Patent Publication Number: US-11656291-B2

Title: Fast screening method for used batteries using constant-current impulse ratio (CCIR) calibration

Description:
FIELD OF THE INVENTION 
     This invention relates to battery screening methods, and more particularly for methods to screen aged or retired batteries for re-use. 
     BACKGROUND OF THE INVENTION 
     Electric batteries are widely deployed to power various systems. Traditionally many battery-powered systems have low power, but more recently demand has been growing for batteries for use in Electric Vehicles (EV&#39;s). Each EV requires a large battery pack to provide the significant power required to propel the EV. 
     More expensive Lithium-ion batteries are often used with EV&#39;s. The chemicals used in such advanced batteries pose disposal problems. Toxic chemicals can leak from disposed batteries and contaminate water sources. As EV&#39;s grow in popularity, additional burdens will be placed on disposal landfills as EV batteries are retired from service. 
     Recycling Lithium-ion and other batteries may require acids or furnaces that can cause additional environmental concerns. Low profit margins make battery recycling unattractive. 
     EV battery packs in particular may be replaced prematurely. The EV manufacturer&#39;s recommendations may dictate that repair shops swap out battery packs that fall below a fairly high discharge capacity needed to ensure sporty EV performance. 
     The EV battery packs may be removed before all of the battery cells have worn out. Especially for large battery packs, there may be many cells or groups of cells that still have a significant useful life remaining. These battery cells could be useful for powering other systems that have less stringent power requirements, such as communication and computer backup systems. Rather than dispose of the replaced EV batteries in a landfill or melting them down, re-using EV batteries may extend their useful lifetime by as much as 5 to 7 years, providing a more sustainable and environmentally-friendly approach. 
     The usability of a used battery can be defined by its State-of-Health (SOH) ratio. The SOH is the ratio of the battery&#39;s current energy storage capacity to that battery&#39;s initial or nominal energy storage capacity. Storage capacity is approximated by the battery&#39;s discharge capacity. 
       FIG.  1    shows a prior-art battery capacity test. Many variations are possible and  FIG.  1    is merely for illustration and is not necessarily representative of any particular battery test. 
     Accurately measuring a battery&#39;s full storage capacity can require a lot of time. Rapid charging or discharging can heat the battery and affect measurements. The battery initially may have a residual charge stored that needs to be discharged before capacity measurements. 
     The battery being tested is initially charged to 3.8 volts by applying a Constant Current (CC) having a value of 1C amps, and then once the voltage target of 3.8 volts is reached, reducing this current to maintain a Constant Voltage (CV) or 3.8 volts, step  202 . The current will fall during the CV phase until a low current value is reached, such as 0.01C, or until a time period has elapsed. 
     The battery is allowed to cool for one hour before the next step. Also, the battery may be allowed to cool for 10 minutes before the initial charging of step  202 . 
     After the 1-hour cooling period, the battery is discharged using a Constant Current (CC) having a fixed current value of 1C. Once the battery&#39;s voltage falls from 3.8 volts to 2.8 volts, discharging stops and the battery is allowed to rest and cool for an additional hour, step  204 . 
     The battery is then charged to a higher voltage of 4.25 volts by applying a Constant Current (CC) of 1C, and then when the battery voltage reaches 4.25 volts, Constant Voltage (CV) charging is performed where the current is reduced to maintain a constant battery voltage of 4.25 volts. After the charging current falls below a lower threshold, charging ends and the battery allowed to rest and cool for another hour, step  206 . 
     Finally the battery is slowly discharged using a Constant Current (CC) of only 5% of the earlier discharge current, or 0.05C. This discharge current continues until the battery voltage reaches 2.8 volts, step  208 . The battery&#39;s discharge capacity is measured by integrating the 0.05C discharge current over the time required to reach the 2.8-volt endpoint. This integrated current can be compared with the specified charge for a similar test on a new battery to calculate the SOH ratio. 
     The 0.05C low-current in discharge step  208  may require a long time period, such as 20 hours, when the 1C discharge of step  204  exceeds one hour. The total test time may exceed 26 hours, including the hour-long rest periods in steps  202 ,  204 ,  206 . This lengthy test time is costly and undesirable. 
