Description: FIELD OF THE INVENTION
[0001] The present disclosure relates generally to monitoring of an energy storage device. In particular, the present invention relates to a system and a method for estimation of a state of charge (SoC) for the energy storage device.
BACKGROUND OF THE INVENTION
[0002] Energy storage devices are prevalent and have been used in a variety of applications for many years. Energy storage devices, such as batteries, play a critical role in this context by storing excess energy during low demand periods and releasing it during peak demand, thus contributing to a sustainable and balanced energy ecosystem.
[0003] One of the most significant challenges faced in the practical implementation of energy storage devices is the accurate estimation of their State of Charge (SoC). The State of Charge represents the current energy capacity or remaining charge level of the energy storage device and is crucial for optimizing its performance, prolonging its lifespan, and ensuring safe and efficient operation.
[0004] Various methods have been proposed in the prior art for estimating the State of Charge in energy storage devices. Conventional techniques, such as open-circuit voltage (OCV) based SoC estimation have shown limitations in accuracy, especially during aging and environmental changes, and often require extensive calibration. Other methods, such as model-based approaches, often suffer from complexity and computational overhead, making them less practical for real-time applications.
[0005] In an example, a patent application CN110286324B discloses a method to estimate the SoC of a battery which comprises estimating an initial SoC of the battery using the open-circuit voltage when the battery is in a stable state. It also includes the estimation of SoC of the battery by ampere-hour integration method while using the initial SoC value obtained from OCV method, when current is transferred from or to the battery (charging/discharging state). It further discloses using a battery equivalent circuit model to establish a relation between the OCV, battery model parameters R0, R1, C1 and the battery state of charge through testing data of a battery sample under laboratory conditions. Lastly, it discloses the weighted calculations using a Kalman method to obtain a final SoC value.
[0006] In another example, the patent application US10295600B2 discloses a method to estimate SoC of a battery. It includes a method of applying a current integration method to update a primary charge estimate value representative of the charge stored in the battery. It also includes a method of calculating the open-circuit voltage using a battery equivalent circuit model and obtaining a relative SoC value using a predetermined calibration curve. Lastly, it includes correction of the estimated SoC value by applying an update step of the linear quadratic estimation method such as the Kalman Filter.
[0007] In yet another example, the patent application CN112415410A discloses a method of estimating the SoC of a battery which can improve the accuracy of SoC estimation values. The method includes obtaining an initial SoC value by an open-circuit voltage method i.e., the initial SoC value is obtained by detecting the terminal voltage of the battery at the initial charging and discharging moment; and the predicted OCV- SoC curve. Thereafter, current data of charging and discharging of the battery is collected and ampere-hour integration is performed according to the current data and then taking the quotient of the ampere-hour capacity and the rated capacity as the theoretical SoC variation value.
[0008] Existing solutions present in the knowledge for estimating State-of-charge (SOC) of the battery involves complex computationally intensive methodologies such as recursive and/or convergence principles. These algorithms require multiple calculation steps in order to arrive at an estimated value of the SOC of the battery during run-time (online estimation). These algorithms are also prone to not converge at a solution quick enough (non-trivial algorithms) under certain conditions of operation leading to overloading of microcontrollers.
[0009] Hence, there is a need for an innovative and robust method for State of Charge (SoC) estimation that overcomes the limitations of existing approaches and offers improved accuracy, adaptability, less complexity and ease of implementation. Such a method would significantly enhance the efficiency, reliability, and overall performance of energy storage devices.
[0010] Therefore, in the present invention, a first SoC and a second SoC is determined using different SoC estimation methods. Further, SoCerror, which is a function of the first SoC and the second SoC and a Kalman gain are provided as inputs to a Kalman filter for the estimation of a SoC of a battery, which is not disclosed in the above prior arts. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.
OBJECTIVES OF THE DISCLOSURE
[0011] A primary objective of the present invention is to overcome the disadvantages of the prior-arts.
[0012] Another objective of the present invention is to provide a simplified approach for a State of charge estimation by combining two methods of state of charge estimation to estimate the most accurate SoC of an energy storage device.
[0013] Another objective of the present invention is to provide a less intensive SoC estimation method that may be applied to any energy storage device of a device.
