associated with it. In one example, the user may assign a personal value to time, so that time may be assigned a fixed value for a given user.

[0044] Additionally, in this example, the process determines whether excessive DC charging is being used 413. If DC charging is used excessively, battery degradation may be accelerated beyond normal degradation, and the resulting cost of DC charging, including that degradation, may be unacceptably high. If excessive DC charging is occurring, the process may set a notice to notify the user that DC charging should not be used as frequently 415. Any charging choices (both DC and AC) and / or any notices may then be presented to the user 417.

[0045] Because DC loading is faster and may be perfectly acceptable, the process may present DC choices first. The process may also present both choices together, or skip DC loading entirely if DC loading is used too frequently. In this example, the process waits to see which loading choice is chosen from the presented choices.

[0046] If the driver selects DC charging 421, the process checks if any advisory for DC charging has been set 423. If there is a DC charging advisory (i.e., the driver is overusing DC charging), the process may notify the driver that DC charging is not recommended at that time 425. The driver may then choose to maintain the DC selection or switch to the AC selection 427. If DC charging is maintained, or if no advisory is set (meaning that DC charging is not currently flagged as “overusing”), the process may present a route 431 that includes the selected DC charging station. If DC charging is not selected, the AC route is presented.

[0047] If the driver chooses not to maintain the DC route and switches to an AC route 429, the process also presents a route that includes AC charging.

[0048] In another example, the process can "optimize" a charging route for the driver. The optimization strategy can be based on a variety of factors. A driver can choose to optimize route selection based on DC, AC, cost, time, or any other suitable value. These driver preferences can be used in conjunction with other variables to present optimized route suggestions that include recommended charging stops.

[0049] Route selection can be based, for example, but not limited to, route feasibility (e.g., the driver is driving 15 miles in the wrong direction), route time costs, route energy costs, route battery damage costs, and charging session costs. Variables associated with both DC and AC charging can also be considered.

[0050] For DC charging, for example, but not limited to, the following would need to be considered: the cost of the charging session, the cost of battery damage, route travel time, estimated waiting times (charging stations often have waiting times assigned because vehicles take longer to "refuel"), and charging time. For AC charging, for example, but not limited to, the following would need to be considered: the amount of energy required to complete the route (minimum recommended charge), the price of the charge, the waiting time, and the charging time for the minimum charge. Both AC and DC charging optimization strategies can also consider the cost of the energy required to complete the route.

[0051] Using this and any other suitable data, the process can calculate optimized routes and their respective loading times. Based on the results, one or more routes can be presented to drivers for use.

[0052] When there are multiple choices for routes, costs, and time, functions for each route can be used to determine an "optimal route." Optimal routes can be different for each driver, depending on what is important to that driver. A busy working parent may want the shortest charging time, while a high school student with plenty of time but little money may want the cheapest charging option. Below are some example equations that can be used to calculate cost functions and time functions.

[0053] Along with cost and time, other factors can also be considered, including, but not limited to, cost and time weightings based on individual user preferences (W1, W2, etc.). For example, an overall route rating for each route can be calculated as follows.

[0054] In the example above, p1 is the probability that the vehicle will successfully navigate the route (e.g., it may be low if the charge is low and the station is far away). The process can also track and present DC fast charging usage to keep a user informed, including, but not limited to, DC charging speeds, state of charge at the beginning of DC sessions, average battery temperature during charging, and battery aging during charging. This profile can also show the driver how much total DC charging is estimated to remain at recommended usage, allowing the driver to personally decide when to use the limited recommended charging sessions.

[0055] Fig.Figure 5 shows an illustrative example of a route optimization process. In this illustrative example, the process receives the destination input from the driver 501. As before, this determines the "default" route to the destination, since charging may not be necessary. If charging is necessary based on estimated usage of remaining power, the process receives the constraints (time, cost, etc.) 503 associated with the current driver or vehicle. If the driver has not set any constraints, there may be a set of manufacturer-preset constraints that balance time and the potential for battery damage.

