Estimation of battery state of health and of battery state of charge

Abstract

Systems and methods relating to the estimation of battery state of health (SOH) and / or battery state of charge (SOC) without the need for lengthy charge-discharge cycles. A testing system uses a machine learning trained estimation model to provide estimates for battery SOH or battery SOC based on battery test results for specific battery characteristics based on battery chemistry. The testing system, once the battery chemistry is entered, automatically applies tests to the battery and calculates values for specific characteristics based on the test results. The values for the specific characteristics are then used by the trained estimation model to estimate battery SOH or battery SOC. The system is based on a data driven approach to estimating battery state of health and / or battery state of charge based on the selection of specific characteristics (or parameters) and correlating values for these characteristics with battery state of health and / or battery state of charge.

Description

ESTIMATION OF BATTERY STATE OF HEALTH AND OF BATTERY STATE OF CHARGECROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to US Provisional Patent Application No. 63 / 490,448, entitled “BATTERY STATE OF HEALTH ESTIMATION”, filed on March 15, 2023, the entire disclosure of which is hereby incorporated by reference.TECHNICAL FIELD

[0002] The present invention relates to battery state of health and battery state of charge. More specifically, the present invention relates to systems and methods for estimating battery health or battery state of charge without the need for lengthy charge / discharge cycles or for lengthy full charge-discharge capacity measurements.BACKGROUND

[0003] As the number of electric vehicles (EVs) and plug-in electric vehicles (PHEVs) in the world is projected to increase significantly, the investigation of potential applications of used EV batteries with residual capacity over 70-80% of their capacity is necessary, especially for expensive EV batteries. Although EV batteries after 10 years of average use or warranty replacement can be recycled for material recovery, there are other more economical options. One option is to remanufacture End-of-Life (EOL) EV batteries and to reuse them in mobile applications. A second option is to repurpose End-of-Life EV batteries for stationary energy storage applications such as backup power, peak shaving, arbitrage, and balancing intermittent renewable generation for homes and commercial buildings.

[0004] Remanufactured batteries can be utilized in vehicles after preliminary screening and diagnosis of modules and cells of the battery packs to lower vehicle batterycosts. The remanufacturing process includes partial disassembly of the battery pack or module, removal of substandard cells, replacement of these cells, and reassembly of the module and pack. In addition to the growth of EVs and PHEVs markets, advances in longer life EV batteries and potential benefits of energy storage integrated in power grids have provided new opportunities for secondary use of EV batteries in stationary applications (in addition to the primary use of EV batteries in vehicle-to-X applications where X refers to houses, buildings, or grids).

[0005] The design and implementation of a prognosis and state management architecture [prognosis and health management (PHM)] is considered as key to deal with the problem of battery durability. The deployment of a PHM solution allows for the anticipation and avoidance of failures, evaluation of the state-of-health (SOH), estimation of the residual useful life (RUL) and, on the basis of such information, it becomes possible to envision control and / or maintenance actions to ensure the continuity of the battery operations. However, the different batteries states, such as state-of-charge (SOC), SOH, and RUL, are not directly observable. These usually require estimation and prediction algorithms that provide diagnosis and prognosis of the battery.

[0006] The main role of the diagnosis is to estimate the failure parameters in order to identify the EOL of the system once the faulty mode is detected. For electric vehicle applications, the limit is generally set to 80% of the nominal capacity. Other studies define another limit based on the internal resistance when the internal resistance increases to 160% of its initial value for the same SOC and operating temperature condition. On the other hand, the prognosis models allow for the prediction of the EOL based on the estimation of failure parameters and the SOH diagnosis of the battery storage system. In the case of lithium-ion batteries, the diagnostic model should be able to identify aging parameters such as capacity and internal resistance in real time for online estimation of the battery storage system’s SOH. Based on the diagnosis results, the main role of the prognosis model is to predict the future degradation and to estimate the RUL of batteries, thereby presenting the useful life left on an asset at a particular time of operation.

[0007] In order to separate EOL batteries into recycling or remanufacturing / repurposing applications, it is necessary to diagnose, screen and select used EV batteries by using safe, reliable and efficient methodologies.

[0008] Current SOH assessment technologies are based on determining battery health by running the battery through multiple charge / discharge cycles. Unfortunately, such testing can take several hours before a suitable SOH is found. In addition, rapid charge / discharge cycles can greatly degrade battery condition.

[0009] For a battery state of charge (SOC), it is known that the state of charge (SOC) is the ratio of the capacity (amp-hours) available in a battery at a specific point in time relative to its full capacity. International air transport limits the SOC to 30% of the rated capacity of batteries. Li battery shipping guidelines specify that Li ion cells and batteries must be offered for transport with a state of charge (SOC) not exceeding 30% of their rated capacity. However, there is no portable SOC estimator available for checking the SOC of batteries prior to transportation by air, rail, etc., in a fast and reliable manner at airports or any rail stations. Therefore, there is a need for a portable tool that provides fast and reliable off-board SOC estimation of batteries to be transported.

[0010] There is therefore a need for systems and methods that provide battery health, battery SOH, and battery SOC that is quicker than current technologies while still providing useful results.SUMMARY

[0011] The present invention provides systems and methods relating to the estimation of battery state of health (SOH) and / or battery state of charge (SOC) without the need for lengthy charge-discharge cycles or for lengthy full charge-discharge cycles. A testing system uses a machine learning trained estimation model to provide estimates for battery SOH or for battery SOC based on battery test results for specific battery characteristics based on battery chemistry. The testing system, once the battery chemistry is entered, automatically applies tests to the battery andcalculates values for specific characteristics based on the test results. The values for the specific characteristics are then used by the trained estimation model to estimate battery SOH or to estimate battery SOC. The system is based on a data driven approach to estimating battery state of health and / or battery state of charge based on the selection of specific characteristics (or parameters) and correlating values for these characteristics with battery state of health and / or battery state of charge.

