Evaluation method and device for state of charge estimation, equipment, medium and product
By collecting battery data under static conditions, determining the representative open-circuit voltage and calculating the SOC reference value, the problem of difficulty in evaluating the accuracy of SOC estimation in the prior art is solved, and reliable evaluation of SOC estimation and optimization of BMS algorithm are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-05
- Publication Date
- 2026-04-07
AI Technical Summary
Existing technologies struggle to quickly and reliably assess the accuracy of battery state of charge (SOC) estimation from massive amounts of data generated during real-world vehicle operation, and voltage queries at single moments lead to unstable assessment results.
By collecting battery data during periods of vehicle idling, a representative open-circuit voltage is determined. Combined with a preset SOC-OCV mapping relationship, a SOC reference value is calculated, and an error analysis is performed with the real-time estimate from the BMS to evaluate the accuracy of the SOC estimation.
It enables reliable evaluation of SOC estimation accuracy, improves the optimization of BMS algorithm and the accuracy of battery status monitoring, and supports automated and large-scale verification.
Smart Images
Figure CN121805850A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery management technology, and in particular to an evaluation method, apparatus, device, medium, and product for estimating state of charge. Background Technology
[0002] With the rapid development of the new energy vehicle industry, the accuracy of estimating the state parameters of the power battery, as a core component, directly affects the vehicle's range and driving safety. Among these parameters, the State of Charge (SOC) is a key parameter reflecting the remaining charge. Accurate SOC estimation can effectively alleviate users' "range anxiety" and prevent battery damage due to overcharging and over-discharging, which is of great significance for improving battery life and vehicle reliability. Onboard battery management systems (BMS) generally use the ampere-hour integration method for real-time SOC estimation. However, under complex actual operating conditions, this method is susceptible to interference from factors such as current sampling accuracy, battery aging, and temperature changes, resulting in varying degrees of cumulative error.
[0003] In practical applications, effectively verifying and evaluating the SOC estimation accuracy of a BMS has become a key technical challenge in the industry. Existing methods often rely on calibration under standard laboratory operating conditions, making it difficult to quickly and accurately verify the data from massive amounts of real-world vehicle operation data. Furthermore, some methods look up reference SOC values based on voltage at a given moment, but directly using voltage at a single moment can lead to significant fluctuations in the lookup results, failing to achieve a stable and accurate assessment. Therefore, there is an urgent need for a method that can reliably evaluate the SOC estimation accuracy based on massive amounts of real-world vehicle operation data. Summary of the Invention
[0004] This application provides a method, apparatus, device, medium, and product for estimating the state of charge (SOC) to achieve a reliable assessment of the accuracy of SOC estimation.
[0005] In a first aspect, embodiments of this application provide an evaluation method for estimating the state of charge, including:
[0006] Collect battery data of the vehicle within a set time period, which covers the period of resting operation. The battery data includes the voltage data of individual battery cells and the real-time SOC estimate reported by the BMS.
[0007] Based on the voltage data of the individual battery cells during the resting period, determine the representative open-circuit voltage of the battery pack after resting.
[0008] Determine the corresponding SOC reference value based on the representative open-circuit voltage;
[0009] The accuracy of the SOC estimation is evaluated based on the error between the estimated SOC value of the vehicle at the start of the stationary operating period and the SOC reference value.
[0010] Secondly, embodiments of this application also provide an evaluation apparatus for estimating the state of charge, comprising:
[0011] The data acquisition module is used to collect battery data of the vehicle within a set time period, which covers the period of resting operation. The battery data includes the voltage data of individual battery cells and the real-time SOC estimate reported by the BMS.
[0012] The open-circuit voltage determination module is used to determine the representative open-circuit voltage of the battery pack after resting, based on the voltage data of the individual battery cells during the resting period.
[0013] A reference value determination module is used to determine the corresponding SOC reference value based on the representative open-circuit voltage.
[0014] The evaluation module is used to evaluate the accuracy of SOC estimation based on the error between the estimated SOC value of the vehicle at the start of the stationary operating condition period and the SOC reference value.
[0015] Thirdly, embodiments of this application provide an electronic device, including:
[0016] One or more processors;
[0017] Storage device for storing one or more programs;
[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the evaluation method for state of charge estimation as described in the first aspect.
