Method and guidance-unit for guiding battery-operated transportation means to reconditioning stations

• US 9,103,686 B2
• Filed: 04/27/2011
• Issued: 08/11/2015
• Est. Priority Date: 05/06/2010
• Status: Active Grant

• Current Assignee: Leica Geosystems AG (Hexagon AB)
• Original Assignee: Leica Geosystems AG (Hexagon AB)
• Inventors: Pettersson, Bo
• Primary Examiner(s): Algahaim, Helal A
• Assistant Examiner(s): Smith, Jelani
• Application Number:
  US13/639,449
• Publication Number:
  US 20130046457A1
• Time in Patent Office: 1,567 Days
• Field of Search: 701/70, 701/54, 701/538, 701/533, 701/532, 701/439, 701/409, 701/33.7, 701/31.9, 701/31.4, 340/901, 340/457.1, 340/425.5, 320/101, 320/110, 320/116, 705/7.14, 705/7.13, 705/39, 705/34
• US Class Current: 1/1
• CPC Class Codes: B60L 1/003 to auxiliary motors, e.g. for pumps, compressors
• CPC Class Codes: B60L 1/02 to electric heating circuits
• CPC Class Codes: B60L 1/14 to electric lighting circuits
• CPC Class Codes: B60L 2200/12 Bikes
• CPC Class Codes: B60L 2200/18 Buses
• CPC Class Codes: B60L 2200/40 Working vehicles
• CPC Class Codes: B60L 2200/46 Vehicles with auxiliary ad-on propulsions, e.g. add-on electric motor kits for bicycles
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/66 Ambient conditions
• CPC Class Codes: B60L 2250/16 by display
• CPC Class Codes: B60L 53/80 Exchanging energy storage elements, e.g. removable batteries
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: B60L 8/003 Converting light into electric energy, e.g. by using photo-voltaic systems
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 : Electric charging stations
• CPC Class Codes: Y02T 90/14 : Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

Methods and apparatus for guiding a mobile transportation means of a set of transportation means to a selected reconditioning station of a set of reconditioning stations, comprising determining a position of the battery, determining a condition of the battery, forecasting a consumption characteristic of the transportation means, evaluating an achievable range of mobility of the transportation means, assigning the selected reconditioning station of the set of reconditioning stations, which is located within the range of mobility of the transportation means along a path to a desired target and guiding the transportation means to the selected reconditioning station, an optimization of the assignment and/or the path is executed by a search algorithm for assigning the set of transportation means to the set of reconditioning stations and batteries, based on actual and/or forecasted information about multiple entities of the sets of transportation means, stations and batteries as well as their conditions.

Claims

1. A method for guiding a mobile transportation means of a set of transportation means, to a selected reconditioning station of a set of reconditioning stations which transportation means have each associated a powering battery of a set of batteries, in a geographically distributed arrangement of the transportation means and the reconditioning stations, comprising the steps of:

determining a position of the battery;

determining a condition of the battery;

forecasting a consumption characteristic of the transportation means;

evaluating an achievable range of mobility for the transportation means according to its consumption characteristic and the condition of its powering battery;

assigning the selected reconditioning station, out of the set of reconditioning stations, to the transportation means for charge or substitute of the powering battery, which is located within the range of mobility of the transportation means along a path to a desired target;

guiding the transportation means to the selected reconditioning station, wherein;

an optimization of the assignment or the path is executed by a search algorithm for assigning the set of transportation means to the set of reconditioning stations and batteries, based on actual or forecasted information about multiple entities of the sets of transportation means, stations and batteries as well as their conditions;

the optimization being done on a cost function combining at least;

a spatial distribution of the set of batteries over the reconditioning stations, and a forecast of desired stocks;

a travelling time of transportation means;

an overall energy consumption of one or multiple entities of the set of transportation means; and

a priority of one or multiple entities of the set of transportation means,

and wherein

the assignment of the selected reconditioning station is based on an environmentally dependent driving behavior, and the environmentally dependent driving behavior is represented by a model or historical data with a forecasting of the consumption characteristic according to an altitude profile of the path or a section of the path from a three-dimensional terrain information and the characteristics of a driver of the transportation means,

the condition of the battery comprises at least a subset of;

temperature, and

actual state of usage;

the condition of the reconditioning station comprises at least actual or forecasted information about;

a restriction of usage for public, private or certain other groups;

a battery stock at the station;

a booking or reservation information of the battery stock or the reconditioning capacity; and

ambient temperature conditions are taken into account in a prediction of consumption.

