Method for adapting anode overvoltage of a lithium-ion battery, method for improving the state of capacity aging of a lithium-ion battery

Abstract

The invention relates to a method for adapting the anode overvoltage of a lithium-ion battery (310). The invention furthermore relates to a method for improving the state of capacity aging of a lithium-ion battery (310). The invention also relates to a vehicle having at least one lithium-ion battery (310), the anode overvoltage of which is adapted in accordance with the method for adapting the anode overvoltage of a lithium-ion battery (310) and / or the state of capacity aging of which is improved in accordance with the method for improving the state of capacity aging of a lithium-ion battery (310). The invention also relates to a fleet management system which is set up to carry out the method for adapting the anode overvoltage of a lithium-ion battery (310) and / or the method for improving the state of capacity aging of a lithium-ion battery (310).

Description

Technical Field

[0001] The present invention relates to a method for adapting the anode overvoltage of a lithium-ion battery. The present invention further relates to a method for improving the capacity aging state of a lithium-ion battery. The present invention also relates to a vehicle having at least one lithium-ion battery, the anode overvoltage of which is adapted according to the method for adapting the anode overvoltage of a lithium-ion battery and / or the capacity aging state of the lithium-ion battery is improved according to the method for improving the capacity aging state of a lithium-ion battery. The present invention also relates to a fleet management system configured to execute the method for adapting the anode overvoltage of a lithium-ion battery and / or the method for improving the capacity aging state of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries are considered the energy storage devices of the future. They can store a large amount of current in a small space and at a relatively low weight. However, lithium-ion batteries have the problem of lithium deposition, in which metallic lithium forms and deposits during charging, reducing the battery's lifespan or potentially causing a short circuit or fire.

[0003] To estimate the capacity aging state (SOHC) of lithium-ion batteries, the anode overvoltage of the lithium-ion battery is used. It is assumed that as long as the anode overvoltage is well above 0 mV, the capacity aging state of the lithium-ion battery is normal, and that the capacity aging state of the lithium-ion battery is deteriorated, that is, once the anode overvoltage approaches 0 mV, the aging of the lithium-ion battery is accelerated by increased lithium precipitation.

[0004] Document US 2014 / 0312912 A1 discloses a method and circuit for adaptively charging a battery.

[0005] A method for detecting battery properties is known from US 2006 / 0238168 A1. Summary of the Invention

[0006] A method is proposed for adapting a limit value of an anode overvoltage of a lithium-ion battery.

[0007] First, a signal curve of a lithium-ion battery is provided. The signal curve includes at least the voltage, current, and operating temperature curves of the lithium-ion battery, as well as a capacity aging state curve. Here, the capacity loss of the lithium-ion battery is represented by the capacity aging state curve.

[0008] The signal curves and capacity aging curves of the lithium-ion batteries are then transferred to a storage device. Data for multiple lithium-ion batteries is stored in the storage device, including at least the voltage, current, and operating temperature curves of the respective lithium-ion batteries, as well as the capacity aging curves and anode overvoltage limit values.

[0009] The signal curves of the lithium-ion batteries are then compared with the data stored in the storage device, and comparison batteries are selected whose signal curves are similar to the signal curves of the lithium-ion batteries. The similarity between the signal curves can be defined using a least squares method. The lithium-ion batteries are preferably compared over similar time intervals.

[0010] Subsequently, the data of the comparative battery are examined to obtain the average capacity aging curve and average capacity loss of the comparative battery, and the optimized limit value of the anode overvoltage corresponding to the average capacity loss is also obtained.

[0011] The capacity aging state curve of the lithium-ion battery is then compared with the average capacity aging state curve of the comparison battery.

[0012] If it is subsequently determined that the capacity aging curve of the lithium battery is below the average capacity aging curve of the comparison battery, the limit value of the anode overvoltage of the lithium-ion battery is adapted to the optimized limit value of the anode overvoltage. With the limit value of the anode overvoltage of the lithium-ion battery adapted to the optimized limit value, the capacity loss of the lithium-ion battery is consistent with the average capacity loss.

[0013] The signal curve and the capacity aging state curve of the lithium-ion battery are preferably transmitted to the storage device by means of a wireless network.

[0014] In this case, the wireless network can be designed as a WLAN network. The wireless network is preferably designed as a mobile radio network, such as a UMTS or LTE network.

[0015] The storage device is preferably designed as a cloud storage. However, it is also conceivable that the storage device is designed as a storage medium, a memory of a control unit such as a lithium-ion battery, or an external memory.