[0019] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the evaluation method for state of charge estimation as described in the first aspect.
[0020] Fifthly, embodiments of this application also provide a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the state of charge estimation evaluation method as described in any of the above embodiments.
[0021] This application provides a method, apparatus, device, medium, and product for estimating the state of charge (SOC). The method includes: collecting battery data from a vehicle over a set time period, where the set time period covers a period of inactivity; the battery data includes voltage data of individual battery cells and a real-time SOC estimate reported by the battery management system (BMS); determining a representative open-circuit voltage of the battery pack after inactivity based on the voltage data of the individual battery cells during the inactivity period; determining a corresponding SOC reference value based on the representative open-circuit voltage; and evaluating the SOC estimation accuracy based on the error between the SOC estimate at the start of the inactivity period and the SOC reference value. This technical solution improves the reliability of evaluating SOC estimation accuracy by determining a reliable representative open-circuit voltage based on battery data during the inactivity period, combining the mapping relationship between open-circuit voltage and SOC to calculate a more accurate SOC reference value, and performing error analysis between this SOC reference value and the real-time estimated SOC reported by the BMS. Attached Figure Description
[0022] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.
[0023] Figure 1 A flowchart illustrating an evaluation method for estimating the state of charge (SOC) provided in this application embodiment;
[0024] Figure 2 A schematic diagram of the structure of an evaluation device for estimating the state of charge, provided for an embodiment of this application;
[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0026] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.
[0027] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.
[0028] It should be noted that the concepts of "first" and "second" mentioned in the embodiments of this application are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order of functions performed by these devices, modules, units or other objects or their interdependencies.
[0029] Furthermore, the embodiments and features described in this application may be combined with each other, unless otherwise specified.
[0030] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.
[0031] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the relevant content of the solution.
[0032] Figure 1 This is a flowchart illustrating a method for estimating the state of charge (SOC) according to an embodiment of this application. This embodiment is applicable to acquiring eye images. Specifically, the SOC estimation method can be executed by a SOC estimation evaluation device, which can be implemented through software and / or hardware and integrated into an electronic device. The electronic device includes, but is not limited to, devices with control functions such as computers, smartphones, host computers, or servers.
[0033] like Figure 1 As shown, the method specifically includes the following steps:
[0034] S110. Collect battery data of the vehicle within a set time period, wherein the set time period covers the period of static operation, and the battery data includes the voltage data of individual battery cells and the real-time SOC estimate reported by the BMS.
[0035] The "idle condition" mainly refers to the battery's operating state when the vehicle is not running and not undergoing charging or discharging operations. For example, during a long period of vehicle parking, the battery only maintains its own chemical balance and a small self-discharge process. The set time period can be the idle condition period and a period before and after it. For example, the idle condition period is from T_start to T_end, and the set time period is from T_0 to T_1, where T_0 ≤ T_start < T_end ≤ T_1.
[0036] A vehicle's battery pack is typically composed of multiple battery cells connected in series and parallel, and integrates a battery management system (BMS). State of Charge (SOC) estimation is one of the core functions of the BMS. By analyzing parameters such as battery voltage, current, and temperature in real time, the BMS calculates the current percentage of charge and obtains an estimated SOC value. The BMS can report the real-time SOC estimate, battery cell voltage data, and timestamps to the cloud for storage.
[0037] For example, a big data cloud platform supporting access to massive amounts of vehicle terminal data can be built. This platform can adopt distributed computing frameworks such as Hadoop, Spark, or Flink. Through a wireless communication network (such as 4G or 5G), data uploaded from the vehicle is received at preset intervals (e.g., every 10 or 30 seconds). This data includes at least the voltage of all individual battery cells in the battery pack and the real-time SOC estimate of the entire vehicle obtained from the BMS (denoted as SOC_bms). Additionally, it may include the Vehicle Identification Number (VIN), data timestamps, and the total current value of all individual battery cells in the battery pack.
[0038] S120. Determine the representative open-circuit voltage of the battery pack after resting based on the voltage data of the individual battery cells during the resting period.
[0039] For example, the data uploaded by the vehicle is cleaned and preprocessed to remove abnormal data such as jumps and breaks. Then, the data of each vehicle can be comprehensively analyzed based on time series to identify the static condition, mainly to identify the start and end time of the vehicle being in a long static state and to determine the representative open circuit voltage of the battery pack after static.
[0040] The representative open circuit voltage can be determined based on the voltage data of all battery cells during the resting period. For example, it can be determined based on the voltage extremes of all battery cells (including the highest voltage V_max and the lowest voltage V_min). Specifically, the arithmetic mean or weighted average of V_max and V_min can be used as the representative open circuit voltage (OCV) of the battery pack after this resting period.
[0041] S130. Determine the corresponding SOC reference value based on the representative open-circuit voltage;
[0042] For example, the corresponding real SOC reference value can be queried from a preset relationship table based on OCV. The preset relationship table refers to the correspondence table between SOC and OCV, and the SOC reference value corresponding to the open circuit voltage is denoted as SOC_r.
[0043] S140. Evaluate the accuracy of the SOC estimation based on the error between the estimated SOC value of the vehicle at the start of the stationary operating period and the SOC reference value.
[0044] For example, during the analysis of data for each vehicle based on time series, the BMS-estimated State of Charge (SOC) value at the beginning of the stationary period can be extracted as the evaluation object, denoted as SOC_c. The accuracy of the SOC estimation is evaluated based on the error between SOC_c and SOC_r. The error between SOC_c and SOC_r can be the absolute error between the two, i.e., |SOC_c - SOC_r|. The larger the error between SOC_c and SOC_r, the lower the accuracy of the SOC estimation; if the error exceeds a set error threshold, it can be determined that the SOC estimation accuracy of the BMS under this stationary condition does not meet the requirements.
[0045] The state-of-charge (SOC) estimation evaluation method in this embodiment obtains the battery's voltage data under static conditions and, combined with the correspondence between the battery's open-circuit voltage and SOC, calculates a more realistic, stable, and reliable SOC reference value. By comparing and analyzing this reference value with the real-time SOC estimate reported by the battery management system (BMS), the accuracy and reliability of the BMS's SOC estimation under different operating conditions can be systematically evaluated. Furthermore, this provides effective data support for the optimization of the BMS algorithm and the accurate monitoring of battery status. In addition, through big data technology, automated, large-scale verification and long-term performance evaluation of the BMS's estimated SOC can be achieved, providing a solid data foundation for the optimization of the BMS algorithm and the diagnosis of battery health status.
[0046] In one embodiment, the method further includes:
[0047] S112. If the absolute value of the vehicle's current is always lower than the set threshold within a continuous time window during the set time period, then the start time of the time window shall be taken as the start time of the stationary working condition period.
[0048] For example, if the absolute value of the vehicle's current remains below a set threshold (e.g., 5A) for a continuous period of time (e.g., more than 3 hours), the vehicle can be considered to have entered a stationary state, and the start time of this time window is taken as the start time of the stationary operating period. If the absolute value of the vehicle's current reaches the set threshold (e.g., 5A) at a certain moment, that moment can be taken as the end time of the stationary operating period. The start time T_start and end time T_end of the stationary operating period are recorded. To obtain a stable OVC, data after the end of the stationary operating period, such as battery data at time T_end, is mainly used to determine the OVC.
[0049] In one embodiment, determining the representative open-circuit voltage of the battery pack after resting, based on the voltage data of the individual battery cells during the resting period, includes:
[0050] The voltage characteristic value is determined based on the voltage data of all individual cells during the resting period, and the voltage characteristic value is used as the representative open-circuit voltage of the battery pack after resting.
[0051] The voltage characteristic value includes one of the following:
[0052] The arithmetic mean of the highest and lowest voltages;
[0053] The weighted average of the highest and lowest voltages;
[0054] The median of the voltage values in multiple sampling cycles at the end of the static operating period.
[0055] For example, based on the voltage data of individual battery cells during the resting period [T_start, T_end], the voltage extremes, including the highest voltage V_max and the lowest voltage V_min, can be determined. The arithmetic mean of V_max and V_min, such as OCV_rep = (V_max + V_min) / 2, can be used as the OCV of the battery pack after the resting period. Alternatively, a weighted average of V_max and V_min can be calculated based on battery characteristics, such as OCV_rep = (w1 × V_max + w2 × V_min), which can be used as the OCV of the battery pack after the resting period, where w1 and w2 are the weights corresponding to the highest and lowest voltages, respectively. Alternatively, the median OCV_rep of the voltage values from multiple sampling periods at the end of the resting period (such as 5 consecutive sampling periods with a cutoff time of T_end, totaling 2.5 minutes) can be used as the OCV of the battery pack after the resting period. In this case, the interference of voltage sampling spike noise can be effectively eliminated, making the calculated OCV value more representative.
