JP7616101B2 - SOC Estimation Method - Google Patents

Description

This disclosure relates to a method for estimating the SOC of a lithium iron phosphate ion battery that includes a positive electrode formed from lithium iron phosphate.

In recent years, lithium iron phosphate ion batteries including a positive electrode (hereinafter referred to as "LiFePO positive electrode") formed of lithium iron phosphate such as _LiFePO4 are being adopted as a replacement for secondary batteries mounted on electric vehicles (BEVs) and hybrid vehicles (HEVs) and lead-acid batteries (12V batteries) mounted on vehicles. The LiFePO positive electrode of a lithium iron phosphate ion battery exhibits peculiar behavior (polarization behavior) such as memory effect, path dependency, and voltage hysteresis in which the OCV (open circuit voltage) changes between charging and discharging. In addition, as a model for explaining the behavior of the LiFePO positive electrode, a many-particle model is known in which the LiFePO positive electrode is assumed to be composed of a large number of spherical particles with different particle radii that are electronically and ionically connected to each other (see, for example, Non-Patent Document 1). By using such a multi-particle model, the behavior of the LiFePO positive electrode can be comprehensively grasped.

Comprehensive Study of the Polarization Behavior of LiFePO4 Electrodes Based on a Many-Particle Model Hiroki Kondo, Tsuyoshi Sasaki, Pallab Barai and Venkat Srinivasan Journal of The Electrochemical Society, Volume 165, Number 10 Published 6 July 2018

In ternary lithium-ion batteries and the like, the SOC is generally estimated by combining a current integration method, which estimates the SOC by integrating measured current values, with an estimation method using the relationship between SOC and OCV. In contrast, in lithium iron phosphate batteries, the rate of change in OCV relative to changes in SOC is very small over a wide range other than the high and low SOC ranges, and the above-mentioned voltage hysteresis and the like exist, making it difficult to accurately estimate the SOC from the relationship between SOC and OCV. For this reason, a method for accurately estimating the SOC of lithium iron phosphate batteries is required, but the above-mentioned non-patent document 1 does not disclose any method for estimating the SOC of lithium iron phosphate batteries.

Therefore, the primary objective of this disclosure is to enable accurate estimation of the SOC of a lithium iron phosphate ion battery that includes a positive electrode formed from lithium iron phosphate.

The SOC estimation method disclosed herein is an SOC estimation method for estimating the SOC of a lithium iron phosphate ion battery including a positive electrode formed of lithium iron phosphate, and includes: calculating an overvoltage caused by a reaction of the positive electrode based on at least a measured voltage of the lithium iron phosphate ion battery and an OCV obtained from a first map that is predetermined to define the relationship between the SOC and OCV of the lithium iron phosphate ion battery; calculating an overvoltage of each particle when it is assumed that the positive electrode is composed of a plurality of particles each containing lithium, using the overvoltage caused by the reaction of the positive electrode and a second map that is predetermined based on the relationship between the lithium composition of the lithium iron phosphate and the OCV of the positive electrode; calculating a current density of each particle based on the overvoltage of each particle; calculating an estimated battery current of the lithium iron phosphate ion battery based on the current density of each particle; and estimating the SOC of the lithium iron phosphate ion battery based on the estimated battery current.

The SOC estimation method of the present disclosure uses a multi-particle model that assumes that the positive electrode formed of lithium iron phosphate is composed of multiple particles each containing lithium. When estimating the SOC of the lithium iron phosphate ion battery, the overvoltage due to the reaction of the positive electrode is calculated based on at least the measured voltage of the lithium iron phosphate ion battery and the OCV obtained from a first map that is predetermined to define the relationship between the SOC and OCV of the lithium iron phosphate ion battery. Furthermore, the overvoltage of each of the multiple particles that constitute the positive electrode is calculated using the overvoltage due to the reaction of the positive electrode and a second map that is predetermined based on the relationship between the lithium composition of the lithium iron phosphate and the OCV of the positive electrode. Then, the current density of each of the multiple particles is calculated based on the overvoltage of each of the multiple particles, and the estimated battery current of the lithium iron phosphate ion battery is calculated from the current density of each of the multiple particles. As a result, the SOC of the lithium iron phosphate ion battery can be estimated based on the estimated battery current calculated to offset the deviation between the OCV obtained from the first map and the actual OCV. As a result, it is possible to accurately estimate the SOC of a lithium iron phosphate-ion battery that includes a positive electrode made of lithium iron phosphate, which exhibits peculiar behaviors such as memory effect, path dependency, and voltage hysteresis.

In addition, the overvoltage due to the reaction of the positive electrode may be calculated by subtracting from the measured voltage value of the lithium iron phosphate battery at least the previous OCV corresponding to the previous SOC obtained from the first map, and the overvoltage of each particle may be calculated from the overvoltage due to the reaction of the positive electrode and the reference voltage of each particle obtained from the second map. The current SOC of the lithium iron phosphate battery may be estimated from the previous SOC, the estimated battery current, and the battery capacity, and the current OCV corresponding to the current SOC may be obtained from the first map. This makes it possible to converge the estimated SOC value to an actual value by repeatedly estimating the SOC of the lithium iron phosphate battery.

Furthermore, the first map may be created by subtracting the OCV of the negative electrode of the lithium iron phosphate battery from the average voltage of the two spinodal points of the positive electrode for each SOC, and the second map may be created by offsetting the relationship between the lithium composition of the lithium iron phosphate and the OCV of the positive electrode so that the average voltage of the two spinodal points of the positive electrode becomes 0 volts. This makes it possible to more accurately calculate the overvoltage of each of the multiple particles using the overvoltage due to the reaction of the positive electrode and the second map.

In addition, the amount of change in the lithium concentration of each particle may be calculated based on the current density of each particle, and the current lithium concentration may be calculated from the previous lithium concentration and the amount of change, and the reference voltage of each particle corresponding to the lithium composition based on the previous lithium concentration may be obtained from the second map. This makes it possible to more appropriately calculate the reference voltage and therefore the overvoltage of each of the multiple particles.

Furthermore, the overvoltage due to the reaction of the positive electrode may be calculated by subtracting the previous OCV, the salt concentration overvoltage, and the product of the measured current and internal resistance of the lithium iron phosphate battery from the measured voltage value of the lithium iron phosphate battery. This makes it possible to more accurately calculate the overvoltage due to the reaction of the positive electrode while reducing the computational load.

1 is a schematic configuration diagram of a vehicle equipped with a battery including a lithium iron phosphate battery to which an SOC estimation method of the present disclosure is applied; 1 is a diagram showing the relationship between the lithium composition of lithium iron phosphate and the OCV of a LiFePO positive electrode. 1A is a graph showing the relationship between the SOC of a lithium iron phosphate ion battery and the average voltage of the OCV of the positive electrode, FIG. 1B is a graph showing the relationship between the SOC of a lithium iron phosphate ion battery and the OCV of the negative electrode, and FIG. 1C is a graph showing an example of a first map that specifies the relationship between the SOC and OCV of a lithium iron phosphate ion battery. 4 is a flowchart for explaining an SOC estimation method according to the present disclosure. 4 is a chart showing an example of a second map used in the SOC estimation method of the present disclosure.

Next, the form for implementing the invention of this disclosure will be described with reference to the drawings.

Figure 1 is a schematic diagram showing a vehicle V equipped with a battery 1 including a lithium iron phosphate battery to which the SOC estimation method of the present disclosure is applied. The vehicle V shown in the figure is an electric vehicle (BEV) or hybrid vehicle (HEV, PHEV) that includes, in addition to the battery 1, a motor generator (three-phase AC motor) MG that is connected to the battery 1 via a power control device (not shown) including an inverter and the like and that can exchange power with the battery 1 to output driving power and regenerative braking force.

The battery 1 is a so-called high-voltage battery including a battery stack including a plurality of battery modules connected in series, for example, and a case that houses the battery stack, and each battery module includes a plurality of battery cells 2 that are connected in series and housed in a module case, for example. In this embodiment, the battery cells 2 constituting the battery module are lithium iron phosphate ion batteries that include a positive electrode (LiFePO positive electrode ₎ formed of lithium iron phosphate having an olivine crystal structure, which is LiFePO4, and a negative electrode formed of a graphite-based carbon material or the like, and the positive electrode and the negative electrode are housed inside an exterior body together with a separator and an electrolyte solution, which is an organic solvent or the like.

FIG. 2 is a diagram showing the relationship between the lithium composition x ("x" in _LixFePO4 ) of lithium iron phosphate and the OCV (open circuit voltage) of the LiFePO positive electrode obtained by an experiment. As shown in the figure, when a lithium iron phosphate ion battery is charged and discharged, the reaction proceeds in the LiFePO positive electrode in a state in which the Li-rich phase, the Li-poor phase, and a region in which the two phases coexist are present. In addition, as shown in the figure, the OCV of the LiFePO positive electrode decreases approximately in proportion to the decrease in the lithium composition x between the spinodal point on the Li-rich phase side and the spinodal point on the Li-poor phase side, and the voltage difference between the two spinodal points is approximately 20 mV. Furthermore, the average voltage of the two spinodal points (the equilibrium voltage in the coexistence phase of the Li-rich phase and the Li-poor phase) is approximately 3.43 V.

