DE102019202014A1 - Device and method for calculating a charging time and method for optimizing a route - Google Patents

Abstract

The present invention enables improved prognosis for the charging time of a traction battery of an electric vehicle. For this purpose, it is intended to evaluate characteristic parameters of the traction battery. Based on the available characteristic parameters of the traction battery, individual sections can be precisely determined during a charging process. This enables an exact forecast of the charging performance in each section of the charging process. In this way, a very precise estimate of expected charging processes for a traction battery of an electric vehicle can be achieved.

Description

The present invention relates to a method and a device for calculating a charging time for charging a traction battery of an electric vehicle. The present invention also relates to a method for optimizing a route with an electric vehicle.

State of the art

Fully or partially electrically powered vehicles are becoming increasingly important. Electrically powered vehicles have a so-called traction battery, which provides the electrical energy to drive the vehicle. This traction battery must be charged from an external energy source. Charging the traction battery generally requires a longer period of time compared to refueling a conventional vehicle with gasoline or diesel. Especially in a transition phase from conventional vehicles with combustion engines to modern electric vehicles, some users need support in planning suitable charging strategies. In addition, it is also helpful for the providers of charging stations and the operators of the energy supply networks that supply the charging stations with electrical energy to gain as precise knowledge as possible about the expected charging processes for the electric vehicles. In this context, the most precise possible knowledge of the expected charging time for charging a traction battery of an electric vehicle is desirable.

The pamphlet DE 10 2010 014 291 A1 discloses a method for determining a total travel time of an electric vehicle. In the method, among other things, the charging time is determined which is necessary to supply the required amount of energy to the electrical storage device at a charging station. Based on this, a total travel time can be determined, which includes the required charging time.

Disclosure of the invention

The present invention provides a method and a device for calculating the charging time for charging a traction battery of an electric vehicle, as well as a method for optimizing a route with an electric vehicle having the features of the independent claims. Further advantageous embodiments are the subject of the dependent claims.

Accordingly, it is provided:

A method of calculating the charging time for charging a traction battery in an electric vehicle. The method comprises a step of determining at least one characteristic parameter of the traction battery. The method further comprises a step of dividing the charging process for charging the traction battery of the electric vehicle into several sections. The charging process is subdivided using the determined at least one characteristic parameter of the traction battery. The method further comprises a step of calculating an individual charging duration for each section of the charging process for charging the traction battery. Finally, the method includes a step for determining the total charging time for charging the traction battery. The total charging time for charging the traction battery results, for example, from the sum of the previously calculated individual charging times for each section of the charging process.

It is also planned:

A method of optimizing a route using an electric vehicle. The method includes a step of receiving multiple waypoints of a route. The method further comprises a step of calculating a route along the waypoints and the consumption for the calculated route. The method further comprises a step of determining one or more charging points for charging a traction battery of the electric vehicle. Finally, the method includes the inventive calculation of the charging time for charging the traction battery of the electric vehicle. The charging time is calculated using the calculated consumption.

Finally, it is planned:

A device for calculating a charging time for charging a traction battery of an electric vehicle with an analysis device and a computing device. The analysis device is designed to determine at least one characteristic parameter of the traction battery. The computing device is designed to subdivide the charging process for charging the traction battery of the electric vehicle into several sections. In particular, the charging process is divided into several sections using the determined at least one characteristic parameter of the traction battery. The computing device is also designed to calculate an individual charging time for each section of the charging process for charging the traction battery of the electric vehicle, and to determine the total charging time for charging the traction battery of the electric vehicle. The total charging time results in particular from the sum of the individual charging times for each section of the charging process.

The present invention is based on the knowledge that precise knowledge of an expected charging time for charging a traction battery of an electric vehicle is of great importance. For example, if a user has precise knowledge of the expected charging time, he can coordinate his further actions with the expected charging time. When planning a total travel time for a route, it is very helpful to have as precise a knowledge of the interruptions as possible for charging the traction battery. In addition, the operators of charging infrastructures can optimize the available resources if they know the charging time as precisely as possible. Last but not least, an energy supply company, which provides the electrical energy at the individual charging points, can plan the energy requirement with knowledge of the expected charging time and thus coordinate the energy generation accordingly.

It is therefore an idea of the present invention to take this knowledge into account and to enable the charging time for charging a traction battery of an electric vehicle to be calculated as precisely as possible. In addition, it is an idea of the present invention, based on such a precisely determined charging time for charging the traction battery of an electric vehicle, to include the exact charging time in the planning of a route, in particular a route with several waypoints. In this way, not only can the comfort of the user of such an electric vehicle be increased, but it is also possible to develop an optimal charging strategy that enables the most efficient possible use of the electric vehicle, as well as the charging infrastructure and the power supply network, which provides the energy for charging provides, can be optimally matched to the expected charging processes.

According to the invention, it is taken into account that the charging time for charging a traction battery depends on numerous factors. On the one hand, a charging process for a traction battery can be divided into several phases, with the traction battery being charged with different charging powers during the different phases. In addition, there are numerous other influencing factors that determine the charging time of a traction battery. On the one hand, there are different types of traction batteries, for example. Each individual type can have a different charging behavior, for example due to the design. Therefore, for example, such construction-related differences in the individual traction batteries can also be included in the calculation of the expected charging time. Furthermore, other parameters, such as manufacturing tolerances or production-related influencing variables, can also influence the charging time of a traction battery.

In addition, a traction battery will also be subject to aging effects during its service life, depending on its load and use, which can also have an influence on the expected charging time of the traction battery. In order to be able to make as precise a statement as possible about the expected charging time of a traction battery, as many of these parameters as possible can be included in the calculation of the expected charging time. While some of the parameters, for example capacity, operating temperature or the like, can be determined directly, other characteristic parameters of the traction battery can possibly only be determined indirectly. If necessary, some characteristic parameters can also be determined or derived from a comparison with other, for example, similar or structurally identical traction batteries.

In particular, the transitions between the individual charging phases during charging of the traction battery can depend on one or more characteristic parameters of the traction battery. It is therefore of great benefit not to make general assumptions for a precise determination of the expected charging time for the traction battery, but to adapt the parameters of the charging process individually and as dynamically as possible for the respective traction battery.

If a user knows the exact charging time for charging the traction battery, he can, if necessary, coordinate further actions, for example a purchase, a meeting or the like, with the expected charging time. In particular, if there are time-dependent costs for charging the traction battery, the user will also be interested in returning to his vehicle in good time and not unnecessarily prolonging the charging process or the occupancy of the charging point. The operator of the charging infrastructure will also be interested in a charged vehicle being removed from the charging point as quickly as possible so that the charging point can be used to charge another vehicle. Precise knowledge of the expected charging time is therefore of crucial importance. Furthermore, an energy supply company can also plan the load on the energy supply network very precisely with precise knowledge of the expected charging time.

According to one embodiment, the lengths of the individual sections of the charging process can be dynamically adapted as a function of the at least one determined characteristic parameter. As previously explained, the Duration of the individual phases while charging the traction battery depend on one or more parameters of the traction battery. However, at least some of these parameters are not constant over the entire service life of the traction battery. Some parameters that influence the length of the individual sections of the charging process vary even while the traction battery is in operation. For example, the traction battery can heat up while driving or charging, which can shift the transitions between individual phases of the charging process.

According to one embodiment, the multiple sections for charging the traction battery include a section for initializing the charging process (pre-charge or pre-charge), a section for charging with constant current (CC) and a section for charging with constant voltage (constant voltage, CV). Such charging strategies are widespread in conventional charging processes. Of course, depending on the application and design of the traction battery, in particular for future or new types of traction batteries, other charging strategies and, in particular, a different subdivision of the charging process are also possible.

According to one embodiment, the at least one characteristic parameter of the traction battery comprises at least one of the following parameters: an operating temperature of the traction battery, a maximum cooling capacity of the traction battery, an aging condition of the traction battery, a maximum charging current of the traction battery, a chemical or physical property of the traction battery, such as a cell -Chemistry, form factor or something similar. Some of the characteristic parameters of the traction battery are fixed due to the design. These can be derived from a type designation or the like, for example. Further parameters can be measured directly or indirectly using suitable sensors, for example. Still other parameters can possibly only be determined indirectly by observing the operating behavior of the traction battery over a longer period of time or, if necessary, by comparing it with data from other traction batteries.

