Vehicle control device and battery charging control method

Abstract

The invention relates to a vehicle control apparatus and a battery charging control method. The vehicle control apparatus includes a processor configured to optimize a battery model based on an optimization algorithm and generate a variable charging map based on the optimized battery model. The processor is further configured to acquire state information of the battery and determine an optimal charging current corresponding to the state information of the battery based on the variable charging map. The processor is further configured to control the charging device to perform battery charging based on the determined optimal charging current.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to U.S. Provisional Patent Application No. 63 / 683,009, filed August 14, 2024, and Korean Patent Application No. 10-2025-0076701, filed June 11, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to vehicle control devices and battery charging control methods. Background Technology

[0004] The statements in this section are merely background information related to the present invention and do not constitute prior art.

[0005] Lithium-ion batteries are a critical component of electric vehicles and hybrid electric vehicles in terms of cost and performance. Electric vehicles equipped with these lithium-ion batteries suffer from long charging times and limited range. Therefore, existing technologies aim to shorten charging time by charging at high current rates (C-rate, C-rate), but this results in severe degradation and excessive heat generation.

[0006] Recently, a multi-stage constant current (MCC) charging protocol has been developed for battery charging. The MCC charging protocol is an algorithm used to control battery charging to limit overpotentials so that they do not exceed the lithium plating (LiP) overpotential in the initial state of the battery. As the battery deteriorates, lithium dendrites grow during charging, which can lead to internal short circuits, thus reducing the margin for safe operation. Summary of the Invention

[0007] This invention aims to solve the aforementioned problems in the prior art while fully maintaining the advantages of the prior art.

[0008] The present invention provides a vehicle control device and a battery charging control method for optimizing charging current and cut-off voltage to reduce or minimize lithium deposition, suppress heat generation, and shorten battery charging time.

[0009] Other aspects of the present invention provide a vehicle control device and a battery charging control method for optimizing fast charging schemes based on (or utilizing) a nonlinear model predictive control (NMPC) algorithm based on an electrochemical-thermal-lifetime model.

[0010] The technical problems to be solved by this invention are not limited to those described above. Those skilled in the art should gain a clearer understanding of other technical problems not mentioned herein through the following description.

[0011] According to one aspect of the present invention, a vehicle control device is provided. The vehicle control device includes a processor configured to optimize a battery model based on an optimization algorithm. The processor is further configured to generate a variable charging map based on the optimized battery model. The processor is further configured to acquire battery state information and determine an optimal charging current corresponding to the battery state information based on the variable charging map. The processor is additionally configured to control a charging device to perform battery charging based on the determined optimal charging current.

[0012] Optimization algorithms can include nonlinear model predictive control algorithms.

[0013] Battery models can include electrochemical-thermal-lifetime models.

[0014] A variable charge mapping can be a table that defines the optimal charging current corresponding to the battery's voltage and state of health (SOH).

[0015] The processor can be configured to determine whether the battery's current SOC is less than the fast-charging upper limit SOC. The processor can also be configured to determine whether the battery's current voltage is greater than or equal to the fast-charging upper limit voltage based on the determination that the battery's current SOC is less than the fast-charging upper limit SOC. The processor can be further configured to reduce the charging current by a predetermined factor based on the determination that the battery's current voltage is greater than or equal to the fast-charging upper limit voltage.

[0016] The processor can be configured to determine whether the optimization algorithm's start condition is met based on whether the battery's current voltage is lower than the fast-charging upper limit voltage. The processor can also be configured to execute the optimization algorithm based on the determination that the start condition is met.

[0017] The processor can be configured to determine whether the constant current application time is greater than the constant current application end time and whether the overpotential is less than the allowable overpotential, and based on the determined results, determine whether the optimization algorithm start condition is met.

[0018] The processor can be configured to determine the optimal charging current under predetermined constraints using an optimization algorithm.

[0019] The processor can be configured to maintain the previous charging current based on the determination that the start conditions of the optimization algorithm are not met.

[0020] The processor can be configured to acquire at least one of the following as battery state information: battery voltage, battery SOH, or any combination thereof, based on one or more signals acquired from one or more sensors installed in the battery.

[0021] According to another aspect of the present invention, a battery charging control method is provided. The battery charging control method includes optimizing a battery model based on an optimization algorithm and generating a variable charging map based on the optimized battery model. The battery charging control method further includes acquiring battery state information and determining an optimal charging current corresponding to the battery state information based on the variable charging map. The battery charging control method further includes controlling a charging device to perform battery charging based on the determined optimal charging current.

[0022] Optimization algorithms can include nonlinear model predictive control algorithms.

[0023] Battery models can include electrochemical-thermal-lifetime models.

[0024] A variable charge mapping can be a table that defines the optimal charging current corresponding to the battery's voltage and state of health (SOH).

[0025] Optimizing the battery model may include determining whether the battery's current SOC is less than the fast-charging upper limit SOC. Optimizing the battery model may also include determining whether the battery's current voltage is greater than or equal to the fast-charging upper limit voltage based on the determination that the battery's current SOC is less than the fast-charging upper limit SOC. Optimizing the battery model may further include reducing the charging current by a predetermined factor based on the determination that the battery's current voltage is greater than or equal to the fast-charging upper limit voltage.

[0026] Optimizing the battery model may include determining whether the start condition for the optimization algorithm is met based on the determination that the current voltage of the battery is less than the fast charging upper limit voltage, and executing the optimization algorithm based on the determination that the start condition for the optimization algorithm is met.

[0027] Determining whether the start conditions of the optimization algorithm are met may include determining whether the constant current application time is greater than the constant current application end time and whether the overpotential is less than the allowable overpotential, and determining whether the start conditions of the optimization algorithm are met based on the determined results.