SUMMARY OF THE INVENTION
[0014] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0015] An embodiment of the present invention relates to a method for estimating a state of charge (SoC) of a battery. The method includes estimating a first SoC (SoC1) by one or more processors. The estimation of the SoC1 includes a calculation that combines an initial SoC (SoCstart) of the battery with an ampere-hour integral of a current transferred from or to the battery. In addition, the method includes estimating a second SoC (SoC2). The estimation of the SoC2 includes measuring an open circuit voltage (OCV) across the battery terminals using a battery equivalent circuit model and determining a relative SoC corresponding to the measured OCV using a look-up table, wherein the relative SoC is the SoC2. Further, the method includes determining a state of charge error (SoCerror) as a function of the SoC1 and the SoC2 by the one or more processors. Furthermore, the method includes determining a Kalman filter gain as a function of a battery charge storage capacity variance, a current variance and a voltage variance of the battery by the one or more processors.
[0016] In accordance with an embodiment of the present invention, the method further comprises the step of estimating the SoC of the battery at a first time by a Kalman filter as a function of the SoCerror and the Kalman filter gain determined at the first time by the one or more processors. The final step includes estimating the SoC of the battery at a second time by the Kalman filter as a function of the SoC of the battery estimated at the first time, and both the SoCerror and the Kalman filter gain determined at the second time.
[0017] In accordance with an embodiment of the present invention, the first SoC (SoC1) and the second SoC (SoC2) are estimated using an ampere-hour integration method and an OCV_SoC method, respectively.
[0018] In accordance with an embodiment of the present invention, the initial SoC (SoCstart) is used as a reference or a starting point for estimating SoC1.
[0019] In accordance with an embodiment of the present invention, the SoCstart is obtained by measuring the open-circuit voltage (OCV) across the battery terminals of a device, and then the relative SoC is determined corresponding to the measured OCV from the look-up table, wherein the relative SoC is the initial SoC (SOCstart).
[0020] In accordance with an embodiment of the present invention, the open-circuit voltage (OCV) across the battery terminals of the device for obtaining the initial SoC (SoCstart) is that under the condition that the battery is kept still or left standing for a predefined time interval.
[0021] In accordance with an embodiment of the present invention, the look-up table represents an OCV_SoC curve having a one-dimensional relationship between the OCV measured across the battery terminals and the relative SoC corresponding to the measured OCV.
[0022] In accordance with an embodiment of the present invention, the device refers to one of a consumer electronic appliance, a smart electronic device, or a vehicle.
[0023] In accordance with an embodiment of the present invention, either of the SoC1 or the SoC2 is used to calculate the SoC of the battery during a condition when anyone of the ampere-hour integration method and the OCV_SoC method fails to determine SoC1 and SoC2, respectively.
[0024] In accordance with an embodiment of the present invention, the battery includes at least one of an electrochemical battery, a lithium-ion (Li-ion) battery, a nickel-cadmium battery, a nickel-metal hydride battery, an ultra-capacitor, and a solid-state battery.
[0025] In accordance with an embodiment of the present invention, the method further comprises displaying the estimated SoC of the battery to a user.
[0026] In accordance with an embodiment of the present invention, the estimated SoC of the battery is displayed to the user on a display unit of the device including but not limited to one of a CRT display, an LCD display, an LED display, an OLED display, an AMOLED display, and a PMOLED display.
[0027] In accordance with an embodiment of the present invention the display unit of the device is touch-sensitive or non-touch sensitive.
[0028] In accordance with an embodiment of the present invention, the method further comprises sending a notification alert to the user indicative of a critical SoC of the battery when the estimated SoC of the battery is below a predefined threshold SoC value.
[0029] In accordance with an embodiment of the present invention, the notification alert includes at least one of a text message, an audio warning, a visual warning, haptic feedback, and vibrations provided on the display unit of the device or a user handheld device.
[0030] These and other aspects herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawing. It should be understood, however, that the following descriptions are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the invention herein without departing from the spirit thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure. Having thus described the disclosure in general terms, reference will now be made to the accompanying figures.
[0032] Fig. 1 illustrates a system 100 for estimating a state of charge (SOC) of an energy storage device 108, in accordance with an embodiment of the present invention.