2. A method according to claim 1, wherein the search algorithm is based on the principles of:

Heuristics;

incomplete search;

genetic algorithms;

tabu search;

simulated annealing;

Greedy-Algorithm;

Prim-Algorithm;

or

Kruskal-Algorithm.

3. A method according to claim 1, wherein the optimization is executed in a centralized way, by a stationary dedicated means, or a group of such dedicated means, or decentralized in each battery or transportation means, or by cloud computing.

4. A method according to claim 1, wherein the optimization includes a narrowing of the solution space while progressing towards the selected reconditioning station or a stopover location for each transportation means, resulting in an optimized path of travel.

5. A method according to claim 1, wherein the path or a section of the path or assignment will be re-determined for the transportation means while travelling.

6. A method according to claim 1, wherein:

the path or a section of the path or assignment will be re-determined for the transportation means while travelling by involving a preliminary reservation of the assigned reconditioning station or an entity of battery stocked there, or

the path, section of the path or assignment will be fixed for the transportation means in advance of arrival at the reconditioning station by a fixed booking of the assigned reconditioning station or an entity of the set of batteries stocked there.

7. A method according to claim 1, wherein the selected reconditioning station is determined by further involving actual or forecasted information about:

a condition of the reconditioning stations of the respective set;

a condition and position of the at least one other battery out of the according set;

the path of the transportation means;

or

a path and allocation of at least one other transportation means out of the according set.

8. A method according to claim 7, wherein the condition of the reconditioning station comprises at least actual or forecasted information about:

a reconditioning capacity of the station;

a usage of the reconditioning capacity;

a number of available slots for reconditioning;

a price for reconditioning of the battery;

or

an information about leisure or business activities located around the station.

9. A method according to claim 1, wherein the actual information is exchanged by a communication network, either updated generally online while travelling or at least updated occasionally, in particular by a wireless communication means.

10. A method according to claim 1, wherein the actual information is exchanged by a communication network, either updated generally online while travelling or at least updated occasionally, by a wireless communication means.

11. A method according to claim 1, wherein the environmentally dependent driving behavior is represented by a model or historical data.

12. A method according to claim 1, wherein the path is determined according to:

the position of the transportation means;

the position of the desired target of the transportation means;

the position of the desired target of the transportation means according to a driver'"'"'s input;

the positions of the reconditioning stations;

information on a planned continuation after the desired target has been reached;

information on planned or demanded stopovers of the transportation means and expected times for stopovers;

or

information from traffic information or traffic guidance systems.

13. A method according to claim 1, wherein the condition of the battery comprises at least a subset of:

actual capacity;

full capacity;

estimated remaining power;

estimated time to recharge to full capacity;

age;

date of production;

elapsed operating hours;

elapsed regeneration cycles;

health or weighted age, according to a usage-history;

required maintenance times;

life expectancy;

load history;

desired recharging characteristics;

max recharging power;

desired supplying characteristics;

max peak power;

max continuous power;

or

manufacturer, type, model, serial number.

14. A method according to claim 1, wherein the model used for the forecast of the consumption characteristic comprises at least a subset of:

the path or a section of the path of the transportation means, in particular comprising a stop and go zone, a road type and a road condition;

a consumption characteristic of the transportation means, in particular comprising a characteristic energy consumption of the transportation means, a class of transportation means, a weight or a load;

a drivers characteristic of an actual driver of the transportation means, either human or non-human;

an information history of other transportation means or drivers previously travelling the path;

a forecast of holiday, seasonal or rush-hour conditions to be expected on the path;

or

an information from traffic information or traffic guidance systems.

