Lithium ion battery state of charge estimator using hysteresis model

Abstract

The invention relates to a lithium ion battery state-of-charge estimator using a hysteresis model. Electric vehicles (EV) and systems and methods are presented in aspects of the present disclosure that use a battery management system (BMS) for one or more lithium ion (Li +) battery cells. The BMS includes a device coupled with an external terminal of the one or more Li + battery cells and configured to estimate an open circuit voltage (VOC) across the Li + battery cells. The apparatus also includes a memory, at least one processor coupled with the memory. The at least one processor, when executing the code stored in the memory, is configured to generate a state of charge (SOC) observer that includes a hysteresis model to account for hysteresis in the one or more Li + battery cells.

Description

Background Art

[0001] State of charge (SOC) is a parameter currently used in various applications to estimate the remaining charge (charge) in lithium-ion (Li+) batteries. While there are multiple applications for Li+ batteries, one such application includes using Li+ batteries to power electric vehicles (EVs). In this case, the SOC is needed to determine how much time the driver has before the EV stops working, which makes the SOC an important parameter like a gas gauge in a combustion-based vehicle. To this end, multiple techniques for estimating the SOC in LI+ batteries have been implemented or proposed in the literature. For the EV industry, these techniques are typically deployed in a battery management system (BMS) coupled to the terminals of the Li+ cell or pack.

[0002] In general, SOC can be accurately modeled as a curve of voltage (vertical axis) versus SOC (horizontal axis). In this model, SOC can be sufficiently interpreted to resemble a single curve so that measurements will produce more or less a single voltage value within an acceptable error margin for each SOC, whether the battery is charging or discharging. For many Li+ battery configurations, approximating SOC gives sufficiently accurate measurements.

[0003] There are different configurations of Li+ batteries. For reasons that are not precisely understood, some of these configurations are more susceptible to inaccurate SOC measurements. That is, some otherwise valid Li+ battery configurations may produce undesirably significant hysteresis effects. For example, each SOC in a charge-discharge cycle may be plotted as an open circuit voltage (V OC ) (vertical axis) versus SOC (horizontal axis), where each SOC in the charge-discharge cycle corresponds to a number of V OC In these cases, existing models for measuring SOC are likely to fail by returning inaccurate SOC results. Summary of the invention

[0004] Aspects of the present disclosure include using an enhanced SOC observer model executed on one or more processors in a battery management system. In some embodiments, accurate SOC estimation can be achieved by enhancing a standard SOC observer model with a hysteresis model, wherein the combined model is configured as executable code for running on (one or more) processors, whether in a BMS coupled to battery terminals at a cell or pack level or otherwise.

[0005] In one aspect of the present disclosure, a battery management system (BMS) for one or more lithium-ion (Li+) battery cells includes: a device coupled to external terminals of the one or more Li+ battery cells and configured to estimate an open circuit voltage (VOC ). The apparatus further comprises: a memory; and at least one processor coupled to the memory and configured to generate a state of charge (SOC) observer when executing code stored in the memory, the SOC observer comprising a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

[0006] In another aspect of the present disclosure, an electric vehicle (EV) includes a body. The body is coupled to an instrument panel and houses an interior compartment. The interior compartment has a plurality of controls and dials, the plurality of controls and dials including an indicator that identifies an amount of power or a time period remaining for the EV to continue operating. The identified amount of power or time is based on the state of charge (SOC) of a lithium-ion (Li+) battery cell. The EV also includes: a suspension system coupled to the body and coupled to at least four wheels; and a battery management system (BMS) for one or more of the Li+ battery cells. The BMS includes: a device coupled to external terminals of one or more Li+ battery cells and configured to estimate the state of charge (SOC) of the Li+ battery cells. The device also includes: a memory; and at least one processor coupled to the memory and configured to generate a state of charge (SOC) observer when executing code stored in the memory, the SOC observer including a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

[0007] In yet another aspect of the present disclosure, a method for determining a state of charge (SOC) of an electric vehicle (EV) powered by a lithium-ion (Li+) battery exhibiting hysteresis includes: coupling a battery management system (BMS) to external terminals of the Li+ ion battery; executing code on at least one processor to determine an open circuit voltage (VOC); coupling the BMS to external terminals of the Li+ battery cell; and executing code by at least one processor within the BMS to generate a state of charge (SOC) observer, the SOC observer including a hysteresis model to account for hysteresis in the Li+ battery cell.

[0008] In various configurations of each of the above aspects, the SOC observer may include in the hysteresis model: charge to discharge state and discharge to charge state, which when combined result in the open circuit voltage (V OC ) in a continuous transition between a charging direction and a discharging direction. The SOC observer is configured to predict the V OC The SOC observer may further include a SOC model further coupled with: a resistance model for estimating a voltage drop due to current; an overpotential model for accounting for changes to the terminal voltage; and the hysteresis model.

[0009] In various embodiments, at least one processor, when executing code to generate a hysteresis model, is configured to determine a static nonlinearity based on a direction of current flow. At least one processor, when executing code to generate a hysteresis model, is configured to determine an initial state of the hysteresis model from charge to discharge or from discharge to charge when a change in the direction of current is detected. The SOC observer may include a Kalman filter or another type of circuit.