     Existing fast screening methods such as Coulomb Counting and Internal Resistance methods may suffer from such long test periods. The goodness of fit may be lower for the Internal Resistance method. Complex setups may be needed with these methods. 
     What is desired is a screening method for used batteries. It is desired to measure the discharge capacity of used batteries using a higher current to speed testing. It is desired to more rapidly determine battery health using a combination of Constant-Current and Constant-Voltage methods. A calibrated method using Artificial Intelligence (AI) is desired to more rapidly screen used batteries. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    shows a prior-art battery capacity test. 
         FIGS.  2 A- 2 B  are graphs of CC-CV charging of new and used batteries. 
         FIG.  3    is a method to test and sort batteries based on their CCIR ratios measured during CC-CV charging. 
         FIG.  4    shows the CCIR test in more detail. 
         FIG.  5    shows a process to age a new battery to obtain CCIR values to model a calibration curve. 
         FIG.  6    is a graph of a calibration curve of SOH as a function of CCIR. 
         FIG.  7    illustrates a neural network for modeling the calibration curve of SOH as a function of CCIR. 
         FIG.  8    shows training a neural network using the measured SOH as the target to generate a used-battery calibration model. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention relates to an improvement in battery screening. The following description is presented to enable one of ordinary skill in the art to make and use the invention as provided in the context of a particular application and its requirements. Various modifications to the preferred embodiment will be apparent to those with skill in the art, and the general principles defined herein may be applied to other embodiments. Therefore, the present invention is not intended to be limited to the particular embodiments shown and described, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed. 
       FIGS.  2 A- 2 B  are graphs of CC-CV charging of new and used batteries. In  FIG.  2 A , a new battery is charged by applying a Constant Current (CC), such as 0.2C, until the battery voltage, curve  312 , reaches V 2  at time T 1 . The Constant Current may be determined based on the working current or the nominal current defined in the specification file of the batteries. Then at time T 1  the charging mode changes from CC to CV. In Constant-Voltage (CV) mode, the voltage applied to the battery remains fixed at V 2  while the charging current is adjusted. In particular, the charging current, curve  310 , drops from I 1  during CV mode as the battery nears full charge. CV mode can be determined and charging stopped when some endpoint is reached such as the charging current falling to a threshold value, such as 10% of CC, or 0.02C. 
     In  FIG.  2 B , an older used battery is charged using the same CC-CV method. An older used battery will store less charge than a new battery. Thus when the same constant current of I 1  is applied during CC mode, the used battery voltage reaches target voltage V 2  at an earlier time T 1 ′ for the used battery of  FIG.  2 B  than for the newer battery of  FIG.  1 A . Voltage curve  312 ′ rises more rapidly to V 2  for the older battery of  FIG.  2 B  than for curve  312  for the newer battery of  FIG.  2 A . 
     Internal resistance within the used battery may have increased, requiring a shorter time for CC mode. Current curve  310 ′ for the used battery of  FIG.  2 B  tends to drop off at a slower rate than does current curve  310  for the new battery of  FIG.  2 A . 
     The Constant-Current Impulse is initial constant-current impulse that is needed for the battery to reach a target voltage. The Constant-Current Impulse period for the new battery of  FIG.  2 A  is time T 1 , while the Constant-Current Impulse period for the used battery of  FIG.  2 B  is time T 1 ′. The charge Q CC  supplied to the battery during the Constant-Current Impulse is I 1 *T 1  for the new battery, and I 1 *T 1 ′ for the used battery. 
     The remaining charge provided to the battery during the CV period, Q CV , can be obtained by integrating the current over time during the CV period. Current curve  310  is integrated over the CV phase, from T 1  to the endpoint, to obtain Q CV  for the new battery, while current curve  310 ′ is integrated over the CV phase, from T 1 ′ to the endpoint, to obtain Q CV  for the used battery. 
     The aging or health of the battery can be expressed as the Constant-Current Impulse Ratio (CCIR) of the CC charge to the total charge, or
 
CCIR= Q   CC /( Q   CC   +Q   CV ).