According to one embodiment, the charging process is divided into several sections and / or the individual charging time is calculated for each section of the charging process using previously stored reference values. For example, the reference values can be determined in the form of a table, for example a so-called lookup table (LUT) or the like. For example, the relationships between the beginning and / or end of a section for the charging process of the traction battery can be stored in a memory as a two- or multi-dimensional array as a function of one or more determined parameters of the traction battery. The data can be stored in a memory of the electric vehicle, for example. Alternatively, it is also possible to save the data in a central server or the like, for example in a cloud, and to transmit it to the vehicle if necessary. Any other desired strategies are of course also possible.

According to one embodiment, the charging process is divided into several sections and / or the individual charging time is calculated for each section of the charging process using comparison data from further traction batteries. For example, the operating data of a traction battery can be transmitted to a central point, for example a central server, for example a server in a cloud or the like. Such a central server can thus record, evaluate and save data from several traction batteries. In particular, it is possible to determine more precise data about the traction battery of the electric vehicle on the basis of comparison data, for example data about traction batteries from other electric vehicles. For example, a type designation of the traction battery can be used as soon as it is available. In addition, measured values from previous charging processes or operating data can also be recorded while the electric vehicle is driving and compared with corresponding data from other electric vehicles in order to obtain information about the traction battery of the electric vehicle from this comparison.

According to one embodiment, the method comprises a step of transmitting the calculated charging duration to a charging point. In particular, the information about the calculated charging time can be transmitted in advance to a charging point at which the electric vehicle is to be charged. In this way, the charging point knows in advance how long the charging process will take. This enables efficient use of the charging point and, in particular, planning of the available resources. In addition, the charging point can also pass the information received on to a network operator, so that the load on an energy supply network that feeds the charging point is also known. This makes it possible, if necessary, to provide sufficient power in advance by the energy supply network.

The above-described calculation of the charging time can in particular also be used for a Optimizing a route can be used with an electric vehicle. In particular, if one or more charging points for charging the traction battery are available along a route, an expected charging time can also be determined at these charging points and included in the planning process. Since, as already described above, the charging process can be divided into several sections, whereby the charging power can be different in the individual sections, the individual charging times can be different if the route varies, so that these variations in the charging times also lead to a variation in the Total travel time. Therefore, the route can be optimized by taking into account forecasts that are as precise as possible for the expected charging times.

According to one embodiment, several routes are calculated along the predetermined waypoints for the optimization of the route. In particular, predetermined user specifications can be received for calculating the route, and an optimal route can then be selected from the calculated multiple routes using the user specifications. For example, the user specification can include the fastest possible travel route taking into account the expected charging times. Further user specifications can include breaks to be specified at individual waypoints. Furthermore, the route can optionally also be optimized taking into account the shortest possible charging time. If necessary, cost specifications for charging the traction battery, in particular time-dependent costs, can also be taken into account. Of course, any other specifications for the route are also possible.

The above configurations and developments can be combined with one another as desired, as far as this makes sense. Further refinements, developments and implementations of the invention also include combinations, which are not explicitly mentioned, of features of the invention described above or below with regard to the exemplary embodiments. In particular, the person skilled in the art will also add individual aspects as improvements or additions to the respective basic forms of the present invention.

Figure list

The present invention is explained in more detail below with reference to the exemplary embodiments specified in the schematic figures of the drawings.

• 1: ein Spannungs-/Strom-Zeitdiagramm eines Ladevorgangs einer Traktionsbatterie gemäß einer Ausführungsform;
• 2: eine schematische Darstellung eines Blockschaubilds mit einem Elektrofahrzeug in einer Vorrichtung zur Berechnung einer Ladedauer gemäß einer Ausführungsform;
• 3: eine schematische Darstellung eines Ablaufdiagramms, wie es einem Verfahren zur Berechnung einer Ladedauer für das Aufladen einer Traktionsbatterie gemäß einer Ausführungsform zugrunde liegt; und
• 4: eine schematische Darstellung zur Veranschaulichung eines Verfahrens zur Optimierung einer Route gemäß einer Ausführungsform.