[0056] The costs of the various routes to charging stations and the destination can then be calculated 505. This results in base costs for the routes and can include the factors listed previously, as well as other appropriate factors. The base costs are then weighted according to driver preferences 507. In the example presented previously, the parent may value their time at $50 per hour, while the high school student may value their time at $10 per hour. Using these values, the costs can be weighted based on the total time, and a total cost for each route can be determined.

[0057] In another example, the parent can set "always present the fastest route unless DC charging above a threshold is harmful," and the high school student can set "always present the cheapest route unless DC charging is currently harmful," and the results can be weighted accordingly. In this example, the parent would receive DC charging choices until the possibility of damage reaches a threshold, and the high school student would receive AC charging choices once the recommended number of DC charging stops for the current point in the battery's life cycle has been used up, or, for example, when a maximum number of recommended DC charging stops for a day / week / month, etc., has been used up.

[0058] The process then selects a preferred route based on the route weighting 509 and ensures that any driver constraints have been met 511 (e.g., the vehicle may have a constraint set to "never use DC if damage is above a certain threshold"). If the constraints have not been met, the process selects a next-best route 513 and repeats until a route is discovered that satisfies all constraints. This route is then presented to the driver 515.

[0059] Although exemplary embodiments are described above, these embodiments are not intended to describe all possible forms of the invention. The words used in the specification are words of description, not limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Furthermore, the features of various implementation embodiments may be combined to form further embodiments of the invention.

Claims

[1] System (1), comprising: a processor (3) designed to Discovering charging station locations within a predefined proximity to a route, which requires vehicle charging to complete; Determining charging costs at each discovered station (203, 205), including at least battery damage costs resulting from charging; Presenting multiple charging stations (203, 205), including charging costs and an estimated total route time for each station (203, 205); Receiving a station selection; and Presenting a route that includes a stop at a station (203, 205) selected by a user. [2] A computer-implemented method comprising: discovering charging station locations within a predefined proximity of a route requiring vehicle charging for completion; determining charging costs via a vehicle-related computer at each discovered station (203, 205), including at least battery damage costs resulting from charging; presenting a plurality of charging stations (203, 205), including the charging costs and an estimated total route time for each station (203, 205); receiving a station selection and presenting a route including a stop at a station (203, 205) selected by a user. [3] A non-transitory computer-readable storage medium for storing instructions that, when executed by a processor (3), cause the processor (3) to perform a method that includes discovering charging station locations within a predefined proximity of a route requiring vehicle charging for completion, the method further comprising determining a charging cost at each discovered station (203, 205), including at least battery damage costs resulting from charging; and the method further comprising: presenting a plurality of charging stations (203, 205), including the charging cost and an estimated total route time for each station (203, 205), and receiving a station selection and presenting a route that includes a stop at a user-selected station (203, 205). [4] System (1) according to claim 1 or method according to claim 2, wherein the processor (3) is adapted to extend the predefined proximity until at least two charging stations (203, 205) have been discovered. [5] The system (1) of claim 1 or the method of claim 2, wherein the costs comprise stochastic charging costs. [6] System (1) according to claim 1 or method according to claim 2, wherein the costs comprise stochastic time costs. [7] System (1) according to claim 6 or method according to claim 6, wherein the time costs comprise stochastic travel time costs. [8] System (1) according to claim 6 or method according to claim 6, wherein the time costs comprise stochastic waiting time costs. [9] System (1) according to claim 6 or method according to claim 6, wherein the time costs comprise stochastic loading time costs. [10] System (1) according to claim 1, wherein the processor (3) is further configured, or method according to claim 2 comprising: Weighting charging costs and total route time based on user-defined preferences; Determining an overall rating for each station (203, 205) based on the weighted charging costs and total route time; and Presenting a station (203, 205) that has the lowest overall rating. [11] The system (1) of claim 10 or the method of claim 10, wherein the user-defined preferences comprise a time-assigned cost value. [12] The storage medium of claim 3, wherein the method further comprises: Weighting the charging cost and the total route time based on user-defined preferences; determining an overall rating for each station (203, 205) based on the weighted charging cost and the total route time; and presenting a station (203, 205) with the lowest overall rating.

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