BATTERY MANAGEMENT SYSTEM WITH STATE OF POWER PREDICTION FOR AN ACCUMULATOR

Abstract

A battery management system with state of power prediction for an accumulator based on a calculated current of the accumulator, a method and a control device are provided. The battery management system for an accumulator is configured and arranged to predict a state of power of the accumulator, the prediction being based on a calculated current of the accumulator.

Claims

1. A battery management system for an accumulator, wherein the battery management system is configured and arranged to predict a state of power of the accumulator, wherein the prediction is based on a calculated current of the accumulator.

2. The battery management system according to claim 1, wherein the battery management system is configured and arranged to use an inverted single-particle model to calculate the current.

3. The battery management system according to claim 2, wherein the battery management system is configured and arranged to iteratively solve the inverted single-particle model to calculate the current.

4. The battery management system according to claim 2, wherein the battery management system is configured and arranged to use a voltage and a pulse length as inputs for the inverted single-particle model.

5. The battery management system according to claim 3, wherein the battery management system is configured and arranged to perform the following steps in iteratively solving the inverted single-particle model: calculating the current based on the inverted single-particle model; verifying the calculated current; and updating parameters of the inverted single-particle model and repeating the steps of calculating and verifying, if the step of verifying indicates that recalculating the current is required.

6. The battery management system according to claim 1, wherein the accumulator is implemented as a lithium-ion accumulator.

7. The battery management system according to claim 1, wherein the battery management system is configured and arranged to control the accumulator based on the predicted state of power.

8. A method for predicting a state of power of an accumulator, comprising the steps of: calculating a diffusion factor; calculating a current based on an inverted single-particle model;verifying the calculated current; updating parameters of the inverted single-particle model and repeating the steps of calculating and verifying, if the step of verifying indicates that recalculating the current is required.

9. A controller for controlling a power flow of an accumulator, the controller being configured and arranged to control the power flow based on a predicted state of power, wherein the controller is configured and arranged to determine the predicted state of power according to a method according to claim 8.

Description

DRAWINGS

[0028] In order that the disclosure may be well understood, there will now be described various forms thereof, given by way of example, reference being made to the accompanying drawings, in which:

[0029] FIG. 1 shows an embodiment of a vehicle with a control unit for predicting a state of power of an accumulator; and

[0030] FIG. 2 shows an embodiment example of steps performed by a battery management system for predicting a state of power of an accumulator.

[0031] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

DETAILED DESCRIPTION

[0032] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0033] FIG. 1 shows an at least partially electrified vehicle 1 with an accumulator configured as a traction battery 2 and a control unit configured as a vehicle control unit 3 (VCU). The traction battery 2 in this case is a lithium-ion accumulator configured as an NMC accumulator.

[0034] The VCU 3 is configured and arranged to detect a load request from a vehicle driver, but also sensor signals from brakes, and to control a power flow in the vehicle 1 based on the detected information. Controlling the power flow includes providing torque to the wheels and controlling an electric motor, the traction battery 2 and a charging system.

[0035] The VCU 3 is configured and arranged to control the power flow based on a predicted state of power of the traction battery 2. For this purpose, the VCU 3 communicates with a battery management system 4, which is configured and arranged to predict the state of power of the traction battery 2, the prediction being based on a calculated current of the traction battery 2.

[0036] The battery management system 4 comprises a computing unit 5 which is configured and arranged to execute a first computing program. The first computing program comprises instructions to perform the method steps for calculating the current shown in FIG. 2: calculating a diffusion factor in step S10; calculating a current based on an inverted single-particle model in step S20; verifying the calculated current in step S30; updating input parameters β and ΔSOC of the inverted single-particle model in step 40; and in step S50, repeating the steps of calculating and verifying if the step S30 of verifying indicates that recalculation of the current is required.

[0037] Referring to FIG. 2, the first computing program performs a preparation step (not shown in FIG. 2) preparing input data in a step preceding the step S10. Input data of the calculation program are a battery resistance R and an associated angular frequency ω, a state of charge SOC of the traction battery 2, an initial voltage Uini, a battery temperature T.sub.Bat, a current direction, a pulse length Δt and a desired voltage U.sub.tar.

[0038] The pulse length Δt and the voltage U.sub.tar represent target values, for which the calculation program calculates the current I.sub.ges which can be provided by the traction battery 2. The other input data represent boundary conditions which influence the available current I.sub.ges.

[0039] Here, the computing unit 5 of the battery management system 4 includes a second computing program that includes instructions to calculate the battery state SOH.sub.R. In this regard, the second computing program determines a battery resistance R, and an angular frequency ω as battery model parameters for a single-particle model used to calculate the battery state SOH.sub.R. Battery state SOH.sub.R and battery model parameters R, ω are passed by the second calculation program to the first calculation program.

[0040] In the preparation step, the first calculation program first processes the input data in such a way that it can use them to calculate the current I.sub.ges. In addition, it checks the input data with regard to its value range and format. If the first calculation program cannot process the input data due to an invalid value range or an invalid format, an error signal is output and the calculated current I.sub.ges and the state of power of the traction battery 2 are set to zero.

[0041] When the first calculation program has successfully performed the preparation step, it starts the calculation of the diffusion factor β in step S10. The diffusion factor β is an indicator for a transitory shift of the SOC for the considered pulse length Δt. The first computing program calculates the diffusion factor β in step S10 for the given pulse length Δt before the actual calculation of the current I.sub.ges. The calculation is performed according to the following equation:

[00001]$\beta =\frac{f_1\ast \Delta t}{1+f_2\ast \omega \ast \frac{\Delta t}{2}}+{\beta }_{prev}\ast \frac{1-f_2\ast \omega \ast \frac{\Delta t}{2}}{1+f_2\ast \omega \ast \frac{\Delta t}{2}},$

where f.sub.1 and f.sub.2 are factors, ω an angular frequency of the traction battery 2, Δt the given pulse length and β.sub.prev an initial diffusion factor.

[0042] After calculating the diffusion factor β in step S10, the first computing program performs the calculation of the current I.sub.ges in step S20 based on an inverted single-particle model.

[0043] A single-particle model is used in the normal, i.e. non-inverted, formulation to calculate a voltage of the traction battery 2. The single-particle model uses Fick’s law to determine diffusion, determines an open-circuit voltage OCV based on a map stored in the calculation unit 5, considers contact resistance and charge transfer, and the electrodes are approximated as circular particles.

[0044] To run the single-particle model, the battery model parameters R and ω have to be determined. This is done here by the second computing program, which passes the battery model parameters R, ω to the first computing program.

[0045] The first computing program uses the single-particle model in an inverted form and calculates a section-wise current I of the traction battery 2 according to the following equation:

[00002]$I=\frac{U_{tar}-U_{ini}}{b\ast \left(\Delta t+{\beta }_{max}\right)+R},$

where U.sub.tar is the desired voltage, U.sub.ini the initial voltage, b a linearization factor and β.sub.max a maximum diffusion factor for the considered pulse length Δt.

[0046] The initial voltage U.sub.ini is determined by the first calculation program based on the state of charge SOC of the traction battery 2. The linearization factor b is obtained by the first calculation program by linearizing a curve of the open-circuit voltage OCV provided. The linearization is performed by the first calculation program in such a way that the deviation between the linearization and the actual curve of the OCV is as small as possible. To calculate the current I, the first program uses the linearization factor b which corresponds to the state of charge SOC.

[0047] The first computing program calculates a section-by-section current I of the traction battery 2 for each section resulting from the linearization by iteratively executing the inverted single-particle model.

[0048] After calculating the current I, the first calculation program performs the step S30 of verifying the calculated current I. For this purpose, it determines whether the used section of the linearized state of charge SOC of the pulse length Δt is exceeded by calculating a time interval Δt* according to the following equation:

[00003]$\Delta t*=\frac{\Delta SOC}{I}-{\beta }_{max},$

where ΔSOC is a difference between an actual state of charge of the traction battery 2 and a maximum or minimum state of charge in the considered linearization section. Whether the maximum or minimum state of charge is used depends on the current direction, i.e., whether the traction battery 2 is charged or discharged.

[0049] If the time span Δt* is greater than the pulse length Δt the considered linearization section is not exceeded and the calculated current I is valid. If the time span Δt* is smaller than the pulse length Δt the considered linearization section is exceeded and for a following linearization section an update of parameters in step S40 is performed.

[0050] Before the parameter update in step S40, the first calculation program still performs a calculation of the time actually spent in a linearization section Δt.sub.act according to the following formula:

[00004]$\Delta t_{act}=\frac{\Delta SOC}{I}-{\beta }_{act},$

where β.sub.act is a diffusion factor corresponding to an actual diffusion factor in the considered linearization section.

[0051] The step S40 of updating parameters includes recalculating initial voltage Uini, taking into account a change of ΔSOC due to a charging or discharging process, redetermining the linearization factor b factor based on the recalculated initial voltage U.sub.ini,n and determining the remaining pulse length Δt.sub.n by subtracting the time spent in the preceding linearization section Δt.sub.act.

[0052] Based on the updated parameters in step S40, the first calculation program repeats steps S20 and S30 and again calculates the current for the considered linearization section, now according to the following equation:

[00005]$I=\frac{U_{tar}-U_{ini,n}}{b_n\ast \left(\Delta t_n+{\beta }_{max}\right)+R}.$

[0053] For the recalculated current I, the first calculation program also verifies in step S30 again whether the considered linearization section is exceeded by the pulse length Δt. The first calculation program performs the step S40 of updating parameters of the inverted single-particle model and the step S50 of repeating step S20 of calculation and the step S30 of verification until the considered linearization section is no longer exceeded by the pulse length Δt.

[0054] When the first calculation program has determined and verified the currents I for all linearization sections, it calculates a current that can be provided by the traction battery 2 I.sub.ges by a weighted averaging of the currents I calculated for the linearization sections. For weighting, the actual time spent in each linearization section Δt.sub.act is used.

[0055] In a further intermediate step, the first calculation program takes into account a limitation S60 of the total current I.sub.ges which is preset for the traction battery 2.

[0056] In a final step S70, the first computing program predicts the state of power of the traction battery 2 based on the calculated total current I.sub.ges. Finally, the battery management system 4 transmits the predicted state of power to the VCU 3, which uses the predicted state of power to control the power flow in the vehicle 1.

[0057] In an alternative embodiment not shown, the battery management system 4 is configured and arranged to control the traction battery 2 based on the predicted state of power. This may be in addition or as an alternative to communicating the predicted state of power to the VCU. By controlling the traction battery 2 based on the predicted state of power, for example, an overload of the traction battery 2 or an excessive discharge can be avoided.

[0058] Unless otherwise expressly indicated herein, all numerical values indicating mechanical/thermal properties, compositional percentages, dimensions and/or tolerances, or other characteristics are to be understood as modified by the word “about” or “approximately” in describing the scope of the present disclosure. This modification is desired for various reasons including industrial practice, material, manufacturing, and assembly tolerances, and testing capability.

[0059] As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C), using a non-exclusive logical OR, and should not be construed to mean “at least one of A, at least one of B, and at least one of C.”

[0060] In this application, the term “controller” and/or “module” may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components (e.g., op amp circuit integrator as part of the heat flux data module) that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.

[0061] The description of the disclosure is merely exemplary in nature and, thus, variations that do not depart from the substance of the disclosure are intended to be within the scope of the disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure.