EP2296934A1

EP2296934A1 - Adapter device and method for charging a vehicle

Info

Publication number: EP2296934A1
Application number: EP09779409A
Authority: EP
Inventors: Jakob Doppler; Alois Ferscha; Marquart Franz; Manfred Hechinger; Doris Zachhuber; Andreas Zeidler; Marcos JANSCH DOS SANTOS ROCHA
Original assignee: Siemens AG
Current assignee: Siemens AG
Priority date: 2008-07-08
Filing date: 2009-05-06
Publication date: 2011-03-23
Also published as: JP2011527556A; CN102089178A; US20110270476A1; WO2010003711A1; CN102089178B; JP5583124B2

Abstract

The invention relates to an adapter device (10) and a method for charging a vehicle (20), having an interface (11) for detecting internal vehicle operation data (30) containing factors which report driving habits according to lifestyle, and an interface (12) for detecting details related to the fluctuation of energy prices (31), having a device (13) for detecting and planning requirements, said device (13) being designed for deducing an energy requirement profile (40) using the vehicle operation data (30) and for producing a future requirement plan based on at least one of the named factors, said device (13) further being suitable for deducing the duration and frequency of vehicle down times (41, 41') by incorporating the requirement plan, having a charging optimizing device (14) which is designed for comparing the vehicle down times (41, 41') with the energy price fluctuation (31) details and for producing a vehicle charging plan (42) which is optimized for time and/or price and is based on the results of the comparison, and having a charging control unit (15) which is designed for charging the vehicle (20) from an energy storage (21) in a manner controlled by the charging plan.

Description

description

Adapter device and method for energetically charging a vehicle

The invention relates to an adapter device and a method for energetically charging a vehicle according to claim 1 or claim 8.

Caused by the increasing use of vehicles on the one hand and the scarcity of fossil fuels walls ^¬ hand, expected regulatory intervention in the automotive market are essential. The mandatory reduction of C0 ₂ emissions forces manufacturers to consider low-emission and more efficient drive technologies. This requirement is also reflected in the European Energy Efficiency Directive and the Action Plan. In its own words, the Commission wants to create markets for cleaner, smarter, safer and more energy-efficient vehicles and make the public aware of this.

On the consumer side, fossil fuel cost development is driving demand for and acceptance of lower-cost alternatives to traditional combustion-powered vehicles. These tendencies are confirmed by recent sales statistics of hybrid vehicles, which are considered by many to be precursors of purely electric vehicles, as can be seen in FIG. The figure shows a bar chart in which the number of electric motors sold (in millions

Units) in Europe, the US and Japan over the years 2005 to 2007 is plotted away, and the expected decision ^¬ development until the year 2012.

In addition to the reduced energy consumption, which can preferably be covered by renewable energy sources, ^¬ se technology brings currently mainly short-haul one we ^¬ sentliche reducing emissions. The plugin hybrid Concept promises subsequently even to emission-free ^¬ en operation by the short-term exclusive use of the electric motor, as shown in FIG. 2 This figure shows the CO ₂ emissions in g / km of a hybrid (Kangoo) and a plug-in hybrid vehicle (Cleanova) over a distance traveled in km. The individual curves (Curve 1) to C5 (Curve 5) apply to Cleanova II 2004 (33% ao - Wind 20g), Cleanova II 2004 (33% ao - Mix 650g), Cleanova II 2004 (66% ao - Mix 650g ), Kangoo 2006 (66% ao) and Kangoo 2006 (33% ao) in the order of the named curves.

However, vehicles with electric drive require repeated charging of the electrical energy storage, the battery. This charging takes place during the life of the vehicle, but at times unplanned and usually only when full charging is required. Such an approach is inefficient before the current energy price development.

It is therefore an object of the present invention to provide an optimized method for charging the energy store of a driving ^¬ stuff that is feasible, reliable and cost-effective particularly simple.

This object is solved tereinrichtung device side by means of a adapters specify an interface for detecting vehicle operating data including factors that lifestyle-dependent driving habits, and an interface for detecting information on the energy price ^¬ winding; a Bedarfserkennungs- and planning unit which is adapted to derive an energy demand profile from the Fahrzeugbe ^¬ operating data and for creating a future demand plan based on at least one of said factors, and further for deriving the duration and frequency of downtime of the vehicle by taking the demand plan is adjusted; a charging optimization unit for comparing the service life of the vehicle with the information on the development of energy prices and for creating a time and / or price optimized charging plan for the Vehicle is formed on the basis of the comparison result, and a charging control unit, which is designed for charging-dependent controlled charging of an energy storage of the vehicle.