[0016] The storage device preferably includes an artificial intelligence (AI)-based model for adapting the anode overvoltage limit value of the lithium-ion battery. Using the AI-based model, a comparison battery is selected based on the voltage, current, and operating temperature profiles of the lithium-ion battery stored in the storage device. Using the AI-based model, the comparison battery data is examined to determine the average capacity aging curve and average capacity loss of the comparison battery, as well as an optimized anode overvoltage limit value corresponding to the average capacity loss. Through comparison, the AI-based model can identify when the capacity aging curve of the lithium-ion battery falls below the average capacity aging curve and provide an optimized anode overvoltage limit value. The anode overvoltage limit value of the lithium-ion battery is then adapted to the optimized limit value.

[0017] Furthermore, a method for improving the capacity aging state of a lithium-ion battery is proposed.

[0018] Here, the capacity aging state of the lithium-ion battery is first calculated using a battery model. The anode overvoltage limit of the lithium-ion battery is estimated. Of course, the battery model can also calculate other parameters of the lithium-ion battery, such as the state of charge and lithium ion concentration.

[0019] The optimal charging current of the lithium-ion battery is then calculated with the help of the charging model so that the anode overvoltage of the lithium-ion battery is not lower than the limit value, that is, the anode overvoltage of the lithium-ion battery should be greater than or at least equal to the limit value.

[0020] The lithium-ion battery is then charged with an optimal charging current and monitored. A capacity aging state curve and a signal curve of the lithium-ion battery are detected, the signal curve comprising at least voltage, current, and operating temperature curves.

[0021] The limit value of the anode overvoltage of a lithium-ion battery is subsequently adapted with the aid of the method according to the invention for adapting the limit value of the anode overvoltage of a lithium-ion battery.

[0022] The optimal charging current is then recalculated using the charging model with the adapted limit values ​​for the lithium-ion battery.

[0023] Preferably, the lithium-ion battery is equipped with a battery management system for monitoring and controlling the lithium-ion battery and for detecting a capacity aging state curve and a signal curve of the lithium-ion battery. The battery model is preferably integrated into the battery management system.

[0024] The optimal charging current is preferably calculated based on optimal control theory. The charging model is also preferably integrated into the battery management system. The limit value for the anode overvoltage estimated by the battery model can be directly transmitted to the charging model. It is also possible to adjust the limit value via the battery management system.

[0025] The battery management system is preferably equipped with a telematics control unit for data transmission between the battery management system and the storage device.

[0026] The battery management system may also include a memory for storing data of the lithium-ion battery, such as the anode overvoltage and other electrochemical parameters of the lithium-ion battery, which are measured in the laboratory.

[0027] A vehicle comprising at least one lithium-ion battery is also proposed, the anode overvoltage of the lithium-ion battery being adapted according to the method for adapting the anode overvoltage of a lithium-ion battery according to the present invention and / or the capacity aging state of the lithium-ion battery being improved according to the method for improving the capacity aging state of a lithium-ion battery according to the present invention.

[0028] In this case, further information or parameters of the vehicle, such as a usage profile of the vehicle and the driving style of the driver, can also be transferred to the storage device.

[0029] Furthermore, a fleet management system is proposed, which is configured to carry out the method according to the invention for adapting the anode overvoltage of a lithium-ion battery and / or the method according to the invention for improving the capacity aging state of a lithium-ion battery.

[0030] Fleet management systems are used to manage, plan, control, and monitor fleets of vehicles, also known as carpools. Vehicle routes are coordinated and defined, taking into account certain influencing factors. Fleet management systems are designed to identify problems early, eliminate them, or prevent them from occurring.

[0031] The fleet management system can have a cloud-based storage device in which the vehicle data are stored. The vehicle data include not only the data of the lithium-ion batteries used in the vehicle but also the vehicle usage profile and the driving style of the vehicle driver.

[0032] Advantages of the invention

[0033] In recent years, efforts have been underway to develop accurate battery models at high voltages in order to model internal states such as the state of charge (SOC), lithium-ion concentration, and anode overvoltage, as well as external states, particularly voltage, current, and operating temperature. The goal of accurately describing the battery's internal lifetime is to optimize charging algorithms so that the battery ages as little as possible or lasts as long as possible.

[0034] Until now, only vague arguments have been made about how limit values ​​for anode overvoltage should be defined. Too high a limit value would lead to overly conservative charging and increased charging times. Too low a limit value would result in overly aggressive charging and rapid battery aging.

[0035] The method according to the present invention can be used to update the limit value of the anode overvoltage of a lithium-ion battery online so that the limit value is always at the best possible value, thereby eliminating the need for offline calibration.

[0036] The method according to the invention makes it possible to develop a robust charging algorithm which regulates the charging current in such a way that the lithium-ion battery is handled as kindly as possible, while the charging duration and energy content are optimized.

[0037] With the method according to the invention, the charging duration of a lithium-ion battery can be optimized either with sensible aging of the lithium-ion battery or with the same sensible aging of the lithium-ion battery.

