Apparatus and method for controlling vehicle

Abstract

The invention relates to an apparatus and method for controlling a vehicle. The vehicle control apparatus predicts a state of the battery based on a battery model reflecting at least one of an electrical characteristic, a thermal characteristic, a deterioration characteristic, and any combination thereof of the battery, determines a target charging current for shortening a charging time of the battery based on at least one of a negative pulse that induces peeling, and the state of the battery, and any combination thereof, wherein the metal precipitated in the battery during the stripping process is oxidized into metal ions, and the battery is charged based on the target charging current.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to U.S. Patent Application No. 63 / 704,143, filed October 7, 2024, and Korean Patent Application No. 10-2025-0115737, filed August 20, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to vehicle control devices and methods thereof, and more specifically, to techniques for charging vehicle batteries. Background Technology

[0004] Secondary batteries are used in various applications due to their high energy density and excellent power output characteristics. In particular, with the increasing prevalence of electric vehicle technologies, the demand for fast charging performance and long lifespan is growing. Therefore, various charging control methods are being researched to improve battery charging efficiency while maintaining safety and lifespan.

[0005] In the past, relatively simple charging protocols such as constant current (CC) and constant voltage (CV) methods were used, and were easy to implement. However, these protocols did not take into account the battery's state or degradation, which could lead to problems such as overcharging, lithium deposition (Li deposition), and overheating. These problems can shorten battery life and, in severe cases, may affect safety.

[0006] Therefore, advanced charging control techniques that precisely consider the electrical, chemical, and thermal characteristics of batteries are currently attracting attention. For example, methods have been proposed to measure or predict various battery variables (such as temperature, voltage, resistance, and state of being) and adjust the charging current accordingly. Additionally, methods based on battery models to predict the future state of the battery and controlling charging conditions based on the prediction results (model-based control) are also being actively researched.

[0007] Meanwhile, because the performance of model-based control is highly dependent on the accuracy of the predictive model, state estimation techniques (e.g., algorithms such as Kalman filtering) are also widely used to reduce the error between actual battery measurements and model predictions. Furthermore, various control strategies are being considered in parallel to suppress battery degradation phenomena such as lithium deposition and heat generation that may occur during charging.

[0008] However, conventional technologies still have room for improvement in real-time performance, accuracy, and coverage, and advanced charging control technologies that can respond flexibly to changes in battery state are needed. Summary of the Invention

[0009] This disclosure is made to address the aforementioned problems in the prior art, while maintaining the advantages achieved by the prior art.

[0010] One aspect of this disclosure provides a vehicle control device and method capable of inducing lithium stripping by including a negative pulse in the current used to charge a battery.

[0011] One aspect of this disclosure provides a vehicle control device and method that can improve battery life and safety by removing lithium deposited from the battery surface via lithium stripping.

[0012] One aspect of this disclosure provides a vehicle control device and method capable of determining whether a negative pulse is applied and determining the optimal charging current for a battery by reflecting its lithium stripping effect.

[0013] One aspect of this disclosure provides a vehicle control device and method that can derive an optimal charging current that satisfies both charging performance and safety by taking into account the metal stripping reaction induced by a negative pulse.

[0014] One aspect of this disclosure provides a vehicle control device and method that can dynamically determine the optimal current for a battery being charged based on battery state data.

[0015] One aspect of this disclosure provides a vehicle control device and method capable of determining the amplitude of a negative pulse in order to minimize the growth of lithium deposited from the surface of the battery without increasing the total charging time.

[0016] The technical problems to be solved by this disclosure are not limited to those described above, and any other technical problems not mentioned herein should be clearly understood by those skilled in the art from the following description.

[0017] According to one aspect of this disclosure, the vehicle control device includes a memory storing program instructions and a processor executing the program instructions. The processor predicts or determines the state of the battery based on a battery model reflecting or taking into account at least one of the battery's electrical characteristics, thermal characteristics, degradation characteristics, and any combination thereof; determines a target charging current for shortening the battery's charging time based on at least one of the induced stripping negative pulse, the battery's state, and any combination thereof; wherein metal deposited within the battery during the stripping process is oxidized to metal ions; and charges the battery based on the target charging current.

[0018] In one implementation, the processor can generate an algorithm for optimizing battery charging using a battery model, and can determine a target charging current by applying the algorithm to data associated with the battery's state.

[0019] In one implementation, the processor can determine a target charging current that minimizes or shortens the charging time of the battery while satisfying charging conditions, which are at least one of the following: the rate of heat generation of the battery, the degree of inhibition of lithium (LiP) deposition in the metal, the degree of lithium (LiS) stripping induction, the minimization of battery capacity degradation (or the degree of reduction in battery capacity degradation), and any combination thereof.

[0020] In one implementation, the processor can determine the target charging current, including negative pulses, based on nonlinear model predictive control (NMPC).

[0021] In one implementation, if the condition that a negative pulse is included in the target charging current is met (or when the condition that a negative pulse is included in the target charging current is met), the processor can determine the target charging current including the negative pulse based on NMPC. The condition that a negative pulse is included in the target charging current may include: a condition that the charging time for which a constant current is applied exceeds a predetermined first reference time; a condition that the charging time for which a current including a negative pulse is applied is less than or equal to a predetermined second reference time; and at least one of any combination thereof.

[0022] In one implementation, the processor may determine the amplitude of the negative pulse based on at least one of the following conditions: a first condition that the battery terminal voltage does not exceed a predetermined voltage range (or is within a predetermined voltage range); a second condition that the difference between the metal ion concentration on the battery surface and the average metal ion concentration of the battery does not exceed (or is less than or equal to) a predetermined threshold; and any combination thereof.

[0023] In one implementation, the processor can determine the amplitude of the negative pulse based on a third condition: the current density of metal deposition within the battery does not exceed (or is less than or equal to) the current density of metal ions stripped from the battery.

[0024] In one implementation, the processor can determine the target charging current as the current used to maintain the predetermined voltage value based on whether the maximum or highest value of the battery's terminal voltage exceeds a predetermined voltage value.

[0025] In one implementation, the processor may use one or more sensors installed in the battery to identify data associated with the battery's state. Data associated with the battery's state may include: state of charge (SOC), state of health (SOH), battery terminal voltage, overvoltage of metal deposition within the battery, metal ion concentration on the battery surface, average metal ion concentration of the battery, current density of metal deposition within the battery, current density of metal ion stripping within the battery, and at least one of any combination thereof.

[0026] In one implementation, the processor can predict or determine the state of the battery based on a model based on reduced-order physics.

[0027] According to one aspect of this disclosure, a vehicle control method includes: a processor predicting or determining the state of a battery based on a battery model reflecting or considering at least one of electrical characteristics, thermal characteristics, degradation characteristics, and any combination thereof; the processor determining a target charging current for shortening the charging time of the battery based on at least one of a negative pulse induced by stripping, the state of the battery, and any combination thereof, wherein metal deposited in the battery during the stripping process is oxidized to metal ions; and the processor charging the battery based on the target charging current.

[0028] In a vehicle control method according to one embodiment, determining a target charging current for shortening battery charging time by a processor may include: generating an algorithm for optimizing battery charging by the processor using a battery model; and determining the target charging current by the processor applying the algorithm to data associated with the state of the battery.

[0029] In a vehicle control method according to one embodiment, determining a target charging current for shortening the charging time of a battery by a processor may include: determining a target charging current that minimizes or shortens the charging time of the battery while satisfying charging conditions, the charging conditions being at least one of the following: the battery's heating rate, the degree of inhibition of lithium (LiP) deposition contained in the metal, the degree of lithium (LiS) stripping induction, the minimization of battery capacity degradation (or the degree of reduction in battery capacity degradation), and any combination thereof.

[0030] In a vehicle control method according to one embodiment, determining a target charging current for shortening the battery charging time by a processor may include: determining a target charging current including negative pulses by the processor based on NMPC.

[0031] In a vehicle control method according to one embodiment, determining a target charging current for shortening battery charging time by a processor may include: if a condition is met that a negative pulse is included in the target charging current (or when the condition is met), then the processor determines the target charging current including the negative pulse based on NMPC. The condition that a negative pulse is included in the target charging current may include at least one of the following: a charging time for which a constant current is applied exceeds a predetermined first reference time; a charging time for which a current including the negative pulse is applied is less than or equal to a predetermined second reference time; or any combination thereof.

[0032] In a vehicle control method according to one embodiment, determining the target charging current for shortening the charging time of the battery by the processor may include: determining the amplitude of the negative pulse by the processor based on at least one of the following conditions: a first condition that the terminal voltage of the battery does not exceed a predetermined voltage or is within a predetermined voltage range; a second condition that the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery does not exceed or is less than or equal to a predetermined threshold; and any combination thereof.