[0010] In one aspect of the present disclosure, a battery management system (BMS) for one or more lithium-ion (Li+) battery cells includes: a device coupled to external terminals of the one or more Li+ battery cells and configured to estimate an open circuit voltage (V OC ), the device also includes: a memory; and at least one processor, coupled to the memory and configured to generate a state of charge (SOC) observer when executing code stored in the memory, the SOC observer including a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

[0011] In various embodiments, the SOC observer is configured to include in the hysteresis model: a charge-to-discharge state and a discharge-to-charge state, which when combined result in the open circuit voltage (V OC ) continuous transition between the charging direction and the discharging direction.

[0012] In various embodiments, the SOC observer is configured to predict the V OC .

[0013] In various embodiments, the SOC observer further includes: a SOC model further coupled with: a resistance model for estimating a voltage drop due to current; an overpotential model for accounting for changes to the terminal voltage; and the hysteresis model.

[0014] In various embodiments, the at least one processor, when executing the code to generate the hysteresis model, is configured to determine static nonlinearity based on a direction of current flow.

[0015] In various embodiments, the at least one processor, when executing the code to generate the hysteresis model, is configured to determine an initial state of the hysteresis model from charging to discharging or from discharging to charging when a change in direction of current is detected.

[0016] In various embodiments, the SOC observer includes a Kalman filter.

[0017] In one aspect of the present disclosure, an electric vehicle (EV) includes: a body coupled to an instrument panel, the body housing an interior compartment having a plurality of controls and dials, the plurality of controls and dials including an indicator identifying an amount of power or a time period remaining for continued operation of the EV, the identified amount of power or time period being based on a state of charge (SOC) of a lithium-ion (Li+) battery cell powering the EV; a powertrain coupled to the body and to at least four wheels; and a battery management system (BMS) for one or more of the Li+ battery cells, the BMS including: a device coupled to an external terminal of the one or more of the Li+ battery cells and configured to estimate the state of charge (SOC) of the Li+ battery cells, the device also including: a memory; and at least one processor coupled to the memory and configured to generate a state of charge (SOC) observer when executing code stored in the memory, the SOC observer including a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

[0018] In various embodiments, the SOC observer is configured to include in the hysteresis model: a charge-to-discharge state and a discharge-to-charge state, which when combined result in an open circuit voltage (V OC ) continuous transition between the charging direction and the discharging direction.

[0019] In various embodiments, the SOC observer is configured to predict V using the charge-to-discharge state and the discharge-to-charge state. OC .

[0020] In various embodiments, the SOC observer further includes: a SOC model further coupled with: a resistance model for estimating a voltage drop due to current; an overpotential model for accounting for changes to the terminal voltage; and the hysteresis model.

[0021] In various embodiments, the at least one processor, when executing the code to generate the hysteresis model, is configured to determine static nonlinearity based on a direction of current flow.

[0022] In various embodiments, the at least one processor, when executing the code to generate the hysteresis model, is configured to determine an initial state of the hysteresis model from charging to discharging or from discharging to charging when a change in direction of current is detected.

[0023] In various embodiments, the SOC observer is a Kalman filter.

[0024] In one aspect of the present disclosure, a method for determining a state of charge (SOC) of an electric vehicle (EV) powered by a lithium-ion (Li+) battery cell exhibiting hysteresis comprises: coupling a battery management system (BMS) to external terminals of the Li+ ion battery; executing code on at least one processor to determine an open circuit voltage (V OC ); coupling the BMS to external terminals of the Li+ battery cell; and executing code by at least one processor within the BMS to generate a state of charge (SOC) observer, the SOC observer including a hysteresis model to account for hysteresis in the Li+ battery cell.

[0025] In various embodiments, the SOC observer includes in the hysteresis model: charge to discharge state and discharge to charge state, which when combined result in an open circuit voltage (V OC ) continuous transition between the charging direction and the discharging direction.

[0026] In various embodiments, the SOC observer uses the charge-to-discharge state and the discharge-to-charge state to predict V OC .

[0027] In various embodiments, the SOC observer further includes a SOC model further coupled with: a resistance model for estimating a voltage drop due to current; an overpotential model for accounting for changes to the terminal voltage; and the hysteresis model.

[0028] In various embodiments, the method further includes executing the code, by the at least one processor, to generate the hysteresis model to determine static nonlinearity based on a direction of current flow in the Li+ battery.

[0029] In various embodiments, the method further includes executing the code by the at least one processor to generate the hysteresis model to determine an initial state of the hysteresis model from charging to discharging or from discharging to charging when a change in direction of current is detected.

[0030] The above summary of the invention is not intended to represent each embodiment or each aspect of the present disclosure. On the contrary, the above summary of the invention only provides examples of some novel concepts and features set forth herein. When understood in conjunction with the accompanying drawings and the appended claims, the above features and advantages of the present disclosure and other features and accompanying advantages will be easily apparent according to the following detailed description of the illustrated examples and representative modes for performing the present disclosure. In addition, the present disclosure explicitly includes various combinations and sub-combinations of the elements and features presented above and below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, explain the principles of the present disclosure.

[0032] Figure 1 is the open circuit voltage of the Li+ battery cell V OC An exemplary graph of a single charge-discharge cycle versus SOC, including at lower V OC More pronounced hysteresis at charge and discharge levels.

[0033] Figure 2 is a conceptual diagram of a model for estimating various SOCs in a battery charge / discharge cycle.

[0034] Figure 3 is an example graph of hysteresis that occurs in a conventional battery and a selected charge-discharge cycle among a plurality of charge-discharge cycles identified in the graph.

[0035] Figure 4A and 4B Techniques for developing hysteresis models according to aspects of the present disclosure are described.