[0028] Performing optimization algorithms may include determining the optimal charging current under predetermined constraints.

[0029] Optimizing the battery model can include maintaining the previous charging current based on the determination that the starting conditions of the optimization algorithm are not met.

[0030] Obtaining battery status information may include acquiring at least one of the following as battery status information: battery voltage, battery state of health (SOH), or any combination thereof, based on one or more signals obtained from one or more sensors installed in the battery. Attached Figure Description

[0031] The above and other objects, features and advantages of the present invention should be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0032] Figure 1 This is a configuration block diagram illustrating a battery charging system according to an embodiment of the present invention;

[0033] Figure 2 This is a block diagram schematically illustrating a fast charging scheme related to an embodiment of the present invention;

[0034] Figure 3A This is a schematic diagram illustrating a microcell related to an embodiment of the present invention;

[0035] Figure 3B This is a diagram illustrating the structure of an electrochemical-thermal-lifetime model according to an embodiment of the present invention;

[0036] Figure 4 This is a diagram illustrating the electrochemical degradation mechanism according to an embodiment of the present invention;

[0037] Figure 5A This is a graph showing the state of battery health (SOH) as a result of verifying the degradation model related to the embodiments of the present invention;

[0038] Figure 5B This is a graph showing the root mean square error (RMSE) of the capacity, which demonstrates the results of verifying the degradation model related to the embodiments of the present invention.

[0039] Figure 5C This is a graph showing the voltage RMSE of the results of verifying the degradation model related to the embodiments of the present invention;

[0040] Figure 6 This is a flowchart illustrating a method for optimizing a fast charging algorithm according to an embodiment of the present invention;

[0041] Figure 7 The results of simulation optimization of multi-stage constant current (O-MCC) in the initial battery SOH range according to an embodiment of the present invention are shown;

[0042] Figure 8A A current curve diagram showing the results of a simulated O-MCC according to an embodiment of the present invention is shown;

[0043] Figure 8B An overpotential curve of the simulated O-MCC result according to an embodiment of the present invention is shown;

[0044] Figure 8C A voltage curve showing the results of a simulated O-MCC according to an embodiment of the present invention is shown;

[0045] Figure 8D A SOC curve of the simulated O-MCC result according to an embodiment of the present invention is shown;

[0046] Figure 9A This is a graph showing the results of comparing the SOH of the battery using O-MCC and MCC according to an embodiment of the present invention;

[0047] Figure 9B This is a graph showing the results of comparing charging time using O-MCC and MCC according to an embodiment of the present invention;

[0048] Figure 9C This is a graph showing the results of comparing capacity using O-MCC and MCC according to an embodiment of the present invention;

[0049] Figure 10 This is a flowchart illustrating a battery charging method according to an embodiment of the present invention;

[0050] Figure 11A This is a graph showing the results of comparing the SOH of the battery by applying different constraints to O-MCC and MCC according to an embodiment of the present invention;

[0051] Figure 11B This is a graph showing the results of comparing charging time with O-MCC and MCC with different constraints according to an embodiment of the present invention;

[0052] Figure 11C This is a graph showing the results of comparing the total heat generation by applying different constraints to O-MCC and MCC according to an embodiment of the present invention;

[0053] Figure 11D This is a graph showing the results of comparing the capacity of O-MCC and MCC with different constraints according to an embodiment of the present invention; and

[0054] Figure 12 This is a graph illustrating the performance of the fast charging algorithm according to an embodiment of the present invention. Detailed Implementation

[0055] In the following, some embodiments of the invention are described in detail with reference to the accompanying drawings. When adding reference numerals to components in each drawing, it should be noted that the same components are represented by the same reference numerals even if they are shown in different drawings. Furthermore, in describing embodiments of the invention, detailed descriptions of well-known features or functions are omitted if it is determined that such detailed descriptions would obscure the gist of the invention.

[0056] In describing the components of embodiments of the present invention, the terms first, second, A, B, (a), (b), etc., may be used herein. These terms are used only to distinguish one component from another. These terms do not limit the corresponding components, regardless of the order or priority of the corresponding components. Furthermore, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms such as those defined in general dictionaries should be interpreted as having the same meaning as in the context of the relevant field, and should not be interpreted as having an idealized or overly formal meaning, unless expressly defined as having such a meaning in this application.

[0057] In this invention, when the components, controllers, devices, elements, equipment, units, etc., of this invention are described as having a purpose or performing an operation or function, such components, controllers, devices, elements, equipment, units, etc., are to be regarded herein as "configured" to satisfy that purpose or perform that operation or function. Each component, controller, device, element, equipment, unit, etc., may be implemented individually or may include one or more processors and memories (e.g., non-volatile computer-readable media) as part of a device.

[0058] Figure 1 This is a block diagram illustrating the configuration of a battery charging system according to an embodiment of the present invention.

[0059] The battery charging system may include a battery 10, a charging device 20, and a vehicle control device 100.

[0060] Battery 10 can store electrical energy. Battery 10 can be used in electronic devices, electric vehicles, hybrid electric vehicles, energy storage systems (ESS), etc. Lithium-ion batteries can be used as battery 10.

[0061] Although not shown in the accompanying drawings, a lithium-ion battery may include or consist of a positive electrode (or cathode), a negative electrode (or anode), an electrolyte, and a separator. The positive electrode is the space in the lithium-ion battery where lithium is inserted. The capacity and voltage of a lithium-ion battery can be determined based on the active material of the positive electrode. The positive electrode can be made of lithium metal oxide. The negative electrode serves to store and release lithium ions from the positive electrode, allowing current to flow through an external circuit. Graphite can be used as the active material for the negative electrode. As the process of storing and releasing lithium ions at the negative electrode is repeated, the structure of graphite changes, and the amount of ions that can be stored decreases. This reduces the state of health (SOH) of the battery. The electrolyte is the medium that helps lithium ions move between the positive and negative electrodes. The electrolyte may include or consist of an organic solvent in which lithium salts are dissolved. The separator serves to physically block contact between the positive and negative electrodes. The separator can be an electrical insulator but can selectively be permeable, allowing lithium ions to pass through. Polyethylene (PE), polypropylene (PP), etc., can be used as separators.