[0033] Fig. 2. illustrates a different implementation of a battery equivalent circuit model, in accordance with an embodiment of the present invention.
[0034] Fig. 3 is a block diagram illustrating a method 300 for a state of charge estimation for an energy storage device 108, in accordance with an embodiment of the present invention.
[0035] It should be noted that the accompanying figure is intended to present illustrations of a few examples of the present disclosure. The figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE INVENTION
[0036] In the following detailed description of the invention, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be obvious to a person skilled in the art that the invention may be practiced with or without these specific details. In other instances, well known methods, procedures and components have not been described in detail so as not to unnecessarily obscure aspects of the invention.
[0037] The accompanying drawing is used to help easily understand various technical features and it should be understood that the alternatives presented herein are not limited by the accompanying drawing. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawing. Although the terms first, second, etc. may be used herein to describe various elements or values, these elements or values should not be limited by these terms. These terms are generally only used to distinguish one element or values from another.
[0038] It will be apparent to those skilled in the art that other alternatives of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention. While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific aspect, method, and examples herein. The invention should therefore not be limited by the above described alternative, method, and examples, but by all aspects and methods within the scope of the invention. It is intended that the specification and examples be considered as exemplary, with the true scope of the invention being indicated by the claims.
[0039] Conditional language used herein, such as, among others, "can," "may," "might," "may," “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain alternatives include, while other alternatives do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more alternatives or that one or more alternatives necessarily include logic for deciding, with or without other input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular alternative. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.
[0040] Terms Open circuit voltage or OCV may be used interchangeably for convenience.
[0041] Terms State of charge or SoC may be used interchangeably for convenience.
[0042] Terms Battery management system or BMS may be used interchangeably for convenience.
[0043] Terms energy storage device or ESD or battery may be used interchangeably for convenience.
[0044] Fig. 1 illustrates a system 100 for estimating a state of charge (SOC) of an energy storage device 108, in accordance with an embodiment of the present invention. The system 100 includes a battery pack 101, a control unit (CU) 110, a display unit 116 and a user handheld device 118. In an embodiment of the present invention, the battery pack 101 includes a battery management system 102 and energy storage device 108. Further battery management system 102 includes a memory unit 104 and one or more processors 106. In addition, the control unit 110 includes a memory unit 112 and one or more processors 114. In an embodiment, the battery management system 102 is associated with a device. The device refers to anyone of a consumer electronic appliance, a smart electronic device, or a vehicle.
[0045] Further, in various embodiments, the vehicle is one of, but not limited to, a battery electric vehicle (BEV), a hybrid electric vehicle (HEV), a Plug-in Hybrid electric vehicle (PHEV), a Fuel Cell electric vehicle (FCEV). Further, the vehicle is a two-wheeled vehicle or a multi-wheeled vehicle. Additionally, the consumer electronic appliances and the smart electronic devices are also included, but not limited to a desktop computer, a laptop computer, a user computer, a tablet computer, a personal digital assistant (PDA), a cellular telephone, a communication network appliance, a camera, a smartphone, a smartwatch, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console and other electric and electronic devices known to a person skilled in the art which includes at least one rechargeable battery.
[0046] In accordance with an embodiment of the present invention, the battery 108 includes at least one of an electrochemical battery, a lithium-ion (Li-ion) battery, a nickel-cadmium battery, a nickel-metal hydride battery, an ultracapacitor, and a solid-state battery. Further, the battery 108 may include any rechargeable battery for any use, which includes a plurality of electrochemical cells that convert a chemical energy into an electrical energy.
[0047] In accordance with an embodiment of the present invention, the battery management system 102 is configured with the memory unit 104 and the one or more processors 106. The one or more processors 106 is in communication with the memory unit 104 to perform a series of computer-executable instructions stored in the memory unit 104 for estimation of the SoC of the energy storage device 108.
[0048] The one or more processors 106 associated with the BMS 102 may be any well-known processor, but not limited to processors from Intel Corporation. Alternatively, the processor may be a dedicated controller such as an ASIC or ARM, MIPS, SPARC, or INTEL® IA-32 microcontroller or the like. In yet another embodiment of the present invention, the one or more processors 106 comprise a collection of processors which may or may not operate in parallel. Alternatively, the one or more processors 106, which may be any processor-driven device, such as one or more microprocessors and memories or other computer-readable media operable for storing and executing computer-readable instructions.
