Techniques for estimating high-resolution surface temperature maps in battery packs

Abstract

Systems and methods for estimating battery surface temperatures involve generating a core temperature estimate for the battery based on a battery model. Based on the core temperature, a set of concentrated temperature states can be generated, containing temperature estimates for different regions of the battery. Additional aggregated information about the battery can be recovered. Using a mapping function, a surface temperature map for the battery can be generated from this set of concentrated temperature states and the additional aggregated information.

Description

PRIORITY CLAIMS

[0001] This patent application claims the priority benefit of the preliminary US patent application, Ind. No. 63 / 696,125, entitled “TECHNIQUES FOR ESTIMATION OF HIGH-RESOLUTION SURFACE TEMPERATURE MAPS IN BATTERY PACKS”, filed on September 18, 2024, and the US patent application, Ind. No. 19 / 330,531, entitled “TECHNIQUES FOR ESTIMATION OF HIGH-RESOLUTION SURFACE TEMPERATURE MAPS IN BATTERY PACKS”, filed on September 16, 2025, which are incorporated herein in full by reference. TECHNICAL AREA

[0002] The present disclosure relates to battery state estimation. BACKGROUND

[0003] Rechargeable batteries, such as lithium-ion batteries, are widely used in portable electronics and electric vehicles (EVs), as well as in various other applications, including military and aerospace. Monitoring the battery's condition, such as its temperature, is crucial to maximizing its performance.

[0004] Continuous monitoring of battery health is important, for example, for the safe introduction of EVs. High currents during rapid charging and discharging can cause uneven heating of the battery cells, making it important to measure their surface temperatures, such as the maximum and minimum surface temperatures. SUMMARY

[0005] The present disclosure describes systems and methods for estimating battery surface temperatures, comprising generating a core temperature estimate for the battery based on a battery model; generating a set of concentrated temperature states based on the core temperature, wherein the set of concentrated temperature states includes temperature estimates for different regions of the battery; retrieving additional condensed information regarding the battery; generating a surface temperature map for the battery based on the set of concentrated temperature states and the additional condensed information using a mapping function; and determining at least one surface temperature for the battery based on the surface temperature map. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The various accompanying drawings merely illustrate exemplary embodiments of the present disclosure and should not be regarded as limiting its scope of protection. Fig. Figure 1A illustrates a simplified model for a battery. Fig. Figure 1B illustrates a model for estimating the core temperature of a battery. Fig. Figure 2 is a block diagram illustrating a method for estimating surface temperatures using surface temperature maps. Fig. Figure 3 is a block diagram illustrating a method for estimating surface temperatures in a battery pack using a common low-rank decomposition. Fig. Figure 4 is a process diagram illustrating the low-rank decomposition of Kalman filter gain matrices over several battery cells. Fig. Figure 5 is a block diagram illustrating a method for estimating surface temperatures in a battery pack using subspace-based super-resolution techniques. Fig. Figure 6 is a graphical representation illustrating an example of a battery temperature grid. Fig. Figure 7 illustrates a block diagram of an example that includes a machine on which one or more of the techniques (e.g., methodologies) discussed here can be performed. DETAILED DESCRIPTION

[0007] This disclosure describes techniques for estimating surface temperature maps in battery packs, which can be used to estimate various surface temperature values, such as the maximum, minimum, and average surface temperature. These techniques can be used, for example, in electric vehicles and other applications to monitor battery safety and performance.

[0008] Other battery monitoring approaches generally lack the ability to accurately track the maximum and minimum surface temperatures of individual cells within a battery pack without the extensive use of thermocouples. Other computational techniques may be limited by memory and computing constraints and may not provide the spatial resolution needed to identify localized hot or cold spots that could lead to safety hazards or reduced battery lifespan.

[0009] The present disclosure describes techniques that combine model-based temperature estimates using sensor data with additional condensed information, such as physics-based models or data-driven representations derived from simulations, to generate a high-resolution characteristic map of surface temperatures across the entire battery pack, thereby enabling accurate identification of temperature extremes.

