Device capable of being connected to a battery and method for calculating the state of charge of the battery

Abstract

This invention relates to a device capable of being connected to a battery and a method for calculating the state of charge (SOC) of the battery. The device can determine a first current according to a first technique and a second current according to a second technique. The device can use a Kalman filter to calculate the SOC of the battery, wherein the Kalman filter uses either the first current or the second current based on a comparison of the second current with a threshold current.

Description

Technical Field

[0001] The present invention relates to a device capable of being connected to a battery and a method for calculating the state of charge of a battery. Background Technology

[0002] Battery capacity is a measure of the amount of charge a battery stores (usually expressed in ampere-hours) and is determined by the mass of the active materials contained within the battery. Battery capacity represents the maximum amount of energy that can be extracted from a battery under certain specified conditions. Battery capacity can also be expressed as a percentage and is referred to as the state of charge or relative state of charge (RSOC).

[0003] Fuel gauges are typically provided to measure various parameters of a battery and monitor its state of charge (SOC). The battery current (based on the battery's internal resistance) is usually used to determine the SOC. However, during high-current cycles, the measured current may contain errors, which can lead to inaccurate SOC readings. Summary of the Invention

[0004] The present invention relates to a device capable of being connected to a battery and a method for calculating the state of charge of a battery.

[0005] Various embodiments of this technology can provide methods and apparatus for a battery. The apparatus can determine a first current according to a first technique and a second current according to a second technique. The apparatus can use a Kalman filter to calculate the state of charge of the battery, wherein the Kalman filter uses either the first current or the second current based on a comparison of the second current with a threshold current.

[0006] The technical problem solved by this invention is that in conventional systems, fuel gauges are typically provided to measure various parameters of a battery and monitor the battery's state of charge by using the measured current of the battery; however, during high-current cycles, the measured current may contain errors, which may lead to inaccurate state of charge.

[0007] According to a first aspect, an apparatus capable of being connected to a battery includes: a voltage detector configured to measure the voltage of the battery; a current sensor configured to detect a first current of the battery; and a processor configured to: calculate a differential voltage based on the measured voltage and an open-circuit voltage; determine a second current of the battery based on the differential voltage; generate a first output, including comparing the second current with a predetermined threshold current; select one of the first current and the second current based on the first output; and calculate the state of charge of the battery using a prediction and correction algorithm and the selected current.

[0008] In one embodiment, the device further includes a memory configured to store battery data indicating the relationship between voltage and current values.

[0009] In one implementation, the processor selects the first current when the second current is greater than or equal to the threshold current; and selects the second current when the second current is less than the threshold current.

[0010] In one embodiment, the processor is further configured to: compare the difference voltage with a threshold voltage; select a second current if the second current is greater than or equal to the threshold current and the difference voltage is less than the threshold voltage; and select a first current if the second current is less than the threshold current and the difference voltage is greater than the threshold voltage.

[0011] In one implementation, the processor includes a multiplexer that selects one of a first current and a second current based on a first output.

[0012] In one embodiment, the processor includes: a Kalman filter that uses a selected current to calculate the state of charge of the battery; and a comparator that compares a second current with a predetermined threshold current.

[0013] In one implementation, determining the second current includes accessing the memory and selecting a second current corresponding to the voltage difference.

[0014] According to a second aspect, a method for calculating the state of charge (SOC) of a battery includes: detecting a first current of the battery; measuring the voltage of the battery; calculating a difference voltage based on the measured voltage and an open-circuit voltage; determining a second current of the battery based on the difference voltage; comparing the second current with a predetermined current; comparing the measured voltage with a predetermined voltage; and calculating the SOC of the battery, including using the first current to perform an algorithm when the second current is less than a predetermined threshold and the difference voltage is greater than a predetermined voltage; and using the second current to perform an algorithm when the second current is greater than or equal to a predetermined threshold and the difference voltage is less than a predetermined voltage.

[0015] In one implementation, the algorithm performs prediction and correction.

[0016] In one implementation, detecting the first current includes measuring the voltage across a sensing resistor; and determining the second current includes retrieving the second current from a lookup table based on the voltage difference.

[0017] The technical effect achieved by this invention is to provide a solution that uses a Kalman filter to calculate the state of charge of a battery, which uses feedback of the uncertainty variables of the battery model to minimize the error between the estimated output and the measured output in real time. Attached Figure Description

[0018] The present invention can be more fully understood by referring to the specific embodiments when considered in conjunction with the following exemplary drawings. Throughout the following drawings, similar reference numerals are used to refer to similar elements and steps in the various drawings.