[0036] Figure 5 is a schematic diagram of a SOC observer module that takes hysteresis into account to provide an accurate SOC according to aspects of the present disclosure.

[0037] Figure 6 is a set of schematic diagrams including estimating SOC using a hysteresis model according to aspects of the present disclosure.

[0038] Figure 7 is a plan view illustration of a vehicle and a battery system coupled to an electronic control unit (ECU) for controlling aspects of the battery.

[0039] The drawings are not necessarily drawn to scale and may present simplified representations of various features of the present disclosure, including, for example, specific dimensions, orientations, positions, and shapes. In some cases, well-known features may be omitted from certain drawings to avoid overly obscuring the concepts of the present disclosure. The details associated with such functionality will be determined in part by the specific intended application and use case environment. DETAILED DESCRIPTION

[0040] The present disclosure allows for embodiments in many different forms. Representative examples of the present disclosure are shown in the accompanying drawings and described in detail herein as non-limiting examples of the disclosed principles. For this reason, elements and limitations described in the abstract, background technology, summary of the invention, and detailed description of the embodiments but not explicitly stated in the claims should not be incorporated into the claims individually or collectively by implication, inference, or otherwise.

[0041] For the purposes of this description, unless specifically disavowed, the use of the singular includes the plural and vice versa, the terms "and" and "or" shall be both conjunctions and disjunctions, and the words "include", "comprises", "including", "having", etc. shall mean "including, but not limited to". In addition, approximate words such as "approximately", "almost", "substantially", "generally", "approximately", etc. may be used herein in the sense of "within, close to, or nearly within" or "within 0-5% of..." or "within acceptable manufacturing tolerances" or their logical combinations. As used herein, a component "configured to" perform a specified function is capable of performing the specified function without modification, rather than merely having the potential to perform the specified function after further modification. In other words, when expressly configured to perform a specified function, the hardware described is specifically selected, created, implemented, utilized, programmed and / or designed for the purpose of performing the specified function. It should also be noted that for the purposes of this disclosure, the term V OC and OCV (open circuit voltage) are often used interchangeably.

[0042] A BMS is an electronic management system that performs important measurements, collects relevant metrics, and performs other safety and performance related functions for the battery pack to which it is coupled (e.g., in an EV). Although a BMS can perform a variety of actual functions, the term "BMS" is used herein without limitation to refer to the hardware used to perform the measurements and functions identified herein and to operate a Li+ battery, as described with respect to the embodiments herein.

[0043] Figure 1 is the open circuit voltage of the Li+ battery cell V OC An example plot of a single charge-discharge cycle versus SOC. This particular Li+ cell shows that at lower V OC Correctly addressing the underlying complexity of the SOC parameter requires first understanding how SOC is modeled within an estimator or observer device (see Figure 2 ). The observer device may be implemented on a circuit in a battery management system (BMS), the circuit comprising, for example, a processor (which itself may include one or more processors) in which code may be executed, a DSP, an application specific integrated circuit (ASIC), a system on a chip (SoC), a field programmable gate array (FPGA), a digital combinational logic element, or a physically implementable device that may execute code or other hardware-based functions to estimate the SOC of a given battery cell or group. First refer to Figure 1 , the vertical axis of the curve represents V OC, which is the open circuit voltage of the battery, or the voltage measured across the battery terminals when the current through the battery is 0 amperes (Amp). The horizontal axis represents SOC, which is defined as the ratio of the available battery capacity to its rated maximum capacity. Curve 115 represents the charging of the battery 108. As the battery charges, its SOC and V OC The values ​​of both increase. Curve 120 represents the discharge of the battery 106. Similarly, as the battery discharges, its SOC and V OC As noted, V OC is the current through the battery (i batt ) is measured when V OC The voltage may be measured across the terminals of a battery cell, across a pack, or using another configuration. If the pack voltage is measured, an estimator (e.g., a processor in a battery management system) may divide the measured voltage by the number of cells in series to obtain a cell level voltage. For purposes of this disclosure, the term "battery" may be used broadly to encompass Li+ cells or packs having series and / or parallel connections, where appropriate in the context.

[0044] To reproduce Figure 1 The BMS may charge the battery 108 for a specified time and then stop charging 108 to allow the estimator to measure V OC and the corresponding SOC. Thereafter, charging 108 may resume and periodically pause to obtain V OC Until the battery is fully charged. The BMS can then discharge the battery 106 using the same general techniques in reverse (e.g., periodically stopping the discharge 106 to measure the V at the SOC level). OC ). It should be noted that if the BMS measures some internal current in the battery after stopping the current flow to measure VOC, the BMS should also capture the direction of this current in the SOC estimator ( Figure 2 ).

[0045] It will be appreciated that in an alkaline Li+ cell, the hysteresis may be so small that it is within an acceptable error margin and can be ignored. An example of such a cell is a Li+ cell using graphite as the anode. However, in other configurations, such as when SiO2 is used as the anode material, or some other chemical or physical change is incorporated into the cell, the error introduced by the more significant hysteresis cannot be ignored.

[0046] Figure 2 is a conceptual diagram of an equivalent circuit model 200 for estimating various SOCs in a battery charge / discharge cycle. The model can be used to obtain V by approximating nonlinear behavior and other well-known phenomena within the battery, such as by taking into account additional numerical phenomena. OC) to build the SOC estimator module. There may be many different models. Figure 2 The model is an example of different functions that represent the operating characteristics of a Li+ cell for the purpose of estimating SOC while taking into account other internal chemical or electrical functions that may have an impact on SOC. In short, understanding SOC begins with understanding the battery characteristics (including, for example, V OC , the impact of internal current flow within the model, nonlinear characteristics, etc.) and add the different characteristics to produce a result that can be used to accurately estimate SOC.