[0010] Improved safety can be achieved by accurately monitoring maximum and minimum surface temperatures, which helps prevent thermal runaway and other safety risks. Enhanced performance results from high-resolution temperature mapping, enabling better thermal management and leading to optimized charging, discharging, and overall battery health.

[0011] In particular, the techniques described here can achieve high accuracy without the use of a dense network of physical sensors, such as thermocouples, or excessive computing resources, making them suitable for edge-based use in compact battery systems, such as in electric vehicles.

[0012] Various types of models can be used to estimate battery state information, such as the core temperature (also known as the internal temperature). The core temperature can be estimated, for example, using electrochemical impedance spectroscopy (EIS). According to some examples, a multivariable polynomial regression model can be used.

[0013] As is generally the case in Fig. As illustrated in Figure 1A, a core temperature is estimated using EIS. According to some examples, polynomial regression with multiple variables can be used to estimate the core temperature of a battery (such as a lithium-ion battery) using EIS techniques. For example, terminal impedance measurements can be obtained at multiple frequencies of an applied sinusoidal current. According to some examples, the frequencies to be used are selected based on a battery type.

[0014] Fig. Figure 1B illustrates a block diagram of a system 100 for using a polynomial regression model with multiple variables to estimate the core temperature of a battery from impedance measurements at several frequencies. As shown in Fig. As shown in 1B, the connection impedance measurements determined at different frequencies (shown in Fig. 1B (designated with reference 101) are entered into a model 102 of a polynomial regression with several variables, the output of which is an estimate of the battery core temperature.

[0015] Core temperature estimates can provide an average internal temperature of the battery cell, but they typically do not capture local heating or cooling that may occur at the cell surface. Surface temperature measurements can be useful because thermal extremes, such as hot spots or cold spots, can develop at the surface, posing safety risks like thermal runaway and affecting battery performance and lifespan. Monitoring surface temperatures can enable more effective thermal management and safer operation.

[0016] Next, techniques for estimating surface temperatures based on core temperature estimation techniques are described.

[0017] Fig. Figure 2 is a block diagram illustrating a method 200 for estimating surface temperatures using surface temperature maps.

[0018] EIS measurements are taken in block 202. As described above, for example, connection impedance measurements can be taken at several frequencies of an applied sinusoidal current.

[0019] In block 204, a core temperature estimate is generated based on the EIS measurements using a battery model, as described above. For example, a polynomial regression model with multiple variables can be used to estimate the battery's core temperature from the impedance measurements at several frequencies.

[0020] In block 206, the core temperature estimate can be inputted into a concentrated parameter Kalman filter module (KF module) to output a set of concentrated temperature states. According to some examples, additional sensor data, such as temperature readings from a small number of thermocouples located at predetermined battery locations, can also be input into the concentrated KF module to generate the set of concentrated temperature states. The thermocouples can have negative temperature coefficient (NTC) thermistors.

[0021] Concentrated temperature states can include temperature estimates calculated for different areas or segments within a battery cell, rather than for individual points or the entire cell as a whole. The concentrated KF module can aggregate the input data and the battery's physical properties to estimate the average temperature in each area (or state). Concentrated temperature states can provide values ​​such as T core , T lumped1 , T lumped2 , ..., T lumpedk , and T NTC , exhibiting, where T core represents the core temperature estimate, T lumped1 , T lumped2 , ..., T lumpedk the estimated temperatures of different areas and T NTCA temperature reading is represented by one or more NTC thermistors positioned at specific locations on the surface of the battery cell. While concentrated temperature states can provide more spatial detail than a single core temperature estimate, they may still lack the resolution to accurately identify localized hot or cold spots on the cell surface.

[0022] Block 208 contains additional condensed information regarding the battery. This additional condensed information may include supplementary data or model representations that improve the accuracy of the surface temperature estimate, as further described below.

[0023] The information is described as "condensed" because it can capture, for example, dominant features or patterns of heat distribution within the battery using a reduced dataset or model parameters, rather than relying on full, high-dimensional thermal models or extensive sensor networks. The information can be condensed to save storage and computational resources. The condensed information can summarize the most relevant aspects in a compact form, minimizing storage and computational usage while still enabling an accurate surface temperature estimate. The additional condensed information may include sparse thermal models, dominant components identified from simulation data, or other physics-based or data-driven features that capture the heat distribution characteristics within the battery.

[0024] In block 210, a surface temperature map is generated based on the concentrated temperature states and the additional condensed information. A mapping function can be used to combine the concentrated temperature states and the additional condensed information. For example, a mapping function Φ can generate a high-resolution surface temperature map as a function of the concentrated temperature states and the position S on the cell surface.

[0025] A surface temperature map can display the temperature values ​​distributed across the surface of a battery cell. The map can be provided in high resolution. The surface temperature map can provide detailed spatial information about how the temperature varies at different locations on the cell surface.

[0026] In block 212, surface temperature estimates are generated based on the surface temperature map. For example, the maximum, minimum, and average surface temperatures across the entire battery surface can be generated. The maximum surface temperature T max and the minimum surface temperature T min They can be calculated, for example, as follows: Tmax=maxSΦ(Tcore,Tlumped[1…k],TNTC,S), Tmin=minSΦ(Tcore,Tlumped[1…k],TNTC,S).

[0027] By integrating the condensed information (rather than the complete information) with the concentrated temperature states, the system can generate high-resolution surface temperature maps, which can then be used for surface temperature estimation without relying on extensive sensor networks or computing resources. As such, the method can be implemented on an edge-based device, such as an electric vehicle, where storage and computing resources may be limited.

[0028] Fig. Figure 3 is a block diagram illustrating a method 300 for estimating surface temperatures in a battery pack using a common low-rank decomposition.

[0029] EIS measurements are taken in block 302. For example, connection impedance measurements can be obtained at several frequencies of an applied sinusoidal current, as described above.

[0030] In block 304, a core temperature estimate is generated based on the EIS measurements using a battery model, as described above.

[0031] In block 306, the core temperature estimate can be input into a concentrated KF module to output a set of concentrated temperature states, as described above.

[0032] In block 308, additional condensed information regarding the battery is obtained, which in this case includes the parameters of a sparse thermal model, such as asymptotic KF gain values, obtained using a common low-rank decomposition, as further described below.

[0033] In block 314, a joint low-rank decomposition is performed to condense the Kalman filter gain matrices across multiple battery cells, as described in more detail below. The pre-calculated asymptotic KF gain shown in block 316 is periodically updated via calibration (block 320).

[0034] In block 318, the concentrated temperature states are refined by applying KF update equations. The system incorporates the condensed KF gain matrices obtained through joint low-rank decomposition. By integrating these gain matrices with the concentrated temperature states, the KF update module efficiently computes improved temperature estimates for various battery regions.

[0035] In block 310, a surface temperature map is generated based on the KF update equations. A mapping function can be used to generate a high-resolution surface temperature map as a function of the concentrated temperature states and the position on the cell surface, as described above. A surface temperature map provides a representation of the temperature values ​​distributed across the surface of a battery cell and offers high-resolution, detailed spatial information about how the temperature varies at different locations on the cell surface.

[0036] Block 312 generates surface temperature estimates, such as the maximum, minimum and average surface temperature, based on the surface temperature map, as described above.

[0037] Fig. Figure 4 is a process diagram illustrating the low-rank decomposition of Kalman filter gain matrices over multiple battery cells. As described below, the use of low-rank decomposition can enable efficient and scalable battery state estimation. Joint low-rank decomposition allows for efficient compaction and distribution of Kalman filter gain matrices, effectively utilizing the asymptotic stability and highly sparse convolutional nature of the thermal model.

[0038] As in Fig. As shown in Figure 4, individual high-dimensional Kalman filter gain matrices K1, K2, ..., K are first used. Bcollected from each battery cell, each matrix having dimensions N × n (e.g., 165 × 6), where N is the number of grid points in the high-resolution state and n is the number of concentrated KF states. These individual matrices are combined to form a single aggregated gain matrix K of size N × (nB), where B is the number of battery cells.

[0039] This aggregation and compression process is described by the following set of equations, the first equation being: K=[K1|K2|…|KB]∈ℝN×(Bn).

[0040] It represents the concatenation of the individual Kalman filter gain matrices K1, K2, ..., K B from each of the B battery cells. Each matrix K i It has dimensions N × n. The resulting aggregated matrix K has N rows and nB columns.

[0041] The second equation K≈LR=[LR1LR2…LRB] shows that the aggregated amplification matrix K can be efficiently approximated by a product of two matrices: a global prefactor matrix L of size N × M and a set of cell-specific postfactor matrices R. i of size M × n, where M is the rank of the reduced-rank decomposition. This reduced-rank representation significantly reduces the storage and computational requirements, since only the global matrix L and the smaller cell-specific matrices R are needed. i Instead of the complete set of high-dimensional enhancement matrices, they can be stored, for example, in the edge-based setup and used for further calculations.

[0042] A low-rank decomposition is performed on K using the singular value decomposition (SVD), resulting in a set of low-rank matrices designated L and R. The decomposition is solved for L and R such that: L,R=arg minL,R‖K−LR‖ holds, where L is a global prefactor matrix of size N × M and each R j a cell-specific post-factor matrix of size M × n, where M is the rank of the reduced-rank matrices (which is typically much smaller than nB).

[0043] After decomposition, the process distributes the results into the global prefactor matrix L, which is stored once for the entire battery pack, and the cell-specific postfactor matrices R. i , which are distributed across each battery cell. This approach reduces the storage footprint from nNB to NM + MnB and achieves a reduction of at least two orders of magnitude compared to storing complete gain matrices for each cell.

[0044] The joint low-rank decomposition enables efficient condensation and distribution of the Kalman filter gain matrices, effectively exploiting the asymptotic stability and highly sparse convolutional nature of the thermal model. According to some examples, the joint low-rank decomposition of the Kalman filter gain matrices, as described above, can be performed in a central location, such as on a server or cloud-based system, in a manufacturing facility, or on a vehicle's central computing system, where computing resources are more extensive and readily available. In this process, the high-dimensional gain matrices of multiple battery cells are aggregated and decomposed into a global prefactor matrix and cell-specific postfactor matrices.Once the decomposition is complete, only the compact gain values ​​exhibiting the global and cell-specific matrices are stored and used in the edge-based battery management systems. This approach minimizes the storage and computational requirements at the edge and enables efficient real-time temperature estimation in various environments, such as electric vehicles.

[0045] Fig. Figure 5 is a block diagram illustrating a Method 500 for estimating surface temperatures in a battery pack using subspace-based super-resolution techniques.

[0046] EIS measurements are taken in block 502. For example, connection impedance measurements can be obtained at several frequencies of an applied sinusoidal current, as described above.

[0047] In block 504, a core temperature estimate is generated based on the EIS measurements using a battery model, as described above.

[0048] In block 506, the core temperature estimate can be input into a concentrated KF module to output a set of concentrated temperature states, as described above.

[0049] Block 508 contains additional condensed information regarding the battery, which in this case represents the dominant components of the battery's heating profile. For example, simulation data can be used to identify subspaces that have a dominant effect on the battery's heating profile. These subspaces can then be used for super-resolution imaging, as described below.

[0050] Block 514 provides high-resolution simulation data representing detailed temperature profiles under various operating conditions.

[0051] Block 516 performs cell-specific standardization to account for differences between individual battery cells, ensuring that simulation data and measured conditions are correctly aligned.

[0052] In block 518, a subspace identification is performed, e.g., using principal component analysis (PCA), to extract a set of basis vectors that capture the dominant modes of the temperature variation observed in the simulation data.

[0053] Block 520 contains additional features, such as a current (I), a squared current (I). 2 ) and a coolant flow rate was included to further refine the temperature estimation process.

[0054] In block 522, the identified subspace basis vectors and additional features are combined with the concentrated temperature states using a super-resolution algorithm. This process effectively utilizes the learned relationship between the low-dimensional concentrated states and the high-resolution temperature profiles, enabling the generation of a higher-resolution temperature map in block 510.

[0055] A mapping function for a high-resolution surface temperature map can be used as a function of the concentrated temperature states and the position on the cell surface, as described above. A surface temperature map provides a representation of the temperature values ​​distributed across the surface of a battery cell and offers high-resolution, detailed spatial information about how the temperature varies at different locations on the cell surface.

[0056] Block 512 generates surface temperature estimates, such as the maximum, minimum and average surface temperature, based on the surface temperature map, as described above.

[0057] Next, techniques for determining the dominant components of the battery's heating profile are described. Fig. Figure 6 is a graphical representation illustrating an example of a battery temperature grid.

[0058] The temperature profiles under normal conditions can be quite regular and uniform, and can be assumed to lie in a low-dimensional affine subspace: x=Uz,z∈ℝK, where x represents the high-resolution temperature grid vector containing temperature values ​​at every location on the battery cell surface; U is a matrix of basis vectors capturing the dominant patterns of temperature variation; and z is a latent variable vector providing the coefficients for combining the basis vectors in U to reconstruct the complete temperature profile x across the cell surface. U can be determined by performing PCA on the simulated data.

[0059] The condensed KF state vector s can be related to the vector × via the operator H, such that s = H X is.

[0060] It is assumed that the high-resolution temperature lattice itself lies in a low-dimensional manifold. A possible example of such a manifold is an affine subspace parameterized by the basis vectors U and the latent variables z such that x = U. Z .

[0061] Given a concentrated KF state vector and a subspace basis U, the interpolated temperature profile can be calculated using the following relationship: x=U(HU)+s, where (HU) + the Moore-Penrose pseudoinverse.

[0062] To account for the differences between individual battery cells, cell-specific normalization and Tikhonov regularization can be included, leading to the following calculation: x=∑iU(H∑iU)+s+[I−∑iU(H∑iU)+H]mi, where m i and Σ iThese are the cell-specific pixel-wise mean or standard deviation.

[0063] According to some examples, the calculation can be further reduced to the following: x'^=U(Vσi)λ+s+μi, where V ∈ ℝ M×K×4 , σ i ∈ ℝ 4 ,µ i ∈ ℝ nsearch , n search : the number of pixels over which the maximum and minimum values ​​are to be searched.

[0064] This approach allows the system to store and process only a few dozen additional numbers per cell, instead of the hundreds required by conventional concentrated Kalman filtering methods. As a result, the storage cost for adding an additional state is similar, making the method highly efficient and scalable for edge-based battery management applications.

[0065] According to some examples, subspace identification can be performed at a central location, such as on a server, a cloud-based system, a manufacturing facility, or a vehicle's central computing system, using high-resolution simulation data collected under various operating conditions. In this process, PCA or similar techniques are applied at the central location to extract a set of basis vectors that capture the dominant modes of temperature variation across the battery cells. Once the subspace basis vectors and cell-specific normalization parameters are determined, only these compact representations are stored and used in edge-based battery management systems. This enables efficient real-time reconstruction of high-resolution temperature profiles at the edge device while minimizing storage and computational requirements.

[0066] The techniques shown and described in this document can be implemented using part or all of the battery monitoring system described above, or otherwise using a Machine 700 as described below. Fig. 7 will be discussed and executed. Fig. Figure 7 illustrates a block diagram of an example featuring a machine 700 on which one or more of the techniques (e.g., methodologies) discussed here can be executed. According to various examples, the machine 700 can operate as a standalone device or be connected (e.g., networked) with other machines.

[0067] In a networked environment, Machine 700 can operate as a server machine, a client machine, or both in server-client network environments. For example, Machine 700 can act as a peer machine in a peer-to-peer (P2P) network environment (or any other distributed network environment). Machine 700 can be a personal computer (PC), a tablet device, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web device, a network router, a network switch or bridge, or any other machine capable of executing instructions (sequentially or otherwise) that specify operations to be performed by that machine.While only a single machine is illustrated, the term "machine" should also be understood to encompass any collection of machines that, individually or collectively, execute a set (or multiple sets) of instructions to perform one or more of the methodologies discussed here, such as cloud computing, software as a service (SaaS), or other computer cluster trainings.

[0068] Examples such as those described here may contain or operate through logic or a number of components or mechanisms. A circuit arrangement is a collection of circuits implemented in tangible entities that contain hardware (e.g., simple circuits, gates, logic, etc.). Membership in a circuit arrangement can be flexible over time due to the variability of the underlying hardware. Circuit arrangements contain elements that, alone or in combination, can perform predefined operations when operating. For example, the hardware of the circuit arrangement may be designed to perform a specific operation in an immutable way (e.g., hardwired). Alternatively, the hardware containing the circuit arrangement may consist of variably connected physical components (e.g., execution units, transistors, simple circuits, etc.).) including a computer-readable medium that is physically modified (e.g., magnetically, electrically, such as by changing its physical state or transforming another physical property, etc.) to encode the instructions for the specific operation. When connecting the physical components, the underlying electrical properties of a hardware component can be changed, for example, from an insulating property to a conductive property, or vice versa. The instructions enable the embedded hardware (e.g., the execution units or a loading mechanism) to generate elements of the circuit arrangement in the hardware via the variable connections in order to execute portions of the specific operation when it is running.Accordingly, the computer-readable medium is communicatively coupled to the other components of the circuit arrangement when the device is operating. For example, each of the physical components can be used in more than one element of more than one circuit arrangement. The execution units can, for instance, be used in operation at one time in a first circuit of a first circuit arrangement and reused at another time by a second circuit in the first circuit arrangement or by a third circuit in a second circuit arrangement.

[0069] The machine 700 (e.g., a computer system) may comprise a hardware-based processor 701 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), main memory 703, and static memory 705, some or all of which may communicate with each other via an intermediate connection 730 (e.g., a bus). The machine 700 may further comprise a display device 709, an input device 711 (e.g., an alphanumeric keyboard), and a user interface navigation device (UL navigation device) 713 (e.g., a mouse). By way of example, the display device 709, the input device 711, and the UL navigation device 713 may comprise at least sections of a touchscreen display. The machine 700 can additionally include a storage device 720 (e.g. a drive unit), a signal generation device 717 (e.g.a loudspeaker), a network interface device 750, and one or more sensors 715, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or another sensor. The machine 700 may include an output control unit 719, such as a serial control unit or interface (e.g., a universal serial bus (USB)), a parallel control unit or interface, or other wired or wireless (e.g., infrared control units or interfaces, near field communication (NFC), etc.) coupled to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0070] The storage device 720 can comprise a machine-readable medium in which one or more sets of data structures or instructions 724 (e.g., software or firmware) are stored, embodying or utilizing one or more of the techniques or functions described herein. The instructions 724 can also be stored, in whole or in part, within a main memory 703, within a static memory 705, within a mass storage device 707, or within the hardware-based processor 701 during their execution by the machine 700. According to an example, any combination of the hardware-based processor 701, the main memory 703, the static memory 705, or the storage device 720 can constitute machine-readable media.

[0071] While the machine-readable medium is considered a single medium, the term "machine-readable medium" can refer to a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) that are designed to store the one or more instructions 724.

[0072] The term “machine-readable medium” can include any medium capable of storing, encoding, or transmitting instructions for execution by the Machine 700 that cause the Machine 700 to execute any or more of the techniques disclosed herein, or capable of storing, encoding, or transmitting data structures used by or associated with such instructions. Non-limiting examples of machine-readable media include solid-state storage and optical and magnetic media. Accordingly, machine-readable media are not transitory propagating signals. Specific examples of concentrated machine-readable media include non-volatile storage, 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; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0073] The instructions 724 can also be transmitted or received via a communication network 721 using a transmission medium via the network interface device 750 using any one of a number of transmission protocols (e.g. frame forwarding, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Examples of communication networks include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile networks (e.g., cell-based networks), traditional telephone networks (POTS networks), and wireless data networks (e.g., the IEEE 802.22 family of standards from the Institute of Electrical and Electronics Engineers (IEEE), known as Wi-Fi®, the IEEE 802.26 family of standards, known as WiMax®, the IEEE 802.27.4 family of standards, and peer-to-peer networks (P2P networks).According to one example, the network interface device 750 may have one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connecting to the communication network 721. According to another example, the network interface device 750 may have multiple antennas for wireless communication using at least one of the single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term "transmission medium" shall be understood to include any intangible medium capable of storing, encoding, or transmitting instructions for execution by the machine 700, and includes digital or analog communication signals or any other intangible medium to facilitate the transmission of such software. Various comments

[0074] Each of the above non-restrictive aspects can stand alone or be combined in various permutations or combinations with one or more of the other aspects or any other subject described in this document.

[0075] The detailed description above includes references to the accompanying drawings, which form part of the detailed description. The drawings illustrate specific implementations in which the invention can be practiced. These implementations are also generally referred to as "examples." Such examples may include elements in addition to those shown or described. However, the inventors of the present invention also consider examples in which only the elements shown or described are provided.Furthermore, the inventors of the present invention also consider examples that use any combination or permutation of these elements shown or described (or one or more aspects thereof) either with respect to a particular example (or one or more aspects thereof) or with respect to other examples shown or described herein (or one or more aspects thereof).

[0076] In case of inconsistent usage between this document and any documents referenced, the usage in this document shall prevail.

[0077] In this document, the terms "one" or "a" are used, as is customary in patent documents, to indicate one or more than one, irrespective 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 a non-exclusive or, so that "A or B" indicates "A but not B," "B but not A," and "A and B" unless otherwise specified. In this document, the terms "indicating" and "in which" are used as the plain English equivalents of the respective terms "indicating" and "in which." Furthermore, in the following claims, the terms "indicating" and "indicates" are open, meaning a system, device, article, composition, formulation, or process that includes, or includes, the following:Elements in addition to those listed in a claim following such a term are nevertheless considered to fall within the scope of protection of that claim. Furthermore, in the following claims, the terms "first," "second," and "third," etc., are used merely as designations and are not intended to impose any numerical requirements on their objects.

[0078] The process examples described here may be at least partially machine- or computer-implemented. Some examples may include a computer-readable or machine-readable medium encoded with instructions capable of constructing an electronic device to execute the processes described in the examples above. An implementation of such processes may include code such as microcode, assembly language code, code of a higher-level programming language, or the like. Such code may contain computer-readable instructions for executing various processes. The code may form sections of computer program products. Furthermore, according to one example, the code may be stored in a tangible manner on one or more volatile, non-transient, or non-volatile tangible computer-readable media, such as during execution or at other times.Examples of these tangible, computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, read / write memory (RAMs), read-only memory (ROMs).

[0079] The above description is intended to be illustrative and not limiting. The examples described above (or one or more aspects thereof) may, for example, be used in combination with one another. Other implementations may be used, such as those that a person skilled in the art in this field might devise after reviewing the above description. The summary is provided to enable the reader to quickly determine the nature of the technical disclosure. It is presented with the understanding that it is not intended to interpret or limit the scope of protection or the meaning of the claims. Furthermore, in the above detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as implying that an unclaimed disclosed feature is essential to any claim.Rather, the subject matter of the invention may consist of fewer than all features of a particular disclosed implementation. Consequently, the following claims are hereby included as examples or implementations in the detailed description, each claim constituting a separate implementation, with the intention that such implementations may be combined with one another in various combinations or permutations. The scope of protection of the invention with respect to the attached claims should be determined together with the full scope of protection of the equivalents to which such claims entitle the holder.

[0080] According to one aspect, systems and methods for estimating battery surface temperatures involve generating a core temperature estimate for the battery based on a battery model. Based on the core temperature, a set of concentrated temperature states can be generated, with each set containing temperature estimates for different regions of the battery. Additional aggregated information about the battery can be recovered. Using a mapping function, a surface temperature map for the battery can be generated from this set of concentrated temperature states and the additional aggregated information. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 63 / 696.125

[0001] US 19 / 330.531

[0001] Cited non-patent literature

[0000] TECHNIQUES FOR ESTIMATION OF HIGH-RESOLUTION SURFACE TEMPERATURE MAPS IN BATTERY PACKS,” filed September 18, 2024

[0001] TECHNIQUES FOR ESTIMATION OF HIGH-RESOLUTION SURFACE TEMPERATURE MAPS IN BATTERY PACKS,” filed September 16, 2025

[0001]

Claims

[1] Method for estimating battery surface temperatures, comprising: Generating a core temperature estimate for the battery based on a battery model; Generating a set of concentrated temperature states based on the core temperature, wherein the set of concentrated temperature states includes temperature estimates for different areas of the battery; Retrieving additional condensed information regarding the battery; Generating a surface temperature map for the battery based on the amount of concentrated temperature states and the additional condensed information using a mapping function; and Determine at least one surface temperature for the battery based on the surface temperature map. [2] Method according to claim 1, wherein the quantity of concentrated temperature states is generated using a Kalman filter module with concentrated parameters. [3] Method according to claim 1 or 2, wherein the additional condensed information comprises the components of a sparse thermal model. [4] Method according to claim 3, wherein the components of a sparse thermal model comprise a global prefactor matrix and a set of cell-specific postfactor matrices. [5] Method according to claim 4, wherein the global prefactor matrix and the set of cell-specific postfactor matrices are generated by performing a common low-rank decomposition on Kalman enhancement matrices aggregated from several cells. [6] Method according to one of the preceding claims, wherein the additional condensed information includes dominant components of the heat distribution within the battery based on simulation data. [7] Method according to claim 6, wherein the dominant components are identified by performing a principal component analysis to generate basis vectors representing the dominant modes of temperature variation. [8] Method according to any of the preceding claims, wherein the method is carried out on an edge-based device. [9] Method according to claim 8, wherein the additional condensed information is generated on a central device and stored in the edge-based device and retrieved from a memory in the edge-based device. [10] A method according to any of the preceding claims, further comprising: Receiving electrochemical impedance spectroscopy measurements from a battery, where the core temperature estimation is based on the electrochemical impedance spectroscopy measurements. [11] System which has the following features: at least one hardware processor; and at least one memory that stores instructions which, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations that include the following: Generating a core temperature estimate for the battery based on a battery model; Generating a set of concentrated temperature states based on the core temperature, wherein the set of concentrated temperature states includes temperature estimates for different areas of the battery; Retrieving additional condensed information regarding the battery; Generating a surface temperature map for the battery based on the amount of concentrated temperature states and the additional condensed information using a mapping function; and Determine at least one surface temperature for the battery based on the surface temperature map. [12] System according to claim 11, wherein the quantity of concentrated temperature states is generated using a Kalman filter module with concentrated parameters. [13] System according to claim 11 or 12, wherein the additional condensed information comprises the components of a sparse thermal model. [14] System according to claim 13, wherein the components of a sparse thermal model comprise a global prefactor matrix and a set of cell-specific postfactor matrices. [15] System according to claim 14, wherein the global prefactor matrix and the set of cell-specific postfactor matrices are generated by performing a common low-rank decomposition on Kalman enhancement matrices aggregated from several cells. [16] System according to any one of claims 11 to 15, wherein the additional condensed information includes dominant components of the heat distribution within the battery based on simulation data. [17] System according to claim 16, wherein the dominant components are identified by performing a principal component analysis to generate basis vectors representing the dominant modes of temperature variation. [18] System according to any one of claims 11 to 17, wherein the additional condensed information is generated on a central device and stored in the edge-based device and retrieved from a memory in the edge-based device. [19] System according to any one of claims 11 to 18, wherein the operations further comprise: receiving electrochemical impedance spectroscopy measurements from a battery, wherein the core temperature estimation is based on the electrochemical impedance spectroscopy measurements. [20] Machine storage medium that embodies instructions which, when executed by a machine, cause the machine to perform operations which include: Generating a core temperature estimate for the battery based on a battery model; Generating a set of concentrated temperature states based on the core temperature, wherein the set of concentrated temperature states includes temperature estimates for different areas of the battery; Retrieving additional condensed information regarding the battery; Generating a surface temperature map for the battery based on the amount of concentrated temperature states and the additional condensed information using a mapping function; and Determine at least one surface temperature for the battery based on the surface temperature map.

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