[0019] Figure 1 This is a block diagram of a battery system according to an exemplary embodiment of the present technology;

[0020] Figure 2 A simplified circuit diagram of a logic circuit according to an exemplary embodiment of the present technology;

[0021] Figure 3 A graph illustrating the battery voltage characteristics during charging and discharging according to an exemplary embodiment of the present invention;

[0022] Figure 4A A circuit model of a battery according to an exemplary embodiment of the present technology;

[0023] Figure 4B An alternative circuit model for a battery according to an exemplary embodiment of this technology;

[0024] Figure 5 A flowchart illustrating an exemplary embodiment of the present technology for operating a battery system;

[0025] Figure 6 An alternative flowchart for operating a battery system according to an exemplary embodiment of the present technology;

[0026] Figure 7 For example, a lookup table according to an embodiment of the present technology; and

[0027] Figure 8 This is a block diagram of an SOC calculator according to an exemplary embodiment of the present technology. Detailed Implementation

[0028] This technology can be described in terms of functional block components and various processing steps. Such functional blocks can be implemented by any number of components configured to perform specified functions and achieve various results. For example, this technology can employ various voltage sensors, current sensors, coulomb counters, logic gates, timers, memory devices, switches, semiconductor devices such as transistors and capacitors, etc., capable of performing multiple functions. Furthermore, this technology can be integrated into any number of electronic systems (such as automotive, aerospace, "smart devices," portable devices, e-cigarettes, aromatherapy puff systems, e-cigarette devices, and consumer electronics), and the systems described are merely exemplary applications of this technology.

[0029] The methods and apparatus for batteries according to various aspects of this technology can operate in conjunction with any suitable electronic system and / or device, such as "smart devices," wearable devices, battery-powered consumer electronics, portable devices, battery-powered vehicles, etc. See also Figure 1 The exemplary system 100 can be integrated into an electronic device (not shown) powered by a rechargeable battery 101 (such as a lithium-ion battery) (such as an electronic cigarette device or an electric vehicle). For example, in various embodiments, the battery 101 can operate together with a charger 105 and a fuel gauge circuit 110 to provide power to a host system 115 (i.e., a load).

[0030] According to an exemplary embodiment, system 100 may include a switch 140 to selectively connect charger 105 to battery 101. According to an exemplary embodiment, host system 115 may operate switch 140 according to a pulse-width modulated control signal S1. Switch 140, in conjunction with control signal S1, can control the current flowing from charger 105 to battery 101 (also referred to as charging current I). CC This controls the charging and discharging cycles of battery 101.

[0031] The fuel gauge circuit 110 can be configured to manage various battery operations and monitor various battery conditions. For example, the fuel gauge circuit 110 can be configured to measure the voltage V of battery 101. T ; Measure the first current I DD1 (also known as load current I) DD1 ); calculate the remaining capacity of battery 101 (also expressed as a percentage and called state of charge, SOC); calculate the state of health (SOH) of battery 101; estimate the lifespan of battery 101; determine the energy capacity of the battery, etc.

[0032] Furthermore, the fuel gauge circuit 110 can be configured to store various battery data. For example, the fuel gauge circuit 110 can store predetermined battery characteristics such as the open-circuit voltage value of battery 101 as a function of the capacity (i.e., RSOC) of battery 101. The fuel gauge circuit 110 can also store predetermined values, such as a predetermined threshold voltage value V. TH and the predetermined current value I TH And / or other battery characteristic data.

[0033] In an exemplary embodiment, the fuel gauge circuit 110 may include a voltage detector 120 to measure or otherwise detect the voltage V of the battery 101. T Voltage detector 120 can be connected to battery 101 and may include any circuitry and / or devices suitable for measuring voltage potential.

[0034] In an exemplary embodiment, the fuel gauge circuit 110 may further include a current sensor 125 to measure the load current I to / from the battery 101 and the host system 115. DD1 The current sensor 125 may include any circuitry and / or device suitable for measuring the current of the battery 101. For example, the current sensor 125 may operate in conjunction with a sensing resistor 155, wherein the current sensor 125 measures the voltage across the sensing resistor 155 to determine the load current I. DD1 .

[0035] In an exemplary embodiment, the fuel gauge circuit 110 may further include a memory 130 to store known battery characteristics and / or profile data of the battery 101, such as the open-circuit voltage characteristic of the battery 101. The open-circuit voltage characteristic provides an open-circuit voltage V as a function of the remaining capacity (RSOC) of the battery 101. OC The open-circuit voltage characteristics can be predetermined by testing battery 101 under open-circuit (i.e., no-load) conditions and can be stored in a lookup table or any other data storage device suitable for storing relational data.

[0036] Memory 130 can also store various previously and currently calculated or measured variables, such as battery voltage V. T Current I DD1 etc. The memory 140 can also store predefined variables, such as a threshold voltage V based on the current value. TH Value. For example, memory 130 may contain a threshold voltage V. TH and a lookup table for the corresponding current value (e.g., such as...) Figure 7 As shown), a threshold voltage from the table can be selected based on a known current value. Furthermore, the memory 130 may contain a current value (referred to as a second current value I). DD2 and differential voltage V DIFF A lookup table (not shown) for (i.e., ΔV), which can be used based on the known voltage difference V. DIFF To select the second current I from the table DD2 .

[0037] In an exemplary embodiment, the memory 130 may further include a component containing an open-circuit voltage value (V). OC The lookup table for the open-circuit voltage value is used as a function of the RSOC (relative state of charge) value (where the relative state of charge is the battery capacity expressed as a percentage).

[0038] The memory 130 may include any number of storage devices, such as registers, flash memory devices, EEPROM (Electrically Erasable Programmable Read-Only Memory), ROM (Read-Only Memory), and RAM (Random Access Memory).