[0049] Further, the memory unit 104 associated with the BMS 102 stores instructions executed by the one or more processors 106. The memory unit 104 can be any type of suitable memory, including various types of dynamic random access memory (DRAM) such as SDRAM, various types of static RAM (SRAM), and various types of non-volatile memory (PROM, EPROM, and flash). It should be understood that the memory unit 104 may be a single type of memory component or it may be composed of different types of memory components. As noted above, the memory unit 104 stores instructions for executing one or more methods for estimating the state of charge of the battery 108. For example, the memory unit 104 may store software used by the user device, such as an operating system (not shown), application programs (not shown), and an associated internal database (not shown). In an alternative embodiment, the memory unit 104 may be an external memory to the BMS 102 to store various data and computer executable instructions to be performed by the one or more processors 106 associated with the BMS 102.
[0050] In an embodiment of the present invention, a method of SoC estimation resides in the BMS 102. Further, BMS 102 is communicatively coupled with the battery 108.
[0051] In accordance with an embodiment of the present invention, a plurality of battery parameters is monitored in real-time by the one or more processors 106 of the BMS 102 for the estimation of the SoC of the battery 108. Alternatively, a plurality of sensors may be provided on different parts of the battery 108 (not shown in the figure). The plurality of sensors directly senses and measures the plurality of battery parameters and communicates to the one or more processors 106 of the BMS 102 in order to determine the SoC of the battery 108. The plurality of battery parameters may also be stored in the memory unit 104 of the BMS 102 and used by the one or more processors 106 as per the requirements.
[0052] In an embodiment, the memory unit 104 stores instructions which are executed by the one or more processors 106 of the BMS 102 to perform the method for estimating the state of charge (SoC) of the battery 108.
[0053] In an embodiment, the BMS 102 estimates a first SOC (SOC1) using an ampere-hour integration method, which includes a calculation that combines an initial SoC (SOCstart) of the battery 108 with an ampere-hour integral of a current transferred from or to the battery 108. The initial SoC (SOCstart) is used as a reference or a starting point for estimating SOC1. In addition, the initial SoC (SOCstart) is obtained by measuring the open-circuit voltage (OCV) across the battery 108 terminals of a device and then the relative SoC is determined corresponding to the measured OCV from a look-up table, wherein the relative SoC is the initial SoC (SOCstart).
[0054] In an embodiment, the look-up table represents an OCV_SoC curve having a one-dimensional relationship between the OCV measured across the terminals of battery 108 and the relative SoC corresponding to the measured OCV.
[0055] In an embodiment, the open-circuit voltage (OCV) across the battery 108 terminals of the device for obtaining the initial SoC (SoCstart) is under the condition that the battery 108 is kept still or left standing for a predefined time interval. In an embodiment, the pre-defined time interval may be of any minimum time interval defined by the system 100. Further, the initial SoC (SoCstart) is equal to a starting capacity of the battery 108.
[0056] In an exemplary embodiment, the SoC1 is calculated by using the following formula:
SoC1= (SoC)starting + Integration or Summation of [charging current * time] / Battery capacity,
while the current is flowing into the battery
SoC1 = (SoC)starting - Integration or Summation of [dis-charging current * time] / Battery capacity,
while the current is transferred or drained from the battery
[0057] In an embodiment of the present invention, the BMS 102 estimates a second SoC (SoC2) using an OCV_SoC method by the one or more processors 106. The estimation of the second SoC (SoC2) includes measuring the open circuit voltage (OCV) across the terminals of the battery 108 using a battery equivalent circuit model. Further, determining the open circuit voltage (OCV) across the battery terminals using the battery equivalent circuit model can also be referred to as determining OCV across the battery terminals using an inverted battery model. In addition, the estimation of the second SoC (SoC2) includes determining the relative SoC from the look-up table corresponding to the OCV determined from the battery equivalent circuit model, wherein the relative SoC is the SoC2.
[0058] In one embodiment, the battery equivalent circuit model is as shown in Fig. 2a and Fig. 2b. In Fig. 2a, the battery equivalent circuit model includes a battery having a battery voltage Vb, and battery internal parameters, such as R0, R1 and C1. Likewise, in Fig. 2b, the battery equivalent circuit model includes the battery having the battery voltage Vb, and battery internal parameters, such as R0, R1, R2, C1 and C2.