 
     The inventors have realized that the shift in the CC-CV transition point T 1  can be used as a measure of a battery&#39;s aging or health. In particular, the inventors measure the CC charge Q CC  before T 1 , and then measure the CV charge Q CV  after T 1 , to be able to calculate the Constant-Current Impulse Ratio (CCIR). The CCIR is then compared to a calibration curve to determine the battery&#39;s State of Health (SOH). Batteries are sorted and discarded or reused based on their SOH values. 
       FIG.  3    is a method to test and sort batteries based on their CCIR ratios measured during CC-CV charging. The voltage of each used battery is measured as Vcel, step  102 . When Vcel is above a maximum voltage Vmax or is below a minimum voltage Vmin, step  104 , the battery is disposed of, step  106 . 
     Batteries having initial voltage Vcel between Vmin and Vmax, step  104 , are processed further. CCIR test  110 , shown in  FIG.  4   , is performed on the used battery to measure the charge during CC mode, Q CC , and the charge during CV mode, Q CV . With Q CC  and Q CV , the Constant-Current Impulse Ratio (CCIR) is calculated as Q CC /(Q CC +Q CV ). 
     The CCIR value calculated from the CC and CV charging measurements is compared to a calibration curve to obtain a State of Health (SOH) value, step  112 . A dataset of new batteries are aged by repeated charge/discharge cycles ( FIG.  5   ) and modeled using AI to obtain the calibration curve ( FIG.  6   ). 
     The SOH for the battery being tested is compared to a SOH threshold, such as 75%, step  114 , and batteries with SOH below the threshold are disposed of, step  106 . Batteries above the SOH threshold are sorted into quality bins based on their SOH values, step  116 . The sorted batteries may be reused for various applications based on the quality bins. Some applications may require higher-quality reused batteries than other applications. For example, batteries with SOH above 95% could command a higher price and be used in more demanding applications than batteries with SOH between 80 and 75%. 
       FIG.  4    shows the CCIR test in more detail. The battery to be tested is initially discharged using a constant current of 0.2C until a target minimum voltage Vmin is reached, step  142 . The battery is allowed to cool and rest for one hour. 
     After the rest period, the battery is charged with a Constant Current (CC) of 0.2C until a target maximum voltage Vmax is reached, step  144 . The constant current is integrated over time to obtain Q CC . Q CC  is stored or otherwise recorded. 
     Charging then switches from CC mode to CV mode. The battery voltage is held constant at Vmax while the charging current is reduced over time to maintain Vmax. One a minimum charging current Imin is reached, the CV charging mode ends, and the battery rests and cools for 3 minutes, step  146 . The current that falls from 0.2C at the start of the CV phase to Imin and the end of the CV phase is integrated over the time of the CV phase to obtain the CV charge Q CV . Q CV  is stored in a computer memory or otherwise recorded. 
     The current CCIR is calculated for the battery being tested, step  148 . CCIR is calculated as the ratio of Q CC  to Q CC +Q CV . CCIR represents the percentage of the total charge during the CC phase. The CCIR value for the battery being tested is stored, step  150 , such as being written into a computer memory such as a register file, SRAM, DRAM, or hard disk. 
       FIG.  5    shows a process to age a new battery to obtain CCIR values to model a calibration curve. The process of  FIG.  5    can be repeated using many new batteries to obtain a dataset that can model a calibration curve that can be used for sorting used batteries. 
     The new battery to be tested for calibration is initially discharged using a constant current of 0.2C until a target minimum voltage Vmin is reached, step  122 . The battery is allowed to cool and rest for one hour. 
     After the rest period, the battery is charged with a Constant Current (CC) of 0.2C until a target maximum voltage Vmax is reached, step  124 . The constant current is integrated over time to obtain Q CC . Q CC  is stored or otherwise recorded. 