[0039] In various embodiments, the fuel gauge circuit 110 may further include a remaining capacity calculation circuit (not shown) configured to measure the RSOC of the battery 101. In one embodiment, the remaining capacity calculation circuit may be configured to calculate the remaining capacity based on the voltage V of the battery 125. T To determine RSOC. Generally speaking, the battery voltage V is used. T The method for determining remaining capacity is known as the "voltage method".

[0040] In an alternative implementation, the remaining capacity calculation circuit may be configured to measure the inflow and outflow currents I of battery 125 over a certain period of time. DD It also reports the accumulated charge. This can be achieved using a current sensor 125 and by tracking the measured current I. DD1 To achieve this, in this embodiment, the remaining capacity calculation circuit monitors the voltage across the sensing resistor 1555 as current I during the charging and discharging of the battery 101. DD The instruction. Then, for a certain period of time, the current I... DD1 The remaining capacity is integrated and reported as a percentage (in mAh). Generally, using the current IDD1 to determine the remaining capacitance is called "coulomb counting".

[0041] In an exemplary embodiment, the fuel gauge circuit 110 may include a processor 135, which is capable of performing various calculations and comparisons and determining the state of charge (SOC) of the battery 101. For example, the processor 135 may include arithmetic circuitry 200, selector circuitry 205, first comparator circuitry 210, and multiplexer 220. The processor 135 may also be configured to access various data from memory 130.

[0042] According to an exemplary embodiment, the arithmetic circuit 200 can be configured to perform a subtraction function. In an exemplary embodiment, the arithmetic circuit 200 can receive data from the battery voltage V. T Subtract open circuit voltage V OC To calculate the differential voltage V DIFF The operational circuit 200 can communicate with the memory 130 and is configured to receive or otherwise retrieve the open-circuit voltage V from the memory 130 based on the calculated RSOC. OC The operational circuit 200 can convert the difference voltage V DIFF Transmitted to selector circuit 205.

[0043] Selector 205 can be configured to receive differential voltage V DIFF And select the corresponding second current I DD2 For example, selector 205 can be based on the differential voltage V. DIFF The value is retrieved from memory 130 or otherwise received as a second current I. DD2For example, if the differential voltage V DIFF If the voltage is 20mV, then the corresponding second current I DD2 It can be 0.25. Selector 205 can convert the corresponding second current I... DD2 The data is transmitted to the first comparator 210.

[0044] Alternatively, system 100 can be based on differential voltage V DIFF The second current I is determined directly by calculation with the known internal battery resistance R. DD2 , where I DD2 =V DIFF / R.

[0045] The first comparator 210 can convert the second current I... DD2 With a predetermined threshold current I TH The comparison is performed, and a first comparator output C is generated based on the comparison. OUT_1 For example, the first comparator 210 can receive a second current I at the first input terminal. DD2 And receive the threshold current I at the second input terminal. TH If the second current I DD2 Current greater than the threshold I TH Then the first comparator 210 can generate a first output (such as logic 1). If the second current I DD2 Less than the threshold current I TH Then the first comparator 210 can generate a second output (such as logic 0). The first comparator 210 can convert the first comparator output C OUT_1 The data is transmitted to multiplexer 220, where the first comparator outputs C. OUT_1 It can be used as the first enable signal for multiplexer 220.

[0046] Depending on the implementation scheme, the threshold current I can be selected based on the various specifications of battery 101. TH The value of . In one implementation, the threshold current I TH It can be 2A.

[0047] The second comparator 215 can measure the battery voltage V. T With a predetermined threshold voltage V TH A comparison is made and a second comparator output C is generated based on that comparison. OUT_2 For example, the second comparator 215 can receive the battery voltage V at the first input terminal. T And receive the threshold voltage V at the second input terminal. TH If the battery voltage V T Greater than the threshold voltage V TH Then the second comparator 215 can generate a first output (such as logic 1). If the battery voltage VT Less than the threshold voltage V TH, Then the second comparator 215 can generate a second output (such as logic 0). The second comparator 215 can convert the second comparator output C OUT_2 The data is transmitted to multiplexer 220, where the second comparator outputs C. OUT_2 It can be used as a second enable signal for multiplexer 220.

[0048] Multiplexer 220 can be configured based on the first comparator output C OUT_1 The output C of the second comparator OUT_2 At least one of them is used to select and output the first current I. DD1 Second current I DD2 One of them. The output of the multiplexer 220 is usually referred to as the selected current i, where i is I. DD1 Or I DD2 .

[0049] For example, according to the first embodiment, if the second current I DD2 Greater than or equal to the threshold current I TH Then the multiplexer 220 can select and output the first current I. DD1 Alternatively, if the second current I... DD2 Less than the threshold current I TH Then the multiplexer 200 can select and output a second current I. DD2 .

[0050] According to the second implementation scheme, if the second current I DD2 Greater than or equal to the threshold current I TH And the differential voltage V DIFF Less than the threshold voltage V TH Then the multiplexer 220 can select and output a second current I. DD2 Alternatively, if the second current I... DD2 Less than the threshold current I TH And the differential voltage V DIFF Greater than the threshold voltage V TH Then the multiplexer 220 can select and output the first current I. DD1 .

[0051] According to various embodiments, processor 135 may include any number of circuits, systems, logic gates, and / or software to perform desired calculations and / or analyses, as described above. For example, processor 135 may include field-programmable gate arrays, application-specific integrated circuits, programs, and operating systems.

