Method for charging a vehicle battery of a vehicle group

Abstract

The present invention relates to a method for charging vehicle batteries of a member vehicle group, comprising the following steps: predicting the total energy demand of the member vehicle group for charging vehicle batteries in a future time period, predicting the time distribution of the charging power demand of the member vehicle group during the future time period, calculating a total charging power curve optimized with respect to one or more optimization criteria for the member vehicle group based on the predicted total energy demand and the predicted time distribution of the charging power demand, purchasing an amount of energy corresponding to the optimized total charging power curve on a relevant energy market, and controlling the charging process of each member vehicle in the group over time using the corresponding charging curve so that the sum of the corresponding charging curves of the each member vehicle corresponds to the optimized total charging power curve.

Description

Technical Field

[0001] The invention relates to a method for charging a vehicle battery of a vehicle group and to a central control device for charging a vehicle battery of a vehicle group. Background Art

[0002] In the process of increasing electrification of vehicle drives, solutions are sought that can charge a large number of vehicles in an intelligent and as ecological or economical way as possible. On the one hand, the availability of energy from renewable energy sources such as solar and wind power depends strongly on the weather and sunshine hours. On the other hand, energy prices are therefore especially affected by strong and long-term fluctuations in sunshine hours. These prices are obtained on regional or national electricity markets or electricity exchanges and are valid in the respective region or country.

[0003] More and more electrical power is required for charging electrically operated vehicles, such as pure electric vehicles or so-called plug-in hybrid vehicles, using an electric traction machine powered by the vehicle battery. Users of electric vehicles or plug-in hybrid vehicles may therefore be interested in charging the vehicle battery whenever electricity is particularly cheap and / or as much energy as possible from renewable energy sources is available. However, as an individual user of a vehicle, you cannot trade on the electricity exchange. This is only possible for authorized dealers, grid operators, electricity suppliers or large purchasers such as large companies. Summary of the invention

[0004] The object of the present invention is therefore to enable the driver of an electric vehicle to charge the electric vehicle in an inexpensive and, if appropriate, ecological manner.

[0005] This object is achieved by a method and a control device according to the independent claims. Advantageous developments are specified in the dependent claims.

[0006] The present invention exploits the knowledge that large purchasers of energy can trade on a power exchange.

[0007] The method according to the invention for charging vehicle batteries of a member vehicle group has the following steps:

[0008] - predicting the total energy demand of the member vehicle group for charging vehicle batteries over a future time period;

[0009] - predicting the time distribution of the charging power demand of the member vehicle group during the future time period,

[0010] - calculating, for the member vehicle group, a total charging power curve optimized with respect to one or more optimization criteria based on the predicted total energy demand and the predicted time distribution of the charging power demand,

[0011] - Purchase the amount of energy corresponding to the optimized total charging power curve on the relevant energy market;

[0012] - controlling the charging process of the individual member vehicles of the group over time using the respective charging curves so that the sum of the respective charging curves of the individual member vehicles corresponds to the optimized total charging power curve.

[0013] The method according to the invention is suitable for charging electrically operable vehicles, i.e. vehicles having at least one electric traction machine powered by a vehicle battery. Preferably, the vehicles are networked vehicles. According to the invention, a group consists of X member vehicles having a vehicle battery, which is at least provided for operating the electric traction machine of the vehicle. The number of vehicles X in the group and their common energy demand are preferably large enough to be able to appear as a group on the energy market. The number X therefore depends on the regulations of the corresponding energy market, such as the country. For example, it is conceivable that there are at least 50, 100 or 1000 vehicles in a group. Further preferably, the number X is not predetermined, but corresponds to the number of vehicles and / or drivers participating in the method. In addition, the number X is also not constant or limited, but grows with the increase in the number of users. A user can be, for example, a vehicle registered in the group, whereby the vehicle becomes a member vehicle in the group. All vehicles registered in the group are preferably referred to as member vehicles in the present application. Alternatively, the term "member vehicles" in this context can also only relate to a part of the registered vehicles that are connected to a charging socket and are therefore "ready to charge" at the time of carrying out the method. In certain energy markets, it is also possible to sell energy regionally in the form of minimal markets. For example, electricity can be obtained directly from neighbors who operate photovoltaic facilities. In such minimal markets, which usually operate only at low voltage levels, a group can also consist of only a few vehicles, in extreme cases a single "plugged in" vehicle, i.e. a vehicle ready to be charged. In general, the group according to the invention can also be called a "cluster".

