Classifications

• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
  • H01M10/00—Secondary cells; Manufacture thereof
  • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
  • H01M10/4285—Testing apparatus
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
  • H02J7/007—Regulation of charging or discharging current or voltage
  • H02J7/0071—Regulation of charging or discharging current or voltage with a programmable schedule
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02E60/10—Energy storage using batteries

Definitions

• the present invention relates to the use of a method for estimating the characteristics of an electrochemical system, of battery type, which are not directly measurable.
• the method makes it possible to manage an electrochemical battery, in particular during its operation in a hybrid or electric vehicle.
• the electrochemical battery is one of the most critical components of a hybrid or electric vehicle.
• the correct operation of the vehicle is based on an intelligent battery management system (BMS) which is responsible for operating the battery to the best compromise between the different levels of dynamic solicitation.
• BMS battery management system
• SoC state of health
• SoH state of health
• T thermal state
• SoC state of charge of a battery
• available capacity expressed as a percentage of its nominal capacity
• Health status which is the available capacity after recharging (expressed in Ah) is therefore a measure of the point that has been reached in the life cycle of the battery.
• the thermal state (T) of a battery conditions its performance because the chemical reactions and transport phenomena involved in the electrochemical systems are thermally activated.
• the initial thermal state is linked to the temperature outside the vehicle which can be operated over a wide range of temperatures, typically between -40 0 C and +40 0 C.
• the thermal state in use changes depending on the load load and discharge of the battery, of its design and its environment. The most accurate and reliable estimation of SoC, SoH and thermal state
• T results in several advantages. This estimate makes it possible to prevent the supervisor of the vehicle from behaving in an overly cautious manner in the use of the energy potential of the battery or vice versa. It also makes it possible to avoid oversized safety of the batteries, thus to save weight on board and, consequently, fuel consumed; it also reduces the total cost of the vehicle. A correct estimator is therefore a guarantee for efficient and safe operation of the battery capacity throughout the vehicle's operating range.
• the present invention relates to the use of a method for estimating the internal state of a rechargeable electrochemical system (of the battery type), which consists in particular in estimating the characteristics of the battery which are not directly measurable (reference model ). It will be a question of using measurements easily obtained by conventional means to reconstruct the internal state of the battery by means of a mathematical model of the battery which can advantageously be executed synchronously with the operation of the battery. -Even (real time). In particular the method will make it possible to estimate the state of charge (SoC), the state of health (SoH) and the thermal state (T) of an electrochemical battery, which are the most interesting internal characteristics for applications. concerning hybrid and electric vehicles.
• SoC state of charge
• SoH state of health
• T thermal state
• Said method may include the derivation of the reference model (reduced model) to allow simplified use, in particular for the on-board control and energy management of a hybrid vehicle.
• the invention relates to an intelligent system for energy and thermal management of the battery during its operation.
• Another object of the invention is a battery charger / discharger using said method.
• Said method can also be used in a simulator of the thermal state of an electrochemical system forming part of the management system.
• the measurement of the no-load voltage as a SoC indicator is also known as a method.
• the use of other indicators, for example the estimation of an internal resistance, (patents US 6191590 B1, EP1835297 A1) is a method also known.
• SIE impedance spectroscopy
• EP880710 (Philips), the description of the electrochemical and physical phenomena at the electrodes and in the electrolyte serving as a support for the development of the RC model, the temperature of the battery being simulated by the model, in order to gain precision, compared with a measurement external.
• the invention relates to the use of a method for estimating the internal state of a rechargeable electrochemical system (battery type) in which: at least one input signal of at least one representative parameter is measured a physical quantity of the rechargeable electrochemical system, where a reference model of the system is established comprising at least: a mathematical representation of the kinetics of the electrochemical reactions taking place at the interfaces between each of the electrodes and the electrolyte, taking into account the interface concentrations;
• the potential and / or the state of charge and / or the temperature of the electrochemical system are collected as an output signal.
• the health status of the electrochemical system is collected as an output signal.
• An intelligent electrochemical battery management system comprises an input means connected to a measurement means on the battery for receiving an input value of at least one parameter representative of a physical quantity of battery; a processing means for generating at least one output signal of at least one characteristic calculated by the method using the reduced model. an information / control means for presenting information on the physical quantity of the battery and / or controlling the charging / discharging and / or cooling of the battery in response to the output signal of the processing and / or comparison means .