     Charging then switches from CC mode to CV mode. The battery voltage is held constant at Vmax while the charging current is reduced over time to maintain Vmax. One a minimum charging current Imin is reached, the CV charging mode ends, and the battery rests and cools for 3 minutes, step  126 . The current that falls from 0.2C at the start of the CV phase to Imin and the end of the CV phase is integrated over the time of the CV phase to obtain the CV charge Q CV . Q CV  is stored in a computer memory or otherwise recorded. 
     The battery is discharged using a constant current of 0.2C until Vmin is reached, step  128 . The 0.2C constant current is integrated over time to obtain the aged battery&#39;s present charging capacity, Cnow. The aged battery&#39;s current State of Health (SOH) is calculated as Cnow/Cinit, where Cinit is the initial charging capacity of the battery before aging, which can be measured in step  128  before any aging has occurred from charge/discharge cycling in step  134 . 
     The battery rests and cools for 2 hours, step  128 . CCIR is calculated from Q CC  obtained from step  124 , and Q CV  obtained in step  126 , as CCIR=Q CC /(Q CC +Q CV ). Both CCIR and SOH are stored in a computer memory, step  130 . 
     When SOH is above 30%, step  132 , the battery is aged by performing  50  discharge/charge cycles using a constant current of 1C, step  134 . Then the CCIR sequence is repeated starting at step  122 . The battery&#39;s SOH is gradually reduced by the discharge/charge cycling of step  134 . Once the SOH falls below 30%, step  132 , the stored CCIR and SOH data is applied to an AI engine to generate a model of SOH as a function of CCIR, the calibration curve, step  138 . 
       FIG.  6    is a graph of a calibration curve of SOH as a function of CCIR. The SOH, CCIR datapoints obtained by the aging process of  FIG.  5    is plotted as points in the graph. Calibration curve  302  is a best-fit function that best fits these data points. Calibration curve  302  is used by step  112  of  FIG.  3    to obtain a modeled SOH value for a CCIR calculated from measurements during CC-CV charging of a used battery. 
     Calibration curve  302  can be obtained from AI modeling of these (SOH, CCIR) datapoints, such as using a least-squares method to find parameters to optimize using a neural network. Other statistical methods could also be used. 
     Artificial Neural Networks (ANN) may be used to generate a model of SOH as a function of CCIR. Artificial Neural Networks are especially useful for processing large amounts of non-linear data in complex ways that are hard to define using traditional computer programs. Instead of being programmed with instructions, training data is input to a neural network and compared to the expected output, then adjustments are made within the neural network and the training data is again processed and outputs compared to generate further adjustments to the neural network. After many such training cycles, the neural network is altered to efficiently process data similar to the training data and expected outputs. Neural networks are an example of machine learning, since the neural network learns how to generate the expected outputs for the training data. Real data similar to the training data can then be input to the neural network to process live data. 
       FIG.  7    illustrates a neural network for modeling the calibration curve of SOH as a function of CCIR. Input node  12  receives input data CCIR, while output node  60  outputs the result of the neural network&#39;s operations, SOH_CALC, which is the modeled SOH value for the input CCIR value. Two layers of operations are performed within this neural network. Nodes  20 ,  22 ,  24 , . . .  28 ,  29 , each take inputs from input node  12 , perform a wavelet function operation, and send an output to nodes in the second layer. Second-layer nodes  52 ,  54 , . . .  58 ,  59  also receive multiple inputs, combine these inputs to generate an output, such as by generating products, and sends the outputs on to third-level node  60 , which similarly combines or sums the inputs to generates an output. 
     The inputs at each level are typically weighted, so weighted sums (or other weighted operation results) are generated at each node. Each input at a node can be assigned a weight that is multiplied by that input before all the weighted inputs are summed, multiplied together, or otherwise operated upon by the node to generate the node&#39;s outputs. These weights are designated A ij , B ij  to nodes  20 ,  22 ,  24 , . . .  28 ,  29  in the wavelet layer, and are designated W ij  for nodes  52 ,  54 , . . .  58 ,  59  in the product layer. The values of these A ij , B ij , W ij  weights are adjusted during training. Through trial and error or other training routines or learning algorithms, eventually higher weights can be given for paths that generate the expected outputs, while smaller weights assigned to paths that do not generate the expected outputs. The machine learns which paths generate the expected outputs and assigns high weights to inputs along these paths. 
     These weights can be stored in weights memory  100 , or in another memory. Since neural networks often have many nodes, there may be many weights to store in weights memory  100 . Each weight may require multiple binary bits to represent a range of possible values for that weight. Weights often require 8 to 16 bits. Weights memory  100  can be a SRAM, DRAM, flash memory, disk, or various combinations of these or other computer memory devices. 
       FIG.  8    shows training a neural network using the measured SOH as the target to generate a used-battery calibration model. Measurements of aging batteries are made and the measured CCIR and SOH data are stored in step  130  of  FIG.  5   . The measured CCIR data are used as training data  34 , CCIR_MEAS. The measured SOH data that corresponds to the CCIR_MEAS values are recorded as target data  38 , SOH_MEAS. Each value of SOH_MEAS corresponds to a value of CCIR_MEAS that were measured at the same time in the life of the aging battery tested using the process of  FIG.  5   . 
     Neural network  36  receives training data  34  and a current set of weights A ij , B ij , W ij , and operates on training data  34  to generate a result. This generated result is the modeled value of SOH, SOH_CALC. The generated result from neural network  36 , SOH_CALC, is compared to target data  38 , SOH_MEAS, by loss function  42 , which generates a loss value that is a function of how far the generated result is from the target. The loss value generated by loss function  42  is used to adjust the weights applied to neural network  36 . Many iterations of weights may be applied by loss function  42  onto training data  34  until a minimum loss value is identified, and the final set of weights used to model the calibration curve. 
     Rather than generate a single value of SOH_CALC, neural network  36  may have multiple output nodes  60  to generate many SOH_CALC values in parallel from the parallel inputs of CCIR_MEAS. Loss function  42  may compare in parallel the many values of SOH_CALC to many values of SOH_MEAS to generate a loss function value. 
     ALTERNATE EMBODIMENTS 
     Several other embodiments are contemplated by the inventors. For example the order or sequence of some steps may be changed. Storing the CCIR and SOH data, step  130  in  FIG.  5   , could occur during step  128  rather than after this step, as one example. Various modifications to the neural network may be used, such as having more layers or weights or different functions. More sample points may be inputted, and more iteration cycles or epochs may be used. A very good fit for the model of calibration curve  302  can be obtained using neural network modeling and optimization. 
     Calibration curve  302  may be implemented as a lookup table that outputs a modeled SOH value when a measured CCIR is input to the lookup table. Calibration curve  302  could also be implemented as a function performed by a processor such as a microprocessor, central processing unit, arithmetic logic unit, co-processor, or other programmed machine. Memory may be shared or separate, local, remote, or various combinations, and processors and other computational blocks may be shared, distributed, local, remote, or various combinations. 
     While an endpoint for calibration is shown as step  132  in  FIG.  5    to be based on a SOH threshold, collection of CCIR, SOH data could be halted after a certain number of datapoints are collected, or after a certain period of time has elapsed, or a certain number of aging cycles or repats of step  134 , or some other criteria. The testing technician may simply run out of time and halt further data collection, and proceed to step  138  to generate the model for calibration curve  302 . An initial model could be generated for use, and then later a more refined model from more datapoints is substituted. 
     Overall testing time for a used battery can be reduced from 26 hours to 6 hours using CCIR modeling and 0.2C rather than 0.05C. The improved accuracy of the SOH estimating method may allow for a higher current to be used with a faster test time. 
     While integrating current to generate Q CC  and Q CV  have been described, for constant current integrating may be multiplying the constant current by the time period that the constant current is applied. Various approximations for integrating may be applied, such as using PWL or multiplying current by time for each of several short time periods. Coulomb counting methods may be used for integrating charge over time. Integrating methods may accumulate the charge transferred over small time periods. 
     Although an initial deep discharge is not needed, batteries could be pre-discharged or pre-charged in additional steps if desired. Rest periods could be shortened or lengthened. A simple battery bench test setup may be used rather than complicated test benches. Rather than define CCIR as Q CC /(Q CC +Q CV ), an alternative CCIR could be defined as Q CV /(Q CC +Q CV ), and calibration curve  302  adjusted for the new definition. 