[0033] In a vehicle control method according to one embodiment, determining a target charging current for shortening the charging time of a battery by a processor may include: determining the amplitude of a negative pulse by the processor based on a third condition, wherein the current density of metal deposition within the battery does not exceed or is less than or equal to the current density of metal ions stripped from the battery.

[0034] In a vehicle control method according to one embodiment, determining a target charging current for shortening the charging time of a battery by a processor may include: determining the target charging current as a current for maintaining a predetermined voltage value based on the maximum or highest value of the battery's terminal voltage exceeding a predetermined voltage value.

[0035] A vehicle control method according to one embodiment may further include: a processor using one or more sensors installed in the battery to identify or determine data associated with the state of the battery. The data associated with the state of the battery may include at least one of the following: SOC, SOH, battery terminal voltage, overvoltage deposited within the battery, metal ion concentration on the surface of the battery, average metal ion concentration of the battery, current density deposited within the battery, current density of metal ion stripping within the battery, and any combination thereof.

[0036] In a vehicle control method according to one embodiment, predicting or determining the state of a battery by a processor may include: predicting or determining the state of a battery by the processor based on a model based on reduced-order physics. Attached Figure Description

[0037] The above and other objects, features, and advantages of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0038] Figure 1 This is a block diagram illustrating a vehicle control device according to an embodiment of the present disclosure;

[0039] Figure 2 This is a block diagram illustrating the process by which a vehicle control device according to an embodiment of the present disclosure applies a current to a battery determined based on a battery model and a charging algorithm;

[0040] Figure 3A This is a schematic diagram illustrating a micro-region according to an embodiment of the present disclosure;

[0041] Figure 3B This is a diagram illustrating the structure of an electrochemical-thermal-lifetime model according to an embodiment of the present disclosure;

[0042] Figure 4 This is a diagram illustrating the electrochemical mechanism by which lithium ions are stripped by a vehicle control device according to an embodiment of the present disclosure;

[0043] Figure 5A The graph shows the simulation results, which demonstrate how the vehicle control device according to an embodiment of the present disclosure improves the state of health (SOH) of the battery when it includes a negative pulse in the target charging current.

[0044] Figure 5B The graph shows the simulation results, which demonstrate the reduction in charging time when the vehicle control device according to an embodiment of the present disclosure includes a negative pulse in the target charging current.

[0045] Figure 6A It is a graph obtained by comparing the trend of lithium concentration gradient change when charging with a constant current and charging with a current including a negative pulse, according to an embodiment of the present disclosure and based on SOC.

[0046] Figure 6B The graph is obtained by comparing the change of metal deposition overvoltage when charging with a constant current and charging with a current including a negative pulse, according to an embodiment of the present disclosure and based on the SOC.

[0047] Figure 7 This is a diagram illustrating the process by which a vehicle control device according to an embodiment of the present disclosure uses nonlinear model predictive control (NMPC) to optimally determine the magnitude of a constant current and the amplitude of a negative pulse.

[0048] Figure 8 It is a flowchart used to describe a vehicle control device or vehicle control method according to embodiments of the present disclosure;

[0049] Figure 9 This is a flowchart illustrating the process for determining whether a vehicle control device according to an embodiment of the present disclosure determines a target charging current including a negative pulse; and

[0050] Figure 10 A diagram illustrating a computing system associated with a vehicle control device or vehicle control method according to an embodiment of the present disclosure is shown. Detailed Implementation

[0051] In the following, some embodiments of the present disclosure 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 include the same reference numerals, even though they are shown in another drawing. Furthermore, in describing embodiments of the present disclosure, detailed descriptions associated with well-known functions or configurations are omitted if they might unnecessarily obscure the subject matter of the disclosure.

[0052] In describing the elements of embodiments of this disclosure, the terms first, second, A, B, (a), (b), etc., may be used herein. These terms are used only to distinguish one element from another, but do not limit the corresponding element, and are unrelated to the nature, order, or priority of the corresponding element. Furthermore, the expression "at least one of A, B, or C and any combination thereof" may include "A, B, or C or any combination thereof, such as AB, BC, AC, or ABC."

[0053] Furthermore, unless otherwise defined, all terms used herein (including technical or scientific terms) include the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It should be understood that terms used herein should be interpreted to include meanings consistent with their meanings in the context of this disclosure and related fields, and should not be construed as idealized or overly formal unless expressly defined herein. When a component, processor, controller, device, element, apparatus, etc., of this disclosure is described as having a purpose or performing an operation, function, etc., that component, processor, controller, device, element, apparatus, etc., should be considered herein as "configured" to satisfy that purpose or perform that operation or function. Each component, controller, device, element, apparatus, etc., may be embodied individually as a processor and memory (such as a non-transitory computer-readable medium) or included together with a processor and memory as part of that apparatus.

[0054] In the following text, see references Figures 1 to 10 The embodiments of this disclosure are described in detail.

[0055] Figure 1 This is a block diagram illustrating a vehicle control device according to an embodiment of the present disclosure.

[0056] refer to Figure 1 The vehicle control device 100 according to embodiments of the present disclosure can be implemented inside a vehicle. The vehicle control device 100 can be integrated with the vehicle's internal control unit, or it can be implemented by a separate device for coupling with the vehicle's control unit via a separate connection device.

[0057] According to one embodiment, the vehicle control device 100 can be implemented as part of a battery management system (BMS). A BMS is a system that monitors and manages the voltage, current, temperature, state of charge (SOC), or state of health (SOH) of a battery pack installed in a vehicle, and includes hardware-based sensors and control circuitry. The vehicle control device 100 of this disclosure can be implemented as a configuration including a memory and one or more processors running within the BMS, and can perform functions such as controlling the charging current based on battery state information collected by the BMS, and determining an optimal charging profile. Therefore, it is clear that this disclosure is a concrete and technical approach implemented via physical systems, and not merely a mathematical algorithm or psychological process.

[0058] According to one embodiment, the vehicle control device 100 may include a processor 110 and a memory 120. Figure 1 The configuration of the vehicle control device 100 shown is an example, and embodiments of this disclosure are not limited thereto. For example, the vehicle control device 100 may further include... Figure 1 Components not shown in the diagram.

[0059] According to one embodiment, memory 120 may store instructions or data. For example, memory 120 may store one or two or more instructions that, when executed by processor 110, cause vehicle control device 100 to perform various operations.

[0060] According to one embodiment, the memory 120 may be implemented as a single chipset having a processor 110 and may store various information associated with the vehicle control device 100. For example, the memory 120 may store information about the operating history of the processor 110.

[0061] According to one embodiment, memory 120 may include non-volatile memory (read-only memory (ROM)) and volatile memory (random access memory (RAM)).

[0062] According to one implementation, processor 110 can predict the state of a battery based on a battery model that reflects at least one of the battery's electrical characteristics, thermal characteristics, degradation characteristics, and any combination thereof.

[0063] According to one implementation, the processor 110 can predict or determine the state of the battery based on information such as the battery's state of charge (SOC), state of health (SOH), internal resistance, terminal voltage, current, and temperature.

[0064] According to one implementation, the processor 110 can use sensors installed in the battery to identify or determine data associated with the state of the battery.

[0065] According to one embodiment, data associated with the state of the battery may include at least one of the following: SOC, SOH, battery terminal voltage, overvoltage of metal deposited (plated) within the battery, metal ion concentration on the surface of the battery, average metal ion concentration of the battery, current density of metal deposited within the battery, current density of metal ion stripping within the battery, and any combination thereof.

[0066] State of Charge (SOC) can be defined as the percentage (%) obtained by dividing the amount of charge stored in the battery by a reference charge (maximum charge storage or rated capacity). SOC indicates the current level of charge being supplied to the battery and is therefore a key parameter in charge control.

[0067] State of Health (SOH) can be an indicator of a battery's state of degradation or overall performance level, and can be calculated by comprehensively reflecting the battery's capacity, internal resistance, and output characteristics compared to its initial state (new product). For example, if 90% of the initial capacity can be stored, then SOH is defined as 90%. SOH can be used to predict battery life and determine charging conditions.

[0068] A battery's terminal voltage can be measured by an external circuit between the battery's cathode and anode. Terminal voltage can indirectly reflect the battery's current state of charge and internal electrochemical reactions.