[0014] The statistical total energy demand of all member vehicles of the group in a future time period can be predicted based on the information collected about past charging processes, the current state of charge (State of Charge) of the vehicle batteries of the member vehicles, user profiles (e.g. driving habits, charging habits), in particular their time course, etc. For example, the future time period lasts 12 or 24 hours or a week and can start immediately or in the future, for example delayed by 12, 24, 48 hours or a week.

[0015] In addition, the statistical time distribution of the charging power requirements in future time periods is predicted based on the user data and vehicle data mentioned. This means that it is statistically determined when and how many vehicles will require what charging power. For this purpose, vehicle and user information is preferably taken into account, which allows inferences to be drawn about when the vehicles will be needed again and what charging state is required. For example, based on experience, some member vehicles need to be charged quickly, while others can wait for a more favorable time window.

[0016] Based on the predicted total energy demand and the predicted time distribution of the charging power demand, an optimized total charging power curve for the group of member vehicles is determined, which is optimized with respect to one or more optimization criteria. A possible optimization criterion is, for example, the lowest possible price for the total energy quantity. Another possible optimization criterion is the highest possible proportion of electricity from renewable energy sources. Another possible optimization criterion is the shortest possible charging time, wherein this criterion may conflict with other optimization criteria.

[0017] Based on the optimized total charging power curve thus determined, according to the invention, electrical power is now purchased on the relevant electricity market, i.e., for example, at a power exchange. The power generator is therefore contractually obliged to feed the required power into the grid in accordance with the time curve and at the respective applicable electricity price.

[0018] Finally, individual charging curves are preferably determined for each member vehicle and, according to the invention, the charging process of each member vehicle is controlled accordingly according to the corresponding curve. The charging process of the member vehicles is controlled by a known charging device built into the vehicle, the so-called "on-board charger". In this case, the sum of the individual charging curves of the member vehicles corresponds to the optimized total charging power curve and thus to the amount of energy purchased on the electricity market.

[0019] According to a preferred development of the invention, as an additional step, the expected price development of the electrical power on the relevant energy market in the future time period is predicted. This predicted price development is preferably taken into account when determining the optimized total charging power curve. Thus, for example, charging times with low energy prices can be preferred and the total price of the energy quantity obtained on the electricity market can be reduced.

[0020] For predicting the electricity price development, known forecasting methods which are already used on the electricity market can be used.However, the expected price development of electrical power on the relevant energy market in the future time period is preferably also predicted based on weather data.

[0021] The corresponding weather data are, for example, weather forecasts. A forecast about the availability of wind or solar energy in the future time period can be derived from the weather forecast. The availability of renewable energy sources such as solar or wind energy can in turn influence price developments on the energy market. Therefore, it is preferably possible to predict price developments on the energy market taking into account the weather data or weather forecasts.

[0022] If, for example, one optimization criterion is to use the highest possible proportion of renewable energy, weather data can also be used directly to calculate the optimized total charging power curve. The optimized total charging power curve is therefore preferably determined based on weather data and / or based on predicted price developments on the relevant energy market.

[0023] Additionally, weather data may also be used to predict total energy demand or predict the temporal distribution of charging power demand. For example, high temperatures and high levels of solar radiation may necessitate high air conditioning requirements in member vehicles, thereby increasing energy demand.

[0024] According to a preferred development, a total energy requirement for charging and / or a time distribution of a charging power requirement of the group of member vehicles during the future time period is predicted by means of a statistical evaluation of information about past charging processes of the member vehicles.

[0025] For this purpose, information about past charging processes and / or other vehicle data and / or user data can preferably be sent from the member vehicles to the central computing unit via the wireless communication interface.