[0047] Referring to the resistance module 208, this module can be used to take into account the current that may occur in the registers within the cell. The registers in the cell may create a resistance, which may cause a voltage drop. The resistance module 208 is the part of the model that takes into account this voltage drop. Traditionally, various means in the art have been used to implement the resistance portion of the model when performing SOC estimation. Here, the resistance model 208 can be modeled using the resistance in the battery register with a target value, and the current flows into the resistance to cause a voltage drop, as indicated. V OC The voltage drop may be added to the voltage drop associated with the model resistance value. In this embodiment, the model 200 is losing voltage due to the resistance in the internal register, which is accounted for by the model 200 in the overall addition.

[0048] The battery model 200 may include an input current 202 into a SOC model 204, which is an open-loop model that is intended to take into account relevant internal cell parameters and behaviors that affect the SOC in the Li+ cell to be measured. In other example models, the current 202 may additionally represent the current that is leaving the SOC model when the battery associated with the model is discharged. It is worth noting that parts of the SOC model 204 may be affected by actual factors such as cell temperature, battery aging, and other factors (referred to as X). The SOC model 204 may be modeled or subsequently adjusted to take these factors into account. For example, the intrinsic values ​​within the SOC model 204 may be adjusted based on the factor X. In one configuration, the SOC model may model the capacitance, resistance, passive circuits, temperature, process corners, and other parameters of the battery model 200 to take into account potential SOC estimation errors. When estimating the initial SOC, the SOC model may generate an estimated SOC as an output 225. In this case, the output 225 may be provided to a nonlinear function model 228. The nonlinear function model 228 can be used to take into account SOC and V OC The non-linear relationship between them.

[0049] Battery model 200 may also take into account the overpotential of the cell, as shown in overpotential model 230. When current is applied to a Li+ cell, the resulting terminal voltage may change due to the overpotential. Various understood phenomena are generally considered, which can be used to quantify the overpotential to be based on the current in the internal register (resistance model 208), the voltage present in the static V OC The various nonlinear functions (nonlinear function model 228) and overpotential model 230 in the background of the curve are used to generate the accurate voltage 267. Because the hysteresis model may be highly dependent on the chemistry of the Li+ cell (including the anode), the mathematical equations used to represent the models 208, 228, and 230 may be different. However, a significant advantage of the battery model 200 is that once the model is developed for a specific Li+ battery cell, the SOC model 205 can be used on subsequent models of the cell without structural modifications.

[0050] In addition, nonlinear function models 228 and overpotential models that may affect the total voltage may also be considered. Various parameters including charge and discharge currents may change depending on the direction of the internal battery current. Model 200 is introduced to show how the terminal voltage behaves when various considerations are taken into account, such as the direction of internal current flow as noted above. The direction results in different output values ​​and is therefore important in modeling the behavior of the Li+ battery cell.

[0051] Additional models (either linear or nonlinear, static or dynamic) can be developed to describe the behavior of Li+ cells with new chemistry, structure, or functional properties. One such example is the hysteresis model, which is highly dependent on the chemical configuration of the Li+ cell. Examples include silicon anodes, lithium metal anodes, and others.

[0052] Figure 3 is an example graph of hysteresis occurring in an existing battery and a selected charge-discharge cycle among a plurality of charge-discharge cycles identified in the graph. Figure 3 Portions of were drafted as an article for publication in the 2020 IEEE Journal of the Institute of Electrical Engineering (IEEE) by Carles Pregonas Graells, M. Scott Trimboli, and Gregary L. Plett. Figure 3Parts of this are from Differential hysteresis models for a silicon-anode Li-ion battery cell," 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 2020, pp. 175-180, doi: 10.1109 / ITEC48692.2020.9161591 (Differential hysteresis models for a silicon-anode lithium-ion battery cell", 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 2020, pp. 175-180, doi: 10.1109 / ITEC48692.2020.9161591) Figure 1 Reproducible.

[0053] In more detail refer to Figure 3 Before, it should be noted that in many cases the hysteresis is so small that it can be neglected. For example, in the case of graphite anodes of Li+ cells, other parameters being equal, the hysteresis phenomenon is several orders of magnitude smaller, so the estimation of SOC does not need to take this phenomenon into account.

[0054] Return to reference Figure 3 , the author of the original figure plotted a silicon anode Li+ cell, where the hysteresis on the voltage vs. SOC graph of the Li+ cell is clearly related. Note that Figure 3 There are several transient traces, among which SOC and V OC The values ​​of V converge into a closed loop. To identify such a loop, the inventors over-trace the graph with an arrowed curve, referring to the lower part of the SOC region. Thus, for example, point 325 corresponds to this particular V OC / SOC curve, and point 345 corresponds to the same V OC / The lowest point on the SOC curve. The vertical axis is V OC , and the horizontal axis is SOC.