[0062] The charging device 20 can regulate and supply electrical energy (or voltage and / or current) from the power source to the battery 10 to suit the characteristics of the battery 10, thereby charging the battery 10. Alternating current (AC) voltage or direct current (DC) voltage can be used as the power source.

[0063] Although not shown in the accompanying drawings, the charging device 20 may include a rectifier, a converter, a charging controller, a protection circuit, an interface, etc. The rectifier can rectify the power supplied from the power source. The rectifier can convert AC voltage to DC voltage. The converter can convert the DC voltage rectified by the rectifier into voltage and / or current based on the characteristics of the battery 10. The converter may include at least one of a buck converter, a boost converter, a buck-boost converter, or any combination thereof. The charging controller can monitor the state of charge (SOC), temperature, voltage, etc. of the battery 10. The charging controller can measure the voltage, current, temperature, etc. of the battery 10 using voltage sensors, current sensors, and / or temperature sensors installed in the battery 10 (or based on signals obtained from voltage sensors, current sensors, and / or temperature sensors installed in the battery 10). The charging controller can execute a charging algorithm stored in a memory. The protection circuit can stop charging in case of overcurrent, overpotential, and / or temperature abnormalities to prevent battery damage or fire. The interface may be an output terminal connected to the terminals of the battery 10. The interface can output the voltage and / or current converted by the converter to the battery 10. The interface can enable and / or assist in communication between the charging device 20 and external devices (e.g., vehicle control device 100, battery management system (BMS) etc.).

[0064] The vehicle control device 100 may be an electronic control unit (ECU) installed in the vehicle. The vehicle control device 100 may include a communication circuit 110, a detector 120, a memory 130, a processor 140, etc.

[0065] The communication circuit 110 can realize and / or assist in the execution of wired and / or wireless communication between the vehicle control unit 100 and external devices (e.g., battery 10, charging device 20, BMS, server, etc.). The communication circuit 110 may include wireless communication circuits (e.g., short-range wireless communication circuits, Bluetooth communication circuits, mobile communication circuits, etc.) and / or wired communication circuits (e.g., controller area network (CAN) communication circuits, local area network (LAN) communication circuits, power line communication circuits, etc.).

[0066] Detector 120 can detect the state information (or cell state information) of battery 10. Detector 120 can acquire the state information of battery 10 using one or more sensors installed in battery 10, such as a voltage sensor, a current sensor, and / or a temperature sensor (or based on one or more signals acquired from one or more sensors installed in battery 10). The state information of battery 10 may include at least one of voltage, current, temperature, heat generation rate (HGR), state of charge (SOC), state of health (SOH), or any combination thereof.

[0067] In another embodiment, detector 120 can acquire the status information of battery 10 via communication circuit 110. Detector 120 can acquire the status information of battery 10 from BMS via communication circuit 110.

[0068] The memory 130 can store at least one of an electrochemical-thermal-lifetime model, an electrochemical model, a thermal model, a degradation model (or lifetime model), an optimization algorithm, a fast charging algorithm, or any combination thereof. The optimization algorithm can be a nonlinear model predictive control (NMPC) algorithm. The fast charging algorithm can include a multi-stage constant current (MCC) charging algorithm, an optimized MCC (O-MCC) charging algorithm, etc.

[0069] Furthermore, memory 130 can store a variable charge mapping. The variable charge mapping can be a lookup table (LUT) that defines the optimal charging current corresponding to the voltage and state of equilibrium (SOH) of battery 10.

[0070] Memory 130 may be a non-volatile storage medium that stores instructions that can be executed by processor 140. Memory 130 may include flash memory, hard disk, solid-state drive (SSD), secure digital card (SD card), random access memory (RAM), static RAM (SRAM), read-only memory (ROM), programmable ROM (PROM), electrically erasable programmable ROM (EEPROM), erasable programmable ROM (EPROM), embedded multimedia card (eMMC), or at least one combination thereof.

[0071] The processor 140 can control the overall operation of the vehicle control unit 100. The processor 140 may include at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a microcontroller, a microprocessor, or any combination thereof.

[0072] Processor 140 can optimize fast charging algorithms (or charging algorithms or charging protocols). Processor 140 can perform charging algorithm optimization based on (or utilize) an NMPC algorithm based on a reduced-order electrochemical-thermal-lifetime model that includes electrochemical degradation mechanisms (e.g., side reactions and lithium deposition reactions).

[0073] An optimized fast-charging algorithm can calculate the magnitude of the charging current and the MCC transition point. This algorithm can be designed to minimize lithium deposition formation and shorten charging time. Minimizing lithium deposition formation can be achieved by forcing the lithium deposition overpotential to remain within the upper and lower overpotential limits throughout the battery's state of equilibrium (SOH).

[0074] Processor 140 can generate an optimized fast charging algorithm in the form of a lookup table (or variable charging map). Processor 140 can store the generated lookup table in memory 130. Furthermore, processor 140 can apply the generated lookup table in the BMS.

[0075] Furthermore, processor 140 can generate charging maps tailored to the needs of users (or developers). Processor 140 can generate charging maps for each charging time, temperature, and / or lithium deposition constraint. In other words, processor 140 can generate charging maps considering composite constraints.