[0026] The vehicle data contains, for example, information about the state of charge (SOC) of the vehicle battery, information about past charging processes, and user profiles, such as information obtained based on past driving (driving habits, repeated or regular driving, schedules, and personal charging behavior of users). Data transmission is preferably performed anonymously, without involving personal data. However, it is preferred to enable short-term vehicle identification because each vehicle should be individually controlled in the future. In the central computing unit, information about past charging processes of the vehicles included in the group is preferably evaluated in a statistical manner. Information about past charging processes can be detected and transmitted to a central server unit, for example, during the use time of the charging device (on-board charger) on the vehicle. The central computing unit can be, for example, a server device or in the cloud. The charging device installed in the vehicle adjusts each charging process, and the central computing unit forms the back end of the application. Information is collected here, information is evaluated, predictions are created, the optimized total charging power curve is calculated, individual charging curves are determined, and the on-board charging device is controlled to perform the charging process.

[0027] Alternatively, the individual steps of the method can also be carried out in a vehicle memory or a vehicle computing unit in the member vehicles. Here, data about past charging processes and / or other vehicle data and / or user data from past charging processes can be (temporarily) stored and sent to a central computing unit via a communication interface. The vehicle memory or the vehicle computing unit can also, for example, at least partially perform data evaluation and predictions about future charging requirements.

[0028] According to a further preferred embodiment variant of the method according to the invention for charging a vehicle battery of a component vehicle group, the control over time of the charging process of the individual component vehicles can be adapted as required using corresponding power curves.

[0029] Since the amount of energy is purchased based on forecasts of the total energy demand and the time distribution, and these forecasts may be wrong, the charging curve may need to be corrected. In addition, a group of member vehicles with X energy storage devices (each vehicle battery of a member vehicle is an energy storage device) as a participant in the electricity market can capture current peaks or current lows in the grid if necessary. In this case, even cheaper electricity prices can be achieved on the energy market if necessary, or standard charges can be applied. Feeding energy from vehicle batteries back into the grid may be of particular interest for the smallest markets in the low voltage range.

[0030] The method according to the invention therefore offers the advantage that current peaks or current dips in the grid can be cleverly avoided or utilized and that additional economic or ecological benefits can be generated for the group members. Grid stabilization or frequency equalization can be achieved, in particular in the smallest markets at low voltage levels.

[0031] As already mentioned above, the forecasts for the total energy demand and the time distribution may contain errors or uncertainties. The further into the future the forecast period is, the greater the expected errors in the forecast will generally be. In particular, the number of “plugged in” vehicles, i.e. vehicles ready for charging, may also vary. In the total charging power curve predicted according to the invention, errors are generally located in the curve fluctuations above the basic demand.

[0032] Therefore, according to a preferred extension of the method of the present invention, the steps of predicting the total energy demand, predicting the time distribution of the charging power demand in the future time period, calculating the optimized total charging power curve and purchasing the energy quantity are repeatedly performed with decreasing time intervals from the future time period, wherein when purchasing the energy quantity, first only a part of the optimized total charging power curve is purchased, and when the execution is repeated, part or all of the difference between the optimized total charging power curve and the energy quantity purchased for the future time period is purchased.

[0033] Thus, for example, during a first execution, only the basic requirement can be purchased on the electricity market. The greater the distance to the future time period, the cheaper the price may be. The closer the future time period is, the more accurate the prediction and calculation of the optimized total charging power curve will be. However, there is less flexibility in the timing of the charging process and the applicable electricity price must be paid separately. Since the basic requirement has already been met previously, only the difference in the required amount of electricity must now be purchased separately at the applicable price. This can preferably be repeated several times, wherein the prediction preferably becomes more accurate each time.

[0034] A central control unit for charging vehicle batteries of a group of member vehicles is another aspect of the invention.

[0035] - receiving means for receiving vehicle data and / or user data from member vehicles belonging to the group,

[0036] - a prediction unit for predicting the total energy demand of the group of member vehicles in a future time period,

[0037] - a prediction unit for predicting the time distribution of the charging power requirements of the member vehicles in the future time period,

[0038] - a calculation unit, configured to calculate a total charging power curve optimized with respect to one or more optimization criteria for the member vehicle group according to the predicted total energy demand and the predicted time distribution of the charging power demand,

[0039] A control unit for controlling the charging process of the individual member vehicles of the group over time using the respective charging curves such that the sum of the respective charging curves of the individual member vehicles corresponds to an optimized total charging power curve.