[0055] Based on the above data representing the hysteresis of a Li+ cell, the inventors developed a procedure for estimating SOC to account for this non-negligible hysteresis. Part of this procedure involves being able to move between one OCV curve and another. Figure 4A , Figure 4B Techniques for developing hysteresis models according to aspects of the present disclosure are described. Figures 4A-4Brepresents the hysteresis curve. In the development of the hysteresis model, one goal is to have a model that results in continuous OCV transitions in both directions. The tuning parameters reflected in the figure are: (1) charging and discharging V OC , which means V OC should be closed at each end, and (2) note that V OC The direction of current flow can control whether to use the charge-to-discharge model or the discharge-to-charge model. This information in turn enables the SOC observer ( Figure 5 ) can determine the state decay behavior of static nonlinear and hysteretic models in Li+ battery cells.

[0056] The charge-to-discharge model 402a of FIG. 4 focuses on Figure 1 low SOC area. Figure 4A The technology applies to the charging and discharging state of the battery. Figure 4B It is a reproduction Figure 4B The non-bending portion of the attempt and describes the discharge to the charged state. Figure 4A and 4B The main idea of ​​the hysteresis models 402a and 402b in FIG. 4 is to change between one OCV curve and another OCV curve so that V OC The transaction is continuous. First refer to Figure 4A , the SOC observer begins at circle 4 after charging. Then, when discharge begins, it follows the horizontal arrow A to move instantaneously to the discharge OCV 6, which is the portion of the hysteresis curve corresponding to discharge. More specifically, in this example, the model follows arrow A to instantaneously reach the dashed circle 5. The instantaneous nature of the horizontal movement from circle 4 to circle 5 is intended to show that since the horizontal axis is voltage (V), when discharge occurs, the instantaneous change enables the OCV to transition from one value to another immediately and continuously.

[0057] Still reference Figure 4A , after reaching circle 5 of line 6 as noted above, the algorithm begins its discharge OCV. This time following arrow B, it gradually moves along line 6 in the direction of the discharge curve just above arrow B. This movement causes the cursor to gradually follow the discharge curve and its various circles through line 6. Eventually, if the discharge current is small enough, the cursor ends up at circle 8, which aligns with circle 4 where the charge to discharge state begins. Note that circle 8 shares the same SOC as circle 4, but is at a different voltage. Therefore, the "charge to discharge" scan is complete when the OCV voltage returns to the same SOC as before, but the OCV is now at a reduced value. The time constant of the "charge to discharge" scan can be determined experimentally.

[0058] The direction of current flow during charging and discharging can be determined by a BMS or processor coupled to the battery. This direction enables the processor to determine whether the battery is being charged or discharged. This information, in turn, can be used to determine which technology (i.e., Figure 4A and Figure 4B From this data, the static nonlinearity of the system can be determined and the coefficients of the model can be accurately applied.

[0059] Figure 4B The model 402b in principle is similar to Figure 4A , but analyzing the curve from the discharge to the charge state. The SOC observer starts at circle 8a. The horizontal arrow C is then followed to instantaneously scan to circle 9 on the opposite side of the hysteresis curve, which is the portion of the hysteresis curve corresponding to charging. The instantaneous horizontal movement from circle 8a to circle 9 allows the OCV to change instantly from one value to another. After reaching circle 9 on line 10, the algorithm begins its charge OCV. This time following arrow D, it gradually moves along line 10 in the direction of the charge curve above arrow D. As the SOC gradually begins to increase, this scan causes the cursor to gradually follow the discharge curve and its various circles through line 10. Eventually, if the charging current is small enough, the cursor ends at circle 4a, which aligns with circle 8a where the "discharge to charge" procedure begins. Similar to Figure 4A , circle 4a shares the same SOC as circle 8a, but is at a different voltage. Therefore, the "discharge to charge" sweep is complete when the OCV voltage returns to the same SOC as before, but the OCV is now at an increased value. The time constant of the "discharge to charge" sweep can be determined experimentally.

[0060] Following the above model enables the SOC observer to capture the Si anode Li+ ion battery Figure 3 The traces on the differential hysteresis model of (including the concave and convex curves where the OCV traces begin to converge to a single SOC). Figure 4A and 4B The scan should incorporate the following tuning parameters. First, the charge and discharge OCVs need to be closed at each end. Figure 3 An example of this can be seen at points 325 and 345 of FIG, where each point shares a unique SOC. Compliance with this parameter is shown in FIG. Figure 4A and 4B This is also evident in the graphs where curves 10 and 6 converge to a single point at both ends in both graphs. The second tuning parameter is that the time constant of the OCV transition should be consistent in both the charge to discharge and discharge to charge scenarios in order to ensure that the OCV transition in this graph is accurately modeled. Furthermore, using these tuning parameters enables the model to accurately determine the decay behavior of the states of the hysteresis model.

[0061] exist Figure 4A and4B Using the correct tuning parameters in the model can enable the SOC observer 554 to determine the initial state of the hysteretic model when a change in current direction occurs (such as when a Li+ battery changes from charging to discharging).

