Method for calculating and influencing remaining useful life of component of electric vehicle

Abstract

The invention relates to a method (1) for calculating and influencing the remaining service life (10) of a component of an electric vehicle, in which measured sensor data (3) and calculated and / or predefined parameters (4) are combined into a damage model (5, 6). 5 ', 5' '), the remaining service life (10) being determined after a comparison (8) with data describing the target state of health (9), characterized in that, if the remaining service life (10) is below a threshold value, a service life extension mode (11) is activated, which takes measures in the vehicle to extend the service life.

Description

Technical Field

[0001] The invention relates to a method for calculating and influencing the remaining service life of a component of an electric vehicle in order to determine the expected service life of the vehicle. Background Art

[0002] The core approach of the prognostic and health management system is to use data about the use and status of the individual systems in operation to identify disturbances and failures early and to determine appropriate countermeasures. As a result, decisions can be made with overall economic benefits in mind. For example, costs can be reduced, risks minimized, available operating hours maximized and availability improved at the same time.

[0003] Electric vehicles use a pure electric drive system, called eDrive. The drive system of an electric vehicle includes an electric motor, a mechanical reducer, an inverter, and a motor controller.

[0004] The inverter converts DC power from the battery into AC power, which is used to drive the motor. The motor delivers torque to the vehicle's powertrain. The controller controls the power delivered to the motor through the inverter.

[0005] US2021 / 0 175 835A1 shows a method for determining the health state of a drive. The controller estimates the motor torque using a model of the motor. The model estimates the motor torque based on DC power and motor speed. The controller is also configured to calculate the motor torque based on the sampled current and the magnetic flux of the motor. The controller is also configured to generate a first health state of the electric drive system based on the difference between the estimated motor torque and the calculated motor torque. The controller is also configured to generate a second health state for at least one of a position sensor, a plurality of sensors, and a motor in response to the first health state being less than or equal to a first threshold. The controller is also configured to determine whether there is interference based on a combination of the first health state and the second health state.

[0006] For example, the health indicators include system-level health indicators that account for the power of the electric drive system; component-level health indicators that account for the performance of the current sensor; and health indicators that account for the health of the motor windings.

[0007] The method stores the above health information in the cloud. The historical health information of the vehicle can be used to illustrate trends in component performance, predict failures and plan maintenance, and use machine learning techniques to recommend structural changes to components.

[0008] When the component is damaged, but the damage is not enough to require maintenance, the controller sends an email or text message to remind the driver. Alternatively, when the component is damaged enough to require maintenance, the method will plan maintenance for the component. Summary of the invention

[0009] The object of the present invention is to provide an optimized solution for extending the service life.

[0010] The object is achieved by means of a method for calculating and influencing the remaining service life of a component of an electric vehicle, wherein measured sensor data and calculated and / or predefined parameters are combined to form a damage model, wherein the remaining service life is determined after a comparison with data describing a target health state, wherein if the remaining service life falls below a threshold value, a service life extension mode is activated, which takes measures in the vehicle to extend the service life.

[0011] The measures include thermal and / or mechanical damage distribution or load distribution within the installed drive components, for example between the front axle drive system and the rear axle drive system, and / or limiting operation at high electrical power and / or accessing driver assistance systems in order to achieve route optimization.

[0012] The measures are accompanied by suggestions, instructions or even warnings to the driver for adjusting driving style.

[0013] The service life extension mode provides the driver with information and / or incentives for adapting his driving style, for example by continuously monitoring the status, by a points system or by unlocking additional functions.

[0014] The strategy according to the invention can process the detected data in real time and, for protection purposes, adapt the operating strategy based on the components at risk in each case or encourage the driver to adapt his driving behavior.

[0015] It is possible to increase the remaining service life by adjusting the route according to the health status of the individual components, such as avoiding fully loaded uphill driving.

[0016] The measures lead to optimal utilization of the eDrive system, which has balanced aging of individual components, and / or to optimal utilization of the entire vehicle due to balanced aging of the components, regardless of the area of ​​use and type of driver.

[0017] By transmitting information to the driver, preventive steps are pre-set for the driver, which motivates the driver to adopt an optimized driving style.

[0018] For fleet vehicles, fewer fleet breakdowns, shorter service intervals and reduced maintenance costs associated therewith are all beneficial.

[0019] Through early information, preventive maintenance intervals or reduced maintenance costs are possible for the end customer. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of method 1 for calculating and influencing the remaining useful life is shown;

[0021] Figure 2 The impact potential of the service life extension mode is shown. DETAILED DESCRIPTION

[0022] In the highly integrated eDrive system, there are numerous sensors that monitor variables required for operation, such as oil temperature, phase current, rotor speed, and winding head temperature. In addition to regulating the eDrive system, these measured variables can also be used to determine the health status of components in the vehicle. Here, the sensor evaluation is not limited to the electric machine, but also includes the entire powertrain of the vehicle.