[0040] The central control unit is preferably configured to carry out the method according to the invention in accordance with one or more of the above-described embodiment variants.

[0041] Another aspect of the invention is a vehicle memory or vehicle computing unit, which is configured to store information about past charging processes and / or other vehicle data and / or user data from past charging processes and to send it to the central control unit via a communication interface. The vehicle memory or vehicle computing unit is preferably arranged in a vehicle, in particular a member vehicle.

[0042] According to an advantageous development, the vehicle memory or the vehicle computing unit is also designed to evaluate the stored data and / or to predict future energy requirements and preferably to predict the time course of future energy requirements. A prediction of the future energy requirements of the member vehicles can then preferably be created in the vehicle itself and sent to the central control unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The following is based on Figures 1 to 4 The present invention is explained in more detail.

[0044] Figure 1 A group consisting of N member vehicles is schematically shown;

[0045] Figure 2 Schematically illustrated are the steps of the method according to the invention for determining the energy demand of a group of vehicles and the statistical power distribution during a future time period;

[0046] Figure 3 schematically showing forecast price developments on the energy market; and

[0047] Figure 4 An overview of the method according to the invention is schematically shown. DETAILED DESCRIPTION

[0048] exist Figure 1 In FIG. 1 , group 10 includes X participating member vehicles (1, 2, ..., N, N+1, X), of which subset N is connected to charging station 12 at a certain moment, ie plugged into a charging socket. Subset N may in turn include several area-restricted subgroups (not shown).

[0049] Figure 2 It is schematically shown how N member vehicles send user data and vehicle data to a central computing unit (backend) 20 via wireless communication 13. The vehicle data contains, for example, information about the state of charge (SOC) of the vehicle battery, information about past charging processes, user profiles, such as information obtained based on past driving (driving habits, repeated or regular driving, schedules, user's personal charging behavior).

[0050] In the central computing unit 20, information about past charging processes of the vehicles included in the group is preferably evaluated in a statistical manner. Information about past charging processes can be detected, for example, during the use time of the charging device 14 (on-board charger) on the vehicle and transmitted to the central computing unit 20. Information about past charging processes can be obtained, for example, based on the use time of the charging device installed in the member vehicles. Based on the received data, a forecast 21 is created in the central computing unit 20 about the total energy demand of N vehicles in a future time period T and its time distribution.

[0051] Figure 3 The creation of a forecast 22 of the development of the electricity price during a future period T is shown. To this end, the central computing unit 20 obtains price information 24 from the energy market. Additionally, weather data 23 are taken into account and an improved electricity price forecast 22 during the period T is created.

[0052] Figure 4 The method according to the invention is shown in an overview. N vehicles 1, 2, 3, ... N in a group N are plugged into a charging socket 12 and send user data or vehicle data to a central computing unit 20 via wireless communication 13. There, based on the user data and vehicle data and based on the information already collected about past charging processes and based on weather data 23, the total energy demand of the N vehicles in a time period T, the time distribution of the charging demand during the time period T and a forecast of the development of the electricity price in the time period T are created. The optimized total charging power curve P is thus calculated. OPT , 25 , which is optimized, for example, with respect to low electricity prices or with respect to the proportion of renewable energy. In accordance with this optimized total charging power curve 25 , energy is purchased 27 at an electricity exchange 26 .

[0053] Calculate the corresponding charging power curve P for each of the N member vehicles 1, 2, 3, ... N i , and correspondingly operate the corresponding charging devices 14 of the member vehicles to perform the charging process. Then, the sum of the individual charging power curves Pi corresponds to the optimized total charging power curve P OPT .

[0054] The invention thus makes it possible to predict the common energy demand and charging requirements for a group of vehicles based on user data and vehicle data, weather forecasts. Based on this predicted demand and the predicted electricity price development, energy can be purchased and used optimally in terms of price or utilization of renewable energy. Thus, the economic advantages that can be obtained by appearing as a group on the electricity market can be passed on to the users.