[0012] In a first aspect, the present invention provides a method for estimating a state characteristic of a battery, the method comprising: a) receiving input relating to a composition of said battery; b) based on said input received in step a), determining conditions to be used in testing said battery; c) configuring automated testing equipment based on said conditions such that said testing equipment tests said battery for specific characteristics using said conditions, said testing equipment being for testing said battery and said testing equipment being coupled to said battery; d) automatically testing said battery using said testing equipment to result in test results; e) deriving said specific characteristics of said battery from said test results obtained in step d); e) using a trained model to estimate said state characteristic of said battery based on said specific characteristics; wherein said state characteristic of said battery is at least one of :- a state of health of said battery; and- a state of charge of said battery.

[0013] In a second aspect, the present invention provides a system for testing or estimating a state characteristic of a battery, the system comprising:- a database having entries denoting multiple entries for different battery chemistries and specific characteristics for each battery chemistry that are to be used in estimating said state characteristic of said battery for each battery chemistry of said different battery chemistries, said database also including entries of testing conditions to be used when testing each battery chemistry;- a testing module for testing a battery, tests applied by said testing module being based on said specific characteristics for battery chemistry of said battery and on said testing conditions for said battery chemistry of said battery;- a trained model for estimating said state characteristic of said battery based on calculated specific characteristics;- a main processor for receiving input relating to said battery, said main processor being coupled to said database and to said testing module and to said trained model, said input being used to determine a battery chemistry of said battery; wherein said main processor retrieves specific characteristics and testing conditions from said database for said battery chemistry of said battery; said main processor configures said testing module to apply suitable tests to said battery based on retrieved specific characteristics and on testing conditions for said battery chemistry of said battery; said testing module sends test results from said suitable tests to said main processor and said main processor calculates said calculated specific characteristics from said test results; said calculated specific characteristics are sent by said main processor to said trained module;wherein said state characteristic of said battery is at least one of:- a state of health of said battery; and- a state of charge of said battery.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The embodiments of the present invention will now be described by reference to the following figures, in which identical reference numerals in different figures indicate identical elements and in which:FIGURE 1 is a block diagram of a system according to one aspect of the present invention;FIGURE 2 is a diagram showing the current-voltage profile for a pulse current test to be used to test a battery according to another aspect of the present invention;FIGURE 3 is a flowchart detailing the steps in a method according to another aspect of the present invention;FIGURE 4A is a chart for use in SOH estimation and which displays measured various resistance values as a function of SOC;FIGURE 4B is a chart displaying measured various resistance values as a function of Voltage Vo; andFIGURE 5 is a flowchart detailing the steps of construction and use of one embodiment of a SOH / SOC estimation database and its use during battery testing.DETAILED DESCRIPTION

[0015] It should be clear that, for known on-board SOC estimation, data collected during the operation of electric vehicles (EV) and of stationary battery energy storage systems (BESS) may be used. However, off-board SOC estimation may not need to use battery management system (BMS) information and data collected from the operation of batteries. Off-board SOC estimation may use a simple testing protocol, a fast estimation algorithm, and a portable tool to utilize a limited dataset to be collected indoor or outdoor.

[0016] Referring to Fig 1, a block diagram of a system according to one aspect of the present invention is illustrated. The system 10 includes a main processor 20, a database 30, a testing module 40, and a trained estimation module 50. The testing module 40 is coupled to a battery 60 and is enabled to test the battery and to gather test results from the tests.

[0017] The main processor 20 receives the chemistry and other parameters for the battery to be tested. The processor 20 then uses the specific battery chemistry and other parameters are then used to determine, from the database, what specific characteristics from the battery 60 are to be tested / derived based on the battery chemistry, and what are the conditions and / or parameters to be used when testing the battery 60.

[0018] Once the conditions and characteristics for testing are determined by the processor, these are then used to configure the testing equipment in the testing module 30 so that the proper tests (and testing parameters) can be automatically applied to the battery 60.

[0019] The testing module 30 applies the tests to the battery 60 and the test results are sent to the processor 20. The processor 20 then derives the specific characteristics from the test results. The calculated values for the various specific characteristics are then sent to the trained estimation module 50. The module 50 then determines what battery SOH / battery SOC corresponds to the calculated values and outputs this estimated battery SOH / estimated battery SOC 70.

[0020] The system for SOH estimation is based not on the calculation of a state of health or of charge / discharge cycles but is, rather, based on a data driven approach. Instead of calculating values based on tested results (such as battery capacity assessed through repeated charge / discharge cycles, etc.) and determining this as a percentage of a rated capacity, correlations are determined between gathered experimental data for battery state of health and specific characteristics of the battery being tested. As is known, current technology calculates a ratio of sensed battery capacity with rated capacity to find the state of health. The present invention is based on an analysis of gathered experimental data for batteries of a specific chemistry to determine which specific characteristics are shown to have a pseudo-linearity correlation with respect to battery state of health. These specific characteristics are then calculated / tested for a specific battery. The calculated / tested values resulting from the test are then run through a trained model to estimate the specific battery’s state of health. For clarity, the trained model is trained, using machine learning techniques, on a library of experimentally determined data that includes readings for those specific characteristics and corresponding SOH values. Each battery dataset in the experimentally determined data further identifies the chemistry of the battery alongside other relevant battery conditions (such as, for example, the manufacturer and model year) for each battery dataset that has been used to train the estimation module 50. As such, each battery dataset tracks a number of relevant characteristics for the battery as being tested across a wide range of voltages and provides a final SOH assessment for each of the datasets. These conditions and characteristics serve to determine the relevant testing parameters and tests for a given battery chemistry and the relationships between those parameters and the SOH of the battery.