[0023] During normal driving situations in driving state 2, data from sensors 3 are acquired as input variables for evaluating the actual health state of the vehicle components. These data from sensors 3 are available for further evaluation of all vehicle components. The data are processed together with additional calculated variables 4, 4', 4", ..., which are respectively associated with the individual components of the vehicle, such as the temperature of the contact surface or the torque.

[0024] The two data sets consisting of measured and stored data form the data basis for a damage model 5 , 5 ′, 5 ″, . . . which is stored and calculated for each component of the vehicle.

[0025] In this case, an analytical relationship is established between the measured variables and the calculated variables and the partial damage 6 , 6 ′, 6 ″, . . . is determined therefrom.

[0026] In a next step, these partial damages 6, 6', 6", ... are respectively associated with each component and the total damage 7 of each component is determined. Subsequently, in a step, the total damage of each component is compared with a target health state 9. The target health state 9 comprises the design data and load limits of the vehicle components, as well as driving performance limits and / or total driving distance. In the comparison, the target health state 9 allows the remaining service life 10 of the component to be stated in percentage and / or in terms of still possible driving performance or remaining service life time.

[0027] If comparison 8 shows that remaining service life 10 has reached the value zero, then the total damage to the component, which was assumed in the design, has occurred due to the corresponding driving maneuvers or load conditions.

[0028] At a defined threshold value for the health state of the component, ie a threshold value for the remaining operating time 10, an endurance mode, a service life extension mode 11, is activated. The threshold value can be parameterized according to the desired end-user application.

[0029] The service life extension mode 11 influences the overall operating strategy of the vehicle or also only individual open-loop and closed-loop control parameters in order to keep the overall damage low and to increase the possible overall driving range.

[0030] The service life extension mode 11 includes various possibilities and measures for influencing the driving state 2 actively and without requiring the driver's consent.

[0031] The driving style that extends the remaining service life of the components is supported by directly taking into account the load profile driven by the driver. Thus, the service life extension mode 11 refuses to drive in the "boost" mode 12 of the electric vehicle, i.e. by releasing increased power, in order to extend the service life of the eDrive or to protect critical components from premature failure.

[0032] In the service life extension mode 11 , individual components are also addressed and their closed-loop control parameters are influenced, such as the boost mode 12 .

[0033] An extension of the remaining service life 10 is achieved by actively influencing the thermal and / or mechanical damage distribution or load distribution within the installed drive components via so-called “health vectoring”.

[0034] The service life extension mode 11 also adjusts the existing driver assistance system 13 so as to increase the remaining service life. To this end, the navigation device is informed to avoid avoidable uphill sections when planning the route so as to minimize the damage to the vehicle or components in the vehicle.

[0035] In addition to the automatic setting steps of the service life extension mode 11, the service life extension mode 11 also provides the driver with information 14 and / or incentives for adjusting the driving style, similar to the instruction module, in order to correspondingly encourage the driver to adjust the driving style according to the gentle driving characteristics. For example, this can be achieved by a special "reward system", similar to an arcade game through a point system or by unlocking additional functions.

[0036] The service life extension mode 11 also always provides a warning 15 which is provided to the driver 16 when the service life extension mode 11 automatically performs closed loop control.

[0037] Reference numerals list

[0038] s

[0039] 1 Methods

[0040] 2 Driving status

[0041] 3 Sensor Data

[0042] 4, 4', 4" Calculated parameters

[0043] 5, 5', 5" Calculated damage model

[0044] 6, 6', 6" Calculated Partial Damage

[0045] 7 Total Damage

[0046] 8 Comparison

[0047] 9 Target health status

[0048] 10 Remaining service life

[0049] 11 Life extension mode

[0050] 12 boost modes

[0051] 13. Driving Assistance Systems

[0052] 14 Information

[0053] 15 Warnings

[0054] 16 Driver

Claims

1. A method (1) for calculating and influencing the remaining service life (10) of a component of an electric vehicle, wherein measured sensor data (3) and calculated and / or predefined parameters (4, 4', 4") are combined to form a damage model (5, 5', 5"), wherein the remaining service life (10) is determined after a comparison (8) with data describing a target health state (9), It is characterized in that When the remaining service life (10) falls below a threshold value, a service life extension mode (11) is activated, which takes measures in the vehicle to extend the service life.

2. The method according to claim 1, It is characterized in that The measures include thermal and / or mechanical damage distribution or load distribution within installed drive components and / or limiting operation at high electrical power and / or accessing a driver assistance system (13) in order to achieve route optimization.

3. The method according to claim 1 or 2, It is characterized in that The measures are accompanied by a warning to the driver.

4. The method according to claim 1, It is characterized in that The service life extension mode provides information to the driver.

5. The method according to claim 1, It is characterized in that The service life extension mode includes a reward system for the driver.

6. The method according to claim 1, It is characterized in that Parameterizable threshold values ​​can be used depending on the application for an end customer or a fleet vehicle.

7. The method according to claim 1, It is characterized in that During repair / maintenance work on the vehicle, in the comparison of the calculation basis of the partial damage, the calculated current damage of each component is determined.

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