[0062] Figure 5 is a schematic diagram of an SOC observer module that takes into account hysteresis to provide an accurate SOC according to aspects of the present disclosure. Figure 2 , Figure 5 Model 500 is included, which illustrates how to model a SOC using different mathematical techniques. For example, Figure 5 The SOC model 504 may receive the current 502, which may be Figure 2 The SOC model 204 is similar to the current 202 received. Figure 2 As in the SOC model 504, the SOC model 504 may be affected by factors including cell temperature and aging ("X") of the Li+ battery cells. As these factors become more apparent, the SOC model 504 may be modified. The model 500 may also incorporate a resistance module 508 to account for internal currents in the registers within the Li+ battery and an overpotential module 530 to account for intercalation materials used in the Li+ battery. However, unlike the methods involved in modeling the static OCV curve, the SOC model 504 may be modified to account for internal currents in the registers within the Li+ battery. Figure 2 Nonlinear function 228, Figure 5 The model 500 in attempts to capture dynamic curves with hysteresis. This may include, but is not limited to, Figure 3 Described in and Figure 4A and 4B 4. The differential hysteresis model due to silicon anode modeled in . Additional and alternative changes to the chemistry and structure of the Li+ cell may also induce hysteresis, which requires a similar closed-loop solution to accurately estimate the SOC. Therefore, referring to model 500, hysteresis module 511 can be incorporated into hysteresis models 402a and 402b (see Figure 4) to enhance model 500 to take into account dynamic closed-loop hysteresis behavior. Different mathematical techniques can be used to incorporate these models 402a and 402b into hysteresis module 511. The result is that the hysteresis model 500 and SOC model 504 can now take into account internal currents (module 508), hysteresis phenomena (module 511 with models 402a and 402b), and overpotential module 530. The resulting voltages are summed at node 567 as a modified voltage model.

[0063] A BMS or another circuit near the battery can be coupled to the battery terminals and used to measure the actual battery voltage in real time. The BMS can also include a SOC observer 554. As noted, the SOC observer 554 can, in one embodiment, include code (software, firmware, middleware, etc.) stored in a memory and executed in at least one processor local to the memory. In other configurations, the functions of the SOC observer 554 can be performed using a digital signal processor (DSP), ASIC, SoC, or other circuit types. Figure 5 As shown, the terminal voltage (s) 568 of the Li+ ion battery can be coupled to the SOC observer 554, for example, via a wired connection. In some embodiments, the SOC observer 554 can be implemented as a Kalman filter, although other embodiments are possible and can replace the Kalman filter without departing from the spirit or scope of the present disclosure. The model voltage 567 can be compared to the terminal voltage 568 or more specifically the terminal voltage from the Li+ battery. Among other possibilities, this physical connection between the processor and the terminal voltage can be implemented using a connection line in the BMS.

[0064] Using the SOC observer 554 and the associated state of the model 500, the SOC can be advantageously adjusted in real time so that the voltage of the model 500 matches the measured terminal voltage. This process ensures the accuracy of the SOC value. For example, the SOC value can be transmitted to a processor that controls the instrument panel measurement. If necessary, the SOC can be converted to a more accessible value for the driver to enable the driver to determine the remaining power in the vehicle, or more specifically, how long the vehicle can be driven until it needs to be recharged.

[0065] In summary, the model for estimating SOC adds functionality that enables scanning of the OCV curve to produce SOC values ​​that closely match experimental results. Figure 3 Introduced in and incorporated into Figure 5 The hysteresis model in module 511 of model 500 in enables the actual SOC value for a given voltage to be ascertained with significantly higher accuracy.

[0066] Figure 6 is a set of schematic diagrams including estimated SOC using a hysteresis model according to aspects of the present disclosure. The physical Li+ cell in this exemplary configuration uses a silicon anode, although other physical and chemical configurations that produce hysteresis are contemplated. The horizontal axis on each graph represents time in seconds.

[0067] Referring to the top figure, an open-loop model of the battery voltage (V) with hysteresis is shown. The second figure shows the experimentally measured current (I) through the battery terminals. In the first 20 time units of the graph, the current (I) through the charging and discharging of the battery exhibits similar behavior to the voltage (V) of the battery model with hysteresis. At approximately t=25 time units, the experimentally measured battery current (I) is essentially constant as the Li+ battery slowly discharges. At approximately t=160 time units, as the battery gradually charges, causing V to slowly increase, the experimentally measured current shifts slightly, but remains constant until the open-loop effect causes the voltage V to change along with the current I at approximately t=175 time units.

[0068] The bottom graph represents the estimated SOC from the SOC observer. Although it is not clear from the SOC graph, this graph includes both the estimated SOC and the experimentally obtained value. A different graph for each SOC measurement is not needed in this example because the estimated SOC more or less overlaps with the experimentally obtained value, showing that using a hysteresis model for this silicon anode Li+ cell produces accurate results. It should be noted that certain tuning parameters can be incorporated into the battery model with hysteresis, including, among other things, the amount of time it takes to adjust the battery when experiencing a hysteresis. Figure 3 The charging and discharging in the circuit is used to catch up with the rate of the corresponding circle.

[0069] Figure 7 is a plan view illustration of a vehicle and a battery system coupled to an electronic control unit (ECU) for controlling aspects of the battery. Although the above hysteresis model can be applied to a variety of different physical configurations, Figure 7 One such example is shown. Figure 7 A high voltage battery pack (B HV ) 712 of an electrified powertrain system 710. In a non-limiting example, the battery pack 712 may be embodied as a high capacity battery having a voltage capability of approximately 400-800 volts or more, wherein the actual voltage capability of the battery pack 712 is provided based on the desired operating / SOC range, gross weight, and power rating of the load connected to the battery pack 712. In a possible configuration, the battery pack 712 may be a propulsion battery pack typically composed of an array of lithium ion or lithium ion polymer rechargeable electrochemical battery cells (which may be cylindrical battery cells). The present teachings may also be applied to prismatic battery cells, and possibly to pouch-type battery cells in possible configurations, so the cylindrical battery cells are exemplary and not limiting.