[0052] In various embodiments, processor 135 may further include modules and / or circuitry for determining the remaining capacity (measured in ampere-hours) and / or SOC (remaining capacity expressed as a percentage) based on the selected current i. Generally, the SOC of battery 101 can be expressed by the following equation:

[0053]

[0054] SOC t0 Let C be the initial SOC value, and C OCV This is the reference capacity for battery 101.

[0055] In the first implementation scheme, and see also Figure 2 and Figure 4A Battery 101 can be adjusted according to the open circuit voltage V. OC The ohmic resistance represented by the first resistor R1, the voltage across the first resistor V1, and the output voltage V. T The current i is used for modeling. The characteristics of this battery model can be expressed by the following equation:

[0056] V T =V OC -V1=V OC -R1·i (Equation 2)

[0057] In this implementation, the SOC calculator 225 can calculate the SOC according to the following equation:

[0058]

[0059] Where Δt is the sampling period.

[0060] In alternative implementation schemes, and see also Figure 4B and Figure 8 The SOC calculator 225 may include a Kalman filter 800 to determine the SOC by performing prediction and correction algorithms using one or more battery variables, such as the battery's current and voltage. The Kalman filter 800 may be able to minimize the error between the estimated output and the measured output in real time using feedback from uncertainties in the adjusted / corrected model. Utilizing this model, the Kalman filter 800 allows the system to observe physical parameters of the model that cannot be directly measured.

[0061] Generally speaking, the Kalman filter solves the problem of attempting to estimate the state of a discrete-time control process. The problem is that the discrete-time control process is controlled by the following linear stochastic difference equation.

[0062] x k =Ax k-1 +Bu k-1 +wk-1 (1.1)

[0063] Among the measured values for

[0064] z k =Hx k +v k (1.2)

[0065] random variable w k and v k (Representing process noise and measurement noise respectively). Assuming they are independent and follow a normal probability distribution:

[0066] p(w)~N(0,Q) (1.3)

[0067] p(v)~N(0,R) (1.4)

[0068] In practice, the process noise covariance Q and measurement noise covariance R matrices may change with each time step or measurement, but they are assumed to be constant.

[0069] In the absence of a driving function or process noise, the n×n matrix A in the difference equation (1.1) relates the state at the previous time step k-1 to the state at the current time step k. Note that in practice, A may change with each time step, but here it is assumed to be constant.

[0070] The nxl matrix B will accept any control input. It is related to state x.

[0071] The mxn matrix H in the measurement equation (12) will link the state with the measured value z. k In practice, H may change with each time step or measurement, but it is assumed to be constant here.

[0072] Assuming the process up to time step k is known, then the expression... (Note the superscript minus sign) is defined as the prior state estimate at time step k, and it is assumed that the measured value z is known. k Then the expression It is defined as the posterior state estimate at time step k.

[0073] Therefore, the prior and posterior estimation errors are defined as:

[0074] and

[0075]

[0076] The prior estimation error covariance is given by the following formula.

[0077]

[0078] Furthermore, the covariance of the posterior estimation error is given by the following formula.

[0079]

[0080] When deriving the equations for a Kalman filter, the goal is to find an equation that estimates the posterior state. As a priori estimate and actual measured value z k Compared with the measured predicted value The calculation is performed by a linear combination of the weighted differences between them, as shown in (1.7) below.

[0081] A proof for (1.7) is given in the “Probabilistic Origin of the Filter” section below.

[0082]

[0083] The difference in (1.7) This is referred to as measurement innovation or residual.

[0084] The residual reflects the predicted measurement value Compared with the actual measured value z k The difference between them. A residual of zero means that the two are completely identical.

[0085] In (1.7), the n×m matrix K is chosen as the gain or mixing factor that minimizes the prior error covariance (1.6). This minimization can be achieved by first substituting (1.7) into the above equation for e. k Substitute it into (1.6) in the definition, perform the indicated expectation, take the derivative of the result trace with respect to K, set the result to zero, and then solve for K.

[0086] One form of K that minimizes (1.6) is given by the following equation:

[0087]

[0088] Referring to (1.8), it can be observed that as the measurement error covariance R approaches zero, the gain K has a greater weight on the residual. Specifically,

[0089]

[0090] On the other hand, with the prior estimation error covariance As the value approaches zero, the gain K has a smaller weight on the residual. Specifically,

[0091]

[0092] Another way to consider the weighting of K is that as the measurement error covariance R approaches zero, the actual measured value z... k Increasingly "reliable", and the predicted measurement value It is becoming increasingly unreliable. On the other hand, with the covariance of prior estimation errors... Approaching zero, the actual measured value z k Increasingly unreliable, while predicted measurements It is becoming increasingly credible.

[0093] The proof of (1.7) is based on all previous measurements of z. k Prior estimates The probability is based on this. Now, it is sufficient to point out the first two moments of the Kalman filter that preserve the state distribution.