[0038] Furthermore, the method according to the invention forms the basis for data-based learning and can be further developed, for example, for optimal driving behavior and gentle aging behavior in driving and parking maneuvers. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Embodiments of the present invention are explained in more detail with reference to the drawings and the following description.

[0040] Figure 1 A first flow chart of the method according to the invention for adapting the anode overvoltage of a lithium-ion battery is shown,

[0041] Figure 2 A second flow chart of the method for improving the capacity aging state of a lithium-ion battery according to the present invention is shown, and

[0042] Figure 3 A schematic diagram of a vehicle is shown. DETAILED DESCRIPTION

[0043] In the following description of embodiments of the present invention, identical or similar elements are denoted by the same reference numerals, wherein a repeated description of these elements is omitted in individual cases. The figures merely schematically illustrate the subject matter of the present invention.

[0044] Figure 1 FIG. 3 shows a method for adapting a lithium-ion battery 310 according to the present invention (see FIG. Figure 3 ) is a first flow chart 100 of a method for anode overvoltage.

[0045] In step 101 of first flowchart 100 , a signal curve of lithium-ion battery 310 is provided. The signal curve includes at least voltage, current, and operating temperature curves of lithium-ion battery 310, as well as a capacity aging state curve. The capacity loss of lithium-ion battery 310 is represented by the capacity aging state curve.

[0046] Subsequently, in a second step 102 of the first flowchart 100 , the signal curve and the capacity aging state curve of the lithium-ion battery 310 are transmitted to the storage device 330 (see Figure 3 ). Data of the plurality of lithium-ion batteries 310 are stored in the storage device 330 , wherein the data includes at least voltage, current, and operating temperature curves of the respective lithium-ion batteries 310 , as well as capacity aging curves and anode overvoltage limit values.

[0047] In third step 103 of first flowchart 100 , the signal curve of lithium-ion battery 310 is compared with data stored in storage device 330 , and a comparison battery whose signal curve is similar to the signal curve of lithium-ion battery 310 is selected.

[0048] In the fourth step 104 of the first flowchart 100, the data of the comparison battery is examined to obtain the average capacity aging curve and average capacity loss of the comparison battery. Here, the optimized limit value of the anode overvoltage corresponding to the average capacity loss is also obtained.

[0049] In the fifth step 105 of the first flowchart 100 , the capacity aging state curve of the lithium-ion battery 310 is compared with the average capacity aging state curve of the comparison battery.

[0050] In the sixth step 106 of the first flowchart 100, if it is determined that the capacity aging state curve of the lithium-ion battery 310 is below the average capacity aging state curve of the comparison battery, the limit value of the anode overvoltage of the lithium-ion battery 310 is adapted to the optimized limit value of the anode overvoltage. With the limit value of the anode overvoltage of the lithium-ion battery 310 adapted to the optimized limit value, the capacity loss of the lithium-ion battery is consistent with the average capacity loss.

[0051] Figure 2 2 shows a second flow chart 200 of the method for improving the capacity aging state of a lithium-ion battery 310 according to the present invention.

[0052] In a first step 201 of second flowchart 200 , the capacity aging state of lithium-ion battery 310 is calculated using a battery model. In this process, a limit value for the anode overvoltage of lithium-ion battery 310 is estimated.

[0053] In the second step 202 of the second flowchart 200 , an optimal charging current for the lithium-ion battery 310 is calculated with the aid of a charging model so that the anode overvoltage of the lithium-ion battery 310 does not fall below a limit value, i.e., the anode overvoltage of the lithium-ion battery 310 should be greater than or at least equal to a threshold value.

[0054] In the third step 203 of the second flowchart 200 , the lithium-ion battery 310 is charged with an optimal charging current and monitored. Here, the capacity aging state curve and the signal curve of the lithium-ion battery are detected, the signal curve including at least the voltage, current and operating temperature curves.

[0055] In the fourth step 204 of the second flow chart 200, by means of Figure 1 The method for adapting the limit value of the anode overvoltage of a lithium-ion battery according to the present invention described in is used to adapt the limit value of the anode overvoltage of the lithium-ion battery 310 .

[0056] In a fifth step 205 of second flowchart 200 , the optimal charging current is recalculated with the aid of the charging model using the adapted limit values ​​of lithium-ion battery 310 .

[0057] Figure 3 A vehicle 300 is schematically shown, which has a lithium-ion battery 310 and a battery management system 320 .

[0058] The vehicle 300 further comprises a telematics control unit (not shown), which is preferably integrated into the battery management system 320 for data transmission between the vehicle 300 or the lithium-ion battery 310 and the storage device 330 via communication 340 .

[0059] In this case, storage device 330 is designed as a cloud storage and is used to store data of a plurality of lithium-ion batteries 310 .

[0060] The data of the lithium-ion battery 310 of the vehicle 300 is detected by the battery management system 320 and transmitted to the storage device 330. The data of the lithium-ion battery 310 is then compared with the data stored in the storage device 330 to select a comparison battery.

[0061] In this case, storage device 330 is configured to carry out the method according to the invention for adapting the anode overvoltage of lithium-ion battery 310 .

[0062] Once the anode overvoltage of the lithium-ion battery 310 is adapted, the adapted anode overvoltage is sent to the battery management system 320 via the communication 340 between the battery management system 320 and the storage device 330. The charge model (not shown) of the battery management system 320 recalculates the optimal charge current according to the adapted anode overvoltage so that the capacity loss of the lithium-ion battery 310 of the vehicle 300 is in line with the average capacity loss of the comparison batteries.

[0063] The vehicle 300 can be assigned to a vehicle fleet and the storage device 330 can be assigned to a fleet management system for managing, planning, controlling and monitoring all vehicles of the vehicle fleet.

[0064] The application is not limited to the embodiments described here and to the aspects emphasized therein. Rather, a large number of modifications is possible within the scope of the application as it is specified by the claims.

Claims

1. A method for adapting a limit value of an anode overvoltage of a lithium-ion battery (310), the method comprising the following steps: - providing a signal curve of the lithium-ion battery (310), the signal curve comprising at least voltage, current and operating temperature curves and a capacity aging state curve of the lithium-ion battery (310); - transmitting the signal curve and capacity aging state curve of the lithium-ion battery (310) to a storage device (330), storing data of a plurality of lithium-ion batteries (310) in the storage device, the data including at least the voltage, current and operating temperature curves of each lithium-ion battery (310) as well as the capacity aging state curve and the limit value of the anode overvoltage; - comparing the signal curve of the lithium-ion battery (310) with the data stored in the storage device (330) and selecting a comparison battery; - checking the data of the comparative battery to obtain an average capacity aging curve and an average capacity loss of the comparative battery and an optimized limit value of the anode overvoltage corresponding to the average capacity loss; - comparing the capacity aging state curve of the lithium-ion battery (310) with the average capacity aging state curve of the comparison battery; If it is detected that the capacity aging curve of the lithium-ion battery (310) lies below the average capacity aging curve of the comparison battery, the limit value of the anode overvoltage of the lithium-ion battery (310) is adapted to the optimized limit value of the anode overvoltage.

2. The method according to claim 1, characterized in that The signal curve and capacity aging state curve of the lithium-ion battery (310) are transmitted to the storage device (330) by means of a wireless network.

3. The method according to claim 2, characterized in that The wireless network is designed as a mobile radio network.

4. The method according to any one of claims 1 to 3, characterized in that The storage device (330) is designed as a cloud storage.

5. The method according to any one of claims 1 to 4, characterized in that The storage device (330) has an artificial intelligence-based model for adapting a limit value for an anode overvoltage of the lithium-ion battery (310).

6. A method for improving the capacity aging state of a lithium-ion battery (310), the method comprising the following steps: - calculating the capacity aging state of the lithium-ion battery (310) and estimating the limit value of the anode overvoltage of the lithium-ion battery (310) by means of a battery model; - calculating an optimal charging current by means of a charging model so that the anode overvoltage of the lithium-ion battery (310) is greater than or equal to a limit value of the anode overvoltage; - charging the lithium-ion battery (310) with an optimal charging current and monitoring the lithium-ion battery, wherein a capacity aging state curve and a signal curve of the lithium-ion battery (310) are detected, the signal curve comprising at least voltage, current and operating temperature curves; - adapting the limit value of the anode overvoltage of the lithium-ion battery (310) according to the method according to any one of claims 1 to 5; - calculating the optimal charging current with the aid of the charging model using an adapted limit value for the anode overvoltage of the lithium-ion battery (310).

7. The method according to claim 6, characterized in that The lithium-ion battery (310) is equipped with a battery management system (320) for monitoring and controlling the lithium-ion battery (310) and for detecting a capacity aging state curve and a signal curve of the lithium-ion battery (310), and the battery model and / or the charging model are integrated into the battery management system.

8. The method according to claim 6 or 7, characterized in that The optimal charging current is calculated based on optimal control theory.

9. The method according to any one of claims 7 to 8, characterized in that The battery management system (320) is equipped with a telematics control unit for data transmission between the battery management system (320) and the storage device (330).

10. A vehicle (300), comprising at least one lithium-ion battery (310), characterized in that: The anode overvoltage of the at least one lithium-ion battery (310) is adapted according to the method according to any one of claims 1 to 5 and / or the capacity aging state of the at least one lithium-ion battery (310) is improved according to the method according to any one of claims 6 to 9. 11 . A fleet management system configured to carry out the method according to claim 1 and / or the method according to claim 6 .

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