• the processing means comprises a recursive filter.
• Said management system can be used for on-board control and energy management of a hybrid vehicle.
• the invention also relates to a battery charger / discharger comprising said management system.
• the invention also relates to a simulator of the thermal state of a battery using said method and forming part of the intelligent management system. Detailed description of the invention
• Figures 1 to 8 illustrate the invention without limitation and relate to a Ni-MH battery, although the model according to the invention can be applied to any electrochemical system.
• FIG. 1 is a diagrammatic representation of a NiMH battery cell, where MH-el designates the porous negative electrode based on a metal hydride, Ni-el the porous positive electrode based on nickel, ReG the reserve compartment of gas, sep the separator electrically isolating the two electrodes, col the current collectors, and x the prevailing direction.
• the electrodes and the separator are impregnated with a concentrated alkaline solution.
• the gas (oxygen) that can be released during charging of the battery is concentrated in a common space above the cells.
• Figure 2 represents a possible schema of the model that is used in the method, where the acronyms have the following meaning:
• BMa material assessment; (8): c n , (9): c m / (10): p 0 ; (11): V, (12): q;
• the current at the terminals of the cell is considered as an input of the model, while the voltage is one of its outputs.
• the input signals, current and temperature, are representative of physical quantities measured on the battery.
• Processing means based on the Butler Volmer equations, the load balance, the material balance and the energy balance calculate the state of the battery on the basis of the input signals and generate output signals derived from the calculation, like potential, state of charge and temperature.
• FIG. 3 represents an example of associated SoC estimation curves obtained by integrating the current (bold dotted line) and using the models according to the invention, the reduced model (fine dashed line) and the reference model (full fat line). ).
• FIG. 4 represents a diagram of the Kalman filter which is applied to an electrochemical cell according to the method of the invention, with X: internal state calculated by the estimator, U: input, Y: output, F: variation of the internal state according to the model.
• FIG. 5 represents an operating diagram of the SoC estimation algorithm, with Sph: physical system, M: model, FNL: nonlinear filter, East: estimator, U: measured inputs, Y: measured outputs, Ye: outputs calculated by the model, Xe: internal state calculated by the estimator, F: variation of the internal state according to the model, L: output gain of the nonlinear filter.
• FIG. 6 is a functional diagram of a hybrid vehicle simulator using the method for estimating the internal characteristics according to the invention.
• FIGS. 7a and 7b show an example of charge / discharge curves at different current regimes and at ambient temperature; a) IC load, IC slot; b) load IC, slot 1OC Dashed line curve: reduced model according to the invention; solid line curve: reference model according to the invention.
• FIG. 8 shows an example of an electrochemical impedance spectroscopy curve simulated from the method used in the invention, representing the imaginary part of the Imag (Z) impedance as a function of the real part of the impedance Real (Z).
• Electrochemical reactions take place at the interfaces between the electrodes and the electrolyte.
• the positive electrode is the seat of electrochemical reactions of reduction of the oxidizing species, during the discharge, while the negative electrode is the seat of oxidation reactions of the reducing species.
• the kinetics of electrochemical reactions can be described by the Butler-Volmer equations, whose general form for the generic "z" reaction is
• J z Jo, z ⁇ exp [a a> z K ( ⁇ .- U eg , z )] - exp [-a c> z K ( ⁇ z - U eg , z )] ⁇ •
• J z is the charge transfer current density
• J z0 is the exchange current density
• ⁇ Z is the potential difference between the solid phase (electrode) and the electrolyte
• U eq z is the equilibrium potential of the reaction
• ⁇ z is a symmetry factor (different for the positive electrode, index "c", and the negative electrode, index "a")
• E a , z is the activation energy.
• the active species are nickel oxyhydroxide NiOOH, nickel hydroxide Ni (OH) 2 , metal hydride MH, oxygen O 2 partially dissolved in the electrolyte in equilibrium with the gas phase. Electrochemical reactions at the positive electrode are, during the discharge,
• C n is the concentration of protons in the positive electrode (nickel hydroxide)
• C e is the electrolyte concentration of OH ions ie "
• C 0 is the concentration of oxygen in the negative electrode and the same crossed-out variable is the interfacial concentration of oxygen, in equilibrium with the gaseous phase
• c m is the concentration of hydrogen in the negative electrode (metallic material)
• the indices "ref” and “max” are refer to reference and maximum values, respectively
• ⁇ , z is the stoichiometric coefficient of the species "i” in the “z” reaction and a (k ) is the interface specific surface in the "k” region.
• the conversion rate is classically evaluated as
• Equations (12) - (14), (16) constitute a system of four equations with four variables c e , C 0 ⁇ e and i e .
• the equations are differential to partial derivatives in the x domain, as shown in Figure 1.
• the resolution of this system requires appropriate boundary conditions.
• the boundary conditions for the two OH " and oxygen species are determined by the continuity at the interfaces between the electrodes and the separator, as well as by the zero flow condition at the ends of the cell (current collectors). in the liquid phase is also zero because the total current of cell I passes only through the solid phase.
• i s (t) - ⁇ (k) (l- ⁇ (k) ) V ⁇ s (t) (18)
• ⁇ (k) the conductivity in the region "k”
• ⁇ s the potential in the solid phase.
• index j is assigned to both nickel, with the current density Ji, and the metal hydride, with the current density J 3 .
• the method used in the invention distinguishes local concentration c (x, t) and interfacial concentration in the reference model.
• the interface concentrations ç m and ç n are used instead of the average concentrations in the Butler-Volmer equations (4) and (6).
• Interface concentrations are calculated by the following approximation that replaces equation (20):
• V is the volume of the liquid phase, where oxygen is generated
• R 0 eg is calculated for each zone "k" by the
• K is an interfacial mass transport coefficient.
• Thermal balance The temperature of the cell can be calculated as the output of the energy balance, from FIG. 2.
• the internal heat flux ⁇ gen generated by the activity of the cell is given by:
• ⁇ tra (t) hA cel (T (t) - T a ) (2 ⁇ )
• Cp is the specific heat capacity of the cell and the M ⁇ its mass.
• Equation (4) becomes where C (i) is the double layer capacity of the electrode 1 (eg nickel).
• the function f represents the right term of equation (4)
• equation (6) becomes:
• C ( 3 ) is the double layer capacity of the electrode 3 (for example MH).
• the function g represents the right term of equation (6).
• N 0 O 5
• equation (24) is visibly equivalent to:
• r is the radius of the macro-particle representing the metal hydride
• y (1) the thickness of the active substrate which encircles the cylindrical macro-particle representing nickel.
• the method used in the invention distinguishes mean concentration of the c (t) region and interfacial concentration in the reduced model.
• Interface concentrations c. m and n are used instead of the mean concentrations in the Butler-Volmer (4) and (6) equations.
• the interface concentrations are calculated, as in the reference model, by the following approximation that replaces equation (20):
• Equation (28) thus separates into two equations, each valid for one of the electrodes,
• C d i is the double layer capacitance, which can change value between the two electrodes.
• the 0-d model is completed by an overall assessment of the electrical potential on the cell:
• V (t) A ⁇ poa (t) - ⁇ , J ⁇ a (*) + Mintl ⁇ t) (30)
• the reduced model of the method used in the invention comprises equations (4) - (8), (25) - (27), (29) - (30), a total of 15 equations, for 15 variables 3 lr ..., J 4 , ⁇ i, • -, n *, c m , C n , p 0 , ⁇ pos , ⁇ neg , V, T.
• the other quantities appearing in the equations that constitute the method are treated as parameters to be calibrated.
• a special formulation is assigned to the parameter U eq , r e r, i appearing in the first relation of equations (8).
• the state of charge of the cell in the method used in the invention, q (t), is given by the concentration of one of the reactive species, in particular by C n in the example of a Ni-type battery. -MH,
• the estimate of q is therefore based on the estimate of C n , whereas this variable is not directly measurable from a battery, in particular on board the vehicle.
• the method advantageously uses a recursive filter to estimate the state of the dynamic system from the available measurements, a schema of which is proposed in Figure 4. Notable characteristics of this estimation problem are the fact that the measurements are affected by noise, and the fact that the system modeled according to the method is strongly non-linear.
• a recursive filter preferably used in the method will be the extended Kalman filter known to those skilled in the art.
• the available measurements are the cell terminal voltage and the battery temperature, which represent the y output of the model, and the I current at the terminals which represents the u input of the model.
• the method then provides a step (M in Fig. 5) where the model provides the vector of the variations f (F in Fig. 5) and the calculated output y (Ye in Fig. 5) according to equation (34). Then, these two variables are manipulated by a second step (East in FIG. 5) which reconstructs the state Xe from F, Ye, and the measure Y.
• the estimation algorithm thus uses the output a third step (FNL in Figure 5) that provides the variable L as a function of the reconstructed state, the characteristics of the electrochemical system (according to the model of the method) and the characteristics of the noise that affects the measurements.
• the FNL step may be performed with a method known to those skilled in the art, for example the extended Kalman filter.
• the model directly represents the state of charge as a state variable of the model.
• the known methods use so-called “equivalent electric circuit” models, where the state of charge is not a dynamic variable of the model, but an exogenous variable, according to which other dynamic or static variables are set.
• BMS electrochemical battery management system
• the reduced model according to the invention is based on physical parameters of the system, and not already on equivalent global parameters such as the RC models known in the prior art. This property makes it easier to estimate the aging and therefore the state of health of the battery.
• the estimated variations of the parameters of the reduced model will be used to detect possible macroscopic variations in the behavior of the battery, and thus alterations in its performance, which is commonly understood as "aging".
• Other uses of the models of the method Battery simulator
• the reference model is also useful as an aid for the dimensioning of traction chains for hybrid vehicles.
• An example of a hybrid vehicle simulator incorporating a battery model is given in FIG. 6.
• these applications do not need simulation models capable of running in real time, even if a certain speed of calculation is still desirable.
• the reference model (Model 1-D) of the method used in the invention can simulate the dynamic behavior of a traction battery more efficiently and faithfully than equivalent electric circuit type models, and thus it can be used in a battery simulator.
• the electrochemical reference model can be used to test "offline" the efficiency of the online estimator (which uses the reduced 0-d model according to the invention) and to calibrate the parameters, adapting them to the specific battery under examination.
• the reference model as the reduced model of the method used in the invention can calculate the variations in time of all internal electrochemical variables of the battery, and in particular the state of charge.
• the input of the models is the current at the terminals of the battery
• the simulated cases depend on the choice of this last variable. For example, it is possible to represent a charge or a controlled discharge with a constant current, or a variable current according to a fixed profile, or with a variable current depending on the voltage.
• This last case is representative of the conditions of solicitation of the battery in a vehicle, where the current imposed on the battery depends on the voltage, according to the characteristics of the associated electrical components (power electronics, electric motor (s), etc. .).
• Typical results of the battery simulator using the models according to the invention are presented in FIG. 7. Impedance spectroscopy simulator
• Equation (30) is then modified to take into account the inductive effects due to the connections between the cells and with the terminals.
• the presence of the energy balance in the reduced model and in the reference model of the method used in the invention makes it possible to simulate the thermal evolution of the system. Consequently, the method used in the invention can thus be used for the sizing of the battery and the validation of the thermal management systems, which must necessarily equip the battery itself.
• the heat fluxes generated and the temperature of the battery are input variables for these systems, which are intended to adjust these flows and this temperature around the allowable values.
• thermal transients thus makes it possible to synthesize and validate the control and optimization strategies associated with thermal management systems. These strategies can thus benefit from the presence of a reduced model during their online use, to obtain estimates of certain variables that are not measurable (temperatures in specific points, heat fluxes, etc.), or that are measurable, but with response times of the associated sensors too slow.

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• 2008
  • 2008-03-28 FR FR0801709A patent/FR2929410B1/fr active Active
• 2009
  • 2009-03-27 WO PCT/FR2009/000339 patent/WO2009133262A1/fr active Application Filing
  • 2009-03-27 JP JP2011501266A patent/JP2011519118A/ja active Pending
  • 2009-03-27 EP EP09738296A patent/EP2274631A1/de not_active Withdrawn
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  • 2009-03-27 US US12/919,731 patent/US8532945B2/en not_active Expired - Fee Related
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  • 2009-03-27 WO PCT/FR2009/000340 patent/WO2009130405A2/fr active Application Filing

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