[0094]

[0095]

[0096] The posterior state estimate (1.7) reflects the mean of the state distribution (first moment). If conditions (1.3) and (1.4) are satisfied, then the distribution is normally distributed. The posterior estimate error covariance (1.6) reflects the variance of the state distribution (second non-central moment). In other words,

[0097]

[0098] Generally, a Kalman filter estimates a process using some form of feedback control: the filter estimates the process state at a certain time and then receives feedback in the form of (noise) measurements. Therefore, the equations of a Kalman filter are divided into two sets: time update equations and measurement update equations. The time update equations are responsible for projecting the current state and error covariance estimate forward (in time) to obtain a prior estimate for the next time step. The measurement update equations are responsible for feedback, i.e., incorporating new measurements into the prior estimate to obtain an improved posterior estimate.

[0099] The time update equation can also be viewed as a prediction equation, while the measurement update equation can be viewed as a correction equation. In fact, the final estimation algorithm is similar to the prediction-correction algorithm used to solve numerical problems.

[0100] The specific equations for time update and measurement update are presented below in the form of "time update equations" (1.9 and 1.10) and "measurement update equations" (1.11, 1.12 and 1.13).

[0101]

[0102]

[0103] The time update equation projects the state estimate and covariance estimate forward from time step k-1 to time step k. A and B are derived from (1.1), while Q is derived from (1.3). The initial conditions of the filter are discussed in the preceding references.

[0104]

[0105]

[0106]

[0107] The first task during the measurement update is to calculate the Kalman gain K. k Note that equation (1.11) given here is the same as (1.8). The next step is the actual measurement process to obtain z. k Then, the posterior state estimate is generated by merging the measurements as in (1.12). For completeness, (1.12) is simply repeated here in (1.7). The final step is to obtain the prior error covariance estimate via (1.13). After each time and measurement update pair, this process is repeated using the previous posterior estimate used to project or predict the new prior estimate.

[0108] In practical implementations of filters, the measurement noise covariance R is typically measured before operating the filter. The measurement error covariance R can be measured by performing offline sample measurements to determine the variance of the measurement noise.

[0109] Determining the process noise covariance Q is generally more difficult because it cannot be directly observed. Sometimes, if sufficient uncertainty is “injected” into the process via the selection of Q, a relatively simple process model can produce acceptable results. Typically, excellent filter performance (statistically speaking) can be obtained by tuning the filter parameters Q and R. In what is commonly referred to as system identification, tuning is usually performed offline using another (different) Kalman filter.

[0110] The correction is weighted by a gain vector K, which allows for the correction of real-time estimates and filter performance. At each iteration, the gain is calculated from the error predictions and uncertainties (noise) of the state and measurements. Then, dynamic filter control is performed based on the initialization of the noise matrix of the state Q and measurement R, and through the initialization of the error covariance matrix P. Generally, the Kalman filter algorithm may include two phases: the first phase involves the initialization of matrices P, Q, and R, and the second phase involves the observations consisting of two steps at each sampling interval. First, the algorithm predicts the values ​​of the current state, output, and error covariance. Second, the algorithm corrects the state estimate and error covariance using measurements of the physical system output.

[0111] In this case, battery 101 can be adjusted according to the open circuit voltage V. OC The following components are considered: the ohmic resistor represented by the first resistor R1, the voltage across the first resistor V1, the polarization resistor represented by the second resistor R2, the polarization capacitor represented by the capacitor C2, the voltage V2 across the capacitor and resistor connected in parallel, and the output voltage V. T The current i is used for modeling. The characteristics of this battery model can be expressed by the following equation:

[0112] V T =V OC -V1-V2=V OC -R1·i-V2 (Equation 4),

[0113]

[0114]

[0115]

[0116]

[0117] Equation 8 is derived from equations 5 and 7.

[0118] The system input is defined as u(t) = i, and the system output is defined as y(t) = V. T .

[0119] Above, Equations 8 and 4 are the discretization of the state equations, observation equations, and continuity equations of the lithium-ion battery model, and the SOC calculation.

[0120] The Kalman filtering algorithm can be divided into two parts as previously described: 1) predicting the system state, system output, and error; and 2) correcting the current state estimate based on the system output value.

[0121] Based on equations 8 and 4, the system state equation can be written as:

[0122] X = [X1, X2] T (Equation 9)

[0123] Where X1(t) = SOC, X2(t) = V2.

[0124] Therefore, the battery model can be written as:

[0125]

[0126] y = g(x, u) + v (Equation 11)

[0127] Where w and v represent errors, and

[0128]

[0129] g(x, u) = μx1 - x2 - R1u + c (Equation 13)

[0130] Where c is a constant and μ is a coefficient.

[0131] Taylor series expansion can be used to linearize equations 10 and 11. The linearized model is:

[0132]

[0133]

[0134] in:

[0135]

[0136]

[0137]

[0138]

[0139] After discretization, the model can be represented as:

[0140] x k+1 =A k ·x k +B k ·u k +w k ,

[0141] y k =C k ·x k +D k ·u k +v k ,

[0142] Furthermore, the calculation steps of the general Kalman filter algorithm are completed.

[0143] According to an exemplary embodiment, the Kalman filter 800 may be connected to the output terminal of the multiplexer 220 and configured to receive a selected current i from the multiplexer 220 and calculate the state of charge (SOC) according to equation 4-13. The Kalman filter 800 may include a conventional Kalman filter 800 suitable for performing prediction and correction based on known and theoretical variables, and may be implemented in hardware, software, or a combination thereof.