15. A guidance unit comprising:

a localization unit for determination of a position datum;

a battery observation unit, for determination a condition of a battery;

a range evaluation unit for determination of an achievable range according to the condition of the battery and to a forecast of a consumption prediction of a transportation means associated to the battery; and

a guiding unit, in particular comprising an interface to a human user, for assigning an entity out of a set of reconditioning stations to the transportation means and providing information for guiding the transportation means to the entity of reconditioning station along a path to a target for the transportation means,

wherein the guidance unit is configured for guiding a mobile transportation means of a set of transportation means, to a selected reconditioning station of a set of reconditioning stations which transportation means have each associated a powering battery of a set of batteries, in a geographically distributed arrangement of the transportation means and the reconditioning stations, comprising the steps of;

determining a position of the battery;

determining a condition of the battery;

forecasting a consumption characteristic of the transportation means;

evaluating an achievable range of mobility for the transportation means according to its consumption characteristic and the condition of its powering battery;

assigning the selected reconditioning station, out of the set reconditioning stations, to the transportation means for charge or substitute of the powering battery, which is located within the range of mobility of the transportation means along a path to a desired target; and

guiding the transportation means to the selected reconditioning station,

wherein;

an optimization of the assignment or the path is executed by a search algorithm for assigning the set of transportation means to the set of reconditioning stations and batteries, based on actual or forecasted information about multiple entities of the sets transportation means, stations and batteries as well as their conditions;

the optimization is done on a cost function combining at least;

a spatial distribution of the set of batteries over the reconditioning stations, and a forecast of desired stocks;

a travelling time of transportation means;

an overall energy consumption of one or multiple entities of the set of transportation means; and

a priority of one or multiple entities of the set of transportation means; and

the assignment of the selected reconditioning station is based on an environmentally dependent driving behavior, and the environmentally dependent driving behavior is represented by a model or historical data with a forecasting of the consumption characteristic according to an altitude profile of the path or a section of the path from a three-dimensional terrain information and the characteristics of a driver of the transportation means, the condition of the battery comprises at least a subset of;

temperature, and actual state of usage; and

the condition of the reconditioning station comprises at least actual or forecasted information about;

a restriction of usage for public, private or certain other groups;

a battery stock at the station;

a booking or reservation information of the battery stock or the recondition capacity; and

ambient temperature conditions are taken into account in a prediction of consumption.

16. A tangible non-transitory computer program product comprising program code stored on a machine-readable storage medium, for carrying out:

a method for guiding a mobile transportation means of a set of transportation means, to a selected reconditioning station of a set of reconditioning stations which transportation means have each associated a powering battery of a set of batteries, in a geographically distributed arrangement of the transportation means and the reconditioning stations, comprising the steps of;

determining a position of the battery;

determining a condition of the battery;

forecasting a consumption characteristic of the transportation means;

evaluating an achievable range of mobility for the transportation means according to its consumption characteristic and the condition of its powering battery;

assigning the selected reconditioning station, out of the set of reconditioning stations, to the transportation means for charge or substitute of the powering battery, which is located within the range of mobility of the transportation means along a path to a desired target; and

guiding the transportation means to the selected reconditioning station,

wherein;

an optimization of the assignment or the path is executed by a search algorithm for assigning the set of transportation means to the set of reconditioning stations and batteries, based on actual or forecasted information about multiple entities of the sets of transportation means, stations and batteries as well as their conditions;

the optimization done on a cost function combining at least;

a spatial distribution of the set of batteries over the reconditioning stations, and a forecast of desired stocks;

a travelling time of transportation means;

an overall energy consumption of one or multiple entities of the set of transportation means; and

a priority of one or multiple entities of the set of transportation means;

the assignment of the selected reconditioning station is based on an environmentally dependent driving behavior, and the environmentally dependent driving behavior is represented by a model or historical data with a forecasting of the consumption characteristic according to an altitude profile of the path or a section of the path from a three- dimensional terrain information and the characteristics of a driver of the transportation means;

the condition of the battery comprises at least a subset of;

temperature; and

actual state of usage; and

the condition of the reconditioning station comprises at least actual or forecasted information about;

a restriction of usage for public, private or certain other groups;

a battery stock at the station;

a booking or reservation information of the battery stock or the reconditioning capacity; and

ambient temperature conditions are taken into account in a prediction of consumption.