[0069] The overvoltage at which metals are deposited in a battery (plating overpotential) can be a potential difference below the threshold potential required to initiate a deposition reaction in which metal ions react with electrons at the anode surface of the battery and are reduced (deposited) into a solid metal. This represents the driving force of the deposition reaction, and deposition can typically be induced if the overvoltage is below 0 (if it is negative).

[0070] Specifically, if metal ions reach the electrode surface, the corresponding ions need a certain electrochemical driving force to be reduced to the metal state through electrochemical reaction, and the potential difference at this time can be understood as the overvoltage for metal deposition.

[0071] If the overvoltage becomes less than zero (i.e., it becomes negative), this indicates that sufficient reducing power is provided to deposit metal ions. Under these conditions, a metal deposition reaction may occur.

[0072] For example, in the case of lithium-ion batteries, if lithium ions migrate from the cathode to the anode via the electrolyte and are intercalated into the carbon-based anode, or if the charging rate is too fast or the voltage conditions are too high, the lithium ions may not be intercalated and may be deposited as metallic lithium on the surface of the anode. In this case, the metal deposition overvoltage can be used as an important reference variable to determine and control these deposition conditions.

[0073] The concentration of metal ions on a battery surface can include the concentration of metal ions present near the anode surface (near the solid-electrolyte interface (SEI) layer). The concentration of metal ions on the battery surface can indicate the likelihood that metal ions may reach the electrode and react with it. For example, a low metal ion concentration can enhance the concentration gradient, thereby increasing the likelihood of metal deposition.

[0074] The average metal ion concentration of a battery can include the average concentration of metal ions distributed across the entire battery electrode (e.g., the anode) or electrolyte region. The average metal ion concentration can be used to determine whether a concentration gradient exists between the electrode surface and its interior. For example, if the difference from the surface concentration is large, the application of excessive current may lead to deposition conditions.

[0075] The current density for metal deposition in a battery can be defined as the amount of current required to reduce metal ions to their metallic form and undergo an electrochemical reaction per unit area of ​​the battery surface. For example, if the current density for metal deposition exceeds a threshold, metal deposition may accelerate, leading to reduced battery life and safety issues.

[0076] The current density for stripping metal ions within a battery can include the current density of an electrochemical reaction in which the deposited metal is oxidized back to its ionic form and diffuses into the electrolyte. For example, processor 110 can induce a stripping current density via an appropriate negative pulse, thereby improving battery safety and lifespan by removing pre-deposited metals.

[0077] According to one embodiment, a battery model that reflects or considers at least one of the electrical characteristics, thermal characteristics, degradation characteristics, and any combination thereof of a battery may include an empirical model, a circuit model, a model based on full-order physics, a model based on reduced-order physics, and at least one combination thereof.

[0078] According to one implementation, empirical models and circuit models (ECMs) are calculated very quickly in methods that use simple mathematical formulas or RC-based circuits to approximate the operation of an actual battery.

[0079] However, empirical and circuit models fail to reflect and / or account for complex internal physical phenomena (e.g., lithium-ion diffusion, changes in electrochemical reaction rates, degradation mechanisms, etc.), resulting in low accuracy and poor power prediction under anomalous conditions such as fast charging.

[0080] According to one implementation, a full-order physics-based model refers to a structure that uses complex partial differential equations (PDEs) based on numerical analysis to model the physical phenomena inside a real battery, such as lithium concentration distribution within the electrodes, electrolyte conductivity, reaction current density, temperature changes, and metal deposition / stripping.

[0081] According to one implementation, models based on full-order physics can provide high accuracy, but are unsuitable for real-time computing due to their complex computational structure, which leads to long computation times and high computational resource consumption.

[0082] According to one implementation, models based on reduced-order physics can numerically simplify the complex electrochemical and thermodynamic reactions that occur within actual battery cells. In this way, battery state can be predicted with sufficient accuracy while saving computational resources.

[0083] According to one implementation, the model based on reduced-order physics may include at least one of an electrochemical module, a thermal module, a degradation module, and any combination thereof.

[0084] For example, the electrochemical module can model the diffusion, insertion, and deintercalation of lithium ions within the electrode, and can predict the lithium ion concentration distribution C, the electrode reaction rate "j", the terminal voltage V, etc.

[0085] For example, taking into account the electrochemical reaction heat and resistance heat generated during charging and discharging, the thermal module can simulate the time-varying temperature of the battery.

[0086] For example, the degradation module can reflect the decrease in SOH by estimating degradation mechanisms such as capacity decrease based on the increase in cycle count, increase in internal resistance, Li precipitation and stripping, and growth of the solid electrolyte interphase (SEI).

[0087] According to one embodiment, the processor 110 can determine a target charging current for shortening the charging time of the battery based on at least one of the following: a negative pulse that induces stripping (in which metal deposited in the battery is oxidized to metal ions), the state of the battery, and any combination thereof.

[0088] According to one implementation, stripping can refer to the reaction in which metal deposited on the anode surface during battery charging is oxidized back to metal ions and returned to the electrolyte.

[0089] Typically, during fast charging, lithium ions (Li+) on the anode surface of the battery can be deposited as metallic lithium (Li) and simultaneously reduced to electrons, which may be the main reason for battery capacity loss or shortened lifespan.

[0090] By removing deposited metals, stripping can play a crucial role in preventing battery performance degradation and inducing stable charging.

[0091] According to one implementation, a negative pulse may include applying a negative current to the anode in the opposite direction within a specific time interval during a normal constant current charging (positive current) process.

[0092] Negative pulses can be used as a method to induce the stripping reaction described above. In other words, negative pulses can oxidize the metallic lithium deposited on the anode surface through a temporary discharge operation to return it to lithium ions.

[0093] According to one embodiment, the processor 110 can charge the battery with a target charging current that appropriately includes negative pulses, thereby suppressing metal deposition problems that may occur during the charging process and improving charging efficiency.

[0094] According to one embodiment, the target charging current may include a current determined to be effective and safe for charging the battery based on the current battery state and charging conditions.

[0095] For example, the target charging current can be set to a value that is dynamically adjusted based on the battery state and whether a negative pulse is applied, rather than a fixed value.

[0096] According to one implementation, the processor 110 can dynamically determine the target charging current by taking into account battery state information and an optimized algorithm for whether to apply a negative pulse.

[0097] For example, if it is determined that a certain amount of metallic lithium or more has been deposited, the processor 110 can induce stripping via a negative pulse, and then adjust the charging mode by temporarily reducing or restoring the charging current thereafter.

[0098] In addition, the processor 110 can limit the target charging current so that the battery terminal voltage does not exceed a specific range, or it can adjust the target charging current so that the difference in metal ion concentration does not exceed a threshold.

[0099] In this way, the processor 110 can derive a charging strategy that satisfies both charging performance and safety, and specifically, can charge the battery in a shorter time compared to conventional charging methods that use multi-level constant current (MCC).

[0100] In this way, the processor 110 can take into account the dynamic behavior (deposition / stripping) of lithium metal in the battery to optimize the charging current based on the negative pulse and battery state, thereby effectively suppressing factors that reduce battery life and safety while shortening battery charging time.

[0101] According to one implementation, processor 110 can generate an algorithm for optimizing battery charging using a battery model, and can determine a target charging current by applying the algorithm to data associated with the state of the battery.

[0102] According to one implementation, processor 110 can generate an algorithm for optimizing battery charging based on a battery model.

[0103] According to one implementation, an algorithm for optimizing battery charging may include an algorithm that can shorten charging time while preventing battery performance degradation by determining an appropriate charging current based on the battery's state.

[0104] For example, based on a battery model that reflects the internal electrochemical reactions of the battery, the algorithm can predict various phenomena that may occur during charging (Li precipitation, increased heat generation, increased metal ion concentration deviation, excessive voltage increase, etc.), and can calculate the optimal charging current in real time to control the predicted results.

[0105] In addition, to shorten charging time while ensuring battery life, the algorithm can determine whether to apply a negative pulse to induce the oxidation of deposited lithium into metal ions, as well as the amplitude of the negative pulse.

[0106] As a specific example, the algorithm can be generated based on nonlinear model predictive control (NMPC).

[0107] According to one implementation, the processor 110 can evaluate the charging conditions of the battery in real time and determine a target charging current suitable for the charging conditions by applying the generated algorithm to data associated with the state of the battery.

[0108] For example, if it is determined based on data associated with the battery state that a phenomenon occurring within the battery (such as lithium deposition or heat generation) may exceed a threshold level, the processor 110 may determine a current including a negative pulse or a low C-rate current as the target charging current.

[0109] For example, if it is determined that the battery condition is good and lithium stripping can be effectively induced, the processor 110 can determine the target charging current for increasing the charging rate by applying a relatively high constant current.