[0144] The host system 115 can be configured to generate a control signal S1 and control the duty cycle of the control signal S1 (which controls the switch 140), and thus control the flow of charging current from the charger 105 to the battery 101. The control signal S1 can be a pulse width modulation signal and is referred to as a charge pulse.

[0145] The host system 115 may also include any circuitry and / or systems suitable for generating pulse width modulated signals, such as a PMW controller (not shown), a timer (not shown), a waveform generator, a trigger, etc.

[0146] In the exemplary operation, and see also Figure 1 , Figure 2 and Figure 5 System 100 may, for example, use current sensor 125 to determine or otherwise calculate SOC, including detecting a first current I. DD1 (500). Determining SOC may also include, for example, using a voltage detector 120 to measure the battery voltage V. T (505). According to an exemplary embodiment, when the battery voltage reaches its peak value, the battery voltage V is measured. T This can occur at the end of the charging pulse. Determining SOC may also include calculating the differential voltage V. DIFF The differential voltage V DIFF Battery voltage V T With open circuit voltage V OC The difference between (510). For example, the fuel gauge 110 can use the arithmetic circuit 200 to perform a subtraction function. Determining the SOC may also include, for example, by determining the voltage difference V. DIFF Extract the second current I from memory 130 DD2 Determine the second current I DD2 (515). Determining the SOC may also include setting the second current I. DD2 With threshold current I TH A comparison is made to determine whether the second current is less than the threshold current I. TH (520). For example, the fuel gauge 110 can use the first comparator 210 to convert the second current I... DD2 With threshold current I TH A comparison is made and a first comparator output C is generated based on that comparison. OUT_1 If the second current I DD2 Less than the threshold current I TH Then the second current I can be used DD2 To calculate the State of Charge (SOC). If the second current is not less than the threshold current, then the first current I can be used. DD1 To calculate the State of Charge (SOC). For example, multiplexer 220 can calculate the SOC based on the output C of the first comparator. OUT_1 To bring the first current I DD1Second current I DD2 One of the currents is selectively transmitted to the Kalman filter 225, where the Kalman filter 225 uses the selected current in the prediction and correction algorithm, as described above. This method can assume that if the second current I... DD2 Current greater than the threshold I TH Then the battery voltage V T Currently experiencing a lag (e.g.) Figure 3 (as shown), and therefore the second current I DD2 The value has an error, and the first current I DD1 It may be more suitable for generating more accurate predictions of SOC.

[0147] In alternative operations, and see also Figure 1 , Figure 2 and Figure 6 System 100 may determine or otherwise calculate SOC, including, for example, using current sensor 125 to detect a first current I. DD1 (600). Determining SOC may also include, for example, using a voltage detector 120 to measure the battery voltage V. T (605). According to an exemplary embodiment, when the battery voltage reaches its peak value, the battery voltage V is measured. T This can occur at the end of the charging pulse. Determining SOC may also include calculating the differential voltage V. DIFF The differential voltage V DIFF Battery voltage V T With open circuit voltage V OC The difference between (610). For example, the fuel gauge 110 can use the arithmetic circuit 200 to perform a subtraction function. Determining the SOC may also include, for example, by determining the voltage difference V. DIFF Extract the second current I from memory 130 DD2 Determine the second current I DD2 (615). Determining the SOC may also include setting the second current I. DD2 With threshold current I TH A comparison is made to determine whether the second current is less than the threshold current I. TH (620). For example, the fuel gauge 110 can use the first comparator 210 to convert the second current I... DD2 With threshold current I TH A comparison is made and a first comparator output C is generated based on that comparison. OUT_1 Determining the SOC may also include taking the differential voltage V. DIFF With threshold voltage V TH Compare to determine the difference voltage V DIFF Is it greater than the threshold voltage V? TH (625). For example, the fuel gauge 110 can use the second comparator 215 to convert the difference voltage VDIFF With threshold voltage V TH A comparison is made, and a second comparator output C is generated based on that comparison. OUT_2 .

[0148] If the second current I DD2 Less than the threshold current I TH If the voltage difference is greater than the threshold voltage, then the first current I can be used. DD1 To calculate SOC. Alternatively, if the second current I... DD2 Not less than the threshold current I TH And the differential voltage V DIFF Less than the threshold voltage V TH Then the second current I can be used DD2 To calculate the State of Charge (SOC). For example, multiplexer 220 can calculate the SOC based on the output C of the first comparator. OUT_1 The output C of the second comparator OUT_2 To bring the first current I DD1 Second current I DD2 One of the currents is selectively transmitted to a Kalman filter 225, which uses the selected current in the prediction and correction algorithm, as described above. This method uses both current and voltage parameters to determine whether the battery is experiencing hysteresis. Generally, if the second current I... DD2 Greater than or equal to the threshold current I TH However, the voltage difference V DIFF If the voltage is less than the threshold voltage, then the battery voltage V T There was no lag, and the system can use the second current I. DD2 To determine the SOC. However, if the second current I DD2 Less than the threshold current I TH However, the voltage difference V DIFF Greater than the threshold voltage V TH Then the battery voltage V T It may be experiencing a lag (e.g.) Figure 3 (as shown), and therefore the second current I DD2 The value has an error, and the first current I DD1 It may be more suitable for generating more accurate predictions of SOC.

[0149] According to various implementation schemes, it can be based on the second current I DD2 The value is used to select the threshold voltage V. TH For example, and see also Figure 7 If the second current I DD2 If the current is 0.1A, then the threshold voltage V TH It is 13mV. However, if the second current I... DD2 If the current is 2A, then the threshold voltage V THIt is 250mV.

[0150] Generally speaking, and see Figure 3 Battery voltage hysteresis manifests as follows: the battery output voltage does not recover to the open-circuit voltage V during non-charging cycles. OC Furthermore, as charging continues, the output voltage and open-circuit voltage V... OC The difference between them increases with each charging cycle. For example, at the end of the first charging cycle, the difference between the battery voltage and the open-circuit voltage increases by V. T_LOW_1 Given that at the end of the second charging cycle, the difference between the battery voltage and the open-circuit voltage is given by V. T_LOW_2 Given that at the end of the third charging cycle, the difference between the battery voltage and the open-circuit voltage is given by V. T_LOW_3 Given, and at the end of the fourth charging cycle, the difference between the battery voltage and the open-circuit voltage is given by V. TLOW4 Given, where V T_LOW_1 <V T_LOW_2 <V T_LOW_3 <V T_LOW_4 In addition, the peak voltage and open-circuit voltage V OC The difference between them (i.e., ΔV) also increases with each charging cycle.

[0151] Similarly, the same battery voltage hysteresis can be observed when the battery discharges. In this case, the battery's output voltage does not recover to the open-circuit voltage V during the discharge cycle. OC Furthermore, as the discharge continues, the output voltage and open-circuit voltage V... OC The difference between them increases with each discharge cycle.

[0152] In the foregoing description, the technology has been described in conjunction with specific exemplary embodiments. The specific embodiments shown and described are for illustrative purposes only and are not intended to further limit the scope of the technology in any way. In fact, for the sake of brevity, conventional manufacturing, connection, fabrication, and other functional aspects of the methods and systems may not be described in detail. Furthermore, the connecting lines shown in the various figures are intended to represent exemplary functional relationships and / or steps between various components. In actual systems, multiple alternative or additional functional relationships or physical connections may exist.

[0153] The technology has been described in conjunction with specific exemplary embodiments. However, various modifications and variations may be made without departing from the scope of this technology. The descriptions and drawings are to be considered in an exemplary and non-limiting manner, and all such modifications are intended to be included within the scope of this technology. Therefore, the scope of the technology should be determined by the general embodiments described and their legally equivalent forms, rather than solely by the specific examples given above. For example, unless otherwise expressly stated, the steps listed in any method or process embodiment may be performed in any order, and are not limited to the explicit order provided in the specific examples. Furthermore, the components and / or elements listed in any apparatus embodiment may be assembled in various arrangements or otherwise configured to produce substantially the same results as this technology, and are therefore not limited to the specific configurations illustrated in the specific examples.

[0154] The beneficial effects, other advantages, and problem solutions have been described above for specific implementation schemes. However, any beneficial effect, advantage, problem solution, or any element that makes any specific beneficial effect, advantage, or solution appear or become more apparent should not be construed as a critical, required, or necessary feature or component.

[0155] The terms “comprising,” “including,” or any variations thereof are intended to refer to a non-exclusive inclusion, such that a process, method, article, composition, or apparatus that comprises a list of elements includes not only those listed but also other elements not expressly listed or inherent to such process, method, article, composition, or apparatus. Except for those not specifically referenced, other combinations and / or modifications of the above-described structures, arrangements, applications, proportions, elements, materials, or components used in the implementation of this technology may vary without departing from its general principles or be otherwise particularly suited to specific environments, manufacturing specifications, design parameters, or other operational requirements.

[0156] The present technology has been described above in conjunction with exemplary embodiments. However, changes and modifications may be made to the exemplary embodiments without departing from the scope of the present technology. These and other changes or modifications are intended to be included within the scope of the present technology, as set forth in the following claims.

[0157] According to a first aspect, an apparatus capable of being connected to a battery includes: a voltage detector configured to measure the voltage of the battery; a current sensor configured to detect a first current of the battery; and a processor configured to: calculate a differential voltage based on the measured voltage and an open-circuit voltage; determine a second current of the battery based on the differential voltage; generate a first output, including comparing the second current with a predetermined threshold current; select one of the first current and the second current based on the first output; and calculate the state of charge of the battery using a prediction and correction algorithm and the selected current.

[0158] In one embodiment, the device further includes a memory configured to store battery data indicating the relationship between voltage and current values.

[0159] In one implementation, the processor selects the first current when the second current is greater than or equal to the threshold current; and selects the second current when the second current is less than the threshold current.

[0160] In one implementation, the processor is also configured to compare the difference voltage with a threshold voltage.

[0161] In one embodiment, the processor selects the second current when the second current is greater than or equal to the threshold current and the voltage difference is less than the threshold voltage; and the processor selects the first current when the second current is less than the threshold current and the voltage difference is greater than the threshold voltage.