[0076] Processor 140 can measure (or acquire) the voltage and state of equilibrium (SOH) of battery 10 using one or more sensors installed in battery 10 (or based on one or more signals acquired from one or more sensors installed in battery 10). Processor 140 can determine the optimal charging current corresponding to the measured voltage and SOH by referring to (or based on, or utilizing) a variable charging map. Processor 140 can transmit the determined optimal charging current to charging device 20 via communication circuit 110. Charging device 20 can control the current (or charging current) supplied to battery 10 based on the determined optimal charging current.

[0077] The process of optimizing the fast charging algorithm according to the implementation plan is described in more detail below.

[0078] When optimization is initiated, processor 140 can perform initialization. Processor 140 can set time t to "1" and can set the initial SOC (1) of battery 10 to the SOC of battery 10 at the start time of charging. start .

[0079] Processor 140 can determine whether the current SOC (t) of battery 10 is less than the fast charging upper limit SOC. end The fast-charging upper limit SOC can be predefined by the system designer for each vehicle type. When it is determined that the current SOC of battery 10 is greater than or equal to the fast-charging upper limit SOC, processor 140 can terminate fast charging. In other words, the fast-charging upper limit SOC can be a criterion used to determine whether to stop (or terminate) charging.

[0080] When it is determined that the current SOC of battery 10 is less than the fast charging upper limit SOC, processor 140 can determine whether the current voltage V(t) of battery 10 is greater than or equal to the fast charging upper limit voltage V. end The upper limit voltage for fast charging can be predefined by the system designer for each vehicle type. The current voltage V(t) corresponds to the calculation result of the electrochemical model.

[0081] When it is determined that the current voltage of battery 10 is greater than or equal to the fast charging upper limit voltage, processor 140 may reduce the charging current (C-rate, C-rate) by a predetermined factor (e.g., by half) or by a predetermined rate k. This can be expressed as Equation 1 below.

[0082] [Equation 1]

[0083] I(t)=I(t-1) / k

[0084] In Equation 1, I(t) is the current during time (t seconds).

[0085] When it is determined that the current voltage of battery 10 is less than the fast charging upper limit voltage, processor 140 can determine whether at least one of the following NMPC start conditions (or optimization algorithm start conditions) is met.

[0086] NMPC Start Conditions

[0087] 1) When the time is 1 second (t=1)

[0088] 2) When the constant current (CC) is applied for a duration tcc longer than the time tcc is the end of the CC application. end And the lithium deposition overpotential η L i P Less than the allowable lithium deposition overpotential η allow (tcc>tcc end And η LiP <η allow )hour

[0089] In this paper, the lithium deposition overpotential η L i P This refers to the voltage (or potential) that begins to cause lithium deposition on the negative electrode surface during the charging of a lithium-ion battery. The lithium deposition overpotential η L i P This could be the result of calculations using an electrochemical model. CC application end time tcc end It can be predefined by the system designer for each vehicle type.

[0090] Processor 140 may execute NMPC when at least one of the NMPC initiation conditions is determined to be met. Processor 140 may execute the NMPC algorithm based on an electrochemical-thermal-lifetime model.

[0091] The objective function of NMPC can be expressed as Equation 2 below.

[0092] [Equation 2]

[0093]

[0094] In this article, N h It is the scheduled time (e.g., 3 seconds).

[0095] Boundary conditions (or constraints) can be set as "η L i P >η lb In this article, η lb It is the lower limit of the lithium deposition (LiP) overpotential.

[0096] After executing NMPC, processor 140 can initialize the CC application time tcc. In other words, processor 140 can set "tcc = 1".

[0097] When it is determined that the NMPC start condition is not met, the processor 140 can maintain the previous C rate and continue constant current charging. In other words, the processor 140 can determine the charging current I(t) at t seconds as the previous charging current I(t-1). The charging current I(t) can be the result of calculation from the electrochemical model.

[0098] Next, the processor 140 can accumulate the constant current duration Δt to the constant current application time to update the constant current application time (tcc = tcc + Δt).

[0099] Processor 140 can execute an electrochemical-thermal-lifetime model in accordance with a predetermined charging current. In other words, processor 140 can optimize the electrochemical-thermal-lifetime model.

[0100] Processor 140 can generate a lookup table (or variable charge map) for each voltage and SOH of battery 10 based on (or utilizing) an optimized electrochemical-thermal-lifetime model. In other words, processor 140 can generate an O-MCC algorithm (or an optimized fast charging algorithm) based on (or utilizing) an optimized electrochemical-thermal-lifetime model. The O-MCC algorithm can include the magnitude of the charging current of the MCC and the transition point of the MCC.

[0101] Figure 2 This is a block diagram schematically illustrating a fast charging scheme related to an embodiment of the present invention.

[0102] The charging device 20 can monitor the voltage V of the battery 10 using one or more sensors installed in the battery 10 (or based on one or more signals acquired from one or more sensors installed in the battery 10). t And SOH. The charging device 20 can refer to (or be based on, or utilize) a variable charge mapping LUT previously stored in the memory 130 of the vehicle control unit 100 or the BMS to determine the optimal charging current OptimizedI corresponding to the monitored voltage and SOH of the battery 10. The variable charge mapping LUT is a lookup table in which an optimal charging current is defined for each voltage and SOH of the battery 10. The charging device 20 can control the charging current I supplied to the battery 10 based on the determined optimal charging current.

[0103] According to another embodiment, the charging device 20 can receive an optimal charging current corresponding to the voltage and state of equilibrium (SOH) of the battery 10 from the vehicle control device 100. The charging device 20 can send the monitored voltage and SOH of the battery 10 to the vehicle control device 100. The vehicle control device 100 can determine the optimal charging current corresponding to the received monitored voltage and SOH of the battery 10 by referring to a previously stored variable charging map LUT. The vehicle control device 100 can send the determined optimal charging current to the charging device 20. The charging device 20 can control the charging current supplied to the battery 10 based on the optimal charging current received from the vehicle control device 100.