[0110] In this way, the processor 110 can determine the optimal target charging current by applying algorithms to data associated with the battery state in order to effectively shorten the charging time while ensuring the stability and lifespan of the battery.

[0111] According to one embodiment, processor 110 can determine a target charging current that minimizes or shortens the charging time of the battery while satisfying charging conditions, which are at least one of the following: battery heating rate, degree of suppression of lithium deposition (LiP) in the metal, degree of lithium stripping (LiS) induction, minimization of battery capacity degradation (or degree of reduction of battery capacity degradation), and any combination thereof.

[0112] According to one embodiment, the processor 110 can determine a target charging current that satisfies the charging conditions of the battery's heating rate.

[0113] According to one implementation, the processor 110 can use a battery model to pre-calculate the expected heating rate at a specific charging current, and can adjust the target charging current so that the heating rate does not exceed a predetermined threshold.

[0114] For example, the processor 110 can perform charging control in a manner that reduces the charging rate in a segment where the rate of heat generation increases rapidly with increasing charging current, and allows for a relatively high current if the heat generation is within a stable range.

[0115] According to one embodiment, processor 110 can determine a target charging current that satisfies the degree of suppression of LiP contained in the metal.

[0116] According to one implementation, the processor 110 can use a battery model to predict the risk of lithium deposition based on the charging current.

[0117] For example, the processor 110 can calculate indicators such as metal deposition overvoltage during charging, metal ion concentration gradient, and deposition current density during charging, and can set conditions for adjusting the charging current so that these values ​​do not exceed a predetermined threshold.

[0118] Furthermore, the processor 110 can proactively identify potential Li precipitation scenarios by analyzing sensor data (such as SOC, temperature, voltage, and metal ion concentration) in real time. If Li precipitation is determined to be possible, the processor 110 can suppress it by reducing the charging current or including a negative pulse in the charging current.

[0119] According to one embodiment, processor 110 can determine a target charging current that satisfies the charging conditions for the degree to which lithium stripping (LiS) is induced.

[0120] According to one implementation, if a negative pulse is applied, the processor 110 can use a battery model to analyze the conditions for effectively inducing Li stripping (LiS).

[0121] For example, the processor 110 can use indicators such as the stripping current density when Li stripping occurs effectively, the stripping overvoltage, and the recovery rate of metal ion concentration to evaluate the stripping induction conditions.

[0122] According to one embodiment, processor 110 can determine the timing and intensity of the negative pulse to be applied by considering the charging history during or after the MCC segment and the battery state. In this way, processor 110 can set the charging conditions for effective removal of deposited lithium by adjusting the induced stripping time and the magnitude of the current including the negative pulse.

[0123] Furthermore, the processor 110 can assess whether stripping should be induced to a specific level or higher based on changes in the battery's state (e.g., a decrease in deposition overvoltage or a recovery in metal ion concentration). If it is determined that stripping has reached a specific level, the processor 110 can adjust the size or duration of the negative pulse.

[0124] According to one implementation, processor 110 can determine a target charging current that satisfies the conditions for minimizing battery capacity degradation.

[0125] For example, high charging current, excessive heat generation, and Li precipitation can cause changes in the electrode structure within the battery, abnormal growth of the SEI layer, and electrolyte decomposition, which can lead to a reduction in battery capacity.

[0126] The processor 110 can analyze key indicators related to battery capacity degradation in real time, such as the rate of change of accumulated charge, charging current distribution, battery temperature, metal deposition overvoltage and SOH, and can dynamically adjust the target charging current so that these indicators do not exceed predetermined reference values.

[0127] According to one embodiment, the processor 110 can determine the target charging current by taking into account each of the following charging conditions: the rate of heat generation of the battery, the degree of inhibition of Li precipitation contained in the metal, the degree of Li stripping induction, and the minimization of battery capacity degradation (or the degree of reduction of battery capacity degradation).

[0128] For example, the processor 110 can calculate a target charging current that satisfies multiple charging conditions, such that Li precipitation contained in the metal is simultaneously suppressed, the rate of heat generation during charging does not exceed a predetermined threshold, effectively inducing Li stripping, and minimizing battery capacity degradation.

[0129] According to one implementation, processor 110 can determine a target charging current including negative pulses based on NMPC.

[0130] NMPC can be one of the predictive control techniques, and it can be designed to satisfy the system's objective function by predicting the future operation of the system and calculating the optimal control input over a certain time interval.

[0131] The processor 110 can calculate the optimal value of the target charging current distribution based on a predefined objective function (e.g., a function that minimizes battery charging time), including whether a negative pulse is applied, the period of the negative pulse, the amplitude of the negative pulse, and the duration of the negative pulse.

[0132] For example, NMPC can simultaneously consider conditions such as whether the constant current charging time exceeds a predetermined reference time, whether the metal deposition overvoltage remains less than or equal to a threshold, whether applying a negative pulse effectively induces Li stripping, and whether the battery terminal voltage does not rise excessively.

[0133] The processor 110 can set a nonlinear objective function for the above conditions and determine the target charging current. For example, if it is determined that including negative pulses in the charging current improves charging efficiency and battery life, the processor 110 can derive the optimal target charging current including negative pulses using NMPC.

[0134] According to one implementation, if the condition that the negative pulse is included in the target charging current is met, the processor 110 can determine the target charging current including the negative pulse based on NMPC.

[0135] According to one embodiment, the condition for including a negative pulse in the target charging current may include: a condition that the charging time during which a constant current is applied exceeds a predetermined first reference time, a condition that the charging time during which a current including a negative pulse is applied is less than or equal to a predetermined second reference time, and at least one of any combination thereof.

[0136] According to one embodiment, if the condition that the charging time during the application of a constant current exceeds a predetermined first reference time is met, the processor 110 can determine a target charging current including negative pulses based on NMPC.

[0137] If a constant current (CC) is continuously applied to the battery, the risk of lithium-ion concentration imbalance or metal deposition inside the battery increases. Specifically, if fast charging is continued for an extended period at a low SOC range, lithium deposition (Li-plating) may occur, where lithium is deposited on the surface of the anode and fixed in metallic form. This can shorten battery life and reduce safety.

[0138] Therefore, the processor 110 can continuously track the charging time during the period when a constant current is applied after the start of charging, and can identify cases where the charging time during the period when a constant current is applied exceeds a predetermined first reference time (e.g., 47 seconds).

[0139] If the charging time during the application of a constant current is met, the processor 110 can induce lithium stripping by including negative pulses in the target charging current, and can adjust the charging algorithm to mitigate overvoltage or concentration gradients within the battery.

[0140] According to one embodiment, if the charging time during which a current including a negative pulse is applied is less than or equal to a predetermined second reference time, the processor 110 can determine the target charging current including the negative pulse based on NMPC.

[0141] By applying a reverse charging current for a short time using a negative pulse, lithium metal deposited on the anode surface can be oxidized (stripped) back into ions. However, if the current, including the negative pulse, is applied for too long, charging efficiency may decrease and problems such as battery voltage instability may occur.

[0142] Therefore, the processor 110 can continuously track the charging time of the applied current, including negative pulses, and can determine the target charging current such that the charging time does not exceed a predetermined second reference time (e.g., 3 seconds).

[0143] According to one implementation, the processor 110 may determine the two conditions individually or in combination.

[0144] For example, if the charging time during which current is applied exceeds a first reference time, and the charging time during which current including a negative pulse is applied is less than or equal to a second reference time, then the processor 110 can determine the target charging current including the negative pulse based on NMPC.

[0145] According to one embodiment, the processor 110 may determine the amplitude of the negative pulse based on at least one of the following conditions: a first condition that the terminal voltage of the battery does not exceed a predetermined voltage range (e.g., within the predetermined voltage range), a second condition that the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery does not exceed a predetermined threshold (e.g., less than or equal to the predetermined threshold).

[0146] According to one embodiment, the processor 110 can determine the amplitude of the negative pulse based on a first condition that the battery terminal voltage does not exceed a predetermined voltage range.

[0147] The processor 110 can prevent safety issues during charging, such as thermal runaway or electrical overload, by controlling the battery's terminal voltage to prevent it from rising excessively. For example, if the terminal voltage rises abnormally while a current including a negative pulse is applied, it could have adverse effects on the entire battery system.

[0148] According to one embodiment, processor 110 can determine the amplitude of a negative pulse that allows the battery terminal voltage to not exceed a predetermined voltage range.

[0149] For example, if continuous MCC charging or constant current charging is performed during the charging process, the battery's terminal voltage can gradually increase proportionally to the current. In this case, if the terminal voltage is detected to be close to the threshold voltage, or if the possibility of the terminal voltage exceeding the threshold voltage is detected, the processor 110 can reverse the current direction by immediately applying a negative pulse to the target charging current, and can temporarily reduce the terminal voltage by switching to a discharging state.

[0150] The processor 110 can calculate the minimum pulse size required to maintain the terminal voltage within a predetermined voltage range.

[0151] According to one embodiment, processor 110 can determine the amplitude of a negative pulse that allows the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery to not exceed (e.g., less than or equal to) a predetermined threshold.

[0152] To prevent the metal ion concentration gradient generated in the battery from becoming too large, the processor 110 can adjust the size of the negative pulse.

[0153] At faster charging rates or higher current densities, the difference between the metal ion concentration on the battery surface and the average metal ion concentration of the battery can be further amplified. If this difference increases, the accumulation of lithium ions on the battery surface increases, thereby increasing the likelihood of Li deposition.

[0154] According to one embodiment, processor 110 can determine the amplitude of a negative pulse whose maximum value of the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery does not exceed (e.g., less than or equal to) a predetermined threshold.

[0155] According to one embodiment, the processor 110 may determine the size of the negative pulse based on a third condition that the current density of metal deposition within the battery does not exceed (e.g., is less than or equal to) the current density of metal ion stripping within the battery.

[0156] For example, the current density at which metals are deposited in a battery can refer to the amount of metallic lithium deposited on the surface of the anode during the charging process. If the current density at which metals are deposited in the battery is too high, the battery life may be reduced, and the risk of short circuits may increase.

[0157] For example, the current density for stripping metal ions can refer to the amount of lithium metal deposited on the anode surface that is reionized and reduced to electrolyte by methods such as negative pulses.

[0158] According to one embodiment, the processor 110 can continuously determine the relative relationship between the current density for depositing metal in the battery and the current density for stripping metal ions in the battery.

[0159] For example, if the current density of the deposited material exceeds the current density of the stripped material, lithium can accumulate in the battery, thereby reducing the battery's charging efficiency.

[0160] Therefore, the processor 110 can adjust the amplitude of the negative pulse to allow the current density of the deposited current to avoid exceeding the current density of the stripping current.

[0161] According to one implementation, the target charging current can be determined as the current used to maintain the predetermined voltage value based on the fact that the maximum value of the battery's terminal voltage exceeds a predetermined voltage value.

[0162] According to one embodiment, the processor 110 can monitor the maximum value of the battery's terminal voltage in real time during the charging process. If the battery reaches an overvoltage state, the risks of electrochemical imbalance, overheating, electrode damage, and shortened lifespan increase, and therefore it is necessary to adjust the charging current to ensure that it does not exceed a specific threshold voltage.

[0163] According to one embodiment, if the battery terminal voltage exceeds a predetermined voltage value, the processor 110 can charge the battery with a current that allows the terminal voltage to remain at the predetermined voltage value.

[0164] In other words, if the battery's terminal voltage exceeds a predetermined voltage value, the processor 110 can determine the target charging current as the current used to allow the terminal voltage to remain at the predetermined voltage value.

[0165] For example, if charging is performed using a constant current method at the start of charging, and then the battery's terminal voltage reaches a predetermined voltage value, the processor 110 can gradually reduce the charging current to maintain the battery's terminal voltage at the predetermined value. In this way, the processor 110 can prevent side effects that occur at high voltages (such as lithium deposition) and can increase the stability of the battery.

[0166] Figure 2 This is a block diagram illustrating the process by which a vehicle control device according to an embodiment of the present disclosure applies a current to a battery determined based on a battery model and a charging algorithm.

[0167] According to one implementation method Figure 2 It can be roughly divided into an upper region 210 and a lower region 220.

[0168] The upper region 210 includes a charger 211 and a battery 212 installed on the vehicle, and may refer to the region where charging operations are performed via hardware.

[0169] The lower region 220 includes the battery model 221, the calibration module 222, and the charging algorithm 223, and may refer to the region where charging operations are performed via software. For example, the vehicle control device 100 may include the battery model 221, the calibration module 222, and the charging algorithm 223.

[0170] According to one implementation, the charger 211 can be based on the optimal charging current I received from the charging algorithm module. opt It supplies current to battery 212.

[0171] According to one embodiment, a terminal voltage V can be obtained from battery 212. t Measurement data such as current "I", heating rate HGR, temperature, and state of equilibrium (SOH) are collected. This measurement data can be sent to the calibration module 222 and used for calibration tasks to improve the accuracy of model-based predictions.

[0172] According to one embodiment, the vehicle control device 100 can receive data measured from a battery (such as battery 212) via a calibration module 222 and can analyze errors. The vehicle control device 100 can improve the accuracy of the simulation by reflecting the analyzed errors into the battery model 221.

[0173] According to one embodiment, the vehicle control device 100 can numerically simulate the electrochemical and thermodynamic characteristics of the battery 212 based on data corrected by the battery model 221.

[0174] For example, the vehicle control device 100 can predict the state data of the battery 212 through the battery model 221, including SOC, SOH, terminal voltage, lithium deposition efficiency, surface concentration, etc.

[0175] According to one embodiment, the vehicle control device 100 can apply a charging algorithm 223 based on the state data of the battery 212 received from the battery model 221. The vehicle control device 100 can derive an optimal charging current suitable for charging the battery 212 through the charging algorithm 223.

[0176] For example, the vehicle control device 100 can calculate the optimal charging current by comprehensively considering various conditions (including lithium deposition suppression, lithium stripping induction, heating rate limitation, capacity degradation suppression, etc.). The vehicle control device 100 can deliver the calculated charging current information to the charger 211 and control the charging process so that the corresponding charging current is actually applied to the battery 212.

[0177] refer to Figure 2 According to one embodiment, the vehicle control device 100 can simultaneously shorten charging time, improve battery life, and ensure safety by accurately predicting battery status and controlling charging conditions in real time.

[0178] Figure 3A This is a schematic diagram illustrating a micro-region according to an embodiment of the present disclosure.

[0179] refer to Figure 3A The micro-region can consist of a composite anode mixed with an electrolyte, a separator, and a composite cathode mixed with an electrolyte. The composite anode can be made of lithium-intercalated graphite. If the battery cell is discharged, lithium ions can dissociate from the composite anode and move to the cathode via the electrolyte. The composite cathode can be made of lithium metal oxide. If the battery cell is charged, lithium ions can move to the composite cathode via the electrolyte and can be stored. The 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 at the anode and cathode can operate as channels for moving electrons generated during the charging or discharging process of the battery cell.

[0180] Figure 3B This is a diagram illustrating the structure of an electrochemical-thermal-lifetime model according to an embodiment of the present disclosure.

[0181] refer to Figure 3B The electrochemical-thermal-lifetime model refers to an electrochemical model based on a reduced-order model obtained by combining a thermal model and a degradation model.

[0182] If internal variables are received, the thermal model can calculate and output the calorific value based on the internal variables. The calorific value can be expressed based on Equation 1.

[0183] Formula 1

[0184]

[0185] In Formula 1, "I" represents current; V t U represents the voltage at "t" seconds; "T" is the temperature; U oc This represents the open-circuit voltage; and dU oc / dT is the entropy coefficient. The first term can represent an irreversible reaction, and the second term can represent a reversible reaction.

[0186] The degradation model can accept internal variables and can use these internal variables to calculate and output SOH and Li deposition overvoltages. (Reference) Figure 4 Electrochemical degradation, such as side reactions and lithium deposition, can occur during battery charging. Degradation models can account for these electrochemical degradations during charging to predict battery degradation and lifespan. In this case, the degradation model can account for electrochemical degradation occurring under the following assumptions.

[0187] Assumption:

[0188] - Deterioration occurs only at the anode.

[0189] - Side reactions are irreversible.

[0190] -Li precipitation is a semi-reversible reaction.

[0191] - Mechanical degradation, gas generation, overcharging, and over-discharging are not considered.

[0192] The degradation model can be used to calculate the reaction rate and overvoltage based on electrochemical degradation using the formulas in Table 1.

[0193] Table 1

[0194]

[0195] In Table 1, a s R represents the specific reaction area. SEI This represents the resistance of the SEI. α ox α rd α rd,side and α rd,Li "F" represents the Faraday constant; "R" represents resistance; and "T" represents temperature. eq U represents the equilibrium potential of the main reaction; eq，side n represents the equilibrium potential of the side reaction. side This represents the number of ions participating in the side reaction. i0 represents the exchange current density, which can be expressed as Equation 2.