[0021] Similarly, the system for SOC estimation is based not on the calculation of a state of charge or of a charge / discharge cycle to measure the full charge / discharge capacity but is, rather, based on a data driven approach. Instead of calculating values based on tested results (such as battery full charge / discharge capacity assessed through a charge / discharge cycle, etc.) and instead of determining the level of the capacity (amp-hours) available in a battery relative to its full capacity, correlations are determined between gathered experimental data for battery stateof charge and specific characteristics of the battery being tested. As is known, current technology calculates a ratio of sensed available batery capacity with its full capacity to find the state of charge. The present invention is based on an analysis of gathered experimental data for batteries of a specific chemistry to determine which specific characteristics are shown to have a pseudo-linearity correlation with respect to batery state of charge. These specific characteristics are then calculated / tested for a specific batery. The calculated / tested values resulting from the test are then run through a trained model to estimate the specific batery’s state of charge. For clarity, the trained model is trained, using machine learning techniques, on a library of experimentally determined data that includes readings for those specific characteristics and corresponding SOC values. Each batery dataset in the experimentally determined data further identifies the chemistry of the battery alongside other relevant batery conditions (such as the manufacturer and model year for example) for each batery dataset that has been used to train the estimation module 50. As such, each batery dataset tracks a number of relevant characteristics for the batery as being tested across a wide range of voltages and provides a final SOC assessment for each of the datasets. These conditions and characteristics serve to determine the relevant testing parameters and tests for a given batery chemistry and the relationships between those parameters and the SOC of the battery.

[0022] Referring to Figs. 4A and 4B (for use in SOH estimation) the batery datasets show that individual characteristics of interest are fairly consistent over tests at a wide range of conditions. In the example depicted in Fig. 4A, various resistance values are displayed at given SOC values for the batery. In the example of Fig. 4A, in the range of approximately 5% - 25% SOC that information displays a much less constant profile. This allows for better analysis of those values within this “distinguishable zone” of SOC values. Similarly, Fig. 4B shows a similar distinguishable zone over a range of voltage Vo levels. These values from within the distinguishable zone can be more readily correlated to SOH given that the observed values more clearly change in a linear way over the distinguishable zone allowing for simplified correlation. The trained estimation model gives particular weight to the characteristic information of interest for tests occurring within thedistinguishable zone in determining relationships between those values and ultimate SOH. This limiting of voltages of interest allows for the testing of later batteries to be conducted over a much narrower range of voltages than classical full SOH estimation testing. While SOC is used for the testing in the example depicted in Fig. 4A, and voltage Vo is used for the testing in the example depicted in Fig. 4B, other voltage values and ranges could be selected based on determining similar “distinguishable zones” where the data of interest displays profiles that can be more readily mapped on to SOH. As explained above, Figs. 4A and 4B relate to the estimation of SOH.

[0023] The trained model is thus trained such that, given specific values for these specific characteristics, within the distinguishable zone for a given battery chemistry, the trained model estimates the SOH / SOC for that specific battery.

[0024] For clarity regarding the example shown in Fig. 4B, a few voltages or at least one voltage is preferably selected in a specific voltage range to obtain effective parameters for LMO / NMC LIBs, e.g. 3.5-3.7V (Preferred V: 3.65 / 3.60 / 3.55V) during the discharging process. As well, for different chemistries, the preferred voltage ranges during discharging are: LMO / NMC: 3.50-3.70V;LFP: 3.0-3. 15V;NMC111 : 3.25-3.50V;NMC622: 3.25-3.45V;NCA: 3.0-3.30V.

[0025] For greater clarity, the testing applied to the battery may involve a pulsed current test where a pulsed current is applied to the battery and the resulting voltages are sensed at specific points in time after the current is applied. The specific characteristics for the battery may include specific resistances and capacitances that can be calculated based on the sensed voltages and currents from the pulsed current test. As noted above, different battery chemistries may require different specific characteristics. Accordingly, different battery chemistries may have different specific characteristics that correlate with battery SOH / SOC.

[0026] It should also be clear that optimal testing conditions may be different for different battery chemistries. Thus, for some battery chemistries, optimal test results may be obtained when the results are within a specific distinguishable zone range. These testing conditions and the specific characteristics to be used in assessing SOH / SOC, both of which are dependent on battery chemistry, are stored in the database. Thus, once the system receives the battery chemistry of the battery to be tested, the relevant specific characteristics and the relevant testing conditions to be used for the battery are retrieved from the database. These testing conditions and specific characteristics are then used to configure the testing module if necessary. As an example, if one or more specific characteristics for the battery require multiple voltage readings at multiple current inputs, the number of times a current is passed to the battery, the level of those currents, and the voltage reading to be taken are used to configure the testing module. Similarly, it has been found that voltage readings within a specific distinguishable zone range provide the best results for a specific battery chemistry, then this desired specific range is used to collect the data for calculating the parameters used to configure the testing module. As such, different current levels can be applied to the battery being tested until voltage readings in the desired range are read. These voltage readings, in conjunction with the other sensed data points for that battery, are then stored as test results for that battery. Other data points may include current, voltage, temperature, and how these readings develop / change over time as the battery is tested.

[0027] In one aspect of the present invention, the testing applied to the battery may take the form of a pulsed current test. This test involves applying a current pulse to the battery with the pulse having a specific and fixed duration. At specific points in time after the pulse is applied, voltage readings are taken at the battery. It should be noted that the pulse is applied at a non-zero initial level. This means that, when the current pulse is applied, there is a non-zero current being fed to the battery. The current and voltage profile for this current pulse is shown in Figure 2.