[0104] According to another embodiment, the vehicle control unit 100 can monitor the voltage and state of equilibrium (SOH) of the battery 10 using one or more sensors installed in the battery 10 (or based on one or more signals acquired from one or more sensors installed in the battery 10). The vehicle control unit 100 can determine the optimal charging current corresponding to the monitored voltage and SOH of the battery 10 by referring to a previously stored variable charge mapping LUT. The vehicle control unit 100 can send the determined optimal charging current to the charging device 20. The charging device 20 can control the charging current supplied to the battery 10 based on the optimal charging current sent from the vehicle control unit 100.

[0105] The vehicle control unit 100 can optimize the fast charging algorithm. The vehicle control unit 100 can generate a variable charge mapping LUT based on (or utilizing) an optimized electrochemical-thermal-lifetime model. The electrochemical-thermal-lifetime model is a battery model that combines a thermal model, a degradation model, and an electrochemical model. In this paper, the degradation model can also be referred to as the lifetime model.

[0106] An electrochemical model can receive data on at least one of current, voltage, or temperature (or ambient temperature), or any combination thereof, as input. The electrochemical model can perform calculations based on (or utilize) the received data and can output at least one of voltage, current, temperature, heat generation rate (HGR), state of charge (SOC), state of oxygen (SOH), or internal variables, or any combination thereof, as the result of the calculation. Internal variables may include model parameters, such as the ion concentration c in the electrode. s ion concentration c in electrolyte e Potential in the electrode Electrolyte potential Or reaction rate.

[0107] Thermal models can use internal variables as model parameters to perform calculations and can output the heat generation rate as the result. Degradation models can perform calculations based on (or using) internal variables and can output SOH and lithium deposition overpotential as the result.

[0108] The electrochemical model can estimate voltage, lithium deposition overpotential, heat generation rate, state of charge (SOC), and state of charge (SOH) based on the heat generation rate output from the thermal model and the state of oxygen (SOH) and lithium deposition overpotential output from the degradation model. The electrochemical model can then output the estimated data to an optimization algorithm. The optimization algorithm can then optimize the current (or charging current) based on the data estimated by the electrochemical model. The optimization algorithm can then apply the optimized current (Optimized I) to (or reflect it in) the electrochemical-thermal-lifetime model.

[0109] Figure 3A This is a schematic diagram illustrating a microcell related to an embodiment of the present invention. Figure 3B This is a diagram illustrating the structure of an electrochemical-thermal-lifetime model according to an embodiment of the present invention. Figure 4 This is a diagram illustrating the electrochemical degradation mechanism according to an embodiment of the present invention.

[0110] refer to Figure 3A The microcell may include or consist of a composite anode mixed with an electrolyte, a separator, and a composite cathode mixed with an electrolyte. The composite anode may be composed of lithium-intercalated graphite Li. x Composed of C6. When the cell discharges, lithium ions can separate from the composite anode and migrate via the electrolyte to the cathode. The composite cathode can be made of lithium metal oxide (Li). x The battery is composed of MO2, M:Ni, Mn, and CO. When the cell is charged, lithium ions are stored by moving through the electrolyte to the composite cathode. A separator allows lithium ions to pass through while physically separating the anode and cathode. The electrolyte allows lithium ions to move between the anode and cathode. Current collectors located in the anode and cathode act as channels for moving electrons generated during the charging or discharging process of the cell.

[0111] refer to Figure 3B The electrochemical-thermal-lifetime model is a reduced-order electrochemical model based on a combination of thermal and degradation models.

[0112] When internal variables are received, the thermal model can calculate and output the heat generation rate based on the internal variables. The heat generation rate (HGR) is expressed as Equation 3 below.

[0113] [Equation 3]

[0114]

[0115] In equation 3, I is the current, and V is the voltage. t It is the voltage over time (t seconds), where T is the temperature, and U is the voltage over time (t seconds). oc It is the open-circuit voltage, dU oc / dT is the entropy coefficient. The first term can represent an irreversible reaction, and the second term can represent a reversible reaction.

[0116] The degradation model can accept internal variables and can calculate and output SOH and lithium deposition overpotential based on (or utilize) these internal variables. (Reference) Figure 4 Electrochemical degradation, such as side reactions and lithium plating, may occur during battery charging. Regarding the electrochemical degradation that occurs during battery charging, degradation models can predict the degradation and state of harmonics (SOH) of battery 10. In this case, the degradation model can consider electrochemical degradation occurring under the following assumptions.

[0117] Assumption:

[0118] - Deterioration only occurs at the anode.

[0119] - Side reactions are irreversible.

[0120] - Lithium deposition is a semi-reversible reaction.

[0121] - Ignoring mechanical degradation, gas generation, overcharging, and over-discharging.

[0122] The degradation model can be based on (or utilize) the formulas in Table 1 below to calculate the reaction rate and overpotential according to electrochemical degradation.

[0123] [Table 1]

[0124]

[0125] In Table 1, a s It refers to the specific reaction area, R. SEI It is the resistance of the solid electrolyte interphase (SEI). α ox α rd α rd,side α rd,Li and α ox,Li F is a constant, R is resistance, and T is temperature. U eq It is the equilibrium potential of the main reaction, U eq,side It is the equilibrium potential of the side reaction, U eq,Li It is the equilibrium potential for lithium deposition and stripping. side i is the number of ions participating in the side reaction. i0 is the exchange current density, which can be expressed as Equation 4 below.

[0126] [Equation 4]

[0127]

[0128] In Equation 4, k is the kinetic rate constant.

[0129] Figure 5A This is a graph showing the state of battery health (SOH) as a result of verifying the degradation model associated with an embodiment of the present invention. Figure 5B This is a graph showing the root mean square error (RMSE) of the capacity, which demonstrates the results of verifying the degradation model related to the embodiments of the present invention. Figure 5C This is a graph showing the voltage RMSE of the results of verifying the degradation model related to the embodiments of the present invention.

[0130] To validate the degradation model (or durability prediction model), the 2C CC, 3C CC, and MCC charging schemes, as well as the 1CCC discharging scheme, were set to test conditions at 25°C for simulation and experimentation.

[0131] refer to Figure 5A It can be verified that the SOH obtained through simulation is similar to that obtained through experimentation.

[0132] refer to Figure 5B The error between the battery capacity predicted by simulation and the actual battery capacity obtained through experiments (i.e., the capacity prediction error (or absolute capacity error)) is expressed as approximately 2%. In other words, it is found that the capacity reduction of the degradation model can be accurately predicted.

[0133] refer to Figure 5C The error between the simulated predicted battery voltage and the actual battery voltage obtained through experiments (i.e., voltage prediction error V) is calculated by... t (or root mean square error (RMSE)) is expressed as a level less than 35mV.

[0134] The degradation model was validated, and the results showed that the model had good accuracy in predicting battery durability.

[0135] Figure 6 This is a flowchart illustrating a method for optimizing a fast charging algorithm according to an embodiment of the present invention.

[0136] When optimization is initiated, during operation S100, the processor 140 of the vehicle control device 100 can be initialized. The processor 140 can set time t to "1" and can set the initial SOC (1) of the battery 10 to the SOC of the battery 10 at the start time of charging. start .

[0137] In operation S110, processor 140 can determine whether the current SOC (t) of battery 10 is less than the fast charging upper limit SOC. endThe fast-charging upper limit SOC can be predefined by the system designer for each vehicle type. When it is determined that the current SOC of battery 10 is greater than or equal to the fast-charging upper limit SOC, processor 140 can terminate fast charging.

[0138] When it is determined that the current SOC of battery 10 is less than the fast charging upper limit SOC, in operation S120, processor 140 can determine whether the current voltage V(t) of battery 10 is greater than or equal to the fast charging upper limit voltage V. end Fast charging upper limit voltage V end It can be predefined by the system designer for each vehicle type. The current voltage V(t) corresponds to the calculation result of the electrochemical model.

[0139] When it is determined that the current voltage of battery 10 is greater than or equal to the fast charging upper limit voltage, processor 140 may reduce the charging rate (C-rate, C-rate) by a predetermined factor (e.g., by half (C / 2)) or by a predetermined rate.

[0140] When it is determined in operation S120 that the current voltage of battery 10 is less than the fast charging upper limit voltage, in operation S140, processor 140 can determine whether the start condition of the optimization algorithm is met. A nonlinear model predictive control (NMPC) algorithm can be used as the optimization algorithm.

[0141] Optimization algorithm start conditions:

[0142] 1) When the time is 1 second (t=1)

[0143] 2) When the constant current (CC) is applied for a duration tcc longer than the time tcc is the end of the CC application. end And the lithium deposition overpotential η LiP Less than the allowable lithium deposition overpotential η allow (tcc>tcc end And η LiP <η allow )hour

[0144] In this paper, the lithium deposition overpotential η LiP This refers to the voltage at which lithium deposition begins to occur on the surface of the negative electrode during the charging of a lithium-ion battery. The lithium deposition overpotential η LiP This could be the result of calculations using an electrochemical model. CC application end time tcc end It can be predefined by the system designer for each vehicle type.

[0145] When at least one of the starting conditions for the optimization algorithm is determined to be met, the processor 140 can execute the optimization algorithm in operation S150. In other words, the processor 140 can execute the optimization algorithm based on the electrochemical-thermal-lifetime model.

[0146] If, in operation S140, it is determined that the start condition of the optimization algorithm is not met, in operation S160, processor 140 can maintain the previous C rate and continue constant current charging. In other words, processor 140 can determine the charging current I(t) for t seconds as the previous charging current I(t-1). The charging current I(t) can be the result of calculation from the electrochemical model.

[0147] During operation S170, processor 140 can initialize the CC application time tcc. In other words, processor 140 can set "tcc = 1".

[0148] After operation S160, in operation S180, processor 140 can accumulate the CC duration Δt to the CC application time tcc to update the CC application time (tcc = tcc + Δt).

[0149] During operation S190, processor 140 can execute an electrochemical-thermal-lifetime model. Processor 140 can optimize the electrochemical-thermal-lifetime model. Processor 140 can generate a variable charge map based on (or utilizing) the optimized electrochemical-thermal-lifetime model.

[0150] In operation S200, processor 140 can perform a calculation to add 1 second to time t, and can return to operation S110. In other words, processor 140 can increment time t by "1".

[0151] Figure 7 The results of the simulation of optimized multi-stage constant current (O-MCC) in the initial battery SOH range according to an embodiment of the present invention are shown.

[0152] MCC can be optimized using a nonlinear model predictive control algorithm based on a reduced-order electrochemical-thermal-lifetime model (which includes electrochemical degradation mechanisms such as side reactions and lithium deposition reactions).

[0153] refer to Figure 7 When lithium deposition overpotential η L i P When the voltage is less than or equal to 0.02V, the vehicle control unit 100 can perform NMPC to optimize the charging current. The vehicle control unit 100 can search for the lithium deposition overpotential η based on (or utilizing) NMPC. L i P Charging current (C-rate, C-rate) I when the voltage is greater than 0.03V cha .