[0056] Based on this, fluctuations at a single voltage point can be effectively avoided, improving the reliability of the true SOC reference value SOC_r.
[0057] In one embodiment, determining the corresponding SOC reference value based on the representative open-circuit voltage includes:
[0058] Find the SOC reference value corresponding to the open circuit voltage in the preset relationship table under different temperatures and health conditions;
[0059] If no SOC reference value corresponding to the open-circuit voltage is found, the SOC reference value corresponding to the open-circuit voltage is calculated using linear interpolation.
[0060] For example, a high-precision SOC-OCV correspondence table, obtained through experiments and precise measurements, can be pre-stored in a big data platform. This table represents the open-circuit voltage OCV_rep under different temperatures and State of Health (SOH) conditions. The value representing the open-circuit voltage OCV_rep is used as the lookup key to match and search the SOC-OCV table, yielding the corresponding SOC reference value SOC_r. If no matching SOC reference value is found in the table, linear interpolation can be used to calculate the corresponding SOC reference value, which is the actual SOC reference value of the battery under the current static condition. For instance, if there is no matching SOC reference value in the table, but the SOC reference value corresponding to the lookup key that is less than and closest to OCV_rep is SOC1, and the SOC reference value corresponding to the lookup key that is greater than and closest to OCV_rep is SOC2, then a value between SOC1 and SOC2, such as (SOC1 + SOC2) / 2, can be used as the SOC reference value SOC_r corresponding to OCV_rep. Based on this, for any given OVC, a more realistic SOC reference value can be determined, providing a reliable basis for evaluating the accuracy of SOC estimation.
[0061] In one embodiment, the method further includes:
[0062] S150. Calculate the average error, standard deviation of error and / or estimated over-standard rate based on the SOC estimation accuracy of each vehicle in the target vehicle group.
[0063] S160. Determine the SOC estimation accuracy of the BMS based on the average error, the standard deviation of the error, and / or the error overshoot rate.
[0064] For example, the target vehicle group includes multiple vehicles. The single-shot SOC estimation error for a single vehicle can be evaluated using the SOC estimate SOC_c at the start of the stationary operating period and the queried SOC reference value SOC_r. For instance, the absolute error |ΔSOC| = |SOC_c - SOC_r| can be calculated, and an error threshold (e.g., 5%) can be set. Then, |ΔSOC| is compared with the threshold.
[0065] If |ΔSOC| ≤ 5%, then the accuracy of the SOC estimation by BMS is determined to be accurate or meets the requirements.
[0066] If |ΔSOC| > 5%, then record this SOC estimation as a "SOC estimation overshoot" event, and store the relevant data (such as VIN, timestamp, and |ΔSOC|, etc.) in the anomaly database.
[0067] For all stationary events of all vehicles within a target vehicle group (e.g., a specific model year or region) over a long period (e.g., one month), the above-mentioned single SOC estimation error assessment is performed. Then, statistical analysis can be conducted on all |ΔSOC| data to calculate at least one of the following parameters as the basis for evaluating the SOC estimation accuracy of the BMS:
[0068] Average error: such as the arithmetic mean of all |ΔSOC|;
[0069] Error standard deviation: such as the standard deviation of all |ΔSOC|, can be used to reflect the stability of BMS estimation;
[0070] Error over-limit rate: the percentage of events where |ΔSOC| > Threshold out of the total number of static events.
[0071] Finally, the platform automatically generates a visual evaluation report, displaying error distribution and exceedance rate trends in chart form for engineers to analyze. Based on this, the method not only focuses on the error of a single static event but also performs aggregated statistical analysis on a large number of static events. From a probabilistic and statistical perspective, it provides a systematic and macro-level assessment of the accuracy and stability of BMS SOC estimation, comprehensively evaluating the accuracy and stability of BMS SOC estimation under different operating conditions and at different life stages. This achieves a comprehensive evaluation of the accuracy of BMS SOC estimation.
[0072] In one embodiment, the method further includes:
[0073] S170. If the SOC estimation accuracy of the BMS for the target vehicle group does not meet the requirements, the firmware of the BMS is remotely upgraded via Over-the-Air Technology (OTA) to enable the upgraded BMS to continuously collect battery data from each vehicle in the target vehicle group.
[0074] – For example, closed-loop feedback and BMS optimization can be implemented based on this. If the SOC estimation accuracy of the BMS for the target vehicle group does not meet the requirements, for example, if the error exceedance rate of a certain batch of vehicles is found to be significantly higher than the normal level, the root cause of the problem can be located (such as current sensor calibration, algorithm parameters not being adapted to local operating conditions, etc.), and an optimization scheme for the BMS control strategy or algorithm can be formulated. The firmware of the BMS of the target vehicle group can be remotely upgraded via OTA. The SOC estimation accuracy of the upgraded BMS will be effectively improved compared with that before the upgrade. Then, the above-mentioned single SOC estimation error evaluation can be performed on the vehicles in the target vehicle group, thereby forming a continuous, data-driven "evaluation-optimization-re-evaluation" closed-loop optimization system, ultimately realizing the evolution and continuous improvement of the BMS algorithm throughout the entire life cycle of the power battery.
[0075] Based on this, by combining the evaluation results with the BMS's OTA upgrade technology, a data-driven closed-loop optimization system is formed: the evaluation results are used to locate problematic vehicles or incompatible algorithm parameters, which are then remotely optimized via OTA. The optimized effect is then evaluated by the same system, thereby enabling the continuous iteration and evolution of the BMS algorithm.
[0076] Figure 2 This is a schematic diagram of a state-of-charge estimation evaluation device provided in an embodiment of this application. Figure 2 As shown, the state of charge estimation evaluation device provided in this embodiment includes:
[0077] The acquisition module 210 is used to acquire battery data of the vehicle within a set time period, the set time period covering the resting condition period, and the battery data includes the voltage data of individual battery cells and the real-time SOC estimate reported by the BMS.
[0078] The open-circuit voltage determination module 220 is used to determine the representative open-circuit voltage of the battery pack after resting based on the voltage data of the individual battery cells during the resting period.
[0079] Reference value determination module 230 is used to determine the corresponding SOC reference value based on the representative open-circuit voltage;
[0080] Evaluation module 240 is used to evaluate the accuracy of SOC estimation based on the error between the estimated SOC value of the vehicle at the start of the stationary operating period and the SOC reference value.
[0081] This device determines a reliable representative open-circuit voltage based on battery data during periods of inactivity. By combining the mapping relationship between open-circuit voltage and SOC, a more accurate SOC reference value is calculated. This SOC reference value is then compared with the real-time estimated SOC reported by the BMS to perform error analysis, thereby improving the reliability of evaluating the accuracy of SOC estimation.
[0082] Based on any of the above embodiments, the device further includes: a start time determination module, used to take the start time of the time window as the start time of the stationary working condition period if the absolute value of the current of the vehicle is always lower than a set threshold within a continuous time window of the set time period.
[0083] Based on any of the above embodiments, the open-circuit voltage determination module 220 is used for:
[0084] The voltage characteristic value is determined based on the voltage data of all individual cells during the resting period, and the voltage characteristic value is used as the representative open-circuit voltage of the battery pack after resting.
[0085] The voltage characteristic value includes one of the following:
[0086] The arithmetic mean of the highest and lowest voltages;
[0087] The weighted average of the highest and lowest voltages;
[0088] The median of the voltage values in multiple sampling cycles at the end of the static operating period.
[0089] Based on any of the above embodiments, the reference value determination module 230 is used for:
[0090] The SOC reference value corresponding to the open-circuit voltage is found in a preset relationship table under different temperatures and health conditions;
[0091] If the SOC reference value corresponding to the representative open-circuit voltage is not found, the SOC reference value corresponding to the representative open-circuit voltage is calculated using linear interpolation.
[0092] Based on any of the above embodiments, the device further includes:
[0093] The calculation module is used to calculate the average error, standard deviation of error, and / or estimated overshoot rate based on the SOC estimation accuracy of each vehicle in the target vehicle group.
[0094] Evaluation module 240 is also used to determine the SOC estimation accuracy of the BMS based on the average error, the standard deviation of the error, and / or the error overshoot rate.
[0095] Based on any of the above embodiments, the device further includes: an upgrade module, configured to remotely upgrade the firmware of the BMS via OTA if it is determined that the SOC estimation accuracy of the BMS for the target vehicle group does not meet the requirements, so that the upgraded BMS can continuously collect battery data of each vehicle in the target vehicle group.
[0096] The state of charge estimation evaluation device provided in this application embodiment can be used to execute the state of charge estimation evaluation method provided in any of the above embodiments, and has corresponding functions and beneficial effects.
[0097] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.
[0098] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0099] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks and wireless networks.
[0100] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above.
[0101] In some embodiments, the methods described above can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the methods of any of the embodiments described above by any other suitable means (e.g., by means of firmware).
[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0103] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0104] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 10. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0107] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0108] This application also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the state of charge estimation evaluation method as described in any of the above embodiments.
[0109] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.
[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method for estimating the state of charge, characterized in that, include: Collect battery data of the vehicle within a set time period, which covers the period of resting operation. The battery data includes the voltage data of individual battery cells and the real-time estimated value of battery state of charge (SOC) reported by the battery management system (BMS). Based on the voltage data of the individual battery cells during the resting period, determine the representative open-circuit voltage of the battery pack after resting. Determine the corresponding SOC reference value based on the representative open-circuit voltage; The accuracy of the SOC estimation is evaluated based on the error between the estimated SOC value of the vehicle at the start of the stationary operating period and the SOC reference value.
2. The method according to claim 1, characterized in that, Also includes: If the absolute value of the vehicle's current is consistently lower than a set threshold within a continuous time window during the set time period, then the start time of the time window is taken as the start time of the stationary operating condition period.
3. The method according to claim 1, characterized in that, Based on the voltage data of the individual battery cells during the resting period, determine the representative open-circuit voltage of the battery pack after resting, including: The voltage characteristic value is determined based on the voltage data of all individual cells during the resting period, and the voltage characteristic value is used as the representative open-circuit voltage of the battery pack after resting. The voltage characteristic value includes one of the following: The arithmetic mean of the highest and lowest voltages; The weighted average of the highest and lowest voltages; The median of the voltage values in multiple sampling cycles at the end of the static operating period.
4. The method according to claim 1, characterized in that, Determining the corresponding SOC reference value based on the representative open-circuit voltage includes: The SOC reference value corresponding to the open-circuit voltage is found in a preset relationship table under different temperatures and health conditions; If the SOC reference value corresponding to the representative open-circuit voltage is not found, the SOC reference value corresponding to the representative open-circuit voltage is calculated using linear interpolation.
5. The method according to any one of claims 1-4, characterized in that, Also includes: Calculate the average error, standard deviation of error, and / or estimated over-standard rate based on the SOC estimation accuracy for each vehicle in the target vehicle group; The SOC estimation accuracy of the BMS is determined based on the average error, the standard deviation of the error, and / or the error overshoot rate.
6. The method according to any one of claims 1-4, characterized in that, Also includes: If the SOC estimation accuracy of the BMS is determined to be insufficient for the target vehicle group, the firmware of the BMS is remotely upgraded via over-the-air (OTA) download, so that the upgraded BMS can continuously collect battery data from each vehicle in the target vehicle group.
7. An evaluation device for estimating the state of charge, characterized in that, include: The data acquisition module is used to collect battery data of the vehicle within a set time period, which covers the period of resting operation. The battery data includes the voltage data of individual battery cells and the real-time SOC estimate reported by the BMS. The open-circuit voltage determination module is used to determine the representative open-circuit voltage of the battery pack after resting, based on the voltage data of the individual battery cells during the resting period. A reference value determination module is used to determine the corresponding SOC reference value based on the representative open-circuit voltage. The evaluation module is used to evaluate the accuracy of SOC estimation based on the error between the estimated SOC value of the vehicle at the start of the stationary operating condition period and the SOC reference value.
8. An electronic device, characterized in that, include: At least one processor; A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the state of charge estimation evaluation method as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the evaluation method for state of charge estimation as described in any one of claims 1-6.
10. A computer program product comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the processor, they implement the evaluation method for estimating the state of charge as described in any one of claims 1-6.