Claims

1. A method for charging a vehicle battery of a group (10) of member vehicles (1, 2, ... N), comprising the following steps: - predicting the total energy demand of the group (10) of member vehicles (1, 2, ... N) for charging vehicle batteries over a future period of time; - predicting the time distribution (21) of the charging power demand of the group (10) of member vehicles (1, 2, . . . N) during the future time period, - calculating a total charging power curve (P) optimized for the group of member vehicles (1, 2, ... N) with respect to one or more optimization criteria based on the predicted total energy demand and the predicted time distribution of the charging power demand (21); OPT ), wherein the optimization criteria include the lowest possible price for the total energy quantity and / or the highest possible share of electricity from renewable energy sources, - purchasing energy on the relevant energy market (26) in accordance with the optimized total charging power curve (P OPT ) corresponding to the amount of energy; and - controlling the charging process of each member vehicle (1, 2, . . . N) in the group over time using the corresponding charging curves so that the sum of the corresponding charging curves (Pi) of the individual member vehicles (1, 2, . . . N) corresponds to the optimized total charging power curve.

2. The method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to claim 1, having the further step of: - predicting the expected price development of electrical power on the relevant energy market in said future time period (22).

3. Method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to claim 2, The expected price development of electrical power on the relevant energy market in the future time period is predicted based on weather data (23).

4. A method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to any one of the preceding claims, The optimized total charging power curve (P) is additionally calculated for the group (10) of member vehicles (1, 2, . . . N) as a function of weather data (23) and / or as a function of predicted price developments (22) on the relevant energy market. OPT ).

5. Method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to any one of claims 1 to 3, Therein, a total energy requirement and / or a time distribution of a charging power requirement of the group of member vehicles for charging during the future time period is predicted by means of a statistical evaluation of information about past charging processes of the member vehicles (21).

6. Method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to any one of claims 1 to 3, The member vehicles transmit information about the charging process and / or other vehicle data and / or user data to the central processing unit via a wireless communication interface.

7. Method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to any one of claims 1 to 3, In order to predict the total energy demand of the group of member vehicles (1, 2, . . . N) during the future time period and to predict the time distribution of the charging power demand of the group of member vehicles (1, 2, . . . N) during the future time period, user data from the drivers of the group are evaluated.

8. A method for charging a vehicle battery of a member vehicle group according to any one of claims 1 to 3, In this case, the corresponding power curves are used, if necessary, to adapt the control of the charging process of the individual member vehicles (1, 2, . . . N) over time.

9. Method for charging a vehicle battery of a group of member vehicles (1, 2, ... N) according to any one of claims 1 to 3, wherein predicting the total energy demand, predicting the time distribution of the charging power demand during the future time period, and calculating the optimized total charging power curve (P OPT ) and the steps to purchase the amount of energy, And wherein at the time of purchase, first only the total charging power curve (P OPT ), and when repeatedly executed, purchase part or all of the difference between the optimized total charging power curve and the amount of energy purchased for the future time period.

10. A central control unit for charging vehicle batteries of a group of member vehicles (1, 2, ... N), having - receiving means for receiving vehicle data and / or user data from member vehicles (1, 2, ... N) belonging to the group, - a prediction unit for predicting the total energy demand of the group of member vehicles in a future time period, - a prediction unit for predicting a time distribution of a charging power demand of said member vehicles (1, 2, . . . N) during said future time period, - a calculation unit, for calculating a total charging power curve (P ) optimized for the member vehicle group with respect to one or more optimization criteria based on the predicted total energy demand and the predicted time distribution of the charging power demand (21) OPT ), wherein the optimization criteria include the lowest possible price for the total energy quantity and / or the highest possible share of electricity from renewable energy sources, - a control unit for controlling the charging process of each member vehicle of the group over time using the corresponding charging curves so that the sum of the corresponding charging curves (Pi) of the individual member vehicles (1, 2, ... N) corresponds to the optimized total charging power curve (P OPT ). 11 . A vehicle computing unit which is configured to store information about past charging processes and / or other vehicle data and / or user data and to transmit the information to a central control unit according to claim 10 via a communication interface. 12 . The vehicle computing unit according to claim 11 , which is configured to evaluate stored data and / or to predict a future energy requirement and / or a time profile of a future energy requirement.

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