[0070] Although the internal details of the battery cells in the battery pack 712 are omitted for simplicity of illustration, it will be understood by those skilled in the art that the battery cells contain electrolyte materials, working electrodes in the form of cathodes and anodes, and permeability separators (not shown) within the cell cavities, which are collectively enclosed within an electrically insulating can or housing. The grouped battery cells can be connected in series or in parallel using electrical interconnect boards and associated buses, sensing hardware, and power electronics (not shown but well known in the art). A dedicated number of battery cells in the battery pack 712 can be arranged in columns and rows relative to the battery tray 713. For example, in a nominal "xyz" Cartesian reference frame, the battery tray 713 can have a length (x dimension) and a width (y direction) when viewed from above or below, and a height (z dimension) extending in an orthogonal direction away from the battery tray 713.

[0071] In a representative use case, the electrified powertrain 710 may be used as part of an EV 711 or another mobile system. As shown, the EV 711 may be embodied as a battery electric vehicle, and the present teachings may also be extended to plug-in hybrid electric vehicles. Alternatively, the electrified powertrain 710 may be used as part of another mobile system, such as, but not limited to, rail vehicles, aircraft, ships, robots, agricultural equipment, etc. Likewise, the electrified powertrain 710 may be stationary, such as in the case of a power plant, crane, drive belt, or conveyor system. Thus, Figure 7 The electrified powertrain 710 in the representative vehicle embodiment is intended to illustrate the present teachings and not to limit the present teachings.

[0072] Figure 7 EV 711 shown in FIG. 7 includes a body 722 and wheels 724F and 724R, where “F” and “R” indicate respective front and rear positions. Wheels 724F and 724R rotate about respective shafts 725 and 750, where wheels 724F, wheels 724R, or both are driven by a rotating electric machine (M) from the electrified powertrain 710. E ) Output torque of 726 (arrow T O) provides power, as indicated by arrow

[24] . Thus, in this embodiment, wheels 724F and 724R represent mechanical loads, while other possible mechanical loads are possible in different host systems. To this end, the electrified powertrain system 710 includes: a power inverter module (PIM) 728 and a high voltage battery pack 712 (e.g., a multi-cell lithium-ion propulsion battery or a battery having another chemistry suitable for the application), both arranged on a high voltage DC bus 727. As understood in the art, the PIM 728 includes a DC side (780) and an alternating current (AC) side 720, wherein when the rotating electric machine 726 is configured as a multi-phase rotating electric machine in the form of a propulsion or traction motor as shown, the alternating current (AC) side 720 is connected to individual phase windings (not shown) of the rotating electric machine 726.

[0073] Figure 7 The battery pack 712 is further connected to the DC side 780 of the PIM 728, so that the battery voltage from the battery pack 712 is provided to the power integrated module (PIM) 728 during the propulsion mode of the EV 711. The PIM 728, or more precisely, a set of semiconductor switches (not shown) resident therein, is controlled via pulse width modulation, pulse density modulation or other suitable switching control techniques to convert the DC input voltage on the DC bus 727 into an AC output voltage suitable for exciting the high-voltage AC bus 720. Therefore, the high-speed switching of the resident semiconductor switches of the PIM 28 ultimately excites the rotating electric machine 726, thereby causing the rotating electric machine 726 to transmit the output torque (arrow T0) as the motor drive torque to the rotating electric machine 726. Figure 1 The mechanical load may be transferred to one or more of the wheels 724F and / or 724R in the illustrated embodiment, or to another coupled mechanical load in other embodiments.

[0074] The electrical components of the electrified powertrain 10 may also include an accessory power module (APM) 729 and an auxiliary battery (B AUX ) 730. The APM 29 is configured as a DC-DC converter connected to the DC bus 27, as understood in the art. In operation, the APM 729, via internal switches and voltage conversion, is able to reduce the voltage level on the DC bus 727 to a lower level suitable for charging the auxiliary battery 30 and / or supplying low voltage power to one or more accessories (not shown) (such as lights, displays, etc.). Thus, "high voltage" refers to voltage levels that far exceed the typical 12-15V low / auxiliary voltage levels, with 400V or more being exemplary high voltage levels in some embodiments of the battery pack 712.

[0075] In some configurations, Figure 7The electrified powertrain system 710 may include an on-board charger (OBC) 32 that is selectively connectable to an off-board charging station 33 via an input / output (I / O) block 132 during a charging mode, during which the battery pack 712 is charged by an AC charging voltage (V CH ) to recharge. The I / O block 732 can be connected to the charging port 717 on the vehicle body 722. For example, the charging cable 735 can be connected to the charging port 717, for example, via an SAE J1772 connection. As understood in the art, in one or more embodiments, the electrified powertrain 710 can also be configured to selectively receive a DC charging voltage, in which case the OBC 32 will be selectively bypassed using a circuit (not shown), which can be used, for example, to gradually charge and / or discharge the battery pack 712 to perform various functions, such as testing the SOC. The OBC 32 can also operate in different modes, including: a charging mode during which the OBC 32 receives an AC charging voltage (V CH ) to recharge the battery pack 712 after a low charge indicator light is displayed on the instrument panel; and a discharge mode represented by arrow VX during which the OBC 32 unloads power from the battery pack 12 to an external AC electrical load (L). In this way, the OBC 32 can embody a bidirectional charger.

[0076] Still refer to Figure 7 The electrified powertrain system 710 may also include an electronic control unit (ECU) 34. The ECU 734 is operable to regulate the ongoing operation of the electrified powertrain system 10 via the transmission of electronic control signals (arrow CCO). The ECU 734 responds to the electronic input signals (arrow CC I ) to do so. In various embodiments, such an input signal (arrow CC I ) may be actively transmitted or passively detected, so that the ECU 34 is operable to determine a particular operating mode. In response, the ECU 34 controls the operation of the electrified powertrain system 710. Thus, the ECU and its accessory components may act as a BMS for performing functions including estimating SOC, etc.

[0077] To this end, the ECU 734 may be equipped with one or more processors (P), such as logic circuits, (one or more) combinational logic circuits, (one or more) application specific integrated circuits (ASICs), (one or more) electronic circuits, (one or more) central processing units, semiconductor IC devices, etc., as well as (one or more) input / output (I / O) circuits, appropriate signal conditioning and buffering circuits and other components, such as a high-speed clock for providing the described SOC functions in the previous figures and the different functions identified by the CC input signal. The ECU 734 also includes an associated computer-readable storage medium, that is, a memory (M) including read-only, programmable read-only, random access, hard disk drive, etc., whether resident, remote or a combination of both. Control routines (including code for executing the SOC model with hysteresis) are executed by the processor to monitor relevant inputs from sensing devices and other networked control modules (not shown) and execute control and diagnostic routines to govern the operation of the electrified powertrain system 10. The I / O circuits may be directly coupled to the ECU 734 along with the memory M and one or more processors P for executing code for estimating the SOC. In one aspect, the BMS system can be implemented collectively as ECU 734, OBC 32, and bus 727. OBC 32 and bus 27 can be devices within the BMS, or included as part of the BMS, so that the BMS can be connected to the external terminals of the battery pack 712 to perform the functions described herein. In some embodiments, the BMS can be directly coupled to the battery pack.

[0078] Note that, like other vehicles, the EV 711 may include an instrument panel embedded in or otherwise connected to the body of the EV 711. The body houses the cabin in which the driver and any occupants reside. The apparatus discussed above may include control signals to the instrument panel and conversion circuitry to enable the driver to assess the remaining SOC based on the amount or percentage of charge remaining, the estimated time the vehicle will stall or will soon need to be recharged, and other data. At least some of these aspects may be calculated by the BMS, which includes the ECU 734 and its associated processor P (running code from the memory M). Messages may be sent to other parts of the vehicle via I / O circuitry, via CC0, or another connection not specifically shown.

[0079] The detailed description and the accompanying drawings or figures are support and description of the present teaching, but the scope of the present teaching is limited only by the claims. Although some best modes and other embodiments for carrying out the present teaching are described in detail, there are various alternative designs and embodiments for practicing the present teaching defined in the appended claims. In addition, the present disclosure explicitly includes combinations and sub-combinations of the elements and features presented above and below.

Claims

1. A battery management system (BMS) for one or more lithium-ion (Li+) battery cells, comprising: device, coupled to the external terminals of the one or more Li+ battery cells and configured to estimate an open circuit voltage (V OC ), the device further comprising: Memory; and At least one processor is coupled to the memory and is configured, when executing code stored in the memory, to generate a state of charge (SOC) observer including a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

2. The BMS according to claim 1, wherein the SOC observer is configured to include in the hysteresis model: charge to discharge state and discharge to charge state, which when combined results in the open circuit voltage (V OC ) continuous transition between the charging direction and the discharging direction.

3. The BMS according to claim 2, wherein the SOC observer is configured to predict the V OC .

4. The BMS according to claim 1, wherein the SOC observer further comprises: SOC model, which is also coupled with: a resistor model for estimating the voltage drop due to current flow; an overpotential model to account for changes to the terminal voltage; and The hysteresis model. 5 . The BMS of claim 1 , wherein the at least one processor, when executing the code to generate the hysteresis model, is configured to determine static nonlinearity based on a direction of current flow. 6 . The BMS of claim 1 , wherein the at least one processor, when executing the code to generate the hysteresis model, is configured to determine an initial state of the hysteresis model from charge to discharge or from discharge to charge when a change in direction of current is detected. 7 . The BMS of claim 1 , wherein the SOC observer comprises a Kalman filter.

8. An electric vehicle (EV), comprising: a body coupled to the instrument panel, the body housing an interior compartment having a plurality of controls and dials, the plurality of controls and dials including an indicator identifying an amount of charge or a time period remaining for continued operation of the EV, the identified amount of charge or time period being based on a state of charge (SOC) of a lithium-ion (Li+) battery cell powering the EV; a drivetrain coupled to the vehicle body and to at least four wheels; and A battery management system (BMS) for one or more of the Li+ battery cells, the BMS comprising: apparatus coupled to the external terminals of the one or more of the Li+ battery cells and configured to estimate a state of charge (SOC) of the Li+ battery cells, the apparatus further comprising: Memory; and At least one processor is coupled to the memory and is configured, when executing code stored in the memory, to generate a state of charge (SOC) observer including a hysteresis model to account for hysteresis in the one or more Li+ battery cells.

9. The EV of claim 8, wherein the SOC observer is configured to include in the hysteresis model: charge to discharge state and discharge to charge state, which when combined results in an open circuit voltage (V OC ) continuous transition between the charging direction and the discharging direction.

10. The EV of claim 9, wherein the SOC observer is configured to predict V using the charge-to-discharge state and the discharge-to-charge state. OC .

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