[0028] As can be seen from Fig 2, the current pulse, at a level DELTA-11 higher than the initial non-zero current level, is applied for 30 seconds and is then interrupted to a zero current level. Voltage readings are taken at 0, 2, 10, 18, 30, 32, 40, 48, 60 and90 seconds after the current pulse is applied. The current pulse level can be read or can be known. The voltage readings and the current readings can be used to calculate relevant resistance / impedance values as necessary. It should be clear that this current and voltage profde for a current pulse test is just one possible profde that may be applied to the battery. In some implementations, the current pulse and the relevant readings are taken multiple times and the current pulse levels may be different for different pulses.

[0029] As can be seen, each application of the pulse current test only takes 90 seconds. In one implementation, the test is repeated three times to result in a data set. As can be imagined, three 90 second tests will take significantly less time than the several hours it takes for the charge-discharge cycles that current battery testing technologies require.

[0030] In one implementation, use of one current injection for 30 seconds at a specific voltage during a charging or discharging process may be enough for SOH estimation. A set of a current injection and a current interruption is good for comparison of parameters in two different modes, but current interruption mode is an option. In one implementation, in terms of time frame for testing, the testing only needs 30 seconds of current injection at one selected voltage to a maximum of 4.5 minutes (i.e., 90 seconds x 3 times at three different voltages) as a pulsed current testing time. Note that this time frame does not include the preconditioning period to reach the selected testing voltage during charging (for a unidirectional EV charger) or discharging (for all the general testing equipment or conditions including bidirectional EV charger application).

[0031] In terms of pre-conditioning, pre-conditioning may take the following steps: 1) Decreasing or increasing a resulting voltage to a voltage range selected for a specific battery chemistry under a selected initial current as a voltage preadjustment step, and 2) After reaching a selected voltage, applying a pulsed current with a specific current based on a selected C-rate (current level) for 30 seconds as a current injection step, and then cut-off the pulsed current for 30-60 seconds as a current interruption step for data collection. Therefore, if the voltage of batteries is not within a voltage range specified for a specific chemistry, then the battery shouldbe charged or discharged to the specified voltage range before applying the pulsed current. If the initially measured voltage of the battery is too high to apply the pulsed current for SOH estimation, then a selected current can be applied to discharge the battery, resulting in a decreasing of the voltage.

[0032] In terms of the specific characteristics to be used in estimating a battery's state of health (SOH), analysis and experimental data have shown that the following specific characteristics may be used to estimate a battery's SOH / SOC:Ro - Ohmic resistanceRCT - Charge transfer resistanceRp - Polarization resistanceRix - Total internal resistanceCPEi - double layer capacitanceVo - last voltage before current injection with anon-zero initial current

[0033] While the above six parameters can be calculated or directly measured from the testing data, a seventh parameter, a function of specific characteristics or parameters may also be used. Based on analysis of the training battery datasets, certain issues have been identified. Although general battery chemistry is predominantly consistent from battery to battery, there can be minor variances in the recorded characteristics between different model years, different manufacturers, etc. While specific causation of these minor variances is unclear, this is likely due to minor changes in where chemicals or components are sourced or modifications in the processes at a given manufacturer over time along with other similar differences. Due to these minor variances it becomes more difficult to properly formulate a more general relationship between battery chemistry and SOH / SOC estimation and these minor variations can compound to introduce unacceptable error into the SOH / SOC estimation over time. As such, while the above six parameters are sufficient for estimating SOH where the battery being tested directly matches the chemistry and battery conditions of a sufficient number of the training battery datasets, a broader general estimation requires the use of a further function.

[0034] The seventh parameter is determined by looking at a number of battery training datasets where the battery chemistry is the same but where the battery conditions between the training datasets differ. Within this collection of battery training datasets, the above identified six parameters can be compared against one another to find a relationship function which exhibits the best pseudo linearity across this subset of the training datasets. This function can then be used to define the seventh parameter, which works akin to a smoothing function to reduce error across the varying battery conditions and this provides a more generalized function for estimating SOH for a given battery chemistry.

[0035] Such a function may be determined using high quality experimental data and machine learning / Al techniques. Similarly, the specific characteristics and the function of specific characteristics may be determined using a Simplified Determination Method. Such a method was used in one implementation and provided useful results that were comparable to the more time-consuming curve fitting method.

[0036] It should be noted that the use of the seventh parameter is extremely effective in enhancing the reliability and accuracy of SOH estimation. The seventh parameter having the best pseudo-linearity as a function of SOH values may be identified by comparing the linearity of a combination of the 1 st to 6th parameters and then using specific constants in a suitable equation as shown below. It should, however, be noted that this seventh parameter has different equations for different battery chemistries because of different charge and discharge behaviours. The seventh parameter is simply a function of the ohmic resistance, the charge transfer resistance, and of the total internal resistance: f(Ro+Rct, Rtotal) where: Ro+Rct=Ohmic Resistance + Charge Transfer Resistance, Rtotai=Total Internal Resistance;

[0037] In addition, the specific characteristics may also be determined using suitable resistor-capacitor equivalent circuit modelling for the batteries.

[0038] It should be clear that numerous analyses of SOH data and specific characteristic data may need to be performed for each battery chemistry to produce the specific characteristics and parameters to be used for each battery chemistry. As well, batteries with specific chemistries that have been manufactured by specific manufacturers may also need their own specialized set of specific characteristics or parameters to be used in SOH testing and estimation.

[0039] Once the pulse current test has been applied as necessary to the battery being tested and once the desired range of voltages / readings has been achieved, the test results are obtained. As noted above, the pulse current test is used with different current levels to obtain voltage readings that are within the desired range. Once this desired range of voltage readings has been reached, the current levels that produce the desired range of voltages are used with repeated applications of the pulse current test. The voltage readings for these repeated applications, as well as the current, time, and temperature readings, form the test result data set for that specific battery being tested. This test result data set can then be used to calculate values which are to be used in estimating the battery SOH.

[0040] It should be clear that, for SOC estimation, all of the above-noted pulsed current profile and selected parameters (except for the seventh parameter) are used in same way as for SOH estimation, but in the full charge / discharge voltage range of a battery with a specific chemistry. Thus, for SOC estimation, the full voltage range for charge / discharge can be used for pulse current testing. This voltage range can, of course, differ for different battery chemistries. For the seventh parameter for SOC estimation, this seventh parameter is simply a function of the last voltage before current injection with a non-zero initial current and a selected voltage for a specific battery chemistry: f(Vo, Vo) where: Vo = Last voltage before current injection with a non-zero initial current, and Vc = A selected voltage for specific battery chemistry.

[0041] In some embodiments the method and system may also include means for checking the temperature and / or the current (or C-rate) applied during the pulsed currenttesting, of the battery that is being tested as preliminary conditions for the test. Each of these factors can affect the measurement of the first five parameters (Ro; RCT; RP; RIN; and CPE1) and, accordingly, the determination of the respective 7thparameter for either SOH or SOC testing (f(Ro+Rct, Rtotai) and f(Vo, Vc) respectively). As such, the model can also include separate look-up tables for normalizing or correcting the above parameters in respect of the battery temperature and applied current (or C-rate) for use in generating the estimated SOH or SOC values.

[0042] As shown in Fig. 5, the trained model that estimates the battery SOH / SOC using the calculated values can be trained using a BRANN (Bayesian Regularized Artificial Neural Network) and suitable data sets for batteries of a specific chemistry with the data sets including values for the specific characteristics and their corresponding battery SOH / SOC values. Other neural network / machine learning methods for training a suitable model may, of course, be used with such data sets. The trained model would be trained such that, given a set of values for the specific characteristics, the model can provide a SOH / SOC estimate that is based on the training data set. As can be imagined, the quality of the training data set can be quite important to the SOH / SOC estimate produced.

[0043] In one implementation, data sets for LMO / NMC EV batteries were analyzed and the 6-7 specific characteristics listed above were selected for SOH estimation. For batteries with this chemistry (LMO being lithium ion manganese batteries while NMC are nickel manganese cobalt batteries), experiments and data have shown that voltage readings in the range of 3.5 -3.7 V (preferably 3.65 / 3.60 / 3.55 V) produce test result data that greatly reduces the time required for SOH estimation. This implementation has produced SOH estimates that are within 1-2% error of measured SOH (as determined using the charge / discharge method).

[0044] It should be clear that the above noted range of 3.5-3.7V is for SOH estimation during discharging process. If the initial voltage of a battery is higher than 3.7V, then a selected Io (e.g. 0.5C) is applied for discharging the battery down to 3.5- 3.7V as part of a preconditioning process. That means there is certain correlation between Io and the selected testing voltage range. In implementations of the presentinvention, a 1.0 C-rate has been applied for the current injection step at 3.5-3.7V during the discharging process. In the case of an initial voltage lower than 3.5V, a charging step may be applied to reach the specific voltage range. Due to an overall over-potential of a battery in charge direction, the selected voltage range may be shifted towards a higher voltage range during its charging process. The selected voltage range of 3.5-3.7 V is effective for estimating SOH during a battery’s discharging process. The overpotential of batteries during charge direction depends on the battery chemistries and structure. Accordingly, a higher voltage range (such as 3.65-3.80V) may be used for the pulsed current test in the charging process for LMO / NMC batteries.

[0045] For SOC estimation for batteries to be tested, there is no need to use any specifically selected voltage range for a battery. The pulsed current test can be done in the full charge / discharge voltage range of a battery for SOC estimation.

[0046] It should also be noted that the above described invention allows for the use of a typical battery cycler, along with suitable automation components, in the testing module. Accordingly, the testing module may use a combination of a power source, a voltmeter / multimeter, an electronic load for the pulsed current, and configurable automation components so that the desired voltage ranges can be automatically achieved while applying the pulse current test. Once the desired voltage ranges are achieved, suitable repeated applications of the pulse current test can be automatically applied to result in a useful test result data set. In the above implementation, once the desired voltage range was achieved, the useful test result data set was collected within 5 minutes.

[0047] In terms of implementation, the main processor can be implemented using a suitably programmed data processing device such as a microcontroller, an ASIC, or any suitable general data processing device. The database can be a suitable data storage device that operates to store suitable amounts of data and which can be coupled to the main processor. The trained model may be a software module or a suitably programmed ASIC or dedicated processor configured to operate according to the trained model’s logic as determined by its training.

[0048] Referring to Figure 3, a flowchart detailing the steps in a method according to one aspect of the present invention is illustrated. As can be seen, this method is executed by the system illustrated in Figure 1. The method begins with step 100, that of receiving the battery chemistry of the battery being tested (by way of the main processor). Step 110 is that of determining the specific characteristics and testing conditions to be used when testing batteries of the given battery chemistry. This is done by retrieving relevant entries from the database. With the specific characteristics and testing conditions retrieved, the testing module is then configured to apply the necessary tests and to implement the testing conditions (step 120). As can be imagined, this includes setting the specific voltage ranges to be searched for when testing the batteries, the length of time that a pulse current is to be applied, when the voltage readings are to be taken, etc., etc. It should be clear that these and other testing parameters and conditions may be different for different battery chemistries (and even for different battery manufacturers).

[0049] After the testing module has been suitably configured, the battery being tested may be preconditioned so that the battery is suitably configured / conditioned for the required / desired tests (step 130). As noted above, the preconditioning part of the test (executed before the test is applied or which may be included in the testing step) may include charging the battery, discharging the battery, or otherwise ensuring that the battery is at a charge state suitable for the test. Step 140 is then the application of the test to the battery. Once the desired range of readings (i.e., voltage readings) has been achieved, then the tests are repeated with the values necessary to achieve the range of readings (step 150). This produces the test result data set(s).

[0050] With the test result data set(s) produced, these data set(s) are then sent from the test module to the main processor and the values for the specific characteristics are calculated as necessary (step 160). As an example, the relevant resistances, impedances, and capacitances are calculated using the voltage, current, temperature, time and other data readings from the data sets. The calculated specific characteristic values are then sent to the trained estimation module and this module produces the estimated SOH / SOC (step 170).

[0051] When compared to other means of estimating battery SOH / SOC the present methods and systems produce similarly accurate results while allowing for much faster estimation of SOH / SOC. In validation comparisons against other techniques the results of the present method consistently show Mean Squared Error (MSE) of less than 2% against measured SOH / SOC values across the tested battery chemistries. As such, the present methods and systems demonstrate very accurate SOH / SOC estimations in a much lower amount of time.

[0052] Specific implementations of the present invention may be used as part of a larger battery centric system. As examples, the SOH / SOC diagnostic processes of the present invention may be incorporated into a battery monitoring and control system such as battery management system (BMS) and / or energy management system (EMS) in a battery energy storage system. Similarly, the processes of the present invention may also be implemented into a centralized computer for online-based SOH / SOC estimation and any unidirectional / bidirectional Electric Vehicle Supply Equipment (EVSE) for localized SOH / SOC estimation of EV batteries.

[0053] For clarity, to perform SOH estimation, the estimation method begins receiving the battery chemistry of the battery being tested. The specific characteristics and testing conditions to be used when testing batteries of the given battery chemistry are then determined. This is done by retrieving relevant entries from the database. With the specific characteristics and testing conditions retrieved, the testing module is then configured to apply the necessary tests and to implement the testing conditions. This step includes setting the specific voltage ranges to be searched for when testing the batteries, the length of time that a pulse current is to be applied, when the voltage readings are to be taken, etc., etc. It should be clear that these and other testing parameters and conditions may be different for different battery chemistries (and even for different battery manufacturers). As examples, some voltage ranges for testing specific battery chemistries are:LMO / NMC: 3.50-3.70V;LFP: 3.0-3.15V;NMC111: 3.25-3.50V;NMC622: 3.25-3.45V;NCA: 3.0-3.30V.

[0054] After the testing module has been suitably configured, the battery being tested may be preconditioned so that the battery is suitably configured / conditioned for the required / desired tests. This preconditioning part of the test (executed before the test is applied or which may be included in the testing step) may include charging the battery, discharging the battery, or otherwise ensuring that the battery is at a charge state (based on the battery chemistry) suitable for the test. As part of the preconditioning, this may include checking the temperature of the battery. After the battery has been properly preconditioned, the test is then applied to the battery. This test may be the current pulse test.

[0055] For one implementation of this current pulse test, a current pulse, at a non-zero current level, is applied for 30 seconds and is then interrupted to a zero current level. Voltage readings are taken at 0, 2, 10, 18, 30, 32, 40, 48, 60 and 90 seconds after the current pulse is applied. The current pulse level can be read or can be known. The voltage readings and the current readings can be used to calculate relevant resistance / impedance values as necessary. It should be clear that this current and voltage profile for a current pulse test is just one possible profile that may be applied to the battery. The current pulse and the relevant readings can be taken multiple times and the current pulse levels may be different for different pulses. Part of the current pulse test may include determining / checking the current applied during the various repetitions / applications of the current pulse test.

[0056] After the desired range of readings (i.e., voltage readings) have been measured and recorded, the tests are repeated with the values necessary to achieve the range of readings and to thereby produce the test result data set(s).

[0057] The data set(s) are then used to calculate the values for specific characteristics as necessary. As an example, the relevant resistances, impedances, and capacitances are calculated using the voltage, current, temperature, time and other data readings from the data sets. These specific characteristics can include six characteristics as:Ro - Ohmic resistanceRCT - Charge transfer resistance Rp - Polarization resistance RIN - Total internal resistance CPEi - double layer capacitance Vo - last voltage before current injection with a non-zero initial current

[0058] A seventh characteristic can be a function of the ohmic resistance, the charge transfer resistance, and of the total internal resistance: f(Ro+Rct, Rtotal) where: Ro+Rct=Ohmic Resistance + Charge Transfer Resistance, Rtotai=Total Internal Resistance.

[0059] The values for these specific characteristics may need to be normalized or corrected based on the sensed battery temperature and / or applied current applied during the current pulse test. As noted above, each of these factors can affect the measurement of the first five parameters (Ro; RCT; RP; RIN; and CPEi) and, accordingly, the determination of the respective 7thparameter for SOH testing (f(Ro+Rot, Rtotal)). As such, one or more look-up tables for factors that normalize or correct the above specific characteristics (based on the sensed battery temperature and based on the applied current (or C-rate)) may be consulted and suitable values for such correction / normalizing factors are determined. These correction / normalizing factor values are then applied, if necessary, to the readings for the specific characteristics.

[0060] These various calculated specific characteristic values (after correction or normalization as necessary) are then sent to a trained estimation module that includes a trained model that estimates the battery SOH. Training can be performed using a BRANN (Bayesian Regularized Artificial Neural Network) and suitable data sets for batteries of a specific chemistry with the data sets including values for the specific characteristics and their corresponding battery SOH values. The trained model would be trained such that, given a set of values for the specific characteristics, the model can provide a SOH estimate that is based on the training data set.

[0061] For clarity, to perform SOC estimation, the estimation method begins receiving the battery chemistry of the battery being tested. The specific characteristics and testing conditions to be used when testing batteries of the given battery chemistry are then determined. This is done by retrieving relevant entries from the database. With the specific characteristics and testing conditions retrieved, the testing module is then configured to apply the necessary tests and to implement the testing conditions. This step includes setting the specific voltage ranges to be searched for when testing the batteries, the length of time that a pulse current is to be applied, when the voltage readings are to be taken, etc., etc. It should be clear that these and other testing parameters and conditions may be different for different battery chemistries (and even for different battery manufacturers). For clarity, testing for SOC estimation can be performed over the full charge-discharge voltage range and that this charge-discharge voltage range may be different for different battery chemistries. Thus, for battery chemistry A, the battery may be considered discharged at voltage vt and may be considered as fully charged at voltage V2. Thus, for this battery chemistry A, testing may be performed at any voltage between vi and V2.

[0062] After the testing module has been suitably configured, the battery being tested may be preconditioned so that the battery is suitably configured / conditioned for the required / desired tests. This preconditioning part of the test (executed before the test is applied or which may be included in the testing step) may include charging the battery, discharging the battery, or otherwise ensuring that the battery is at a charge state (based on the battery chemistry) suitable for the test (i.e., the battery charge is within the charge-discharge voltage range for that battery chemistry). As part of the preconditioning, this may include checking the temperature of the battery. After the battery has been properly preconditioned (if necessary), the test is then applied to the battery. This test may be the current pulse test.

[0063] For one implementation of this current pulse test, a current pulse, at a non-zero current level, is applied for 30 seconds and is then interrupted to a zero current level. Voltage readings are taken at 0, 2, 10, 18, 30, 32, 40, 48, 60 and 90 seconds after the current pulse is applied. The current pulse level can be read or can be known. The voltage readings and the current readings can be used to calculaterelevant resistance / impedance values as necessary. It should be clear that this current and voltage profile for a current pulse test is just one possible profde that may be applied to the battery. The current pulse and the relevant readings can be taken multiple times and the current pulse levels may be different for different pulses. Part of the current pulse test may include determining / checking the current applied during the various repetitions / applications of the current pulse test.

[0064] After the desired range of readings (i.e., voltage readings) have been measured and recorded, the tests are repeated with the values necessary to achieve the range of readings and to thereby produce the test result data set(s).

[0065] The data set(s) are then used to calculate the values for specific characteristics as necessary. As an example, the relevant resistances, impedances, and capacitances are calculated using the voltage, current, temperature, time and other data readings from the data sets. These specific characteristics can include six characteristics as:Ro - Ohmic resistanceRCT - Charge transfer resistanceRp - Polarization resistanceRix - Total internal resistanceCPEi - double layer capacitanceVo - last voltage before current injection with a non-zero initial current

[0066] A seventh characteristic for SOC estimation can be a function of the last voltage before current injection with a non-zero initial current and a selected voltage for a specific battery chemistry: f(Vo, Vo) where: Vo = Last voltage before current injection with a non-zero initial current, and Vc = A selected voltage for specific battery chemistry.

[0067] The values for these specific characteristics may need to be normalized or corrected based on the sensed battery temperature and / or applied current applied during the current pulse test. As noted above, each of these factors can affect the measurement of the first five parameters (Ro; RCT; RP; RIN; and CPEi) and, accordingly, thedetermination of the respective 7thparameter for SOC testing (f(Vo, Vc)). As such, one or more look-up tables for factors that normalize or correct the above specific characteristics (based on the sensed battery temperature and based on the applied current (or C-rate)) may be consulted and suitable values for such correction / normalizing factors are determined. These correction / normalizing factor values are then applied, if necessary, to the readings for the specific characteristics.

[0068] These various calculated specific characteristic values (after correction or normalization as necessary) are then sent to a trained estimation module that includes atrained model that estimates the battery SOC. Training can be performed using a BRANN (Bayesian Regularized Artificial Neural Network) and suitable data sets for batteries of a specific chemistry with the data sets including values for the specific characteristics and their corresponding battery SOC values. The trained model would be trained such that, given a set of values for the specific characteristics, the model can provide a SOC estimate that is based on the training data set.

[0069] It should be clear that the various aspects of the present invention may be implemented as software modules in an overall software system. As such, the present invention may thus take the form of computer executable instructions that, when executed, implements various software modules with predefined functions.

[0070] The embodiments of the invention may be executed by a computer processor or similar device programmed in the manner of method steps, or may be executed by an electronic system which is provided with means for executing these steps. Similarly, an electronic memory means such as computer diskettes, CD-ROMs, Random Access Memory (RAM), Read Only Memory (ROM) or similar computer software storage media known in the art, may be programmed to execute such method steps. As well, electronic signals representing these method steps may also be transmitted via a communication network.

[0071] Embodiments of the invention may be implemented in any conventional computer programming language. For example, preferred embodiments may be implemented in a procedural programming language (e.g., "C" or "Go") or anobject-oriented language (e.g., "C++", "java", "PHP", "PYTHON" or "C#"). Alternative embodiments of the invention may be implemented as pre-programmed hardware elements, other related components, or as a combination of hardware and software components.

[0072] Embodiments can be implemented as a computer program product for use with a computer system. Such implementations may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (e.g., a diskette, CD-ROM, ROM, or fixed disk) or transmittable to a computer system, via a modem or other interface device, such as a communications adapter connected to a network over a medium. The medium may be either a tangible medium (e.g., optical or electrical communications lines) or a medium implemented with wireless techniques (e.g., microwave, infrared or other transmission techniques). The series of computer instructions embodies all or part of the functionality previously described herein. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e.g., shrink-wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server over a network (e.g., the Internet or World Wide Web). Of course, some embodiments of the invention may be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments of the invention may be implemented as entirely hardware, or entirely software (e.g., a computer program product).

[0073] A person understanding this invention may now conceive of alternative structures and embodiments or variations of the above all of which are intended to fall within the scope of the invention as defined in the claims that follow.

Claims

We claim:

1. A method for estimating a state characteristic of a battery, the method comprising: a) receiving input relating to a composition of said battery; b) based on said input received in step a), determining conditions to be used in testing said battery; c) configuring automated testing equipment based on said conditions such that said testing equipment tests said battery for specific characteristics using said conditions, said testing equipment being for testing said battery and said testing equipment being coupled to said battery; d) automatically testing said battery using said testing equipment to result in test results; e) deriving said specific characteristics of said battery from said test results obtained in step d); f) using a trained model to estimate said state characteristic of said battery based on said specific characteristics; wherein said state characteristic of said battery is at least one of:- a state of health of said battery; and- a state of charge of said battery.

2. The method according to claim 1, wherein said input received in step a) relates to a chemical composition of said battery.

3. The method according to claim 1, wherein said conditions include a voltage range for testing said battery.

4. The method according to claim 1, wherein said specific characteristics of said battery include at least one of:ohmic resistance, charge transfer resistance, polarization resistance, total internal resistance, double layer capacitance, last voltage before current injection with a non-zero initial current, and a function of specific characteristics or parameters.

5. The method according to claim 1, wherein said testing equipment uses a pulsed current method to test said battery.

6. The method according to claim 5, wherein said pulse current method applies a DC current pulse with a non-zero initial current while taking voltage readings at specific instances in time after said current pulse is applied, said voltage readings forming part of said test results.

7. The method according to claim 6, wherein said current pulse lasts for 30 seconds and wherein said pulse current method comprises applying an applied current and then interrupting said applied current 30 seconds after said applied current is initially applied.

8. The method according to claim 6, wherein voltage readings are taken at specific instances after said current pulse is applied, said specific instances including 0 s, 2 s, 10 s, 18 s, 30 s, 32s, 40s, 48s, 60 s, and 90 s.

9. The method according to claim 5, wherein step d) comprises repeatedly applying said pulse current method within a specified time period.

10. The method according to claim 5, wherein said pulse current method includes taking readings of current and voltage for said battery at specific instances in time after an applied current is applied to said battery, said readings of current and voltage forming part of said test results.

11. The method according to claim 1, wherein said trained model is trained using machine learning methods.

12. The method according to claim 1, wherein said specific characteristics are selected using at least one of: a simplified determination method and resistor-capacitor equivalent circuit models.

13. The method according to claim 1, wherein said trained model is trained using machine learning techniques on values for said specific characteristics to thereby correlate said state characteristic of said battery with values for said specific characteristics.

14. The method according to claim 1 , wherein step e) further comprises determining at least one of: the temperature of the battery and an applied current (or C-rate) during testing, said temperature or applied current being for use in determining said specific characteristics.

15. A system for testing or estimating a state characteristic of a battery, the system comprising:- a database having entries denoting multiple entries for different battery chemistries and specific characteristics for each battery chemistry that are to be used in estimating said state characteristic of said battery for each battery chemistry of said different battery chemistries, said database also including entries of testing conditions to be used when testing each battery chemistry;- a testing module for testing a battery, tests applied by said testing module being based on said specific characteristics for battery chemistry of said battery and on said testing conditions for said battery chemistry of said battery;- a trained model for estimating said state characteristic of said battery based on calculated specific characteristics;- a main processor for receiving input relating to said battery, said main processor being coupled to said database and to said testing module and to said trained model, said input being used to determine a battery chemistry of said battery; wherein- said main processor retrieves specific characteristics and testing conditions from said database for said battery chemistry of said battery;- said main processor configures said testing module to apply suitable tests to said battery based on retrieved specific characteristics and testing conditions for said battery chemistry of said battery;- said testing module sends test results from said suitable tests to said main processor and said main processor calculates said calculated specific characteristics from said test results;- said calculated specific characteristics are sent by said main processor to said trained module; wherein said state characteristic of said battery is at least one of:- a state of health of said battery; and- a state of charge of said battery.

16. The system according to claim 15, wherein said testing module includes at least one of: a power source, a voltmeter, a multimeter, a component for applying an electronic load to said battery, automation components for automating said suitable tests.

17. The system according to claim 15, wherein said tests comprises a pulsed current test.

18. The system according to claim 17, wherein said testing conditions include voltage ranges to be achieved for said pulsed current test.

19. The system according to claim 15, wherein said trained model is trained using machine learning.

20. The system according to claim 15, wherein said specific characteristics include at least one of: ohmic resistance, charge transfer resistance, polarization resistance, total internal resistance, double layer capacitance, last voltage before current injection with a non-zero initial current, and a function of specific characteristics or parameters.

21. The system according to claim 17, wherein said pulsed current test includes applying a current pulse with a non-zero initial current.

22. The system according to claim 17, wherein said pulsed current test applies a DC current pulse with a non-zero initial current while taking voltage readings at specific instances in time after said current pulse is applied.

23. The method according to claim 1, wherein, between steps c) and d), said battery is preconditioned to render said battery suitable for said testing.

24. The system according to claim 15, wherein said battery is preconditioned to render said battery suitable for said suitable tests.

25. The system according to claim 15, wherein at least one of: the temperature of the battery and an applied current (or C-rate) during testing, are used in determining the specific characteristics.

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