Show:

• 1 FIG. 12 shows a voltage / current time diagram of a charging process of a traction battery according to an embodiment;
• 2 : a schematic representation of a block diagram with an electric vehicle in a device for calculating a charging time according to an embodiment;
• 3 : a schematic representation of a flow chart as it is based on a method for calculating a charging time for charging a traction battery according to an embodiment; and
• 4th : a schematic representation to illustrate a method for optimizing a route according to an embodiment.

Description of the embodiments

1 shows a schematic representation of a current / voltage-time diagram to illustrate a charging process of a traction battery. The solid line illustrates the voltage curve U at the terminals of the traction battery. The dashed line illustrates the flow of current I. while charging. As can be seen in this diagram, the charging process can, for example, consist of four phases I. to IV be divided. The first phase is called the pre-charge phase or pre-charge mode. During this first phase, the charging process is initialized and the traction battery is brought into a state for optimal charging. For example, the operating temperature of the traction battery can be set to a predetermined temperature value or temperature range for this purpose. Particularly in the case of very low states of charge (SoC), the charging current can also be limited to a low value at the beginning of the charging process.

In a second phase II between the time t1 and t2 the traction battery is charged with a constant charging current I_c. During this phase, the electrical voltage on the traction battery increases continuously.

From a point in time t2 there then takes place the transition to charging the traction battery with a constant voltage U_c. The transition can be determined, for example, as a function of the battery voltage or a predetermined state of charge (SoC). While charging with a constant voltage, the charging current falls I. continuously from.

Towards the end of charging can occur at a time t3 the charging current I. be lowered even further. If necessary, charge equalization processes can be carried out at the end of the charging process take place between individual battery cells (balancing). In this way it can be achieved that finally all battery cells are at the same voltage level as possible. At time t4, the charging process is completely ended and the electric vehicle can be disconnected from the charging point.

As can be seen from the previous diagram, the charging power for charging the traction battery varies in the different sections I. to IV . Assuming a constant average charging power over the entire charging period would therefore lead to a very imprecise result. For a more precise estimate of the duration of the charging process, the charging process can therefore be divided into suitable sections. A separate estimate of the charging time can then be made for each section. For example, the subdivision into different sections can take place in accordance with the scheme described above into a precharge section, a section with constant current and a section with constant voltage. In addition, any other subdivisions are also possible. In particular, new types of battery technologies may also have different charging strategies and thus different charging phases.

The transition between the individual sections depends on battery-specific parameters. For example, the transitions between the individual sections can vary depending on different battery types. If, for example, the exact type or at least the type class of a traction battery is known, this information can also be taken into account in order to determine the times for the transitions between the individual sections. In addition, other parameters, such as the temperature of the traction battery, a possible cooling capacity (which can depend, for example, on an ambient temperature) for the traction battery as well as a possible maximum charging capacity of a charging point or the like can influence the transitions between the individual charging phases. Furthermore, the transitions between the individual phases can also be influenced as a function of the aging status of the traction battery or, if necessary, other parameters.

For the calculation of the charging time of the traction battery and in particular for the subdivision of the charging process into the individual sections, numerous parameters can be taken into account, as already mentioned above. This makes it possible, in particular, to individually adapt the division of the charging process into the individual sections for each planned charging process. Since the transitions between the individual sections of the charging process for different traction batteries can take place at different times, it is necessary to have the most detailed knowledge possible about the traction battery used. Some of the relevant data can be derived from a type designation of the traction battery, for example. For this purpose, for example, the relevant data for known types of traction batteries can be provided in a suitable storage device. For example, the relevant data can be stored in a memory of the electric vehicle. Alternatively, it is also possible to receive the data for the traction battery, for example based on an identification number of the traction battery or the like, from a remote data memory, for example a cloud or the like, via a data connection, for example a cellular connection or the like. If the exact type designation of the traction battery used is not known, for example because it cannot be read out, or if the manufacturer of the traction battery does not provide the relevant information, it may be possible, for example, to determine at least some of the information using sensors or indirectly via further measured values derive.

Furthermore, it is also possible for several electric vehicles to transmit information about parameters of their traction battery to a central memory, for example a cloud or the like. In this case, by comparing data about the traction battery in the electric vehicle with other data stored in a remote memory, a correlation can be established and thus additional information about the traction battery in an electric vehicle can be received from the remote memory. In this way, parameters about the traction battery, in particular parameters which are relevant for transitions between individual charging phases, can also be determined, even if detailed information about the traction battery used is not directly available.

Additionally or alternatively, it is also possible to log the information from previous charging processes, in particular data relating to transitions between individual sections of the charging process, and to evaluate this logged data in order to make a prognosis about the charging time of a planned charging process, in particular about transitions between individual sections of the charging process to determine. For this purpose, a suitable memory can be provided in the electric vehicle, which logs relevant data about previous charging processes. For example, data such as temperature of the traction battery, charging voltage, charging current, charging power, mode of the Charging process, in particular framework conditions are stored during a transition between different charging modes or the like. Data such as the state of charge at the beginning of the charging process and at the end of the charging process or other parameters that specify the health of the traction battery can also be logged. To characterize the traction battery, further parameters can also be logged during operation of the traction battery, in particular during the charging process, but also while driving, i.e. while the traction battery is emitting energy, or even while the vehicle is stationary. A total of the charging processes that have already taken place, in particular possibly in connection with the type of charging process, for example fast charging with direct voltage or slow charging with low power, etc., can also be logged.

In particular, if data on the driving behavior of the electric vehicle are also logged, this information can also be used to assess the health of the traction battery or, if necessary, to infer possible consumption while driving. For example, information on consumption can also be used to draw conclusions about when a further charging process might be necessary. From this information about consumption in connection with the charging behavior of the traction battery, precise forecasts of future planned charging processes can be derived.

2 shows a schematic representation of a block diagram of an arrangement with an electric vehicle 10 , which is a device for calculating the charging time for charging a traction battery 11 includes. The electric vehicle 10 can for example be a traction battery 11 include, which is an electric drive system of the electric vehicle 10 feeds. An analysis facility 12 can the traction battery 11 monitor and information about characteristic parameters of the traction battery 11 capture. The acquisition of the characteristic parameters of the traction battery 11 can be used both when stationary, when driving and when charging the traction battery 11 respectively. As already described, the characteristic parameters can be data such as the operating temperature of the traction battery 11 , State of charge, aging of the traction battery 11 , maximum possible charging current of the traction battery 11 , maximum cooling or heating capacity of the traction battery 11 or any other chemical or physical properties associated with the traction battery 11 act. Also other characteristic parameters of the traction battery not described here 11 are possible. The recorded characteristic parameters 11 can be recorded and, if necessary, directly in a computing device 13th further processed. Additionally or alternatively, it is also possible to transfer data via the traction battery 11 initially in a memory 14th to be saved and thus logged. This also provides historical information about the traction battery 11 available for evaluation. Furthermore, all or at least some of the recorded data can be transmitted via the traction battery 11 to a remote server 20th , for example a storage device in a cloud or the like. It is also possible, if necessary, to send data about the traction battery 11 from the remote server 20th to receive and to use the received data for further evaluation.

In the remote server 20th For example, characteristic data can be permanently stored on traction batteries. For example, these characteristic data can be provided by a manufacturer or another institution. Also metrologically recorded data, for example during the production of the traction batteries, can be recorded and used by the remote server 20th to be provided. Additionally or alternatively, it is also possible for several electric vehicles to record data via their traction batteries and the remote server 20th provide. The remote server 20th can receive and evaluate the data via the traction batteries of the multiple electric vehicles. In this way, information about unknown traction batteries can also be obtained, for example. In particular, if the manufacturer cannot or does not want to make the data available, it is still possible in this way to collect and evaluate relevant data about different traction batteries. In addition, further information, for example about aging behavior or the like, can also be obtained and evaluated in this way. Any further evaluations of data are also possible.