[0059] In an embodiment, a plurality of multi-dimensional look-up tables may be used in the SoC estimation using OCV_SoC method. In accordance with Fig. 2a, there may be three multi-dimensional look-up tables, wherein each multi-dimensional look-up table corresponds to each of the battery internal parameters. Likewise, in accordance with Fig. 2b, there may be five multi-dimensional look-up tables. Therefore, the number of multi-dimensional look-up tables may correspond to the number of internal battery parameters.
[0060] In one embodiment, the multi-dimensional look-up table may be a two-dimensional look-up table. In another embodiment, the multi-dimensional look-up table may include three or more dimensions. In the current implementation, the three-dimensional look-up table is taken into consideration. For example, a three-dimensional look-up table exists for the internal battery parameter R0. Likewise, a separate three-dimensional look-up table exists for the internal battery parameters R1 and C1, respectively. Furthermore, the three dimensions which make up these look-up tables include battery current, battery temperature and the estimated SoC. These three-dimensional look-up tables help in estimating or calculating the values of internal battery parameters R0, R1 and C1 which then ultimately helps in calculating the open-circuit voltage (OCV). Similarly, in accordance with Fig. 2b, there may be separate three-dimensional look-up tables corresponding to internal battery parameters R2 and C2, respectively in addition to the three-dimensional look-up tables for R0, R and C1.
[0061] In another embodiment, once the OCV is determined from the OCV_SoC method, the one-dimensional look-up table as described in the earlier embodiments is used to determine the SoC. This one-dimensional look-up table which is a relation between OCV and SoC is different from the multi-dimensional look-up tables described with respect to Fig. 2a and Fig. 2b.
[0062] In one embodiment, OCV estimation using the battery equivalent circuit model as shown in Fig. 2b may be more accurate compared to OCV estimation using the battery equivalent circuit model as shown in Fig. 2a. One can choose the battery equivalent circuit model as shown in Fig. 2a or Fig. 2b based on system requirements.
[0063] In an embodiment of the present invention, either of the SoC1 or the SoC2 is used to calculate the SoC of the battery 108 during a condition when anyone of the ampere-hour integration method and the OCV_SoC method fails to determine SoC1 and SoC2, respectively.

[0064] In an embodiment of the present invention, the BMS 102 determines a state of charge error (SoCerror) as a function of the SoC1 and the SoC2.
[0065] In an example, the state of charge error (SoCerror) is calculated using the equation mentioned below:
SoCerror = SoC1- SoC2
[0066] In an alternate implementation, the SoCerror may be calculated as an average of the SoC1 and the SoC2.
[0067] Further, the BMS 102 determines a kalman filter gain (Kalmangain) using the one or more processors 106. The kalman filter gain is determined as a function of a battery charge storage capacity variance, a current variance and a voltage variance of the battery 108. In general, variance is a measurement of spread between numbers in a data set. In an embodiment of the present invention, the current variance, capacity variance and the voltage variance is taken in order to cancel out the effect of error in measuring values of current, capacity, and voltage by the BMS 102.
[0068] The current variance normalizes the state of charge error in measured values of current by BMS 102, wherein the current variance is a distributed curve for any number of battery current values having the mean as zero and the variance as one.
[0069] The voltage variance normalizes the state of charge error in measured values of voltage by BMS 102, wherein the voltage variance is a normally distributed curve for any measured values of battery pack voltages having the mean as zero and the variance as one.
[0070] In an example, the Kalman Filter Gain (KalmanGain) is calculated using the equation mentioned below:
Kalman Filter Gain (KalmanGain) = f (Battery charge storage capacityvariance, Currentvariance, Voltagevariance)
[0071] In accordance with an embodiment of the present invention, the BMS 102 estimates the SoC of the battery 108 at a first time by a Kalman filter as a function of the SoCerror and the Kalman filter gain determined at the first time. In addition, the BMS 102 estimates the SoC of the battery 108 at a second time by the Kalman filter as a function of the SoC of the battery 108 estimated at the first time, and both the SoCerror and the Kalman filter gain determined at the second time. In an embodiment, the SoC of the battery 108 is calculated as follows:
[0072] In an example, the SoC of the battery 108 estimation at the first time using the equation mentioned below:
SoCn = (Kalmansoc)n = Kalmangain*SoCerror
[0073] In another example, the SoC of the battery 108 estimation at second, third time and so on using the equation mentioned below:
SoCn+1 = SoCn - Kalmangain*SoCerror (here, Kalman gain and SoC error determined at the (n+1)th time), where n = 1, 2, 3, and so on
SoCn+2 = SoCn+1 - Kalmangain*SoCerror (here, Kalman gain and SoC error determined at the (n+2)th time)
[0074] In an embodiment of the present invention, the BMS 102 communicates the estimated SoC to the control unit (CU) 110 of the device. The CU 110 includes the memory unit 112 and the one or more processors 114. The one or more processors 114 in the CU 110 execute a set of instructions to display the estimated SoC of the battery on the display unit 116 of the device. Further, the BMS 102 estimated SoC of the battery 108 may be further used as an input for various other algorithms residing in the control unit 110 as well as in the BMS 102.
[0075] In an embodiment, the display unit 116 displays the estimated SoC of the battery to a user. The display unit 116 is attached to the device and visible to the user while using the device. The display unit 116 includes but is not limited to one of a CRT display, an LCD display, an LED display, an OLED display, an AMOLED display, and a PMOLED display.
[0076] In some implementations, the display unit 116 is a touch-sensitive display unit or non-touch sensitive display unit.
[0077] In another implementation, the display unit 116 of the device is further associated with the user handheld device 118.
[0078] In an embodiment of the present invention, the control unit 110 sends a notification alert to the user indicative of a critical SoC of the battery 108 when the estimated SoC of the battery 108 is below a predefined threshold SoC value. The notification alert includes at least one of a text message, an audio warning, a visual warning, haptic feedback, and vibrations provided on the display unit 116 of the device or the user handheld device 118.
[0079] The user handheld devices 118 may display notification alert to the user and may be configured with an interface of a desktop computer, a laptop computer, a user computer, a tablet computer, a personal digital assistant (PDA), a cellular telephone, a communication network appliance, a camera, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console, or a combination of any these data processing devices or other data processing devices. Furthermore, the user handheld devices 118 may be any user handheld device that can be provided access to and/or receive application software executed and/or stored on any of the servers.
[0080] In some implementations, the user handheld devices 118 can communicate wirelessly to the device through a communication network (not shown in the figure) such as, but not limited to, the Internet, wireless networks, local area networks, wide area networks, private networks, a cellular communication network, corporate network having one or more wireless access points or a combination thereof connecting any number of mobile clients, fixed clients, and servers and so forth. Examples of the communication network may include the Internet, a WIFI connection, a Bluetooth connection, a Zigbee connection, a communication network, a wireless communication network, a 3G communication, network, a 4G communication network, a 5G communication network, a USB connection, or any combination thereof. For example, the communication may be based through a radio-frequency transceiver (not shown). In addition, short-range communication may occur, such as using Bluetooth, Wi-Fi, or other such transceivers.
[0081] Fig. 3 is a block diagram illustrating a method 300 of state of charge (SoC) estimation for an energy storage device 108, in accordance with an embodiment of the present invention. The energy storage device 108 is associated with one of a consumer electronic appliance, a smart electronic device, or a vehicle.
[0082] In an embodiment of the present invention, the method starts at step 305 and proceeds to step 330. At step 205, a first SOC (SOC1) is estimated by one or more processors 106 (references will be made to Fig. 1). The one or more processors 106 are associated with a battery management system (BMS) 102. In an embodiment, the estimation of the SOC1 includes a calculation that combines an initial SoC (SoCstart) of the battery 108 with an ampere-hour integral of a current transferred from or to the battery 108. In addition, the initial SoC (SOCstart) is used as a reference or a starting point for estimating SoC1.
[0083] In an embodiment of the present invention, the SoCstart is obtained by measuring an open-circuit voltage (OCV) across the battery terminals of a device. In an embodiment, the initial SOCstart may be calculated when the device draws no current from the battery 108. Further, a relative SoC is determined corresponding to the measured OCV from a look-up table, wherein the relative SoC is the initial SoC (SOCstart). The look-up table represents an OCV_SoC curve having a one-dimensional relationship between the OCV measured across the battery terminals and the relative SoC corresponding to the measured OCV. Further, the open-circuit voltage (OCV) across the battery terminals of the device for obtaining the initial SoC (SoCstart) is under the condition that the battery 108 is kept still or left standing for a predefined time interval. In an embodiment of the present invention, the pre-defined time interval may be of any time interval of any numeric value defined by the system 100. Furthermore, the initial SoC is equal to a starting capacity (capacitystart) of the energy storage device 108.
[0084] In an embodiment, the energy storage device 108 includes at least one of an Electrochemical battery, a lithium-ion (Li-ion) battery, a nickel-cadmium battery, a nickel-metal hydride battery, an ultracapacitor, and a solid-state battery.
[0085] At step 310, a second SoC (SoC2) is estimated by the one or more processor 106 associated with the BMS 102. The estimation of the SoC2 further comprises a first step and a second step. The first step includes measuring the open circuit voltage (OCV) across the terminals of battery 108 using a battery equivalent circuit model or an inverted battery model. The second step includes determining the relative SoC from the look-up table corresponding to the measured OCV determined from the battery equivalent circuit model, wherein the relative SoC is the SoC2.
[0086] In an embodiment of the present invention, the first SoC (SoC1) and the second SoC (SoC2) are estimated using an ampere-hour integration method and an OCV_SoC method, respectively. In addition, either of the SOC1 or the SOC2 may be used to calculate the SoC of the battery 108 during a condition when anyone of the ampere-hour integration method and the OCV_SoC method fails to determine SoC1 and SoC2, respectively.
[0087] At step 315, the method includes determining a state of charge error (SoCerror) as a function of the SoC1 and the SoC2 by the one or more processors 106 associated with the BMS 102.
[0088] At step 320, a Kalman filter gain (Kalmangain) is determined as a function of a battery charge storage capacity variance, a current variance and a voltage variance of the battery 108 by the one or more processors 106.
[0089] At step 325, the method includes estimating the SoC of the battery 108 at a first time by a Kalman filter as a function of the SOCerror and the Kalman filter gain (Kalmangain) determined at the first time by the one or more processors 106. At step 330, the SoC of the battery 108 is estimated at a second time by the Kalman filter as a function of the SoC of the battery 108 estimated at the first time, and both the SOCerror and the Kalman filter gain determined at the second time by the one or more processors.
[0090] In an embodiment of the present invention, the method further includes displaying the estimated SOC of the battery 108 to a user. The estimated SOC of the battery 108 is displayed to the user on a display unit 116 of the device including but not limited to one of a CRT display, an LCD display, an LED display, an OLED display, an AMOLED display, and a PMOLED display. The display unit 116 of the device is touch-sensitive or non-touch sensitive.
[0091] In an embodiment of the present invention, the method further includes sending a notification alert to the user indicative of a critical SoC of the battery 108 when the estimated SoC of the battery 108 is below a predefined threshold SoC value. The notification alert includes at least one of a text message, an audio warning, a visual warning, haptic feedback, and vibrations provided on the display unit 116 of the device or a user handheld device 118.
[0092] The user handheld devices 118 is a desktop computer, a laptop computer, a user computer, a tablet computer, a personal digital assistant (PDA), a cellular telephone, a communication network appliance, a camera, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console, or a combination of any of these data processing devices or other data processing devices.
[0093] In an advantageous embodiment of the present invention, this SoC estimation method may be applied simultaneously to the multiple battery packs used in the device such as vehicles which have multiple battery packs and multipack battery management systems. Therefore, this SoC estimation may be applied in a modular way and easy to scale up as per the requirements of the devices.
[0094] In an advantage, this SoC estimation method provides the estimated SoC of the battery 108 in less time, as the cell resistances and capacitances of the inverted battery model is predetermined and stored as look up tables, these parameters do not require online estimation, hence each SoC estimation cycle will take less time.
[0095] Additionally, the present SoC estimation method may use the multi-dimensional look-up tables that help in estimating or calculating the values of internal battery parameters which then ultimately helps in calculating the open-circuit voltage (OCV). Further the measured OCV will be used to determine the relative SoC for the SoC estimation of the battery. Therefore, the present method of SoC estimation is providing the most accurate SoC of the battery in lesser time.
[0096] While the detailed description has shown, described, and pointed out novel features as applied to various alternatives, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the scope of the disclosure. The disclosures and the description herein are intended to be illustrative and are not in any sense limiting the invention, defined in scope by the following claims.
, Claims:We Claim:
1. A method for estimating a state of charge (SoC) of a battery 108, the method comprising:
estimating, by one or more processors 106, a first SoC (SoC1), wherein estimating the SOC1 comprises a calculation that combines an initial SoC (SoCstart) of the battery 108 with an ampere-hour integral of a current transferred from or to the battery 108;
estimating, by the one or more processors 106, a second SoC (SoC2), wherein estimating the SoC2 comprises:
measuring an open circuit voltage (OCV) across the battery 108 terminals using a battery equivalent circuit model, and
determining a relative SoC corresponding to the measured OCV using a look-up table, wherein the relative SoC is the SoC2;
determining, by the one or more processors 106, a state of charge error (SOCerror) as a function of the SoC1 and the SoC2;
determining, by the one or more processors 106, a Kalman filter gain as a function of a battery charge storage capacity variance, a current variance and a voltage variance of the battery 108;
estimating, by the one or more processors 106, the SoC of the battery 108 at a first time by a Kalman filter as a function of the SoCerror and the Kalman filter gain determined at the first time; and
estimating, by the one or more processors 106, the SoC of the battery 108 at a second time by the Kalman filter as a function of the SoC of the battery 108 estimated at the first time, and both the SOCerror and the Kalman filter gain determined at the second time.
2. The method as claimed in claim 1, wherein the first SoC (SoC1) and the second SoC (SoC2) are estimated using an ampere-hour integration method and an OCV_SoC method, respectively.
3. The method as claimed in claim 1, wherein the initial SoC (SoCstart) is used as a reference or a starting point for estimating SoC1.
4. The method as claimed in claim 1 and 3, wherein the SoCstart is obtained by measuring the open-circuit voltage (OCV) across the battery terminals of a device, and then the relative SoC is determined corresponding to the measured OCV from the look-up table, wherein the relative SoC is the initial SoC (SoCstart).
5. The method as claimed in claim 4, wherein the open-circuit voltage (OCV) across the battery terminals of the device for obtaining the initial SoC (SoCstart) is that under the condition that the battery 108 is kept still or left standing for a predefined time interval.
6. The method as claimed in claims 1 and 4, wherein the look-up table represents an OCV_SoC curve having a one-dimensional relationship between the OCV measured across the battery 108 terminals and the relative SoC corresponding to the measured OCV.
7. The method as claimed in claim 1, wherein the device refers to one of a consumer electronic appliance, a smart electronic device, or a vehicle.
8. The method as claimed in claim 1, wherein either of the SoC1 or the SoC2 is used to calculate the SoC of the battery 108 during a condition when anyone of the ampere-hour integration method and the OCV_SoC method fails to determine SoC1 and SoC2, respectively.
9. The method as claimed in claim 1, wherein the battery 108 includes at least one of an Electrochemical battery, a lithium-ion (Li-ion) battery, a nickel-cadmium battery, a nickel-metal hydride battery, an ultracapacitor, and a solid-state battery.
10. The method as claimed in claim 1, further comprises displaying the estimated SoC of the battery 108 to a user.
11. The method as claimed in claim 10, wherein the estimated SoC of the battery 108 is displayed to the user on a display unit 116 of the device including but not limited to one of a CRT display, an LCD display, an LED display, an OLED display, an AMOLED display, and a PMOLED display.
12. The method as claimed in claim 10, wherein the display unit 116 of the device is touch-sensitive or non-touch sensitive.
13. The method as claimed in claim 1, further comprising sending a notification alert to the user indicative of a critical SoC of the battery 108 when the estimated SoC of the battery 108 is below a predefined threshold SoC value.
14. The method as claimed in claim 13, wherein the notification alert includes at least one of a text message, an audio warning, a visual warning, haptic feedback, and vibrations provided on the display unit 116 of the device or a user handheld device 118.