[0196] Formula 2

[0197]

[0198] In Equation 2, “k” represents the kinetic rate constant.

[0199] Figure 4 This is a diagram illustrating the electrochemical mechanism by which lithium ions are stripped by a vehicle control device according to an embodiment of the present disclosure.

[0200] According to one implementation method Figure 4 The various reaction processes that occur at the anode phase interface of a lithium-ion battery are visually represented, and specifically include the processes of Li deposition during charging and Li stripping during discharging.

[0201] According to one implementation method Figure 4 The SEI can be a stable solid layer formed between the electrolyte and the anode (e.g., graphite), and can be formed after the electrolyte is reduced during the initial charging process.

[0202] SEI can include properties that allow lithium ions to pass through the SEI but block electrons.

[0203] exist Figure 4 The upper part shows lithium ions (Li+) moving within the electrolyte. Lithium ions (Li+) can be deposited as metallic lithium (Li) on the electrode surface via a reduction reaction. The process by which lithium ions accept electrons and are converted into metallic lithium is represented as "lithium deposition," an undesirable phenomenon that can occur during excessive current or low-temperature charging.

[0204] In one implementation, the vehicle control device can induce a Li stripping reaction by applying a charging current including a negative pulse to re-oxidize the deposited lithium in order to convert it into lithium ions.

[0205] exist Figure 4 In the diagram, the Li stripping process is shown as a pathway indicated by “oxidation,” which could refer to the process by which deposited metallic lithium releases electrons again and is oxidized into lithium ions to return to the electrolyte.

[0206] According to one embodiment, a vehicle control device can induce a Li stripping reaction by applying a charging current including a negative pulse to the battery, the Li stripping reaction being based on... Figure 4 The mechanism shown oxidizes the lithium metal deposited on the surface of the anode so that it can be returned to lithium ions.

[0207] Figure 5A The graph shows the simulation results, which demonstrate how the vehicle control device according to an embodiment of the present disclosure increases the SOH of the battery when it includes a negative pulse in the target charging current.

[0208] Figure 5A It is a graph used to compare the change in SOH (State of Health) based on the number of charge / discharge cycles when the battery is charged / discharged according to three charging protocols (MCC, O-MCC, and O-MCC+NP).

[0209] The MCC method refers to a method of charging the battery with a constant current in each step, while controlling the charging rate while maintaining a fixed charging current for each step. Typically, this includes initially charging the battery with a high current and gradually decreasing the charging current as the state of charge (SOC) increases. The MCC method has a simple structure and is easy to implement. However, it may not adequately suppress side effects such as lithium deposition or heat generation, leading to rapid battery degradation.

[0210] refer to Figure 5A According to one implementation, the MCC method (Exp:MCC, Sim:MCC) showed a relatively rapid decrease in SOH and rapid degradation after about 250 cycles.

[0211] O-MCC can be based on the MCC method, but can include methods that consider the battery's state (SOC, SOH, voltage, etc.) to optimize the current magnitude and duration of each charging phase. Vehicle control equipment can use battery models and state data to adjust the charging curve to minimize degradation and improve efficiency. In this way, charging efficiency and lifespan characteristics can be improved compared to MCC.

[0212] refer to Figure 5A According to one implementation, the O-MCC method (Exp: O-MCC, Sim: O-MCC) slows down the battery degradation rate by optimizing the charging current, and the SOH degradation curve appears to be flat.

[0213] O-MCC+NP can be a method of adding negative pulses to the O-MCC method, and can apply negative current (discharge current) at regular time intervals during optimized constant current charging. By inducing Li stripping through negative pulses, lithium metal deposited on the anode is converted back into lithium ions, and the degradation caused by metal deposition is mitigated. Therefore, SOH can be maintained most stably during long-term cycling.

[0214] refer to Figure 5A According to one implementation, the O-MCC+NP method (Exp: O-MCC+NP, Sim: O-MCC+NP) is identified by adding negative pulses to the O-MCC method to suppress Li precipitation and induce Li stripping, thereby improving the battery's state retention during long-term cycling. Simulation results also show a similar trend to experimental results and demonstrate the effectiveness of the proposed vehicle control device and the proposed charging algorithm.

[0215] These results indicate that the charging curve, including negative pulses, effectively controls the battery degradation mechanism and thus contributes to longer battery life.

[0216] Figure 5B The graph shows the simulation results, which demonstrate the reduction in charging time when the vehicle control device according to an embodiment of the present disclosure includes a negative pulse in the target charging current.

[0217] According to one implementation method Figure 5B Simulation results are shown demonstrating that a vehicle control device according to an embodiment of the present disclosure can effectively shorten charging time based on the battery's life cycle stage (Start of Life (BoL) / Mid-Life (MoL) / End of Life (EoL)) by applying a charging protocol including negative pulses.

[0218] refer to Figure 5BIn the BOL section, the MCC method appears to have the longest charging time. This is because, in the initial stage, MCC applies the set charging phase uniformly, resulting in unoptimized free current. On the other hand, considering the initial battery state, O-MCC and O-MCC+NP achieve relatively short charging times by using optimized current distribution.

[0219] refer to Figure 5B In the MoL region, the charging times of the three methods were similar to each other, but O-MCC+NP still maintained the shortest charging time. This indicates that the O-MCC+NP method exhibits the most efficient charging performance because Li stripping is effectively induced by negative pulses even in the mid-stage of gradual battery performance degradation.

[0220] refer to Figure 5B Charging time generally tends to increase in the EoL range. In this case, the charging time of O-MCC can be longer than that of MCC or O-MCC+NP, which can be interpreted as O-MCC applying a conservative charging current to reflect battery aging.

[0221] On the other hand, the O-MCC+NP method still maintains the shortest charging time among the three methods. This is because the charging efficiency is consistently maintained by effectively suppressing Li deposition and inducing lithium metal stripping via negative pulses.

[0222] therefore, Figure 5B The O-MCC+NP charging method applied by the vehicle control device according to an embodiment of the present disclosure is visually demonstrated to shorten charging time over the battery life cycle. Specifically, the O-MCC+NP charging method records the shortest charging time in the EoL segment and proposes the possibility of simultaneously satisfying the conflicting objectives of fast charging and battery life preservation.

[0223] Figure 6A The graph is obtained by comparing the trend of lithium concentration gradient change when charging with a constant current and charging with a current including a negative pulse, according to an embodiment of the present disclosure and based on SOC.

[0224] The lithium-ion concentration gradient can represent the difference between the metal ion concentration at the battery surface and the average metal ion concentration of the battery. Figure 6A In the graph shown, solid lines indicate charging methods including negative pulses (NP), while dashed lines indicate constant current (CC) charging methods.

[0225] like Figure 6A As shown, the CC charging method demonstrates a trend where the lithium-ion concentration gradient gradually increases with increasing SOC.

[0226] On the other hand, in charging methods including NP, a segment was observed where the lithium concentration gradient decreased with regular SOC intervals. This can be viewed as a stripping reaction when NP is applied, which alleviates the difference between the lithium concentration on the anode surface and the average concentration.

[0227] Therefore, even with an increase in SOC, the charging method including NP can suppress the cumulative increase in lithium concentration gradient, and thus, the difference in lithium concentration gradient can be kept lower than that of CC charging.

[0228] According to one implementation method Figure 6A This demonstrates that vehicle control equipment can effectively improve the uniformity of lithium-ion concentration distribution by using a target charging current that includes NP.

[0229] Figure 6B The graph is obtained by comparing the change in metal deposition overvoltage when charging with a constant current and charging with a current including a negative pulse, according to an embodiment of the present disclosure and based on the SOC.

[0230] Figure 6B The graph shown illustrates the comparison of metal deposition overvoltage based on the charging method according to SOC. The result of the changing trend. Here, solid lines can indicate charging methods including NP, and dashed lines can indicate CC charging methods.

[0231] exist Figure 6B The curves shown suggest that at a state of charge (SOC) with a metal deposition overvoltage of less than 0V, Li deposition can be promoted on the surface.

[0232] refer to Figure 6B As can be seen, the metal deposition overvoltage increases when charging with a current including a negative pulse. This reflects the phenomenon that the metal Li deposited on the anode surface is oxidized back to the ionic state and then the overvoltage is relieved while the Li stripping reaction is induced by the negative pulse.

[0233] Therefore, compared with charging by constant current, charging by current including negative pulses can maintain a higher average overvoltage level throughout the charging period.

[0234] Therefore, the state of charge (SOC) when the metal deposition overvoltage becomes less than 0 V may be delayed.

[0235] Therefore, the results show that by periodically alleviating the excessive overvoltage state (one of the main causes of Li deposition), the negative pulse helps to delay the time point at which the deposition threshold condition is reached.

[0236] therefore, Figure 6BSimulation results show that a charging control method including negative pulses, which supports embodiments of the present disclosure, can reduce the risk of metal deposition and can help maintain long-term battery performance.

[0237] Figure 7 This is a diagram illustrating the process by which a vehicle control device according to an embodiment of the present disclosure uses NMPC to optimally determine the magnitude of a constant current and the amplitude of a negative pulse.

[0238] Figure 7 It is a diagram that visualizes the process by which a vehicle control device according to an embodiment of the present disclosure uses NMPC to predict and control the magnitude of MCC and the amplitude of NP as a current waveform that varies over time.

[0239] like Figure 7 As shown, negative pulses are periodically inserted during the MCC+NP charging step, which induces Li stripping and ionizes the deposited lithium, thereby helping to improve battery life.

[0240] exist Figure 7 In this context, "p1" and "p2" can refer to the MCC size determined using NMPC. At which point in time, "n1" and "n2" can refer to the NP size determined using NMPC. The point in time.

[0241] According to one implementation, the vehicle control device can determine the MCC size using NMPC based on Formula 3. .

[0242] Formula 3

[0243]

[0244] Formula 3 can represent the vehicle control device establishing a charging strategy to minimize the charging time in the predicted segment. Charging current during the period The sum of . In other words, Formula 3 can be used to find the optimal value. This minimizes the total amount of current distribution.

[0245] According to one implementation, the vehicle control device can apply a current magnitude limit condition. To determine the charging current Size.

[0246] The current magnitude limitation can refer to the conditions used to set the applicable current range within the MCC charging section. In one implementation, parameter "a" can limit the charging current within the Multi-Level Constant Current (MCC) section. The minimum allowable size, and the parameter "b" can limit it. The maximum permissible size. The values ​​of "a" and "b" can be determined based on the battery's safety margin or degradation threshold.

[0247] “C” can be the rated capacity standard (C-rate), and this constraint can be designed to prevent battery degradation by allowing only a certain level or below of gentle charging current instead of a relatively large current.

[0248] According to one implementation, in order to determine the charging current The size of the vehicle control equipment can be determined by applying lithium concentration gradient constraints. At least one of the following. Parameter c can be defined as preventing excessive lithium deposition or degradation of the electrode material by ensuring that internal lithium diffusion does not exceed a critical gradient level during charging.

[0249] This could refer to the lithium concentration on the surface of the battery, and It can refer to the average lithium concentration of the battery.

[0250] According to one implementation, the vehicle control device can determine the NP size using NMPC based on Equation 4. .

[0251] Formula 4

[0252]

[0253] Formula 4 can represent the vehicle control equipment establishing a charging strategy to predict the charging range. Minimize negative pulse current during the period The sum of . In other words, the optimal value for minimizing the total amount of current distribution can be obtained. This prevents the unnecessary application of negative pulses for stripping to the current.

[0254] According to one implementation, the vehicle control device can apply a current magnitude limit condition. To determine the negative pulse current The range.

[0255] Current magnitude limitation conditions can refer to conditions used to set the applicable current range in the negative pulse segment.

[0256] “C” can be the rated capacity standard (C rate), and considering that the negative pulse requires operation with a strong current at or above a certain level to effectively induce Li stripping, this constraint can be designed to limit the current magnitude within a suitable range.

[0257] In addition, vehicle control equipment can apply voltage limiting conditions. To determine the negative pulse current The range.

[0258] Voltage limiting conditions can be designed to maintain battery stability and lifespan by preventing battery cell voltage from becoming too low or too high when a negative pulse is applied.

[0259] In addition, vehicle control equipment can apply lithium concentration gradient limiting conditions. To determine the negative pulse current The range.

[0260] This could refer to the lithium concentration on the surface of the battery, and It can refer to the average lithium concentration of the battery.

[0261] These constraints can be designed to prevent aberrant electrochemical reactions, such as the deposition of metallic lithium, by suppressing the formation of excessive lithium concentration gradients within the battery.

[0262] According to one implementation, the vehicle control device can flexibly determine the magnitude of the current by selectively applying one or more of the above-described constraints or by applying a combination of these constraints in a complex manner.

[0263] exist Figure 7 In this context, the constant voltage (CV) segment represents the final step. If the battery terminal voltage exceeds a set threshold (e.g., 4.15V), this may mean a segment where the charging current is gradually reduced while maintaining a constant voltage. In this segment, under the control of the vehicle's control equipment, the current is adjusted based on the battery voltage to prevent battery damage.

[0264] In summary, Figure 7 This diagram illustrates how an NMPC-based charging control method can optimize the magnitude of the constant current and the amplitude of the negative pulse in real time during each charging segment, thereby simultaneously improving battery performance and lifespan.

[0265] In the following text, see references Figure 8 and Figure 9 Describe a vehicle control device or vehicle control method according to embodiments of the present disclosure.

[0266] In the following text, Figure 1 The vehicle control device 100 can perform Figure 8 or Figure 9 The process.

[0267] Figure 8 This is a flowchart used to describe a vehicle control device or vehicle control method according to embodiments of the present disclosure.

[0268] According to one implementation, the processor of the vehicle control device (such as...) Figure 1 The processor 110 of the vehicle control device 100 can perform an operation to predict the state of the battery based on a battery model that reflects at least one of the battery’s electrical characteristics, thermal characteristics, degradation characteristics, and any combination thereof (S810).

[0269] According to one embodiment, the processor of the vehicle control device can perform an operation to determine a target charging current for shortening the charging time of the battery based on at least one of the following: a negative pulse that induces stripping, the state of the battery, and any combination thereof, wherein the metal deposited in the battery during the stripping process is oxidized to metal ions (S820).

[0270] According to one embodiment, the processor of the vehicle control device can perform an operation of charging the battery based on a target charging current (S830).

[0271] Figure 9 This is a flowchart illustrating the process for determining whether a vehicle control device according to an embodiment of the present disclosure determines a target charging current including a negative pulse.

[0272] According to one implementation, the vehicle control device can initialize the algorithm data as MCC=1, NP=0, t=1. , And SOC(1) = a% (S910).

[0273] According to one implementation, the vehicle control device can determine whether the SOC at the current time "t" is less than a threshold b% (S920). This could mean determining whether the battery needs to be charged.

[0274] According to one embodiment, the vehicle control device can check the conditions for determining whether to execute a charging mode that applies a negative pulse or a charging mode that applies only a constant current (S930).

[0275] For example, the vehicle control equipment can determine whether the condition that the charging time during the application of a constant current exceeds a predetermined first reference time is met (MCC=1 and...). The condition that the charging time for applying a current including a negative pulse is less than or equal to a predetermined second reference time (NP=1 and...) ( ), and at least one of ( ), and any combination thereof.

[0276] If the condition of S930 is not met (which is not the case in S930), the vehicle control device can determine whether the maximum value of the battery terminal voltage (e.g., the maximum value) is less than or equal to a predetermined voltage value. (S940).

[0277] If the maximum value of the battery terminal voltage exceeds the predetermined voltage value (not in S940), the vehicle control device can proceed to constant voltage mode (CV).

[0278] If the maximum value of the battery terminal voltage is less than or equal to a predetermined voltage value (yes in S940), the vehicle control device can determine whether... (S951).

[0279] if If not established (no in S951), the vehicle control device can maintain the magnitude of the previously applied current (S962).

[0280] If the current is applied in constant voltage (CV) mode or maintained at the previously applied magnitude, the vehicle control unit can set the algorithm data to " = (S971)

[0281] if If this condition is met (yes in S951), the vehicle control equipment can derive the optimal MCC current by executing NMPC. (S963). Thereafter, the vehicle control equipment can set the algorithm data to MCC=1. ,as well as (S972).

[0282] If the conditions of S930 are met (yes in S930), the vehicle control equipment can determine whether... (S952).

[0283] if If not established (no in S952), the vehicle control device may maintain the magnitude of the previously applied current (S964).

[0284] If the previously applied current magnitude is maintained, the vehicle control device can set the algorithm data to... (S973).

[0285] if If true (or yes in S952), the vehicle control equipment can derive the optimal negative pulse current by executing NMPC. (S965). Thereafter, the vehicle control equipment can set the algorithm data to NP=1. , , (S974).

[0286] Subsequently, the vehicle control equipment can update the system state or update the prediction results by executing a reduced-order model (ROM) (S980).

[0287] If the prediction result is updated via ROM, the vehicle control device can increment the time "t". (S990) and repeat the loop (S990).

[0288] Figure 10 A diagram illustrating a computing system associated with a vehicle control device or vehicle control method according to an embodiment of the present disclosure is shown.

[0289] refer to Figure 10 The computing system 1000 may include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, a storage device 1600, and a network interface 1700 connected to each other via a bus 1200.

[0290] Processor 1100 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in memory 1300 and / or storage device 1600. Each of memory 1300 and storage device 1600 may include various types of volatile or non-volatile storage media. For example, memory 1300 may include read-only memory (ROM) 1310 and random access memory (RAM) 1320.

[0291] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed in this specification can be directly implemented using hardware modules, software modules, or a combination of hardware and software modules, which are executed by processor 1100. Software modules may reside on storage media (i.e., memory 1300 and / or storage device 1600), such as random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electrically EPROM (EEPROM), registers, hard disk drives, removable disks, or optical disc ROMs (CD-ROM).

[0292] The storage medium can be coupled to the processor 1100. The processor 1100 can read information from the storage medium and write information to the storage medium. Alternatively, the storage medium can be integrated with the processor 1100. The processor and storage medium can be implemented using an application-specific integrated circuit (ASIC). The ASIC can be located in the user terminal. Alternatively, the processor and storage medium can be implemented using separate components in the user terminal.

[0293] The above description is merely an example of the technical concept of this disclosure, and those skilled in the art can make various modifications and changes without departing from the basic features of this disclosure.

[0294] Therefore, the embodiments described herein are not intended to limit but rather to interpret the technical concept of this disclosure, and the scope and spirit of this disclosure are not limited to the described embodiments. The scope of protection of this disclosure should be interpreted by the appended claims, and all equivalents thereof should be interpreted as being included within the scope of this disclosure.

[0295] One or more embodiments of this disclosure can induce lithium stripping by including a negative pulse in the current used to charge the battery.

[0296] One or more embodiments of this disclosure can improve battery life and safety by removing lithium deposited from the battery surface via lithium stripping.

[0297] Furthermore, one or more embodiments of this disclosure can determine whether a negative pulse is applied and can determine the optimal charging current of the battery based on it by reflecting the lithium stripping effect.

[0298] Furthermore, one or more embodiments of this disclosure can derive an optimal charging current that satisfies both charging performance and safety by taking into account the metal stripping reaction induced by the negative pulse.

[0299] Furthermore, one or more embodiments of this disclosure can dynamically determine the optimal current for a battery being charged based on battery state data.

[0300] Furthermore, one or more embodiments of this disclosure can determine the amplitude of the negative pulse so as to minimize the growth of lithium deposited from the surface of the battery without increasing the total charging time.

[0301] In addition, various effects can be provided directly or indirectly through this disclosure.

[0302] Although the present disclosure has been described above with reference to embodiments and accompanying drawings, the present disclosure is not limited thereto, and various modifications and alterations may be made by those skilled in the art to which this disclosure pertains without departing from the spirit and scope of the present disclosure as claimed in the appended claims.

Claims

1. A vehicle control device, comprising: The memory is configured to store program instructions; as well as The processor is configured to execute the program instructions. The processor is configured as follows: The state of the battery is determined based on a battery model that takes into account at least one of the battery's electrical characteristics, thermal characteristics, and degradation characteristics. A target charging current for shortening the charging time of the battery is determined based on at least one of the battery's state and the negative pulse that induces stripping, wherein the metal deposited within the battery during the stripping process is oxidized to metal ions; and The battery is charged based on the target charging current.

2. The vehicle control device according to claim 1, wherein, The processor is further configured to: An algorithm for optimizing the charging of the battery is generated using the battery model; and The target charging current is determined by applying the algorithm to data associated with the state of the battery.

3. The vehicle control device according to claim 1, wherein, The processor is further configured to: The target charging current is determined to minimize the charging time of the battery while satisfying charging conditions, wherein the charging conditions are at least one of the following: the rate of heating of the battery, the degree of inhibition of lithium deposition contained in the metal, the degree of lithium stripping induction, and the degree of reduction in battery capacity degradation.

4. The vehicle control device according to claim 1, wherein, The processor is further configured to: The target charging current, including the negative pulse, is determined based on nonlinear model predictive control.

5. The vehicle control device according to claim 1, wherein, The processor is further configured to: Based on the condition that the negative pulse is included in the target charging current, the target charging current including the negative pulse is determined using nonlinear model predictive control. The negative pulse includes the following conditions in the target charging current: The condition that the charging time for which a constant current is applied exceeds a predetermined first reference time, or the condition that the charging time for which a current including the negative pulse is applied is less than or equal to a predetermined second reference time, is met.

6. The vehicle control device according to claim 1, wherein, The processor is further configured to: The amplitude of the negative pulse is determined based on at least one of the following conditions: a first condition that the terminal voltage of the battery is within a predetermined voltage range and a second condition that the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery is less than or equal to a predetermined threshold.

7. The vehicle control device according to claim 1, wherein, The processor is configured to: The amplitude of the negative pulse is determined based on a third condition, which is that the current density of the metal deposited in the battery is less than or equal to the current density of metal ions stripped from the battery.

8. The vehicle control device according to claim 1, wherein, The processor is further configured to: Based on the fact that the maximum value of the battery's terminal voltage exceeds a predetermined voltage value, the target charging current is determined as the current used to maintain the battery's terminal voltage at the predetermined voltage value.

9. The vehicle control device according to claim 1, wherein, The processor is further configured to: Sensors installed in the battery are used to determine data associated with the state of the battery, and The data associated with the state of the battery includes: State of charge, state of health, terminal voltage of the battery, overvoltage of metal deposition in the battery, metal ion concentration on the surface of the battery, average metal ion concentration of the battery, current density of metal deposition in the battery, and current density of metal ion stripping in the battery.

10. The vehicle control device according to claim 1, wherein, The processor is further configured to: The state of the battery is determined based on a model based on reduced-order physics.

11. A vehicle control method, the method comprising: The processor determines the state of the battery based on a battery model that takes into account at least one of the battery's electrical characteristics, thermal characteristics, and degradation characteristics. The processor determines a target charging current for shortening the charging time of the battery based on at least one of the state of the battery and the negative pulse that induces stripping, wherein the metal deposited in the battery during the stripping process is oxidized to metal ions; as well as The processor charges the battery based on the target charging current.

12. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The processor generates an algorithm for optimizing the charging of the battery using the battery model; and The processor determines the target charging current by applying the algorithm to data associated with the state of the battery.

13. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The processor determines a target charging current that minimizes the charging time of the battery while satisfying charging conditions, the charging conditions being at least one of the following: the battery's heating rate, the degree of inhibition of lithium deposition contained in the metal, the degree of lithium stripping induction, and the degree of reduction in the battery's capacity degradation.

14. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The processor determines the target charging current, including the negative pulse, based on nonlinear model predictive control.

15. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: Based on the condition that the negative pulse is included in the target charging current, the processor determines the target charging current including the negative pulse based on nonlinear model predictive control. The negative pulse includes the following conditions in the target charging current: The condition that the charging time for which a constant current is applied exceeds a predetermined first reference time, or the condition that the charging time for which a current including the negative pulse is applied is less than or equal to a predetermined second reference time, is met.

16. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The amplitude of the negative pulse is determined by the processor based on at least one of the following conditions: a first condition that the terminal voltage of the battery is within a predetermined voltage range and a second condition that the difference between the metal ion concentration on the surface of the battery and the average metal ion concentration of the battery is less than or equal to a predetermined threshold.

17. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The processor determines the amplitude of the negative pulse based on a third condition: the current density of the metal deposited in the battery is less than or equal to the current density of metal ions stripped from the battery.

18. The method according to claim 11, wherein, The processor determines the target charging current for shortening the charging time of the battery by including: The processor determines the target charging current as the current used to maintain the battery's terminal voltage at the predetermined voltage value based on the maximum value of the battery's terminal voltage exceeding a predetermined voltage value.

19. The method of claim 11, further comprising: The processor uses sensors installed in the battery to determine data associated with the state of the battery. The data associated with the state of the battery includes: State of charge, state of health, terminal voltage of the battery, overvoltage deposited by the battery within the battery, concentration of metal ions on the surface of the battery, average concentration of metal ions in the battery, current density deposited by the battery within the battery, and current density of metal ions stripped from the battery within the battery.

20. The method according to claim 11, wherein, The processor determines the state of the battery by including: The processor determines the state of the battery based on a model based on reduced-order physics.

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