[0162] In one implementation, the processor includes a multiplexer that selects one of a first current and a second current based on a first output.

[0163] In one implementation, the processor includes a Kalman filter that uses a selected current to calculate the state of charge of the battery.

[0164] In one embodiment, the processor includes a comparator that compares the second current with a predetermined threshold current.

[0165] In one implementation, determining the second current includes accessing the memory and selecting a second current corresponding to the voltage difference.

[0166] According to a second aspect, a method for calculating the state of charge (SOC) of a battery includes: detecting a first current of the battery; measuring the voltage of the battery; calculating a difference voltage based on the measured voltage and an open-circuit voltage; determining a second current of the battery based on the difference voltage; comparing the second current with a predetermined current; comparing the measured voltage with a predetermined voltage; and calculating the SOC of the battery, including using the first current to perform an algorithm when the second current is less than a predetermined threshold and the difference voltage is greater than a predetermined voltage; and using the second current to perform an algorithm when the second current is greater than or equal to a predetermined threshold and the difference voltage is less than a predetermined voltage.

[0167] In one implementation, the algorithm performs prediction and correction.

[0168] In one implementation, determining the second current includes retrieving the second current from a lookup table based on the voltage difference.

[0169] In one implementation, detecting the first current includes measuring the voltage across a sensing resistor.

[0170] According to a third aspect, a system includes: a battery; and a fuel gauge circuit connected to the battery and including: a voltage detector configured to measure the voltage of the battery; a current sensor configured to detect a first current of the battery; a memory for storing a lookup table containing relevant battery data; and a processor connected to the voltage detector, the current sensor, and the memory, wherein the processor includes: an arithmetic circuit configured to calculate a voltage difference between the measured voltage and an open-circuit voltage; a selector circuit configured to retrieve a second current from the memory based on the voltage difference; a first comparator configured to compare the second current with a predetermined threshold current and generate a first comparator output based on the comparison; a multiplexer configured to output one of the first current and the second current based on the first comparator output; and a state of charge (SCC) calculator connected to the output terminal of the multiplexer and configured to calculate the SCC of the battery using the output current.

[0171] In one implementation, the relevant battery data includes voltage values ​​and corresponding current values.

[0172] In one implementation, the multiplexer outputs a first current when the second current is greater than or equal to a predetermined threshold; and the multiplexer outputs a second current when the second current is less than the predetermined threshold.

[0173] In one embodiment, the system further includes a second comparator configured to compare the difference voltage with a predetermined voltage and generate a second comparator output based on the comparison.

[0174] In one implementation, the multiplexer outputs one of a first current and a second current based on the output of a second comparator.

[0175] In one embodiment, the multiplexer outputs a second current when the second current is greater than or equal to a predetermined threshold and the difference voltage is less than a predetermined voltage; and the multiplexer outputs a first current when the second current is less than the predetermined threshold and the difference voltage is greater than a predetermined voltage.

[0176] In one implementation, the current sensor detects a first current, including measuring the voltage across a sensing resistor connected to the battery.

Claims

1. A device for charging a battery, characterized in that, include: A voltage detector configured to measure the voltage of the battery; A current sensor configured to detect a first current of the battery; and Processor, the processor being configured to: Calculate the difference voltage based on the measured voltage and open-circuit voltage; The second current of the battery is determined based on the voltage difference. Generating a first output includes comparing the second current with a predetermined threshold current; Selecting one of the first current and the second current based on the first output includes: The difference voltage is compared with the threshold voltage; The second current is selected when the second current is greater than or equal to the threshold current and the difference voltage is less than the threshold voltage; and When the second current is less than the threshold current and the difference voltage is greater than the threshold voltage, the first current is selected; and The state of charge of the battery is calculated using prediction and correction algorithms and the selected current.

2. The apparatus according to claim 1, characterized in that, Includes a memory configured to store battery data indicating the relationship between voltage and current values.

3. The apparatus according to claim 1, characterized in that, The processor includes a multiplexer that selects one of the first current and the second current based on the first output.

4. The apparatus according to claim 1, characterized in that, The processor includes: A Kalman filter that uses a selected current to calculate the state of charge of the battery; and A comparator that compares the second current with the predetermined threshold current.

5. The apparatus according to claim 1, characterized in that, Determining the second current includes: accessing the memory and selecting the second current corresponding to the voltage difference.

6. A method for calculating the charging of a battery, characterized in that, include: Detect the first current of the battery; Measure the voltage of the battery; The differential voltage is calculated based on the measured voltage and open-circuit voltage. The second current of the battery is determined based on the voltage difference. The second current is compared with a predetermined threshold current; The measured voltage is compared with a predetermined voltage; as well as Calculating the state of charge of the battery includes: The algorithm is executed using the first current in the following cases: The second current is less than the predetermined threshold current; and The voltage difference is greater than the predetermined voltage; and The second current is used to execute the algorithm in the following cases: The second current is greater than or equal to the predetermined threshold current; and The voltage difference is less than a predetermined voltage.

7. The method according to claim 6, characterized in that, The algorithm performs prediction and correction.

8. The method according to claim 6, characterized in that: Detecting the first current includes measuring the voltage across the sensing resistor; and Determining the second current includes retrieving the second current from a lookup table based on the voltage difference.

Images