Battery thermal model based on electrochemical impedance spectroscopy

Abstract

The invention relates to a battery thermal model based on electrochemical impedance spectroscopy. Surface temperatures of cells in a battery pack may be inferred or estimated using internal or core temperatures of the cells and a thermal model. The internal temperature may be generated using Electrochemical Impedance Spectroscopy (EIS). A delta value from the EIS estimate to the battery surface temperature may be generated based on a thermal model of the battery. A thermal model of the battery may be created using probe points, such as thermocouples, on the surface of the test battery.

Description

Technical Field

[0001] This disclosure generally relates to battery temperature monitoring, and more specifically, to a technique for estimating battery surface temperature using a battery thermal model. Background Technology

[0002] Rechargeable batteries, such as lithium-ion batteries, are commonly used in portable electronics and electric vehicles (EVs), as well as a variety of other applications, such as military and aerospace applications. Monitoring the temperature of these batteries during various operations, such as fast charging and fast discharging, is crucial to maximizing battery performance. For example, temperature monitoring enables the battery temperature to be maintained within specified boundaries or ranges (maximum and minimum) during fast charging, limits current to prevent overheating during fast discharging, and prevents battery damage due to abnormal use to ensure safety. Summary of the Invention

[0003] This disclosure describes a method for estimating the surface temperature of a battery. The method includes: receiving a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; generating a Δ value of the battery cell based on a thermal model; and modifying the first temperature estimate based on the Δ value to generate a second temperature estimate of the battery cell.

[0004] This disclosure also describes a system including one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform the following operations: receiving from a battery cell a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; generating a Δ value of the battery cell based on a thermal model; and modifying the first temperature estimate based on the Δ value to generate a second temperature estimate of the battery cell.

[0005] This disclosure also describes a machine-readable storage medium containing instructions that, when executed by a machine, cause the machine to perform the following operations: receiving from a battery cell a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; generating a Δ value for the battery cell based on a thermal model; and modifying the first temperature estimate based on the Δ value to generate a second temperature estimate of the battery cell. Attached Figure Description

[0006] The various figures in the accompanying drawings illustrate only exemplary embodiments of this disclosure and should not be construed as limiting its scope.

[0007] Figure 1 A block diagram of an example section of the battery monitoring system is shown.

[0008] Figure 2 This is a flowchart of a method for generating surface temperature estimates.

[0009] Figure 3 This is a flowchart of a method for generating surface temperature estimates.

[0010] Figure 4 An example section showing the test battery settings is provided.

[0011] Figure 5 This is a flowchart of the method used to generate a thermal model.

[0012] Figure 6 A block diagram is shown, including an example machine on which any one or more of the techniques (e.g., methods) discussed herein can be performed. Detailed Implementation

[0013] This invention describes an improved technique for estimating the surface temperature of battery cells. The surface temperature of battery cells in a battery pack can be inferred or estimated using the internal or core temperature of the battery cell and a thermal model. The internal temperature can be generated using electrochemical impedance spectroscopy (EIS). A Δ value estimated from EIS to the battery surface temperature can be generated based on the battery's thermal model. The battery's thermal model can be created using probe points (such as thermocouples) on the battery surface.

[0014] Figure 1 A block diagram of an example portion of a battery monitoring system 100 is shown. The battery monitoring system 100 includes a battery pack having multiple battery cells 102.1-102.n. The multiple battery cells 102.1-102.n can be provided in different shapes, such as hexagonal cubes, cylinders, etc. Figure 1 In the example, the battery pack consists of four rows of twelve battery cells, resulting in a total of forty-eight battery cells.

[0015] The battery monitoring system 100 includes electrochemical impedance spectroscopy (EIS) printed circuit boards (PCBs) 104.1-104.m coupled to multiple battery cells 102.1-102.n. The EIS PCBs 104.1-104.m can measure impedance changes in each battery cell 102.1-102.n. As explained in further detail below, impedance changes are used to generate the internal temperature, also known as the core temperature, of each battery cell 102.1-102.n. As used herein, internal or core temperature refers to the volumetric temperature of the battery cell.

[0016] The battery monitoring system 100 includes multiple thermocouples 106.1-106.p. The number of thermocouples 106.1-106.p can be coupled to a selected number of battery cells 102.1-102.n. In this example, only a subset (not all) of the battery cells 102.1-102.n can have corresponding thermocouples 106.1-106.p coupled to them. Figure 1 In the example, four thermocouples are provided for a battery pack with 48 battery cells. Providing a thermocouple for each battery cell (and potentially multiple thermocouples for each individual cell) would be impractical due to the cost and complexity of implementing the necessary thermocouple network to connect to each battery cell.

[0017] The battery monitoring system 100 includes one or more state sensors 108. The state sensors 108 can detect various current states. For example, the state sensors 108 can detect ramp / current (e.g., DC fast charging (DCFC) ramp / current), battery position, battery swelling, oil pressure (in the EV where the battery pack is located), etc.

[0018] The battery monitoring system 100 includes a controller 110. The controller 110 can be configured as one or more processors, microprocessors, etc. The controller 110 can be communicatively coupled to EIS PCBs 104.1-104.m, thermocouples 106.1-106.p, and status sensors 108. As described in further detail below, the controller 110 can generate the internal or core temperature (CoreTemp) of each battery cell based on impedance measurements received from the EIS PCBs 104.1-104.m. However, in some cases, the core temperature may not represent the surface temperature of the individual battery cells.

[0019] The controller 110 can also generate Δ values, such as ΔTSmin (Δ minimum surface temperature) and ΔTSmax (Δ maximum surface temperature). TSmin can represent the minimum surface temperature on the surface of the battery cell, and TSmax can represent the maximum surface temperature on the surface of the battery cell. The Δ values ​​can be generated based on the current state information received from the state sensor 108, and the core temperature can be generated based on the impedance measurement and the temperature measurement from the thermocouples 106.1-106.p.

[0020] As described further below, a thermal model can be generated based on probe points on the surface of the test battery. The test battery may have the same or similar configuration as battery cells 102.1-102.n. In some examples, the thermal model may include a compressed lookup table. In some examples, the thermal model may include a polynomial function with deterministic coefficients. In some examples, the thermal model may include a neural network. Current values, such as state information from state sensor 108, core temperature, and temperature values ​​from thermocouples 106.1-106.p, can be input into the thermal model, and the thermal model can generate Δ values ​​based on these current values.

[0021] The controller 110 can then generate a surface temperature estimate based on the core temperature and the Δ value. For example, the lowest surface temperature (TSmin) of the battery cell can be expressed as:

[0022] TSmin = CoreTemp + ΔTSmin

[0023] The maximum surface temperature (TSmax) of a battery cell can be expressed as:

[0024] TSmax = CoreTemp + ΔTSmax

[0025] Figure 2 This is a flowchart of method 200 for generating surface temperature estimates. In some examples, method 200 can be described using the above reference. Figure 1 The described battery monitoring system 100 is executed.

[0026] In operation 202, impedance measurements are received, for example, by a controller (controller 110). The impedance measurements can represent the impedance changes in the individual battery cells of the battery pack.

[0027] In operation 204, the internal or core temperature of each cell is generated based on impedance measurements using electrochemical impedance spectroscopy (EIS) technology. For example, a multivariate polynomial regression model can be used to estimate the internal (core) temperature of the cell using terminal impedance measurements taken at one or more frequencies at which a sinusoidal current is injected. In some examples, the technique described in U.S. Patent Application No. 17 / 712416, filed April 4, 2022, entitled “Internal Cell Temperature Estimation Technique,” ​​the entire contents of which are incorporated herein by reference, including but not limited to the portions specifically appearing below, except where such application supersedes the preceding application if any part thereof is inconsistent with this application.

[0028] In operation 206, one or more current conditions are received. Current conditions may include charging and discharging ramps / currents (e.g., DC fast charging (DCFC) ramps / currents), battery position, battery swelling, oil pressure (in the EV where the battery pack is located), etc.

[0029] In operation 208, a temperature measurement is received from a thermocouple. The thermocouple can be connected to a subset of the battery cells in the battery pack (also known as a manufacturing thermocouple). In this example, not all battery cells have an associated thermocouple. The temperature measurement can represent the surface temperature at the location of the corresponding thermocouple.

[0030] In operation 210, based on the current conditions, thermocouple temperature measurements, and internal (core) temperature input into the thermal model of the battery pack, Δ values ​​(e.g., ΔTSmin and ΔTSmax) for each battery cell are generated. The thermal model can be a 2D or 3D model. In some examples, the thermal model can be provided as a compressed lookup table. Details of an example of the thermal model are described below.

[0031] In operation 212, surface temperature estimates for each battery cell are generated based on the core temperature and Δ value. For example, the lowest surface temperature (TSmin) of a battery cell can be expressed as:

[0032] TSmin = CoreTemp + ΔTSmin

[0033] The maximum surface temperature (TSmax) of a battery cell can be expressed as:

[0034] TSmax = CoreTemp + ΔTSmax

[0035] Additional fine-tuning of the surface temperature estimate is also possible. This fine-tuning can be performed using current thermocouple measurements coupled to a subset of the battery cells. Figure 3 This is a flowchart of method 300 for generating surface temperature estimates. In some examples, method 300 may be derived from the above. Figure 1 The described battery monitoring system 100 is executed.

[0036] In operation 302, impedance measurements are received, for example, by a controller (controller 110). The impedance measurements can represent the impedance changes in the individual battery cells of the battery pack.

[0037] In operation 304, the impedance measurement is corrected. For example, the impedance measurement may include an error component, and the impedance measurement may be corrected to remove the error component. In some examples, the technique described in U.S. Patent Application No. 18 / 194495, the entire contents of which are incorporated herein by reference, including but not limited to those portions which appear hereinafter, except where such application supersedes the preceding application if any part thereof is inconsistent with this application.

[0038] In operation 306, EIS technology is used to generate the internal or core temperature (CoreTemp) of each battery based on calibrated impedance measurements. For example, a multivariate polynomial regression model can be used to estimate the internal (core) temperature of the battery using terminal impedance measurements taken at one or more frequencies at which a sinusoidal current is injected. In some examples, the techniques described in U.S. Patent Application No. 17 / 712416, filed April 4, 2022, entitled “Internal Battery Temperature Estimation Technique,” ​​the entire contents of which are incorporated herein by reference, including but not limited to the portions specifically appearing below, except where such reference supersedes the preceding application.

[0039] In operation 308, one or more current conditions are received. Current conditions may include DCFC ramp / current, battery position, battery expansion, oil pressure (for the EV containing the battery pack), etc.

[0040] In operation 310, a temperature measurement is received from a thermocouple. The thermocouple may be connected to a subset of the battery cells in the battery pack. In this example, not all battery cells have an associated thermocouple. The temperature measurement may represent the surface temperature at the location of the corresponding thermocouple.

[0041] In operation 312, the core temperature estimate, current status, and thermocouple temperature measurements can be input into the thermal model of the battery pack, for example, through a compressed lookup table. In operation 314, the Δ values ​​(e.g., ΔTSmin and ΔTSmax) for each battery cell are generated based on the compressed lookup table.

[0042] In operation 316, an initial surface temperature estimate for each battery cell is generated based on the core temperature and the Δ value. For example, the minimum surface temperature (TSmin) of the battery cell can be expressed as:

[0043] TSmin = CoreTemp + ΔTSmin

[0044] The maximum surface temperature (TSmax) of a battery cell can be expressed as:

[0045] TSmax = CoreTemp + ΔTSmax.

[0046] In operation 318, a TC temperature estimate is generated for the battery cells to which the thermocouples are connected, based on a compressed lookup table. In operation 320, a Δthermocouple-cell (ΔTC) is generated based on the TC temperature estimate and the current real-time temperature measurement. For example, if the TC temperature estimate for each battery cell with coupled thermocouples is 34˚, but the current real-time temperature measurement for the coupled thermocouples is 35˚, then ΔTC is +1˚.

[0047] In operation 322, the initial surface temperature estimate can be adjusted based on the ΔTC reading (e.g., based on the ΔTC value). In operation 324, final surface temperature and core temperature estimates can be generated for each cell.

[0048] Next, the details of the thermal model are described (e.g., compressed lookup tables). Thermal models can be generated based on a wide variety of different test conditions. For example, a battery pack may be over-insulated when subjected to various test conditions to generate thermal model values.

[0049] Figure 4 An example portion of a test battery setup 400 is shown. The test battery setup shows an example of a battery cell 402 in a test battery pack. The test battery setup 400 shows a single battery cell 402 for illustrative purposes only, and the test battery setup 400 may include multiple battery cells, such as a complete battery pack or rack.

[0050] Battery cell 402 is coupled to battery charger 404. Battery charger 404 can control different charging and discharging conditions on battery cell 402. Charging conditions may be referred to as C-rate. For example, a slow charger can charge at C / 5, while a fast charger can charge at 4C. Discharging is related to driving conditions, such as slow and fast driving. Battery cell 402 is also coupled to EIS PCB 406. EIS PCB 406 can measure the impedance change of battery cell 402 under different charging and discharging conditions. The impedance measurements can be stored and then used to generate a thermal model, as described in further detail below.

[0051] Battery cell 402 is also coupled to multiple thermocouples 408. For example, battery cell 402 may be connected to dozens or hundreds of thermocouples 408 in a test battery setup 400. While coupling these numerous thermocouples 408 to the battery cell is feasible in a test battery setup, it may not be feasible in real-world applications. Thermocouples 408 can measure the surface temperature at their respective locations under different charging and discharging conditions. Surface temperature measurements can be stored and then used to generate thermal models, as described in further detail below. In some examples, a subset of thermocouples 408 may be production thermocouples that will be used in real-world applications. Figure 4 In the example, only one out of dozens or hundreds of thermocouples 408 is a production thermocouple, while the rest are additional thermocouples used only in the testing environment. In a production environment, some or many batteries will not have thermocouples.

[0052] Figure 5 This is a flowchart of method 500 for generating a thermal model. In operation 502, over-insulated battery cells (and battery packs) are provided. For example, it can be provided and operated as described above. Figure 4 The test battery setting is 400.

[0053] In operation 504, an impedance measurement is received from the test battery. The impedance measurement represents the impedance change of the test battery under different charging and discharging conditions.

[0054] In operation 506, the impedance measurement is corrected. For example, the impedance measurement may include an error component, and the impedance measurement may be corrected to remove the error component. In some examples, the technique described in U.S. Patent Application No. 18 / 194495, the entire contents of which are incorporated herein by reference, including but not limited to those portions which appear hereinafter, except where such application supersedes the preceding application if any part thereof is inconsistent with this application.

[0055] In Operation 508, EIS technology is used to generate the internal or core temperature (CoreTemp) of a test battery based on calibrated impedance measurements. For example, a multivariate polynomial regression model can be used to estimate the internal (core) temperature of the battery using terminal impedance measurements taken at one or more frequencies at which a sinusoidal current is injected. In some examples, the techniques described in U.S. Patent Application No. 17 / 612416, filed April 4, 2022, entitled “Internal Battery Temperature Estimation Technique,” ​​the entire contents of which are incorporated herein by reference, including but not limited to the portions specifically appearing below, except where such reference supersedes the preceding application.

[0056] In operations 510 and 512, surface temperature measurements of the produced TC and the TC added to the test battery are received under different charging and discharging conditions, respectively. In operation 514, external conditions during different charging and discharging conditions are received. External conditions may include DCFC ramp / current, battery position, battery expansion, oil pressure (in the EV where the battery pack is located), etc.

[0057] In operation 516, the 3D thermal model is calibrated based on the surface characteristics of the test battery under different charging and discharging conditions, such as thermocouple surface temperature measurements and external conditions. In operation 518, the 3D thermal model is finally determined, providing surface temperature estimates (Tsmax, Tsmin) for the test battery. In this example, the 3D thermal model is based on probe points only on the surface of the test battery, rather than internal probe points.

[0058] In operation 520, a lookup table is generated based on the core temperature estimate and the 3D thermal model. For example, the temperature values ​​from the core temperature estimate and the 3D thermal model are correlated to generate Δ values ​​such as ΔTSmin and ΔTSmax. In operation 522, the lookup table is compressed. In operation 524, the final thermal model (e.g., the compressed lookup table) is stored. The final compressed lookup table can provide ΔTSmin and ΔTSmax values ​​under different core temperatures and other conditions. The final compressed lookup table can be distributed, for example, to EVs using battery packs of the same or similar type as the test battery, and can be used in real-world applications for surface temperature estimation, as described above.

[0059] The techniques shown and described in this document can be implemented using some or all of the battery monitoring system as described above, or in combination with the following. Figure 6 The discussion will be conducted on machine 600. Figure 6 A block diagram is shown that includes an example machine 600 on which any one or more of the techniques (e.g., methods) discussed herein can be performed. In various examples, machine 600 can operate as a standalone device or be connected (e.g., networked) to other machines.

[0060] In a networked deployment, machine 600 can operate as a server machine, a client machine, or both in a server-client network environment. In one example, machine 600 can act as a peer-to-peer (P2P) (or other distributed) network environment. Machine 600 can be a personal computer (PC), tablet device, set-top box (STB), personal digital assistant (PDA), mobile phone, network device, network router, switch, or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying the actions to be taken by that machine. Furthermore, although only a single machine is shown, the term "machine" should also be considered as including any collection of machines that individually or jointly execute a set (or more) of instructions to perform any one or more methods discussed herein, such as cloud computing, Software as a Service (SaaS), and other computer cluster configurations.

[0061] As described herein, examples may include logic or multiple components or mechanisms, or be operated by them. A circuit is a collection of circuits implemented in a tangible entity, including hardware (e.g., simple circuits, gates, logic, etc.). Circuit membership may become flexible over time and with changes in the underlying hardware. A circuit includes members that can perform a specified operation individually or in combination during operation. In one example, the hardware of the circuit may be designed immutably to perform a specific operation (e.g., hardwired). In one example, the hardware including the circuit may include physically connected components (e.g., execution units, transistors, simple circuits, etc.) including physically modified computer-readable media (e.g., magnetic, electrical, such as by changes in physical states or transformations of another physical property, etc.) to encode instructions for a specific operation. When physical components are connected, the underlying electrical characteristics of the hardware components may change, for example, from insulating to conductive, and vice versa. Instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit in the hardware with variable connections to perform a portion of a specific operation during operation. Thus, when the device is operational, the computer-readable media is communicatively coupled to other components of the circuit. In one example, any physical component can be used in multiple members of multiple circuits. For instance, under operation, an execution unit can be used in a first circuit of a first circuit at one point in time, and reused at different times by a second circuit in the first circuit or a third circuit in the second circuit.

[0062] Machine 600 (e.g., a computer system) may include a hardware-based processor 601 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), main memory 603, and static memory 605, some or all of which may communicate with each other via interconnect 630 (e.g., a bus). Machine 600 may also include a display device 609, an input device 611 (e.g., an alphanumeric keypad), and a user interface (UI) navigation device 613 (e.g., a mouse). In one example, display device 609, input device 611, and UI navigation device 613 may include at least a portion of a touchscreen display. Machine 600 may also include a storage device 620 (e.g., a drive unit), a signal generation device 617 (e.g., a speaker), a network interface device 650, and one or more sensors 615, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. Machine 600 may include output controller 619, such as a serial controller or interface (e.g., Universal Serial Bus (USB)), a parallel controller or interface, or other wired or wireless (e.g., infrared (IR) controller or interface, near field communication (NFC), etc.), connected to communicate with or control one or more peripheral devices (e.g., printer, card reader, etc.).

[0063] Storage device 620 may include a machine-readable medium having stored thereon one or more sets of data structures or instructions 624 (e.g., software or firmware) embodying or used by any one or more of the technologies or functions described herein. During execution of instructions 624 by machine 600, instructions 624 may also reside wholly or at least partially within main memory 603, static memory 605, mass storage device 607, or hardware-based processor 601. In one example, one or any combination of hardware-based processor 601, main memory 603, static memory 605, or storage device 620 may constitute a machine-readable medium.

[0064] Although machine-readable media is considered as a single medium, the term “machine-readable media” can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store one or more instructions 624.

[0065] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions executable by machine 600 and causing machine 600 to perform any one or more of the technologies disclosed herein, or any medium capable of storing, encrypting, or carrying data structures used by or associated with those instructions. Examples of non-limiting machine-readable media can include solid-state memory as well as optical and magnetic media. Therefore, machine-readable media are not transient propagating signals. Specific examples of a vast array of machine-readable media may include: non-volatile memories such as semiconductor storage devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic or other phase-change or state-change memory circuits; disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs and DVD-ROMs.

[0066] Instruction 624 can also be transmitted or received on communication network 621 via a transmission medium through network interface device 650, which uses any of a variety of transmission protocols, such as Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc. Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (such as the Internet), mobile phone networks (such as cellular networks), conventional telephone (POTS) networks, and wireless data networks (such as the IEEE 802.22 family of standards known as Wi-Fi®, the IEEE 802.26 family of standards known as WiMax®), the IEEE 802.27.4 family of standards, peer-to-peer (P2P) networks, etc. In one example, network interface device 650 may include one or more physical jacks (such as Ethernet, coaxial cable, or telephone jacks) or one or more antennas for connection to communication network 621. In one example, network interface device 650 may include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-input (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions executed by machine 600, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0067] Various annotations

[0068] Each of the above non-restrictive aspects may exist independently or may be arranged or combined in various ways with one or more other aspects or other topics described in this document.

[0069] The above detailed description includes reference to the accompanying drawings, which form a part of the detailed description. The drawings illustrate, by way of illustration, specific embodiments in which the invention may be practiced. These implementations are generally also referred to as “examples.” These examples may include other elements besides those shown or described. However, the inventors have also contemplated examples that provide only the elements shown or described. Furthermore, the inventors have contemplated examples of any combination or substitution of those elements (or one or more aspects thereof) shown or described, whether relating to a particular example (or another one or more aspects thereof) or to other examples shown or described herein (or two or more aspects thereof).

[0070] In the event of any inconsistency between the usage in this document and any other document incorporated by reference, the usage in this document shall prevail.

[0071] In this document, the terms “a” or “an” are common in patent documents and are used to include one or more, independent of any other instances or uses of “at least one” or “one or more.” In this document, the term “or” is used to refer to non-exclusivity, or “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise stated. In this document, the terms “comprising” and “wherein” are used as their plain English equivalents to the corresponding terms “including” and “in which.” Furthermore, the terms “comprising” and “including” are open-ended, meaning that a system, apparatus, article, composition, formulation, or process that includes elements other than those listed after the term in one aspect is still considered to be within the scope of that aspect. Furthermore, the terms “first,” “second,” and “third,” etc., are used merely as labels and are not intended to impose numerical requirements on their objects.

[0072] The methods described herein can be implemented, at least in part, by a machine or computer. Some examples may include computer-readable media or machine-readable media encoded with instructions operable to configure an electronic device to perform the methods described in the examples above. Implementations of these methods may include code, such as microcode, assembly language code, high-level language code, etc. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Furthermore, in one example, the code may be tangibly stored on one or more volatile, non-transient, or non-volatile tangible computer-readable media, for example, during execution or at other times. Examples of such tangible computer-readable media may include, but are not limited to, hard disks, removable disks, removable optical discs (e.g., optical discs and digital video discs), magnetic tape cassettes, memory cards or memory sticks, random access memory (RAM), read-only memory (ROM), etc.

[0073] The foregoing description is intended to be illustrative and not limiting. For example, the foregoing examples (or one or more aspects thereof) may be used in combination with each other. Other implementations may be used, for example, those that a person skilled in the art would use after reading the foregoing description. The abstract is provided to allow the reader to quickly determine the nature of the technical disclosure. This document is submitted on the premise that it should not be construed as limiting the scope or meaning of these aspects. Furthermore, in the foregoing detailed description, various features may be combined to simplify this disclosure. This should not be construed as meaning that any unclaimed disclosed feature is essential to any claim. Rather, the subject matter of the invention may lie in certain features of a particular disclosed embodiment. Therefore, the following aspects are incorporated into the detailed description as examples or implementations, each existing independently as a separate implementation, and it is conceivable that these implementations may be combined with each other in various combinations or arrangements.

Claims

1. A method for estimating the surface temperature of a battery, the method comprising: Receive a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; The Δ value of the battery cell is generated based on the thermal model; and The first temperature estimate is modified based on the Δ value to generate a second temperature estimate for the battery cell.

2. The method according to claim 1, wherein the second temperature estimate is a surface temperature estimate of the battery cell.

3. The method according to claim 1, further comprising: Receive the current condition associated with the battery, wherein the thermal model is used to further generate the Δ value based on the current condition.

4. The method according to claim 1, further comprising: At least one temperature measurement is received from a thermocouple coupled to the battery, wherein the generation of the Δ value is also based on the at least one measurement.

5. The method according to claim 1, wherein the Δ value includes a minimum surface temperature Δ value and a maximum surface temperature Δ value.

6. The method of claim 1, wherein the thermal model comprises a lookup table.

7. The method of claim 1, wherein the thermal model is created by using probe points on the surface of the test battery.

8. The method of claim 1, wherein the first temperature estimation is based on the electrochemical impedance spectroscopy of the at least one impedance measurement.

9. A system comprising: One or more processors of a machine; and A memory that stores instructions, when executed by the one or more processors, that cause the machine to perform the following operations: Receive a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; The Δ value of the battery cell is generated based on the thermal model; and The first temperature estimate is modified based on the Δ value to generate a second temperature estimate for the battery cell.

10. The system of claim 9, wherein the second temperature estimate is a surface temperature estimate of the battery cell.

11. The system according to claim 9, wherein the operation further comprises: Receive the current condition associated with the battery, wherein the thermal model is used to further generate the Δ value based on the current condition.

12. The system according to claim 9, wherein the operation further comprises: At least one temperature measurement is received from a thermocouple coupled to the battery, wherein the generation of the Δ value is also based on the at least one measurement.

13. The system of claim 9, wherein the Δ value includes a minimum surface temperature Δ value and a maximum surface temperature Δ value.

14. The system of claim 9, wherein the thermal model comprises a lookup table.

15. The system of claim 9, wherein the thermal model is created by using probe points on the surface of the test battery.

16. The system of claim 9, wherein the first temperature estimate is based on the electrochemical impedance spectroscopy of the at least one impedance measurement.

17. A machine-readable storage medium containing instructions that, when executed by a machine, cause the machine to perform the following operations: Receive a first temperature estimate of the battery cell based on at least one impedance measurement, the first temperature estimate representing the volumetric temperature of the battery cell; The Δ value of the battery cell is generated based on the thermal model; and The first temperature estimate is modified based on the Δ value to generate a second temperature estimate for the battery cell.

18. The machine-readable storage medium of claim 17, wherein the second temperature estimate is a surface temperature estimate of the battery cell.

19. The machine-readable storage medium of claim 17, further comprising: Receive the current condition associated with the battery, wherein the thermal model is used to further generate the Δ value based on the current condition.

20. The machine-readable storage medium of claim 17, further comprising: At least one temperature measurement is received from a thermocouple coupled to the battery, wherein the generation of the Δ value is also based on the at least one measurement.

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