     The calibration curve can be approximated by one or more functions, such as a Piece-Wise-Linear (PWL) or multi-variable function. SOH could be modeled by an equation with terms such as square roots, logarithms, etc., of CCIR. 
     The temperature of the battery during testing should be maintained at a constant value, such as room temperature. The length of a rest period after charging or discharging the battery may depend on the charge/discharge current and the thermal properties of the battery. The battery&#39;s thermal properties may change with age, such as due to increased internal resistance causing enhanced heating of older batteries. 
     Many parameters and values may be changed from the examples given. Voltages such as Vmax, Vmin, V 2 , etc. and currents C, I 1  may have different values, or different ratios to one another. Imin can be 0.02C, Vmax can be 4.2 volts, Vmin can be 2.75 volts, as just one of many examples. The number of discharge/charge cycles used for each step in the aging process could be adjusted to other values, such as 10 cycles, 100 cycles, etc., depending on how precise calibration curve  302  needs to be. 
     The number of batteries tested for calibration could be a relatively small number such as 3 batteries when AI modeling is effective, or more batteries, such as 100 batteries, may be tested for calibration when less-effective modeling is used, or when more accurate calibration is needed. Some battery reuse applications may not require accurate SOH modeling. Ideally, the batteries tested for calibration closely match the batteries being screened, such as having the same manufacturer and model. The battery being tested can be a single battery or a battery pack, single cell or multi cell. 
     Some test error may be tolerated, depending on the application or intended use of the reused batteries. A test error of +/−3% of the actual SOH may be obtained in some cases. Test time may be reduced when a larger current is able to be used for a desired test accuracy or error tolerance. 
     Some embodiments may not use all components. Additional components may be added. Loss function  42  may use various error/loss and cost generators, such as a weight decay term that prevents weights from growing too large over many cycles of training optimization, a sparsity penalty that encourages nodes to zero their weights, so that only a small fraction of total nodes are. Many substitutions, combinations, and variations are possible. Other variations and kinds of loss or cost terms can be added to loss function  42 . The values of the relative scaling factors for the different cost functions can be adjusted to balance the impact of the various functions. The training endpoint for the neural network may be set for various combinations of conditions, such as a desired final accuracy, an accuracy-hardware cost product, a target hardware cost, etc. 
     Neural network  36 , loss function  42 , and other components may be implemented in a variety of technologies, using various combinations of software, hardware, firmware, routines, modules, functions, etc. The final product, Calibration curve  302  or a calibration function generator, may be derived from neural network  36  with the final weights, and might be implemented as a program module, or in an Application-Specific Integrated Circuit (ASIC) or other hardware to increase processing speed and lower power consumption. 
     The background of the invention section may contain background information about the problem or environment of the invention rather than describe prior art by others. Thus inclusion of material in the background section is not an admission of prior art by the Applicant. 
     Any methods or processes described herein are machine-implemented or computer-implemented and are intended to be performed by machine, computer, or other device and are not intended to be performed solely by humans without such machine assistance. Tangible results generated may include reports or other machine-generated displays on display devices such as computer monitors, projection devices, audio-generating devices, and related media devices, and may include hardcopy printouts that are also machine-generated. Computer control of other machines is another tangible result. 
     Any advantages and benefits described may not apply to all embodiments of the invention. When the word “means” is recited in a claim element, Applicant intends for the claim element to fall under 35 USC Sect. 112, paragraph 6. Often a label of one or more words precedes the word “means”. The word or words preceding the word “means” is a label intended to ease referencing of claim elements and is not intended to convey a structural limitation. Such means-plus-function claims are intended to cover not only the structures described herein for performing the function and their structural equivalents, but also equivalent structures. For example, although a nail and a screw have different structures, they are equivalent structures since they both perform the function of fastening. Claims that do not use the word “means” are not intended to fall under 35 USC Sect. 112, paragraph 6. Signals are typically electronic signals, but may be optical signals such as can be carried over a fiber optic line. 
     The foregoing description of the embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto.