Intermittent chronoamperometry (ICA) and out-of-equilibrium thermodynamics (OET) for battery charging

Abstract

A method (ICA) for fast charging a battery system comprising one or multiple cells arranged is series and in parallel, within a SOC range (ΔSOC) and a total charge time t(charge), comprising steps of applying a series of intermittent constant voltage plateaus (CVk, k=1, 2, 3... ) for a duration t(CVk) while monitoring the charge current I (C-rate) and the battery temperature Teell, allowing the current Ito go to zero (rest) for a short time duration t(rest) within the constant voltage plateau and measuring the open-circuit voltage during rest, noted as p-OCV or pOCV, between two successive rest times, within a same voltage plateau CVk, applying a voltage pulse for a duration t(pulse), repeating rests and voltage pulses sequences until the current reaches I(min), at Imin applying a voltage step ΔV between two successive voltage plateaus CVk and CVk+i. and measuring the current at the onset of ΔV application noted I(max).

Description

[0001] DESCRIPTION

[0002] TITLE : “Intermittent Chronoamperometry (ICA) and Out-of-Equilibrium Thermodynamics (OET) for Battery Management”

[0003] This PCT application claims priority of the Singapore provisional application n°10202403225S filed October, 162024

[0004] TECHNICAL FIELD

[0005] The invention pertains to the field of battery management systems. More specifically, it relates to methods and systems for the rapid charging and online state determination of rechargeable batteries, such as lithium-ion batteries used in electric vehicles and large-scale energy storage systems.

[0006] BACKGROUND

[0007] The widespread adoption of rechargeable batteries, particularly of the lithium-ion type, in sectors ranging from consumer electronics to automotive and grid storage, has placed ever- increasing demands on their performance, longevity, and safety. A central challenge in battery technology is the inherent trade-off between charging speed, operational lifespan, and safety. Pushing for faster charge times often accelerates battery degradation and can, in extreme cases, compromise safety by inducing undesirable electrochemical phenomena.

[0008] Conventional charging protocols, such as the constant current-constant voltage (CC-CV) method, represent a compromise. The initial constant current (CC) phase is relatively efficient, but the subsequent constant voltage (CV) phase, during which the current tapers off, can account for a significant portion of the total charge time. Attempts to accelerate this process by increasing charging currents or voltages can lead to issues like lithium plating on the anode, which irreversibly reduces capacity and can, over time, form dendrites that pose a risk of internal short circuits.

[0009] Simultaneously, the accurate determination of a battery's state-of-charge (SOC) and state-of- health (SOH) during operation remains a significant technical hurdle. Common methods like coulomb counting are prone to cumulative errors that require periodic recalibration. Other methods, which rely on measuring the open-circuit voltage (OCV), are impractical for online applications as they necessitate long resting periods for the battery voltage to stabilize, which is incompatible with dynamic operational scenarios such as driving an electric vehicle. More sophisticated techniques like electrochemical impedance spectroscopy (EIS) provide detailed insights but are typically complex, computationally intensive, and performed offline.

[0010] Perhaps the most critical challenge lies in the early detection of internal short circuits (ISCs). ISCs are a primary cause of thermal runaway events, which can have catastrophic consequences. These faults are notoriously difficult to detect in their incipient stages using conventional monitoring of external parameters like voltage and current. By the time a significant voltage drop or temperature rise is observed, the thermal runaway process may already be irreversible. There is currently a lack of reliable, online methods capable of identifying the subtle electrochemical changes that signal the onset of an ISC.

[0011] The present invention therefore aims to overcome these drawbacks by providing an integrated solution that enables ultra-fast charging while simultaneously performing accurate, online determination of the battery's state-of-charge, state-of-health, and, critically, its state-of-safety by detecting incipient internal short circuits.

[0012] SUMMARY OF THE INVENTION

[0013] This objective is met with a method (ICA) for fast charging a battery system comprising one or multiple cells arranged is series and in parallel, within a SOC range (ASOC) and a total charge time t(charge), comprising steps of: applying a series of intermittent constant voltage plateaus (CVk, k=l, 2, 3. . .) for a duration t(CVk) while monitoring the charge current I (C-rate) and the battery temperature Tceii. allowing the current I to go to zero (rest) for a short time duration t(rest) within the constant voltage plateau and measuring the open-circuit voltage during rest, noted as pOCV.

[0014] - between two successive rest times, within a same voltage plateau CVk , applying a voltage pulse for a duration t(pulse), repeating rests and voltage pulses sequences until the current reaches I(min), at Imin applying a voltage step AV between two successive voltage plateaus CVk and CVk+i. and measuring the current at the onset of AV application noted I(max). I(min) is advantageously comprised within 20% of the C-rate corresponding to ASOC and t(charge).

[0015] The charge time tcharge is advantageously comprised between 6 min and 60 min for ASOC=100%.

[0016] The voltage plateau tcv is advantageously comprised between 4 seconds and 60 seconds.

[0017] The short time duration trest is advantageously comprised between 1 second and 20 seconds.

[0018] The voltage step AV is advantageously comprised between 10 mV and 200 mV at the cell level.

[0019] The voltage step AV is preferably applied when current drops to a value equal or below a minimum value I(min).

[0020] In an advantageous implementation of the invention, the charging method further comprises a step for assessing the state of charge based on pOCV data according to the equation: in which pOCVo, a, 0 and y are fitting parameters.

[0021] The charging method of the invention can further comprise, a step for assessing the internal cell resistance during the CV step according to equation:

[0022] AV

[0023] ^int / (max) — I (min)

[0024] The charging method of the invention can further comprise a step for assessing the cell temperature T(cell) during the charge process at different ambient temperatures T(amb).

[0025] The charging method of the invention can further comprise a step for determining the entropy p-AS and enthalpy p-AH of a battery cell based on temperature dependence of pOCV, according to the equations:

[0026] It is important to be noted that in equations, the expressions “p - AS”, “p - AH” and “p- - OCV” refer to the same expressions “pAS”, “pAH” and “pOCV” used in the text of the application, and not to subtractions. The method of the invention can further comprise a step for determining the SOC of a cell, using the equation:

[0027] SY = « + ■ p-A A -r • p- AH Eq. 5 where a, 0 and y are fitting parameters being dependent of cell’ chemistry and state of health.

[0028] The method of the invention can further comprise a step for determining the state of health (SOH) of a battery according to the a, 0 and y parameters variation with the battery ageing.

[0029] The method of the invention can further comprise a step for determining the state of health SOH from the internal resistance data variation.

[0030] In an advantageous embodiment of the invention, implementing Out-of-Equilibrium thermodynamics (OET), the charging method further comprises steps of: collecting the voltage, the current and the cell temperature Tceii data during the charge, including the pseudo-open-circuit voltage (pOCV) during the rest time, determining the pseudo entropy data (pAS) and the pseudo enthalpy data (pAH) from said pseudo-open-circuit pOCV as a function of said cell temperature Tceii processing said pseudo entropy data (pAS) and pseudo enthalpy data (pAH) to determine the battery state of charge (SOC) and state of health (SOH).

[0031] This method of the invention can further comprise any of flowing steps: a step of detecting an internal short-circuit (ISC), a step of processing charge parameter to create an augmented battery, a step of using the charge parameters to control the quality of a battery.

[0032] The ambient temperature can be a controlled temperature.

[0033] The determination of the state of charge SOC can be performed online.

[0034] The determination of the state of health SOH can be based: on the pseudo entropy date pAS as a function of pOCV profile analysis, on the pseudo enthalpy data pAH as a function of pOCV profile analysis. on the pseudo entropy data pAS as a function of pOCV differences profile analysis, on the pseudo enthalpy data pAH as a function of pOCV differences profile analysis. on internal resistance data analysis The determination of the state of health SOH can be performed online.

[0035] The detection of an internal short circuit ISC can be based: on the pseudo entropy data pAS as a function of pOCV profile analysis, on the pseudo enthalpy data pAH as a function of pOCV profile analysis, on the pAS as a function of pOCV difference profile analysis, on the pseudo enthalpy data pAH as a function of pOCV difference profile analysis.

[0036] The detection of an internal short circuit ISC can be performed online.

[0037] The creation of an augmented battery can be based on setting up the charge parameters to increase the charge capacity of the battery beyond the rated capacity.

[0038] The quality control of a battery can be based on applying the charge parameters for decreasing charge time tCh until a threshold cell temperature is attained.

[0039] The fitting parameters pOCVo, a, 0, and y of following Eq. 6 (p-OCV vs. SOC) and Eq.7 (SOC vs. pOCV) are adjusted by an artificial intelligence to optimize the battery performances, including the cycle life.

[0040] The a, 0, and y parameters can be adjusted online by artificial intelligence according to the battery SOH.

[0041] It is important to be noted that in equations, the expressions “p - AS”, “p - AH” and “p - OCV” refer to the same expressions “pAS”, “pAH” and “pOCV” used in the text of the application, and not to subtractions.