The invention is based on the assumption that the use of a vehicle is subject to unexpected events as well as recurring utility models which can be detected and statistically evaluated on the basis of vehicle ^operating data. Based on this, an essential point of the invention

Establishment in that factors of lifestyle-dependent driving habits are used and considered to optimize the battery charging process and thus to reduce energy and costs against the background of current electricity price development. Basically, the device is not limited to an electrical application, but can be used in Ver ^¬ connection with all energy sources that are suitable for use in vehicles, such as especially gas.

Both theoretical and practical approaches and prototypical implementations for individual Teilproblema ^¬ tiken the described problem exist. However, with the inventive combination of demand determination and charging optimization with the inclusion of cost-efficient energy trading mechanisms, this has been solved for the first time.

In addition to the main feature of energy consumption per unit time and / or distance there are a number of other factors that can be incorporated into the modeling of driving habits. These include:

(i) Timing factors such as operating times and vehicle lives, journey start and end times, journey time and number of trips per day.

(ii) Routing and elevation profile of the individual routes. (iii) Purpose of the trip, such as the daily commute to work, leisure travel and private errands such as shopping.

(iv) Path chain patterns as recurring sequences of known routes and locations of the vehicle.

(v) environmental factors - e.g. Weather conditions and temperature that affect battery life and driving conditions.

(vi) Vehicle External context information such as traffic flow and disabilities and Kalenderinformatio ^¬ nen most unique occurring events holder of the vehicle.

Current traffic statistics show that significant utility models can be found in motorized private transport. For example, 51% of the Austrian population use a private vehicle as the favorite means of transport on ^weekday public transport. With an average number of 3.7 journeys of 13.5 km in rural areas, more than 50% of all journeys between 2.5 km and 50 km are made by car. The travel time is an average of 23 minutes. These data show that most journeys are short journeys that meet the above-mentioned benefits of electric vehicles.

Also with regard to the purpose of the journey and the periodicity of travel times can draw clear conclusions. 3 shows weekdays temporal profiles of the start ^¬ times of lanes per day depending on each trip purposes in cumulative curves C6 to C12. These correspond to journeys to the workplace, official or business

Rides, training rides, personal rides, private errands, shopping trips and recreational rides in the order of said turns. While Only 4% of all car journeys are made without a known purpose, the remaining 96% (52% work-related journeys, 28% private tending and shopping and 16% leisure activities) are made up of well-known utility models. Particularly in connection with the starting times of journeys to paths, good statements can be made about daily utility models. While journeys of workplaces and training are strongly represented, especially in morning traffic and at 16:30, the trade lanes are between 9:00 and 10:00 and leisure starts at 15:00 to 20:00 significant.

Further developments of the device according to the invention are specified in claims 2 to 7.

Thereafter, a particularly high prediction quality for the future use of a vehicle is achieved if an interface for capturing context information is provided, which describe the current situation of the vehicle in more detail and which affect the consumption, in particular profile data of a vehicle owner and / or traffic ^¬ information and / or weather information, and in which the demand detection and planning unit is formed in addition to Ablei ^¬ th an energy demand profile from this context information. With this additional information, the information base will be broadened, are derived from the current of ^¬ continuously and thus potentially future Fahrgewohnhei ^¬ th. This is a recognition and pre ^¬ hersagequalität increases for the use of the vehicle.

A particularly simple data coupling of the adapter device is achieved by at least one of the interfaces ^{Stel ¬} len for wireless acquisition of the data and / or information and / or information is formed. This corresponding metal connectors can be saved, which also does not need to press the groove ^¬ zer of the vehicle. Alternatively or simultaneously, a storage unit for storing the information on energy price development may be provided, which is kept ak ^¬ tually eg by regular software updates. Thus, the adapter device is independent of the connection to online trading platforms.

On the one hand, the adapter device can be designed as an external adapter between a power source and the energy store of the vehicle, as a result of which it can be used in a particularly versatile manner. Thus, the adapter may be e.g. used to energetically load several and different vehicles and does not have to be purchased again when buying another vehicle.

On the other hand, it may also be preferred if the Adap ^¬ tereinrichtung is designed as integrated executed with the vehicle adapter. This would not have to be purchased separately and carried separately.

In an advantageous use of the adapter device according to the invention, it is finally provided that it is used for the detection of vehicle usage patterns, in particular for recognizing driving habits and / or a driving style for calculating insurance models.

The above object is also achieved by a method comprising the steps of: capturing and storing vehicle include ^¬ internal operating data, the factors which indicate lifestyle-dependent driving habits; Deriving an energy demand profile from the vehicle operating data and compiling a future demand plan formed taking into account at least one of said factors; Deriving the duration and frequency of vehicle life using the demand plan; Recording an energy price development and comparing the service life of the vehicle with this energy price development, and creating a time and / or price optimized charging plan for the vehicle on the basis of the comparison result. An essential point of the method according to the invention consists in its simple structure, which on the one hand ensures high reliability and on the other hand is particularly easy and inexpensive to implement, for example in software, hardware or firmware.

Further developments of the method according to the invention are specified in the claims 9 to 17 and relate in particular to how the already described above factors le ^¬ bensstilabhängiger driving habits enter into this.

In an advantageous embodiment of the method there is initially provided that a demand plan for the vehicle is formed by determined from a history curve of a tatsächli ^¬ chen energy consumption of past trips daily commute times and their average duration ^¬ the. This is a simple model, from which the übli ^¬ chen periods of use of a vehicle can be derived, and vice versa can be closed on its service life. The actual energy consumption is continuously recorded and saved for evaluation.

In a further advantageous embodiment of the method, provision is made for daily operating times and / or route data to be used to form the demand plan for the vehicle. Thus, a temporal classification of the ride on start time, end time and duration is possible, creating a basis for more accurate prediction of the future use of the vehicle is created.

In a further advantageous embodiment, it is also provided that a demand plan for the vehicle is formed by the positions of the vehicle detected and derived therefrom spatial Wegkettenmuster that specify daily recurring trip goals and their sequential sequence. This will lock the route and any breaks, such as stops and longer parking. held, which allows a more accurate detection of life.

The method can additionally be specified by continuing to use external context information for describing a demand plan, which describe the current situation of the vehicle in more detail and which affect the consumption, in particular profile data of a vehicle owner and / or traffic information and / or weather information. formations. In this way, influences are also recorded which have an indirect effect on the energy requirement of the vehicle over any possible speed.

To ensure even with unexpected changes in driving habits egg nen reliable operation of the vehicle, it is advantageous if the user of the vehicle to a lack of energy charge, which was caused by unforeseeable rides, is informed of unscheduled Lademög ^¬ opportunities.

A particularly reliable operation of the vehicle is also ensured by the energy price development is detected by queries of an Internet-based energy trading platform. By always up-to-date data can be determined, for example, each optimal amount of energy at the same cheapest price, more precisely, the start and End ^¬ time point of a charge of the vehicle are set at which it is at a standstill.

Alternatively or simultaneously, it may also be provided that the energy price development is recorded by periodically uploading a software update. This in turn offers the advantage that a determination of the amount of energy and / or price described above does not require a corresponding online connection to an energy trading platform. The method thus works independently of this type of supply of price data. A particularly simple energetic charge is provided ^¬ guaranteed by a charging schedule-dependent controlled energy supply is activated at the vehicle as soon as it is connected to a source, which makes Ener ^¬. The user of the vehicle must then stop worrying, allowing quick access to a power source and the Ak ^¬ tance of the procedure increased about any activation steps and / or Preset ^¬ settings to boot.

To form demand plans are preferred methods for

Pattern recognition and / or machine learning and / or artificial intelligence performed that are already known and easy to implement, and require no further development effort.

The present invention will be explained in more detail below with reference to two adapter devices according to the invention with reference to the accompanying figures. The same or equivalent parts are given the same reference numbers. Show it:

Figure 1 is a bar graph with the known and projected sales figures of electric motors in Europe, the USA and Japan in millions of units, plotted over the years 2005 to 2012;

FIG. 2 shows a diagram with progress curves of CO ₂ -

Emissions in g / km from a hybrid (Kangoo) and a plug-in hybrid vehicle (Cleanova) recorded over the distance traveled in km;

Figure 3 is a diagram with working day curves of

Starting times of driveways depending on paths in cumulative curves; FIG. 4 shows an adapter device according to the invention, illustrating the basic principle of the method according to the invention;

Figure 5 shows the most common day path chain patterns in Vienna, im

Vienna surrounding area 1995 and in the city Salzburg 2004;

FIG. 6 shows an example of factors which influence the determination of future requirements plans in accordance with the invention;

FIG. 7 shows a determination according to the invention of the nocturnal

(Location home) and mid-morning (location working beitsplatz) Aufladezeitpunkte taking into account the price ^¬ information and the possible time frame;

8 shows an inventive adapter device in a first variant, which is embodied as in a vehicle in ^¬ tegrated device, and

Figure 9 shows an inventive adapter device in a second variant, which is designed as an external device between a power outlet and a vehicle.

Figure 1 shows a bar graph with the known and projected sales figures of electric motors in Europe, the US and Japan in millions of units, plotted over the years 2005 to 2012, as already explained in the introduction. The market penetration of hybrid vehicles will increase significantly thereafter.

FIG. 2 shows a graph with curves C 1 to C 5 of C0 ₂ emissions in g / km from a hybrid (Kangoo) and from a plug-in hybrid vehicle (Cleanova) plotted over the respective distance traveled in km, as shown already introducing was cut. From the diagram it is apparent that plug-hybrid vehicles have significant advantages over testify hybrid driving ^¬, see progress curves Cl to C3 versus C4 and C5.

FIG. 3 shows a diagram with working-day course curves C6 to C12 of starting times of travel paths depending on path purposes in cumulative curves, as has already been explained in the introduction. The typical start times are then grouped in the morning at about 07:00 clock, at noon at

12:00 and in the evening at 16:30, which ^reflects in particular the morning and evening drive to work.

FIG. 4 shows an adapter device 10 according to the invention, which illustrates the basic principle of the method according to the invention. The device 10 will also be referred to below as the Power Efficient Charging Adapter (PCA),

The device 10 is connected via an interface 11 to a vehicle 20, whose internal operating data 30 are read in via it. The interface 11 is here aufset ^¬ zen on the on-board diagnostic interface of the vehicle 20, but can also be present in any other suitable form. For the determination of the data 30 by means of embedded sensors, there are a multiplicity of proprietary protocols and common standards of the individual automobile manufacturers. Dedicated bus systems in the vehicle include CAN (Controller Area Network), LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport) and / or FlexRay. As an overarching service-oriented platform, OSGi (Open Service Gateway initiative) is also used in the automotive sector. The acquired measurement data are used at runtime by driver assistance systems such as in the traction control of ABS (Automa ^¬ tic Break System) or ESP (Electronic Stabilization System), but also used for later diagnosis and troubleshooting by authorized specialist workshops. To access the existing sensor data, the on-board diagnostic interface OBD-II in the SAE (Society of Automotive Engineers) standard J1979 specified. About the plug ^¬ tion, which is often mounted on the driver side in the interior of Fahr ^¬ zeugs, sensor information can be read in real time and for later diagnostic purposes on the vehicle bus. A number of parameters (PIDS) are freely accessible ^¬ Lich, others are provided for reasons of safety only the assistance systems of the vehicle itself. The list includes, among others, the following vehicle operating data 30, which are also made available to the driver via various user interfaces:

(i) speed, speed;

(ii) outside temperature;

(iii) turning angle of the steering wheel, pedal positions and switching positions;

(iv) time since engine start, distance traveled;

(v) angle of inclination and centrifugal forces, and

(vi) energy and fuel levels.

Especially for the favored CAN bus, a number of tools (tools) for capturing and evaluating this data 20 are available. The packages HICO. Emtrion CAN-USB-2 (USB-CAN Interface) and Intrepid Control Systems' neoVl FIRE (USB-CAN Interface) include not only the USB-CAN hardware modules but also comprehensive monitoring software. By linking the vehicle operating data already available 20, and an optional positioning by a GPS (Global Po sitioning System) module can be detected lifestyle dependent Fahrge ^¬ habits and used for later Gebrauchsmustererken- planning.

For a meaningful evaluation, the following data can be recorded while driving through the onboard sensors: (i) unambiguous identification of the driver and any passengers;

(ii) Trajectory of the continuously recorded

Energy consumption. This is required for the subsequent Alloc ^¬ voltage to the data of road segments;

(iii) start time, end time and journey time for the time order of the journey;

(iv) Accumulated track data such as height profile (up and down gradients), distance traveled, current Ge ^¬ speed over the duration of the journey etc .;

(v) Current local environmental conditions that can be measured with outdoor sensors, the weather and weather conditions, e.g. Snow, rain, hail, wet, icy road conditions, temperature values, etc.;

(vi) Optional position determination by a GPS module, thereby routing and any Fahrtunterbre ^¬ cations such as stops and longer parking can be held, and

(vii) Optionally, an interaction frequency of the driver with the individual controls such as shift lever, brake pedal position and steering wheel, the information on the frequency, duration and other parameters In ^¬ formations to the economy of the driving style provide and thus can also be incorporated into the needs recognition.

In addition to the determined vehicle operating data 30 of the

On-board sensors can optionally also be used external vehicle data sources to capture context information 32 for the demand calculation. This is a cut point 16 of the device 10 is provided. There are in this case JEG ^¬ Liche important information describing the current situational ^¬ tion of the vehicle 20 in more detail and affect its consumption:

(i) Profile data 32 'of the vehicle owner, such as:

o Scheduling in calendar applications, which contain information about external appointments, which may require a car journey. Explicitly scheduled appointments usually involve non-routine events that occur only once or a few times. Implicit ^assumptions about whereabouts such as the daily commute to work or the ride to the sports club are not included, but due to the frequency they can be easily recognized autonomously, and

o Preferred whereabouts as working or BiI--making workshop, residential area, places for Freizeitverhal ^¬ th etc ..

(ii) Traffic information 32 ", where crucial factors of the traffic information affecting demand are:

o time window of the journey, which contributes significantly to the expected density of the traffic, such. morning rush hour, travel, etc. ;

o assignment to spatial zones, such as Urban area, country road, dirt road etc., and

o At obstacles expected, such as traffic light scarf ^¬ obligations, construction sites, temporarily blocked streets etc .. (iii) weather information 32 ''', since a preview of the expected weather conditions can also have ^¬ effect on the energy demand calculation, for example, in a dependence of the capacitive battery power of the outside temperature, rain and

Snow as factors influencing speed and thus indirectly on demand.

The needs detection and planning unit 13 collects the vehicle operation data 30 and the context information 32 and merges them into an energy demand profile 40 (shown in FIG. 6). For this, factors of lifestyle-dependent driving habits are analyzed and recorded in demand plans. The trajectory of the actual energy consumption of previous journeys is e.g. together with the

Operating times and the route data information about the daily travel times and their average duration. In turn, service lives 41, 41 '(shown in FIG. 7) of the vehicle 20 are derived from the demand plans. The comparison of duration and frequency of life 41,

41 'of the vehicle 20 help possible candidates for the bes ^¬ th charging time of an energy storage, here to find a battery 21. An optional positioning spatial paths chain pattern 43 can (shown in Figure 5) ER are detected and the accuracy of demand forecasting, de ^¬ finiert by daily recurring travel destinations and their se ^¬ quentielle sequence significantly improve ... 43 ''. These can be, for example, recurring events such as the weekday commute to work or the Saturday shopping in the nearby shopping center. In order to further improve the demand prediction, a link to the personal profile data 32 ', such as appointments from a calendar application, work and life location, leisure time behavior, etc., is optionally possible.

The aforesaid service lives 41, 41 'of the vehicle 20 are then fed to a charging optimization unit 14. Alternatively, of course, the requirements plans themselves can also ser unit 14 are fed, and in this finally the service life 41, 41 'are determined. In any case are al ^¬ le relevant vehicle data 30 and optional context information incorporated in the requirement plans 32, 50 optimized time and price charging plans can be created at the Ladeoptimie ^¬ approximation unit 14 42 by incorporating an energy trading platform. This is done under the assumption of a free energy market for end users, the ^¬ which scientific sources found in various reference and has been implemented as a prototype. With the help of a forecast for

Energy price development 50, the required electricity contingent is purchased at the best possible time within the time window specified by the demand.

To include energy offers and price information, two update variants of the device 10 are provided:

In a first variant, there is a periodic up ^¬ date, in which the device 10 waives a connection to the energy trading platform and only by manual

Importing the user via an interface 12 such. USB and supplied software receives an update. The advantage lies in the greater independence from a possibly non-existent Internet connection at the expense of outdated price information. Depending on the settings, the manual update can be performed weekly, monthly or as required.

In a second variant, an online update is carried out in which the device 10 must communicate with each connection to the power network with a trading platform in order to explore the market for the currently cheapest offer. For this purpose, the physical interface 12 to the device must be universally ^¬ laid out. A wireless connection such as IEEE 802.11 WLAN (Wireless Local Area Network) or Bluetooth to the Internet would reduce the integration effort into an existing local

Minimize network. Since the device 10 anyway requires a physical connection to the power grid, would also communicate to the vehicle bus ^¬ communication via a carrier frequency system (Powerline) conceivable. At the protocol level, a TCP / IP-based method such as web services is to be preferred.

After optimization, a calculated charging schedule 42 for the vehicle 20 is finally communicated by the demand detection and planning unit 14 of a charging control unit 15, which switches a relay of a power supply 22 to the battery 21 of the vehicle 20 depending on a charging plan. The mechanism compensates the digital timer and is preferred activated once the vehicle is 20 attached to the power supply ^¬ closed.

FIG. 5 shows the most common day path chain patterns A, B and C, in order in Vienna, in the surrounding area of Vienna in 1995 and in the city of Salzburg in 2004. The probability of P occurring daily path chain patterns resulting from living (W), working ( A), purchasing and private (E) and leisure (F) and can also be determined by a life ^¬ style-dependent vehicle use. The sum S is the proportion of these paths pattern 43 ... 43 '' on tägli ^¬ chen total travel.

6 shows an example of factors that have to he ^¬-making proper identification of future needs plans influence. The trajectory of an energy demand profile 40, plotted in kW over a course of the day, shows some of the factors mentioned above, such as traffic situation, travel times, travel distances, intended use and path chain patterns, which influence the determination of a future demand plan. To make it ^¬ future requirement plans of vehicles operating data are evaluated with 20 methods for pattern recognition and / or machine learning and / or artificial intelligence. Depending on the nature and composition of the features, different algorithms can be used. These include, inter alia, Bayesian networks,

Hidden Markov Models, Bayesian Classifiers, Decision Trees, Neural Networks and Support Vector Machines. 7 shows a determination according to the invention the Night ^¬ lichen (Location Home) and mid-morning (location working ^¬ space) Aufladezeitpunkte taking into account an energy price movements 31 and the possible time window of times 41, 41 'of the vehicle 20, plotted over the course of the day. The arrows indicate this in the portion of the respective window in which in the context of predictive ^¬ tizierten energy price development is possible 31 a particularly favorable purchasing energy, ie an optimal under quantitative and price point of loading of the vehicle can be carried out 20th A maximum price fluctuation within the respective time window of the service lives 41, 41 'is indicated by D31 and D31'. To use the respective lowest energy price sees the calculated loading plan 42, a decrease of a large amount of energy at night between about 4:00 and 5:00 in front, and the acceptance of a smaller amount mor ^¬ gens between about 9:00 and 10: 00 clock, since here again a higher selling price will apply. In the morning high hours between about 05:00 and 08:00 on the other hand, no decrease is provided.

The following two figures show possible embodiments of an adapter device 10. Depending on the technical conditions, other communication interfaces 11, 12 and 16 for receiving the data 31 on the energy trading platform

50 requiring data 30 on the vehicle bus system and context information 32.

FIG. 8 shows an adapter device 10 'according to the invention in a first variant, which is embodied as a device integrated in the vehicle 20. The adapter while a module of the bus system 24 in the vehicle 20. This Adap ^¬ ter 10 'is housed in the front area of the vehicle 20 and controls a power supply 22 from an external energy source 23, here an outlet 21 to the battery of the

Adapter 10 'is shown above the vehicle 20 as a block diagram forth ^¬ . To record the vehicle operating data, this is set on an on-board diagnostic interface 11 of the vehicle zeugs 20 'up. In the event that the vehicle bus does not use stan ^¬ dardized interface, an additional module for the control must be used. To accommodate the energy price development 31 and the context information 32, the interfaces 12 and 16 are formed as an integrated wireless module. The module is based on the WLAN standard, which can communicate with applications in the local network as well as with online services. The data 30, 31 and 32 are an integrated Bedarfserkennungs- and Plans for unit 13, load optimization unit 14 and charging control unit 15 supplied to the charging plans 42 for charging the battery 21, he attests ^¬. The control of the charging of the battery 21 is made via an interface 17 to the bus system of the vehicle 20.

9 shows an adapter device according to the invention 10 '' in a second variant, which as an external device Zvi ^¬ rule of the socket 23 and a vehicle 20 'is executed. The power supply 22 runs via the adapter 10 ", and in contrast to the embodiment of FIG. 8 is controlled by a separate charge control unit 15. Another difference to Figure 9 represent the interfaces 11, 12 and 16, which are integrated into a wireless module, which as usual builds on the WLAN standard. This module can communicate with both local area network applications and online services, as well as with the vehicle bus (not shown). Thus, in-vehicle data 30 and context information 32 can be received in the same way as energy price information 31 for the online update. These data 30, 31 and 32 are transmitted to calculate a charging plan 42 to an integrated Bedarfserkennungs- and Pla ^¬ voltage charging unit 13 and optimization unit 14 which provides the products of the charge control unit 15 for charging the Bat ^¬ terie 21st

An innovative core of the Power Efficient Charging Adapter lies in both cases in the development of a charging control for batteries of electric vehicles resulting from the Combination of two novel and sophisticated components gives:

On the one hand, a requirements recognition and planning unit which uses the vehicle operating data and optional context information acquired by the onboard sensor technology to find lifestyle-dependent vehicle usage patterns, to calculate future energy requirements and to record these in demand plans. The conventional way of purchasing electricity, on the other hand, is the conclusion of a time-bound contract with a supplier. Billing is based on fixed day and night rates. Electricity providers trade with each other on exchanges, such as EEX, to offset overproduction or deficits in their burdens. This can be done both in the short term on spot and long-term through forward contracts, allowing a more accurate and cost-efficient planning of benötig ^¬ th production capacity. The latter is with the legally mandatory involvement of decentralized alternative energy generators continually more difficult because their production volume is heavily dependent on external circumstances often, such as wind, solar, etc .. Dynamic Stromprei ^¬ se, adapted to the current load, therefore, be in many studies ausgewie ^¬ sen as an inevitable future scenario so that power producers continue to supply th the warranty can. They implicitly influence the behavior of the

Electricity consumers and make this predictable. A low electricity price leads to higher consumption and vice versa ^¬, which loads are smoothed. In return, consumers can benefit from lower electricity costs through the targeted use of their electricity consumers. These developments show that the electricity market is in transition. It is likely that will be Wesent ^¬ Lich flexible and freely accessible in the future energy markets for end users. Required electricity quotas are short term from the cheapest provider or already in advance at the

Power Exchange bought. Result from intelligent activity- and be ^¬ may oriented electricity consumers as a result huge savings potential. Secondly, a charging optimization unit, which takes advantage of a free energy market for end users to create battery charging plans that are optimal in terms of electricity costs and energy efficient use of the vehicle, knowing the current energy prices and offers. This is possible through the possible shifts of the current reference time point within a be by the need ^¬ restricted time frame. Although approaches to the modeling of electrical energy storage in automobiles are already concerned with the optimal utilization of energy and power ranges and the monitoring of the state of charge and health. Not only chemical and physical properties such as temperature, weight and chemical composition of the energy source, but also the integration into the Gesamtsys ^¬ tem for efficient conversion to kinetic drive energy play a role. Intelligent solutions in this area, so ^¬ called Smart batteries are limited in design but on the technical development and innovations and do not consider the subsequent individual use of electrically-powered vehicle. However, an advanced energy management is possible with the present invention that provides a contained in the terminal technical procedural ^¬ ren that dealt specifically with the efficient and especially with the lifestyle-related use of the battery storage.

The advantages of the proposed solution lie in the energy cost savings for the end user in comparison to a conventional charging control for energy storage.

The ability to freely select a power purchase time within a predefined frame, the required electricity quotas can be purchased at the best possible price.

Furthermore, the implicit and optimized control of the charging process with minimal user interaction SUC ^¬ gen. In the embodiments of the adapter with wireless Interfaces suffices after a single installation only the anyway necessary connection of the vehicle to a power outlet.

The method is also robust to exceptional treatments of daily energy consumption and the required charging cycles. The user is in a lack of battery power, caused by unforeseen trips and let the Be ^¬ requirements planning aside, informed and made aware of unscheduled charging options.

At the same time there can be increased energy efficiency by re ^¬ production of the power loss for the power generators. Since demand-based purchasing in energy trading platforms presupposes a contractual and legal commitment to the free trading model anyway, the step is one

Market solution for the holistic, demand-oriented production of electricity by transmitting needs planning of individual persons not far away. Due to the Marktme ^¬ mechanisms of supply and demand and automate shopping but it is without the transmission of the exact plans come to a smoothing of the load on the power line.

The method according to the invention offers an additional selling point for electric vehicles, and thus the positioning of electric vehicles as a serious alternative to vehicles with internal combustion engines, especially in the field of short-distance driving and city traffic, without restrictions in use due to short battery life.

The invention is suitable also as a cost-effective and effi ^¬ cient extension of existing systems that use be ^¬ already vehicle operating data in order to save energy for Electronic vehicles. The installation of the device according to the invention as an adapter or an integral part of a

Vehicle is very simple. There are only minimal requirements regarding the available interfaces: (i) It has an interface for energy trading best ^¬ hen to perform the periodic patching the Preisinformatio ^¬ NEN. For this is merely a serial data interface such as USB or integrated into the device memory card and the software for Aktua ^¬ taping requirement. For the frequent online update is a wireless connection such as Blue ^¬ tooth, WLAN, etc. of the adapter or a power line connection via the power connection to the Internet nö ^¬ tig when the device is installed in the vehicle.

(ii) There must also be an interface to the bus system of the vehicle. Here, in the off-vehicle version of the Power Efficient Charging

Adapter a connection to the onboard sensors of the vehicle via a wireless connection such. Bluetooth, WLAN etc. for the bus system possible. In conjunction with the vehicle, the device can be mounted directly on the vehicle bus.

(iii) It may also be provided an interface for optional Kon ^¬ text information for demand planning, which allows a connection to local and online service providers.

(iv) Under the aspect of a flexible model for the detection of driving habits the parts of the Bedarfserkennungs- and demand planning unit which are concerned with the detection of vehicle use patterns can be used of related and / or fahrzeugrele ^¬-relevant problems. This includes, for example, a further development of the pay-as-you-drive insurance model, which can make improved calculations with data on the driving style and the driving habits of a vehicle ^owner .