[0154] Figure 8A A current curve showing the results of a simulated O-MCC according to an embodiment of the present invention is shown. Figure 8B An overpotential curve of the simulated O-MCC result according to an embodiment of the present invention is shown. Figure 8C A voltage curve showing the results of a simulated O-MCC according to an embodiment of the present invention is shown. Figure 8D The SOC curve of the simulated O-MCC result according to an embodiment of the present invention is shown.

[0155] In aging cells, the charging rate (C-rate, C-rate) decreases, and lithium deposition does not occur until the end of life (EoL).

[0156] The constraint remains consistent throughout the entire battery lifespan (beginning of life (BoL), middle of life (MoL), and end of life (EoL)) (see [reference]). Figures 8A to 8D However, it was found that in the 0-1 minute interval, the system temporarily deviated from the lithium deposition overpotential constraint due to the CC constraint.

[0157] Figure 9A This is a graph showing the results of comparing the SOH of the battery using O-MCC and MCC according to an embodiment of the present invention. Figure 9B This is a graph showing the results of comparing charging time using O-MCC and MCC according to an embodiment of the present invention. Figure 9C This is a graph showing the results of comparing capacity using O-MCC and MCC according to an embodiment of the present invention.

[0158] The charging configuration information can be optimized based on (or by utilizing) the NMPC algorithm to optimize the C-rate and voltage standards, and lithium deposition can be suppressed based on (or by utilizing) a variable charging map based on voltage and SOH until EoL.

[0159] refer to Figure 9A Compared to MCC, O-MCC can extend the SOH of battery 10.

[0160] refer to Figure 9B Compared to MCC, O-MCC can reduce the charging time of battery 10 by 11.7%.

[0161] refer to Figure 9C Compared to MCC, O-MCC can reduce capacity fade (CF). Specifically, it can be seen that O-MCC reduces capacity fade by 59.4% over 200 cycles compared to MCC.

[0162] Therefore, O-MCC can suppress lithium deposition throughout the entire battery lifespan, thereby greatly improving battery safety.

[0163] Figure 10 This is a flowchart illustrating a battery charging method according to an embodiment of the present invention.

[0164] During operation S210, the charging device 20 can acquire battery status information. The charging device 20 can acquire the status information of the battery 10 using one or more sensors installed in the battery 10 (or based on one or more signals acquired from one or more sensors installed in the battery 10). The status information of the battery 10 may include voltage, state of equilibrium (SOH), etc.

[0165] In operation S220, the charging device 20 can determine the charging current by referring to a previously stored variable charging map. The charging device 20 can determine the optimal charging current corresponding to the state information of the battery 10 (i.e., voltage and SOH), which is obtained by referring to the previously stored variable charging map.

[0166] In operation S230, the charging device 20 can perform fast charging using a determined charging current. The charging device 20 can control the charging current supplied to the battery 10 based on the determined optimal charging current.

[0167] In operation S240, the charging device 20 can determine whether charging has ended. When it is determined that charging has ended, the charging device 20 can stop charging the battery. If it is not determined that charging has ended, the charging device 20 can return to operation S210.

[0168] Figure 11A This is a graph showing the results of comparing the SOH of the battery with O-MCC and MCC with different constraints according to an embodiment of the present invention. Figure 11B This is a graph showing the results of comparing charging time using O-MCC and MCC with different constraints according to an embodiment of the present invention. Figure 11C This is a graph showing the results of comparing the total heat generation by applying different constraints to O-MCC and MCC according to an embodiment of the present invention. Figure 11D This is a graph showing the results of comparing the capacity of O-MCC and MCC with different constraints according to an embodiment of the present invention.

[0169] MCC can simultaneously consider various factors, such as charging time, degradation, and heat generation. O-MCC can generate charging configuration information according to user requirements.

[0170] For example, O-MCC can generate charging configuration information optimized according to the following constraints.

[0171] O-MCC constraint:

[0172] O-MCC(a): 0.02V≤η LiP ≤0.03V

[0173] O-MCC(b): 0V≤η LiP ≤0.01V

[0174] O-MCC(c): 0V≤η LiP ≤0.01V and HGR<30W

[0175] refer to Figure 11A Compared with MCC, O-MCC(a), O-MCC(b) and O-MCC(c) extended the SOH of battery 10.

[0176] refer to Figure 11B At the start of life (BoL), O-MCC(a), O-MCC(b) and O-MCC(c) shortened the charging time of battery 10 compared to MCC.

[0177] refer to Figure 11C At the beginning (BoL) and middle (MoL) of the battery 10's lifespan, O-MCC(a), O-MCC(b), and O-MCC(c) generated more heat than the MCC. At the end (EoL) of the battery 10's lifespan, O-MCC(a), O-MCC(b), and O-MCC(c) reduced heat generation compared to the MCC.

[0178] refer to Figure 11D Compared with MCC, O-MCC(a), O-MCC(b) and O-MCC(c) can improve the durability of battery 10.

[0179] Figure 12 This is a graph illustrating the performance of the fast charging algorithm according to an embodiment of the present invention.

[0180] Compared to the MCC algorithm, the O-MCC(a) algorithm reduces charging time by 11.7%. Compared to the MCC algorithm, the O-MCC(c) algorithm reduces charging time by 22.6%. Compared to the MCC algorithm, the O-MCC(b) algorithm reduces charging time.

[0181] Compared with the MCC algorithm, the O-MCC(a), O-MCC(b), and O-MCC(c) algorithms improve the battery SOH.

[0182] Compared with the MCC algorithm, the O-MCC(a), O-MCC(b), and O-MCC(c) algorithms improve the heat generation rate.

[0183] The algorithm can shorten charging time and reduce capacity decay, effectively suppressing lithium deposition throughout the battery's state of harmonics (SOH) from BoL to EoL, thereby greatly improving battery safety.

[0184] The embodiments of the present invention can optimize charging conditions suitable for the current cell state of the battery, thereby suppressing lithium deposition and heat generation throughout the battery's lifespan.

[0185] Furthermore, embodiments of the present invention can optimize the charging current and cutoff voltage, thereby minimizing lithium deposition, suppressing heat generation, and shortening charging time.

[0186] Furthermore, embodiments of the present invention can optimize fast charging schemes based on (or utilize) nonlinear model predictive control (NMPC) algorithms based on electrochemical-thermal-lifetime models, thereby suppressing degradation and overheating to improve battery durability and safety.

[0187] Furthermore, embodiments of the present invention can generate charging maps based on user (or developer) requirements, for each charging time, temperature, and lithium deposition constraint.

[0188] While the invention has been described above with reference to exemplary embodiments and the accompanying drawings, it is not limited thereto. Rather, various modifications and alterations can be made by those skilled in the art without departing from the spirit and scope of the invention as claimed in the appended claims. Therefore, embodiments of the invention are not intended to limit the technical spirit of the invention, but are provided for illustrative purposes only. The scope of the invention should be interpreted based on the appended claims, and all technical ideas within the scope of equivalents to the claims should be included within the scope of the invention.

Claims

1. A vehicle control device, comprising: processor, The processor is configured as follows: The battery model is optimized based on the optimization algorithm. A variable charging map is generated based on an optimized battery model. Obtain battery status information. The optimal charging current corresponding to the battery's state information is determined based on the variable charging mapping. Based on the determined optimal charging current, the charging device is controlled to perform battery charging.

2. The vehicle control device according to claim 1, wherein, The optimization algorithm includes a nonlinear model predictive control algorithm.

3. The vehicle control device according to claim 1, wherein, The battery model includes an electrochemical-thermal-lifetime model.

4. The vehicle control device according to claim 1, wherein, The variable charging map is a table that defines the optimal charging current corresponding to the battery's voltage and state of health.

5. The vehicle control device according to claim 1, wherein, The processor is configured as follows: Determine if the battery's current state of charge is lower than the fast charging limit. Based on the determination that the current state of charge of the battery is less than the fast charging upper limit state of charge, it is determined whether the current voltage of the battery is greater than or equal to the fast charging upper limit voltage. Based on the determination that the current voltage of the battery is greater than or equal to the fast charging upper limit voltage, the charging current is reduced by a predetermined factor.

6. The vehicle control device according to claim 5, wherein, The processor is configured as follows: Based on the determination that the current battery voltage is less than the fast charging upper limit voltage, it is determined whether the optimization algorithm start condition is met; Once the starting conditions for the optimization algorithm are determined, the optimization algorithm is executed.

7. The vehicle control device according to claim 6, wherein, The processor is configured as follows: Determine whether the constant current application time is greater than the constant current application end time and whether the overpotential is less than the allowable overpotential; Based on the determined results, determine whether the conditions for starting the optimization algorithm are met.

8. The vehicle control device according to claim 6, wherein, The processor is configured as follows: The optimal charging current is determined under predetermined constraints using an optimization algorithm.

9. The vehicle control device according to claim 6, wherein, The processor is configured as follows: Based on the determination that the starting condition of the optimization algorithm is not met, the previous charging current is maintained.

10. The vehicle control device according to claim 1, wherein, The processor is configured as follows: Based on one or more signals obtained from one or more sensors installed in the battery, at least one of the following is acquired as battery state information: battery voltage, battery health status, or any combination thereof.

11. A battery charging method for a vehicle control device, the battery charging method comprising: Optimize the battery model based on optimization algorithms; Generate a variable charging map based on an optimized battery model; Obtain battery status information; The optimal charging current corresponding to the battery's state information is determined based on the variable charging mapping. Based on the determined optimal charging current, the charging device is controlled to perform battery charging.

12. The battery charging method according to claim 11, wherein, The optimization algorithm includes a nonlinear model predictive control algorithm.

13. The battery charging method according to claim 11, wherein, The battery model includes an electrochemical-thermal-lifetime model.

14. The battery charging method according to claim 11, wherein, The variable charging map is a table that defines the optimal charging current corresponding to the battery's voltage and state of health.

15. The battery charging method according to claim 11, wherein, Optimizing the battery model includes: Determine if the battery's current state of charge is lower than the fast charging limit. Based on the determination that the current state of charge of the battery is less than the fast charging upper limit state of charge, it is determined whether the current voltage of the battery is greater than or equal to the fast charging upper limit voltage. Based on the determination that the current voltage of the battery is greater than or equal to the fast charging upper limit voltage, the charging current is reduced by a predetermined factor.

16. The battery charging method according to claim 15, wherein, Optimizing the battery model includes: Based on the determination that the current battery voltage is less than the fast charging upper limit voltage, it is determined whether the optimization algorithm start condition is met; Once the starting conditions for the optimization algorithm are determined, the optimization algorithm is executed.

17. The battery charging method according to claim 16, wherein, Determining whether the starting conditions for the optimization algorithm are met includes: Determine whether the constant current application time is greater than the constant current application end time and whether the overpotential is less than the allowable overpotential; Based on the determined results, determine whether the conditions for starting the optimization algorithm are met.

18. The battery charging method according to claim 16, wherein, The optimization algorithms include: Determine the optimal charging current under predetermined constraints.

19. The battery charging method according to claim 16, wherein, Optimizing the battery model includes: Based on the determination that the starting condition of the optimization algorithm is not met, the previous charging current is maintained.

20. The battery charging method according to claim 11, wherein, Obtaining battery status information includes: Based on one or more signals obtained from one or more sensors installed in the battery, at least one of the following is acquired as battery state information: battery voltage, battery health status, or any combination thereof.

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