[0042] The charging method according to the invention can be applied to any type of lithium-ion batteries, either those whose cathode consists essentially of mixed oxide of metal M (M==Ni, Mn, Co) and of lithium, with a general formula LiM02, or those whose cathode is phosphate based such as lithium iron mixed phosphate LiFePO4 (LFP). According to another aspect of the invention, there is proposed a system (ICA) for fast charging a battery system comprising one or multiple cells arranged in series and in parallel, within a SOC range (ASOC) and total charge time t(charge), implementing the charging method (ICA) of the invention, comprising: means for applying a series of intermittent constant voltage plateaus (C Vk, k= 1 , 2, 3. . . ) for a duration t(CVk), while monitoring the charge current I (C-rate) and the battery temperature Tceii, means for allowing the current I to go to zero (rest) for a short time duration t(rest) within the constant voltage plateau and measuring the open-circuit voltage during rest, noted as pOCV. means for applying a voltage pulse for a duration t(pulse), between two successive rest times, within a same duration time C Vk , means for repeating rests and voltage pulses sequences until the current reaches I(min), means for applying at Imin a voltage step AV between two successive voltage plateaus C Vk and CVk+i. and measuring the current at the onset of AV application noted I(max).

[0043] The charging system of the invention can further comprise means for assessing a state of charge assessment based on pOCV data according to the equation in which p-OCVo or pOCo, a, 0 and y are fitting parameters

[0044] The charging system of the invention can further comprise means for assessing cell’ internal resistance during a CV step according to equation:

[0045] Rint= ~ / f( -max) - / (mm ,) Eq.2

[0046] The charging system of the invention can further comprise means for determining the entropy p-AS and enthalpy p-AH of battery cell based on temperature dependence of p-OCV, according to the equations: It is important to be noted that in equations, the expressions “p - AS”, “p - AH” and “p- - OCV” refer to the same expressions “pAS”, “pAH” and “pOCV” used in the text of the application, and not to subtractions.

[0047] In a particular embodiment of the invention, combining intermittent chronoamperometry (ICA) and out-of-equilibrium amperometry (OET), the charging system can further comprise: means for collecting the voltage, the current and the cell temperature Tceii data during the charge, including the pseudo-open-circuit voltage (pOCV) during the rest time, means for determining the pseudo entropy data (pAS) and the pseudo enthalpy data (pAH) from said pseudo-open-circuit p-OCV as a function of said cell temperature Tceii means for processing said pseudo entropy data (pAS) and pseudo enthalpy data (pAH) to determine the battery state of charge (SOC) and state of health (SOH).

[0048] The system of the invention can further comprise: means for detecting an internal short-circuit (ISC), means for processing charge parameter to create an augmented battery, means for processing the charge parameters to control the quality of a battery.

[0049] DESCRIPTION OF THE FIGURES

[0050] - FIG.1 illustrates a typical current and voltage profile during a battery’ ICA charge and CC discharge cycling ;

[0051] - FIG.2 illustrates a typical current and cell’ temperature profile during ICA charge and CC discharge cycling ;

[0052] - FIG.3 illustrates a typical current and voltage profile during ICA charge in 20 min ;

[0053] - FIG.4 illustrates a typical voltage profile during a battery’ ICA charge showing constant voltage (CV) plateaus and pOCV ;

[0054] - FIG.5 illustrates a typical current (C-rate) profile during a battery’ ICA charge in 20 min ;

[0055] - FIG.6 illustrates a detailed voltage profile during ICA charge including AV and pOCV ;

[0056] - FIG.7 illustrates a detailed voltage profile during ICA charge including AV ;

[0057] - FIG.8 illustrates a detailed current (C-rate) profile during ICA charge including t(rest) (1=0 A) ;

[0058] - FIG.9 illustrates a detailed current (C-rate) profile during ICA charge including I(min), I(max) and t(pulse) ; - FIG.10A illustrates a pOCV vs SOC profile with fitting curve enabling SOC to be determined for the pOC V data ;

[0059] - FIG.1 OB illustrates a SOC vs pOCV profile with fitting curve enabling SOC to be determined for the pOCV data ;

[0060] - FIG.l 1 represents a SOC determination from pDS and pDH ;

[0061] - FIG.12 represents a Cell’ temperature T(cell) evolution during ICA charge At different ambient temperatures T(amb): 10°C to 45°C ;

[0062] - FIG.13 represents an evolution of an internal resistance vs. SOC of a lithium battery using ICA based method at different ICA charging times ;

[0063] FIG.14A illustrates V, I profiles for a NLV charge in 10 min;

[0064] FIG.14B illustrates T profile for a NLV charge in 10 min;

[0065] FIG.15 illustrates V,I profiles for an ICA charge in 6 min;

[0066] - FIG.16 illustrates ICA parameters such as rest time, current limit and step time;'

[0067] - FIG.17 represents Out-Of Equilibrium (OET) data in controlled temperature, with ISC and without ISC;

[0068] - FIG.18 represents pAS Difference vs. pOCV ;

[0069] - FIG.19 represents pAH Difference vs. pOCV ;

[0070] - FIG.20 represents SOC vs pOCV of cell with non ISC (case 1) ;

[0071] - FIG.21 represents SOC vs. p-OCV of cell with ISC (case 2);

[0072] - FIG.22A and 22B illustrate pAS vs. pOCV profile and pAH vs. pOCV profile, with OET at room temperature ;

[0073] - FIG.23 represents pAS Difference vs. pOCV ;

[0074] - FIG.24 represents pAH Difference vs. pOCV

[0075] - FIG.25 features the SOC theorem in Case 1 (no ISC), under controlled temperature ;

[0076] - FIG.26 features the SOC theorem in Case 2 (with ISC), under controlled temperature ;

[0077] - FIG.27 features the SOC theorem in Case 3 (with ISC), under controlled temperature ;

[0078] - FIG.28 features the SOC theorem in Case 4 (with ISC), under controlled temperature ;

[0079] - FIG.29 features the SOC theorem in Case 1 (no ISC), under uncontrolled room temperature ;

[0080] - FIG.30 features the SOC theorem in Case 2 (with ISC), under uncontrolled room temperature ;

[0081] - FIG.31 features the SOC theorem in Case 3 (with ISC), under uncontrolled room temperature ; - FIG.32 features the SOC theorem in Case 4 (with ISC), under uncontrolled room temperature ;

[0082] - FIG.33 represents evolutions of pAS Difference vs. pOCV ;

[0083] - FIG.34 represents evolutions of pASH Difference vs. pOCV ;

[0084] - FIG.35 illustrates pAHS Profiles: ISC Case 2 & 83.39% SOH ;

[0085] - FIG.36 illustrates pAH Profiles: ISC Case 2 & 83.39% SOH ;

[0086] - FIG.37 represents an evolution of SOC vs. pOCV of cell of SOH=83.39%;

[0087] - FIG.38 represents an evolution of pOCV vs. SOC of LiFePO4 cathode (LFP)-based lithium ion cell.

[0088] DETAILED DESCRIPTION

[0089] The detailed description covers experimental embodiments of systems for charging batteries implementing the ICA charging method according to the invention (Part I) and the ICA+OET charging method according to the invention (part II). However, it is to be noted that an OET method could be implemented in charging systems, independently from the ICA method.

[0090] PART I OF THE DETAILED DESCRIPTION

[0091] A first part of this detailed description is dedicated to the ICA (Intermittent Chrono Amperometry) charging method / system, with reference to Figures 1 to 16.

[0092] The ICA main parameters comprise:

[0093] Charge time: t(charge) State of Health (SOH) The battery temperature T(cell)

[0094] Pulse time: t(pulse) The battery nominal capacity (°C) (Qnom(Ah)) The ambient temperature

[0095] Rest time: t(rest) T(amb) (°C)

[0096] The C-rate (see definition

[0097] The constant voltage plateau below) Upper battery voltage Vmax time: t(CVk)

[0098] Initial SOC: SOCi (%)

[0099] Voltage at rest pOCV Entropy p-AS (J.KTmole-1)

[0100] Final (target) SOC: SOCf (%)

[0101] Minimum current during CV Enthalpy p-AH (kJ. mole'1) pulse: I(min) SOC range ASOC=SOCf- SOO Current intensity I (A)

[0102] The voltage step: AV (%)

[0103] State of charge (SOC) Internal resistance Rmt (Q)

[0104] The Intermittent Chronoamperometry (ICA) is a multitask battery management method and system enabling to: - ultra-fast charge (below 60 min), determine the state of charge (SOC), determine the state of health (SOH), determine the internal resistance, determine entropy and enthalpy. predict the cell temperature T(cell) evolution during charge

[0105] The Intermittent Chronoamperometry (ICA) comprises applying a series of constant voltage pulses (CVk) k=l, 2, 3...

[0106] - Within each constant voltage CVk the current drops to zero (rest) several times ‘j ’ for a short rest time t(rest)kj

[0107] - During the rest time t(rest)kj the voltage drops to an open-circuit voltage pOCVkj.

[0108] The CV pulse between two rest times lasts for t(pulse)kj.

[0109] - During a CV pulse (k,j) the current flowing in the battery is monitored Ikj together with the battery temperature Tk,j. The flowing current Ikj can be expressed as C-rate.

[0110] - When the current Ikj drops below a set value I(min)k the CV is increased by DVkto transit to a new CV plateau CVk+i=CVk+AVk is applied.

[0111] At onset of CVk+i the current goes to a high value I(max)k+i .

[0112] The Intermittent Chronoamperometry (ICA) may use artificial intelligence methods for the above tasks a) to f) implementation

[0113] C-rate definition is provided by the following equations:

[0114] For example, if t(charge) is 15 min=0.25 hr, the C-rate is 4C

[0115] QnomtAh)x SOC(%

[0116] The Imin (A)

[0117] 1 OOxt(charge) (hr)

[0118] The Imin@AF (A)=Imin(A) (1 ± a), a < 0.2

[0119] Let Qnom=50Ah, ASOC=80%, t(charge)=15 min

[0120] 128 A <Imin@AE <192 A 3.2C <C-rate@AE <4.8C Referring to Figures 1 to 9, an ICA charge and CC discharge cycling has been tested in the following conditions: t(charge)=~20 min SOCi=0% SOCf=100% Tamb=25C

[0121] Referring to Figures 10A and 10B, a pOCV vs SOC profile has been obtained with fitting curve enabling SOC to be determined for the pOCV data. pOCV and pOCVo are noted as p-OCV and p-OCVO in the following equations.

[0122] The fitting equation is: - SOC p - OCV = p - OCVa+ «. lo -QC>vEq.6 pOCVo = 3.3991V a = 0.0464 0= 2.7231 y = 97.9172.

[0123] The fitting parameters: pOCVo ,a ,0 and y depend on the battery state of health SOH and temperature T. they can be adjusted to the cell’s SOH using artificial intelligence methods.

[0124] The State of Charge can be computed from the following equation (eq.7) :

[0125] The fitting parameters: pOCVo, a ,0 and y can be determined using artificial intelligence methods.

[0126] With reference to Figure 11, which features SOC determination from pAS and pAH, the fitting equation

[0127] SOC= a + 0 pAS + y pAH Eq.5

[0128] The fitting parameters : a, 0 and y depend on the battery state of health SOH. They can be adjusted to the cell’s SOH using artificial intelligence methods.

[0129] With reference to Figure 12, which features Cell’ temperature T(cell) evolution during ICA charge at different ambient temperatures T(amb): 10°C to 45°C,

[0130] Where 7' is the cell temperature, .s is the SOC in %, and a,b,c represent fitting coefficients.

[0131] Fitting equation’s parameters a, b and c depend on the cell’s SOH and on t(charge). Fitting parameters: a, b and c can be determined online using artificial intelligence methods.

[0132] In Figure 13, which represents Internal resistance vs. SOC of a LIB using ICA based method at different ICA charging times, the internal resistance relates to the SOC, SOH and to the cell temperature T (cell).

[0133] Referring to Figures 14 to 16, NLV charges in 10 min and 6 min have been tested and main parameters of the ICA charge has been identified.

[0134] PART II OF THE DETAILED DESCRIPTION

[0135] A second part of the detailed description is dedicated to the “Out-of-Equilibrium Thermodynamics” (OET) concept that can be applied to the ICA charging method / system.

[0136] OET enables SOC, SOH and SOS (internal short-circuit) on line determination of a battery cell from the ICA / NLV charge data.

[0137] The OET method also applies to augmented batteries and to battery quality control (QC)

[0138] OET uses the temperature dependence of pOCV during NLV charge to determine pseudoentropy (pAS) and pseudo-enthalpy (pAH) in real time using Equations in Part I.

[0139] Two temperature conditions are applied: 1) controlled temperature and, 2) naturally varying ambient (room) temperature.

[0140] NLV tests were carried out at different charging times.

[0141] Cells were aged over hundreds of NLV charge and CC discharge cycles, thus changing their SOH.

[0142] SOH is determined from CCCV cycles following determined number of completed cycles.

[0143] Artificial short-circuits were created into cells before subjecting them to OET tests for internal short-circuit online detection.

[0144] Internal Short-Circuit (ISC) Tests

[0145] Experiment (controlled temperature): - One CCCV cycle @ 25°C

[0146] One NLV cycle at each set temperature (25°C, 30°C, 35°C, 40°C)

[0147] - Experiment at room temperature (varying over day and night):

[0148] One CCCV cycle

[0149] 10 NLV cycles

[0150] - Different case correspond to different artificial short-circuits levels created to the cells.

[0151] Out-of-equilibrium thermodynamics (OET) measurements have been done: at the same SOC level, pOCV variations are observed due to temperature change;

[0152] - they require smoothing of the raw data;

[0153] - Disparate values are obtained around the specific pOCV regions pAS and pAH are determined from Eq. 3 and Eq. 4, respectively.

[0154] A- Internal short-circuits (ISC) detection:

[0155] OET data in Controlled Temperature:

[0156] It is clearly demonstrated that both pAS vs. pOCV and pAH vs. pOCV profiles are significantly different in Case 1 (cell with no internal short) and Case 2 to 4 (cells with internal shorts).

[0157] The [pAS(N)- pAS(l)] and [pAH(N)- pAH(l)] difference profiles are displayed in the figures above.

[0158] The pAS and pAH differences go through a maximum at around pOCV=4.06V±0.01 V

[0159] OET at Room Temperature

[0160] Similarly to the controlled T tests, pAS vs. pOCV and pAH vs. pOCV profiles from OET data are significantly different in Case 1 (cell with no internal short) and Case 2 to 4 (cells with internal shorts)

[0161] The [pAS(N)- pAS(l)] and [pAH(N)- pAH(l)] differences profiles show different pOCV values where differences are larger as highlighted with dashlines at 3.61V, 3.72V, 3.86V and 4.09V. B- State of charge determination:

[0162] 1. Under controlled temperature:

[0163] - Case 1 (no ISC): SOC Theorem (Figure 25)

[0164] - Case 2 (w. ISC): SOC Theorem (Figure 26)

[0165] - Case 3 (w. ISC): SOC Theorem (Figure 27)

[0166] - Case 4 (w. ISC): SOC Theorem (Figure 28)

[0167] 2. Under uncontrolled (room) temperature:

[0168] - Case 1 (no ISC): SOC Theorem (Figure 29)

[0169] - Case 2 (w. ISC): SOC Theorem (Figure 30)

[0170] - Case 3 (w. ISC): SOC Theorem (Figure 31)

[0171] - Case 4 (w. ISC): SOC Theorem (Figure 32) pAS and pAH data were obtained from the OET analysis method performed on cells with and without ISC, under both controlled and uncontrolled (room) temperature conditions.

[0172] Remarkably, the SOC theorem (SOC=a+p.p-AS+y.p-AH) applies as well to cells without ISC as to cells having an ISC, as demonstrated by close to unity of the R2values with corresponding fitting coefficients a, P and y.

[0173] C-State of health determination:

[0174] A SOH Analysis has been implemented by OET and experimented in a chamber (temperature control):

[0175] - One CCCV cycle @ 25°C

[0176] One NLV cycle at each set temperature (25°C, 30°C, 35°C, 40°C)

[0177] Perform 40 NLV cycles @ 25 °C (Charge in 20 mins)

[0178] - Repeat step 1 - 3 until battery SOH below 80% Table 1 below provides SOC Theorem Parameters on aged cells:

[0179] Table 1

[0180] D- Augmented Battery:

[0181] OET / ICA charge method is applied to a battery of nominal rated capacity QnOm for a target charging time tcharge with test parameters AV, Imin, Vmax, Imax and Tmax.

[0182] Parameters are adjusted to increase capacity from nominal capacity to n% augmented target capacity (Qtarget=Q nom (l+n / 100)).

[0183] Highest n% is fixed by one of the two: 1) cycle capacity decay rate significantly larger than before augmentation, 2) reaching temperature safety limit n is generally above 5% and below 25% in capacity augmentation owing the OET and ICA methods

[0184] E- Battery Quality Control :

[0185] Different batteries are tested by OET for QC to determine their suitability for a specific application

[0186] Each battery is subjected to ICA charge at decreasing charge time, typically from 60 min to 10 min while monitoring the battery temperature Batteries of high quality will deliver close to 100% of their nominal capacity at the shortest charge time and the lowest temperature

[0187] A battery with lower quality will reach the safety temperature limit during IC A charge at a charge time higher than suitable for the application, usually of about 30 min.

[0188] OET enable fast QC assessment owing to online pAS and pAH determination and profile analysis.

[0189] F- Machine Learning Models for SOH Estimation:

[0190] Various cycling conditions: have been experimentally tested to train models for SOH estimation:

[0191] Charging protocols

[0192] Controlled temperature

[0193] Cell’s capacity and type

[0194] The charging method of the invention has been tested for a diversity of cells among which :

[0195] Samsung 30T (In the chamber: NLV20, NLV15; at room temperature: CCCV, 2 stage CCCV, CPCV)

[0196] Samsung 40T (At room temperature: CCCV, 2 stage CCCV, CPCV, NLV30)

[0197] Samsung 50E (At room temperature: CCCV)

[0198] Goal: build a generalised Al-based SOH estimation model to deal with different cells under various operating conditions (charging protocols, temperature).

[0199] Referring to Figure 38, which represents an evolution of pOCV (noted as p-OCV in the Figure) vs. SOC for a LiFePO4 cathode (LFP)-based lithium ion cell, it has been found that SOC determination is enabled in such cell by monitoring pOCV, while the OCV vs. SOC ratio is flat between 20% and 80%.

[0200] The charging system according to the invention can be implemented for charging a lithium-ion battery from an electrical energy source, using (i) power electronic components, available or specifically designed, adapted to apply controlled voltage waveforms (voltage plateaus, voltage pulses) to the battery terminals, (ii) voltage, current, and temperature sensors, and (iii) programmable components for processing the captured signals and controlling the power electronic components. The level of integration of a charging system according to the invention can vary, from the use of discrete components to complete integration combining power and information processing, depending on the market and industrial contexts.

[0201] Many other embodiments of the invention may be provided without departing from the scope of the present invention. Thus, the charging method according to the invention can be implemented for charging any type of rechargeable battery.

Claims

CLAIMS1. A method (ICA) for fast charging a battery system comprising one or multiple cells arranged is series and in parallel, within a SOC range (ASOC) and a total charge time t(charge), comprising steps of: a. applying a series of intermittent constant voltage plateaus (CVk, k=l, 2, 3...) for a duration t(CVk) while monitoring the charge current I (C-rate) and the battery temperature Tceii, a. allowing the current I to go to zero (rest) for a short time duration t(rest) within the constant voltage plateau and measuring the open-circuit voltage during rest, noted as pOCV, b. between two successive rest times, within a same voltage plateau CVk , applying a voltage pulse for a duration t(pulse), c. repeating rests and voltage pulses sequences until the current reaches I(min), d. at Imin applying a voltage step AV between two successive voltage plateaus CVk and CVk+i. and measuring the current at the onset of AV application noted I(max). The method of preceding Claim, wherein I(min) is within 20% of the C-rate corresponding to ASOC and t(charge).3 The method of Claim 1, wherein the charge time Charge is comprised between 6 min and 60 min. The method of Claim 1, wherein the voltage plateau tcv is comprised between 4 seconds and 60 seconds.5 The method of Claim 1, wherein the short time duration Lest is comprised between 1 second and 20 seconds.6 The method of Claim 1, wherein the voltage step AV is comprised between 10 mV and 200 mV at the cell level.

7. The method of Claim 1 wherein the voltage step AV is applied when current drops to a value equal or below a minimum value I(min).

8. The method of any of preceding Claims, further comprising a step for assessing the state of charge based on pOCV data according to the equation:in which pOCVo, a, 0 and y are fitting parameters.8 The method of any of preceding Claims, further comprising a step for assessing the internal cell resistance according to equation:AC^int / (max) — I (min)9 The method of any of preceding Claims, further comprising a step for assessing the cell temperature T(cell) during the charge process at different ambient temperatures T(amb).10 The method of any of preceding Claims, further a step for determining the entropy p-AS and enthalpy p-AH of a battery cell based on temperature dependence of pOCV (also noted as p-OCV in equations), according to the equations:11 The method of any of preceding Claims, further comprising a step for determining the SOC of a cell, using the equationSOC = + A ■ p-AS -r o • p- AH where a, 0 and y are fitting parameters being dependent of cell’ chemistry and state of health.

12. The method of any of preceding Claims, further comprising a step for determining the state of health (SOH) of a battery according to the a, 0 and y parameters variation with the battery ageing.

13. The method of any of preceding Claims and Claim 8, further comprising a step for determining the state of health SOH from the internal resistance data variation.

14. The method (ICA+OET) of any of preceding Claims, further comprising steps of: collecting the voltage, the current and the cell temperature Tceii data during the charge, including the pseudo-open-circuit voltage (pOCV) during the rest time, determining the pseudo entropy data (pAS) and the pseudo enthalpy data (pAH) from said pseudo-open-circuit p-OCV as a function of said cell temperature Tceii processing said pseudo entropy data (pAS) and pseudo enthalpy data (pAH) to determine the battery state of charge (SOC) and state of health (SOH).

15. The method of preceding Claim, further comprising a step of detecting an internal short-circuit (ISC).

16. The method of Claim 14, further comprising a step of processing charge parameter to create an augmented battery.

17. The method of Claim 14, further comprising a step of using the charge parameters to control the quality of a battery.

18. The method of Claim 14, wherein the ambient temperature is a controlled temperature.

19. The method of Claim 14, wherein the determination of the state of charge SOC is performed online.

20. The method of Claim 14, wherein the determination of the state of health SOH is based on the pseudo entropy date pAS as a function of pOCV profile analysis.

21. The method of Claim 14, wherein the determination of the state of health SOH is based on the pseudo enthalpy data pAH as a function of pOCV profile analysis.

22. The method of Claim 14, wherein the determination of the state of health SOH is based on the pseudo entropy data pAS as a function of pOCV differences profile analysis.

23. The method of Claim 14, wherein the determination of the state of health SOH is based on the pseudo enthalpy data pAH as a function of pOCV differences profile analysis.

24. The method of Claim 14, wherein the determination of the state of health SOH is performed online.

25. The method of Claim 14, wherein the detection of an internal short circuit ISC is based on the pseudo entropy data pAS as a function of pOCV profile analysis.

26. The method of Claim 14, wherein the detection of an internal short circuit ISC is based on the pseudo enthalpy data pAH as a function of pOCV profile analysis.

27. The method of Claim 14, wherein the detection of an internal short circuit ISC is based on the pAS as a function of pOCV difference profile analysis.

28. The method of Claim 14, wherein the detection of an internal short circuit ISC is based on the pseudo enthalpy data pAH as a function of pOCV difference profile analysis.

29. The method of Claim 14, wherein the detection of an internal short circuit ISC is performed online.

30. The method of Claim 16, wherein the creation of an augmented battery is based on setting up the charge parameters to increase the charge capacity of the battery beyond the rated capacity.

31. The method of Claim 17, where the quality control of a battery is based on applying the charge parameters for decreasing charge time tCh until a threshold cell temperature is attained.

32. The method of Claim 14, wherein the fitting parameters of following equations (p-OCV vs. SOC) and (SOC vs. p-OCV) are adjusted by an artificial intelligence to optimize the battery performances, including the cycle life:With fitting parameters: pOCVo or p-OCVo, a, 0, and y33. The method according to Claims 11 and 14, wherein the a, 0, and y parameters are adjusted online by artificial intelligence according to the battery SOH.

34. A system (ICA) for fast charging a battery system comprising one or multiple cells arranged in series and in parallel, within a SOC range (ASOC) and total charge time t(charge), implementing the charging method (ICA) of any of Claims 1 to 33, comprising: means for applying a series of intermittent constant voltage plateaus (CVk, k=l, 2, 3. . .) for a duration t(CVk), while monitoring the charge current I (C-rate) and the battery temperature Tceii, means for allowing the current I to go to zero (rest) for a short time duration t(rest) within the constant voltage plateau and measuring the open-circuit voltage during rest, noted as pOCV. means for applying a voltage pulse for a duration t(pulse), between two successive rest times, within a same duration time CVk , means for repeating rests and voltage pulses sequences until the current reaches I(min),means for applying at Imin a voltage step AV between two successive voltage plateaus CVk and CVk+i. and measuring the current at the onset of AV application noted I(max).

35. The system of preceding Claim, further comprising means for assessing a state of charge assessment based on pOCV data according to the equationin which pOCVo or p-OCVo, a, 0 and y are fitting parameters36. The system of Claim 34, further comprising means for assessing cell’ internal resistance according to equation:(max)-Z(min) (Eq 2)37. The system of Claim 34, further comprising means for determining the entropy p-AS (or pAS) and enthalpy p-AH (or pAH) of battery cell based on temperature dependence of p-OCV, according to the equations:

38. The system (ICA+OET) of any of Claims 34 to 37, further comprising: means for collecting the voltage, the current and the cell temperature Tceii data during the charge, including the pseudo-open-circuit voltage (pOCV) during the rest time, means for determining the pseudo entropy data (pAS) and the pseudo enthalpy data (pAH) from said pseudo-open-circuit p-OCV as a function of said cell temperature Tceii means for processing said pseudo entropy data (pAS) and pseudo enthalpy data (pAH) to determine the battery state of charge (SOC) and state of health (SOH).

39. The system of preceding Claim, further comprising means for detecting an internal short-circuit (ISC).

40. The system of Claim 38, further comprising means for processing charge parameter to create an augmented battery.41 The system of Claim 38, further comprising means for processing the charge parameters to control the quality of a battery.

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