Based on the recorded characteristic parameters of the traction battery 11 can the computing device 13th then a forecast for the expected charging time of the traction battery 11 at a charging point 30th to calculate. In addition to that of the charging point 30th maximum available charging power also in particular the characteristic properties of the traction battery 11 also to consider which influence on the charging process. As stated above, the loading process is divided into several sections I. to IV divided. The computing device 13th can based on the available characteristic parameters of the traction battery 11 a corresponding division of the charging process into make individual sections. The computing device can then 13th Estimate a required charging time for each of the sections. The total charging time to be expected then results from the sum of the individual charging times for the respective sections.

The times for the transition between the individual sections can be based on the analysis device 12 determined characteristic parameters of the traction battery 11 to be determined. This can take place, for example, on the basis of predetermined mathematical formulas or the like. Alternatively, it is also possible to determine relationships between the characteristic parameters and the transition of the individual sections of the loading process in advance and to store them, for example, in a table or the like. In this case, the transitions can be easily determined on the basis of the previously stored relationships, possibly by means of further interpolations. In the same way, the required charging times for the individual sections can also be based on the characteristic parameters of the traction battery 11 be determined either based on given formulas or previously calculated relationships in tabular form or the like. If necessary, the values calculated in advance can be stored in a remote server 20th determined and by means of a data connection to the vehicle 10 be transmitted.

It is also possible that the vehicle 10 Information about the calculated charging time and, if necessary, information about a planned start or end of charging in advance at a charging point 30th transmits. This way the charging point can 30th Plan resources efficiently and, if necessary, request a power requirement from a connected energy supply network or, if necessary, a charging point 30th to reserve for the expected charging time.

3 shows a schematic representation of a flow chart, as is a method for calculating the charging time for charging a traction battery 11 of an electric vehicle 10 underlying. In one step S1 becomes at least one characteristic parameter of the traction battery 11 determined. In step S2 there is a subdivision of the charging process for charging the traction battery 11 in several sections. The dividing S2 takes place in particular using the determined at least one characteristic parameter of the traction battery 11 . In step S3 an individual charging time is calculated for each section of the charging process for charging the traction battery 11 . In step S4 the total charging time for charging the traction battery is finally determined 11 from the sum of the steps above S3 calculated individual charging times for each section of the charging process.

The calculated charging time can also be used to optimize route planning. In particular, if several charging points are available along an intended route, the journey along the route can be optimized by selecting the charging processes accordingly. For this purpose, a user can optionally specify different specifications for the optimization. For example, a route can be optimized with regard to the total travel time. Alternatively, the route can also be optimized with regard to expected consumption, costs for charging processes or the like. If it is also known that a longer stay is required at one or more of the charging points, for example for a lunch break, shopping, a meeting or the like, then the duration of this stay can also be included in the planning of the route. In particular, if a user is planning to stay near a charging point anyway, the charging time required will not be perceived negatively by the user.

4th shows, for example, a schematic representation of a route with several waypoints WPi. The journey begins at the starting point WP0 and ends at the last waypoint WP4. If necessary, the start and finish can also be identical. The waypoints WP1, WP2 and WP3 should be approached along the route. There is a stop at the waypoints WP1, WP2 and WP3 T1 , T2 and T3 planned. In addition to the route to be traveled, the expected consumption of electrical energy from the traction battery can also be determined for route planning. If necessary, a user can also specify a minimum charge level at destination WP4. Based on this data, it can now be estimated whether the planned length of stay, T1 , T2 , T3 at waypoints WP1, WP2 and WP3 are sufficient for the required charging of the traction battery. If necessary, by varying the order in which the individual waypoints WP1, WP2 and WP3 are approached, the total travel time can be optimized in relation to the required loading times. If, for example, only a relatively short stay is planned at the last waypoint WP3, while a relatively long stay is planned at the first waypoint WP1, the dwell time may possibly be T3 Not sufficient at waypoint WP3 to charge the traction battery to the desired target charge level. In the reverse order, that is, if WP1 is approached as the last waypoint, and a longer dwell time T1 is provided, so can this waypoint WP1 as the last waypoint the desired target charge level can be reached without additional delay.

Furthermore, other framework conditions can also be included in the planning if necessary. If, for example, time-dependent charges for charging the traction battery are charged at one or more of the waypoints WP1, WP2, WP3, the charging process at charging points with high charges can be minimized on the basis of the forecast for the duration of charging, and alternatively the charging time at charging points low or no renewal fees.

In addition, as for example in 1 has been shown, the charging power is not equally high in all sections of the charging process. Especially in sections with constant current strength (CC mode), charging takes place with a particularly high charging power. Correspondingly, for example, route guidance is possible in which the charging process takes place predominantly in sections with constant current charging with as many charging points as possible.

In addition, any other desired strategies for optimizing a planned route are of course also possible.

In summary, the present invention relates to an improved prognosis for the charging time of a traction battery of an electric vehicle. For this purpose, it is intended to evaluate characteristic parameters of the traction battery. Based on the available characteristic parameters of the traction battery, individual sections can be precisely determined during a charging process. This enables an exact forecast of the charging performance in each section of the charging process. In this way, a very precise estimate of expected charging processes for a traction battery of an electric vehicle can be achieved.

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102010014291 A1 [0003]

Claims (10)

Method for calculating the charging time for charging a traction battery (11) of an electric vehicle (10), with the steps: Determining (S1) at least one characteristic parameter of the traction battery (11); Subdividing (S2) a charging process for charging the traction battery (11) into several sections, using the determined at least one characteristic parameter of the traction battery (11); Calculating (S3) an individual charging time for each section of the charging process for charging the traction battery (11); and Determining (S4) the total charging time for charging the traction battery (11) from the sum of the individual charging times for each section of the charging process. Procedure according to Claim 1 , the lengths of the individual sections of the charging process being dynamically adapted as a function of the at least one characteristic parameter of the traction battery (11). Procedure according to Claim 1 or 2 wherein the plurality of sections for charging the traction battery (11) include a section (I) for initializing the charging process, a section (II) for charging with constant current and a section (III) for charging with constant voltage. Method according to one of the Claims 1 to 3 , wherein the at least one characteristic parameter of the traction battery (11) is an operating temperature of the traction battery (11), a maximum cooling capacity of the traction battery (11), an aging condition of the traction battery (11), a maximum charging current of the traction battery (11) and / or chemical or comprises physical property of the traction battery (11). Method according to one of the Claims 1 to 4th , the division (S2) of the charging process into several sections and / or the calculation (S3) of the individual charging duration for each section of the charging process taking place using previously stored reference values. Method according to one of the Claims 1 to 5 , the division (S2) of the charging process into several sections and / or the calculation (S3) of the individual charging duration for each section of the charging process taking place using comparison data from further traction batteries. Method according to one of the Claims 1 to 3 , with a step for transmitting the calculated charging time to a charging point (30). A method for optimizing a route with an electric vehicle (10), comprising the steps of: receiving several waypoints (WP1, WP 2, WP 3) of a route; Calculating a route along the waypoints (WP1, WP 2, WP 3) and a consumption for the calculated route; Determining one or more charging points for charging a traction battery (11) of the electric vehicle 10; Calculating a charging time for charging the traction battery (11) according to the method according to one of the Claims 1 to 7th , using the calculated consumption. Procedure according to Claim 8 wherein the step of calculating the route along the waypoints (WP1, WP 2, WP 3) calculates a plurality of alternative routes, and wherein the method comprises the following steps: receiving a user specification for the route; Selecting an optimal route using the received route preference. Device for calculating a charging time for charging a traction battery (11) of an electric vehicle (10), with: an analysis device (12) which is designed to determine at least one characteristic parameter of the traction battery (11); and a computing device (13) which is designed to subdivide the charging process for charging the traction battery (11) into several sections, the division into several sections using the determined at least one characteristic parameter of the traction battery (11), and wherein the Computing device (12) is further designed to calculate an individual charging time for each section of the charging process for charging the traction battery (11) and the total charging time for charging the traction battery (11) from the sum of the individual charging time for each section of the charging process to determine.

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