Claims

claims

An adapter device (10) for energetically charging a vehicle (20, 20 '), with

- an interface (11) for detecting in-vehicle operational data (30) comprising factors indicative of lifestyle driving habits, and an interface (12) for acquiring information on energy price development (31);

- a Bedarfserkennungs- and planning unit (13), which is for deriving a power demand profile (40) formed of the Fahrzeugbe ^¬ operating data (30) and to create a future demand plan based on at least one of said factors, and further for deriving the duration and frequency of service lives (41, 41 ') of the vehicle (20; 20') is adjusted using the demand plan;

a charging optimization unit (14) for comparing the service life (41, 41 ') of the vehicle (20, 20') with the information about the energy price development (31) and for creating a time and / or price optimized charging plan (42) for the Vehicle (20; 20 ') is formed on the basis of the comparison result, and

a charging control unit (15) which is capable of charging-dependent charging from an energy store (21) of the charging system

Vehicle (20; 20 ') is formed.

2. Device (10) according to claim 1, further comprising an interface (16) for detecting context information (32) which describe the current situation of the vehicle (20; 20 ') in more detail and which affect the consumption, in particular profile data ( 32 ') of a vehicle owner and / or traffic information (32'') and / or weather information (32'''), and in which the demand recognition and planning unit (13) in addition to deriving an energy demand profile (40) from said context information ( 32) is formed.

3. Device (10) according to claim 1 or 2, wherein at least one of the interfaces (11, 12, 16) for the wireless recording of the vehicle operating data (30) and / or the energy price data (31) and / or context information (32) ausge- is forming.

4. Device (10) according to any one of the preceding claims, further comprising a storage unit for storing the information about the energy price development (31).

5. Device (10) according to one of the preceding claims, as an external adapter (10 ') between a power source

(22) and the energy store (21) of the vehicle (20) is executed.

6. Device (10) according to one of the preceding claims, designed as an integrated with the vehicle (20 ') executed Adap ^¬ ter (10'').

7. Use of an adapter device (10) according to one of the preceding claims for the detection of vehicle use ^¬ patterns, in particular for detecting driving habits and / or a driving style for calculating insurance ^{models ¬} dellen.

A method of energetically charging a vehicle (20; 20 '), comprising the steps of:

- acquiring and storing in-vehicle operational data (30) including factors indicative of lifestyle-dependent driving habits;

Deriving an energy demand profile (40) from the vehicle operating ^data (30) and creating a future demand plan formed taking into account at least one of said factors; - Deriving the duration and frequency of life (41, 41 ') of the vehicle (20, 20') using the demand plan; - Recording an energy price development (31) and comparing the service life (41, 41 ') of the vehicle (20, 20') with this energy price development (31), and

- Creating a time and / or price optimized charging plan (42) for the vehicle (20; 20 ') on the basis of Ver ^¬ comparison result.

9. The method of claim 8, wherein a demand plan for the vehicle (20; 20 ') is formed by daily travel times and their average duration are determined from a curve of an actual energy consumption of past journeys ^¬ the trips.

10. The method of claim 8 or 9, wherein to form the demand plan for the vehicle (20; 20 ') continue daily

Operating times and / or route data are used.

11. The method according to any one of claims 8 to 10, wherein a demand plan for the vehicle (20; 20 ') is formed by the positions of the vehicle (20; 20') detected and therefrom spatial Wegkettenmuster (43 ... 43 '') are derived, which specify daily recurring destinations and their sequential Ab ^¬ sequence.

12. The method according to any one of claims 8 to 11, in which for forming a demand plan further vehicle-external context ^¬ information (32) is used which the current situation of the vehicle (20; 20 ') describe in more detail and which have an impact on consumption, in particular profile data (32 ') of a vehicle owner and / or traffic information (32'') and / or weather information (32''').

13. A method according to any one of claims 8 to 12, wherein the user of a vehicle (20; 20 ') is informed of unscheduled charging opportunities in the event of a lack of energetic charge caused by unpredictable trips.

14. The method according to any one of claims 8 to 13, wherein the energy price development (31) by querying an Internet-based energy trading platform (50) is detected.

15. The method according to any one of claims 8 to 14, wherein the energy price development (31) is detected by periodically applying a software update.

16. The method according to any one of claims 8 to 15, wherein a charging-plan-dependent controlled power supply (22) to the vehicle (20; 20 ') is activated as soon as it is connected to an energy source ^¬ (23).

17. The method according to any one of claims 8 to 16, are carried out in the formation of demand plans methods for pattern recognition and / or machine learning and / or artificial intelligence.