Here, in consideration of the peculiar behavior (polarization behavior) of the LiFePO positive electrode, such as the memory effect, path dependency, and voltage hysteresis, when a many-particle model is introduced that assumes that the LiFePO positive electrode is composed (formed) of N spherical particles with different particle radii that are electronically and ionically connected to each other, the OCV of each particle can be obtained from the lithium composition x of each of the particles, as shown in Fig. 2. Furthermore, when an overvoltage occurs due to the reaction of the LiFePO positive electrode, each of the particles that constitute the LiFePO positive electrode has an overvoltage η _j according to the lithium composition (where "j" is an integer from 1 to N). Furthermore, if _the overvoltage due to the reaction of the LiFePO positive electrode during charging and discharging of a lithium iron phosphate ion battery is "ΔV _MPM ," the OCV of the jth particle obtained from Figure 2 is "OCV _j ," and the average voltage of the above two spinodal points is "V _AVE ," then the overvoltage η j can be expressed as in the following equation (1), and the average value of the overvoltage η _j of N particles coincides with the overvoltage ΔV _MPM .

If the current density of the jth particle having an overvoltage η _j is "i _j ", the current density i _j is expressed by the following formula (2), and if the estimated value of the current applied to the LiFePO positive electrode, i.e., the estimated battery current of the lithium iron phosphate ion battery, is "I _app ", the estimated battery current I _app is expressed by the following formula (3). In formula (2), "F" is the Faraday constant, "k" is the reaction rate constant, "c _j " is the lithium ion concentration of the jth particle, "c _e " is the lithium ion concentration of the electrolyte (a predetermined constant value in this embodiment), "c _max " is the maximum lithium concentration in the active material, "R" is the gas constant, "T" is the battery temperature (temperature of the battery cell), and the charge transfer coefficient of the anode reaction and the cathode reaction is 0.5. Furthermore, "S _j " in formula (3) is the surface area of the j-th particle, and if the particle radius of the j-th particle is "r _j ", then S _j = 4πr _j ^2. Thus, by utilizing the relationship between the lithium composition x and the OCV of the LiFePO positive electrode shown in Fig. 2 and the multi-particle model, it is possible to calculate the estimated battery current I _app of a lithium iron phosphate ion battery taking into account the peculiar behavior of the LiFePO positive electrode, such as the memory effect, path dependency, and voltage hysteresis.

On the other hand, the overvoltage ΔV _MPM due to the reaction of the LiFePO positive electrode when the lithium iron phosphate ion battery is charged and discharged can be expressed as in the following formula (4) if the voltage when the lithium iron phosphate ion battery (battery cell) is charged and discharged is "V(t)", the previous value of the OCV of the lithium iron phosphate ion battery is "OCV(t-1)", the salt concentration overvoltage of the electrolyte is "dV _dce ", the current when the lithium iron phosphate ion battery is charged and discharged is "I(t)", and the internal resistance of the lithium iron phosphate ion battery is "Rd". In addition, the voltage V(t) and current I(t) in formula (4) can each be actual measured values (measured values), and the salt concentration overvoltage dV _dce can be calculated based on the following formula (5).

Therefore, by obtaining the previous value OCV(t-1) of the OCV of the lithium iron phosphate battery, it becomes possible to easily calculate the overvoltage ΔV _MPM due to the reaction of the LiFePO positive electrode from formula (4). As described above, by utilizing the relationship between the lithium composition x and the OCV of the LiFePO positive electrode shown in FIG. 2 and the multi-particle model, it is possible to calculate the estimated battery current I _app taking into account the peculiar behavior of the LiFePO positive electrode. In light of this, in this embodiment, the reaction (polarization) of the LiFePO positive electrode is ignored with respect to the OCV of the LiFePO positive electrode, and as shown in FIG. 3(a), the OCV of the LiFePO positive electrode is considered to be the above average voltage V _AVE (constant value) regardless of the SOC. In this embodiment, a first map that defines the relationship between the SOC and the OCV of the lithium iron phosphate battery as shown in FIG. 3(c) is prepared by subtracting the OCV of the negative electrode of the lithium iron phosphate _battery (see FIG. 3(b)) from the average voltage V AVE, and a previous value OCV(t-1) of the OCV of the lithium iron phosphate battery is obtained from the first map.

In this embodiment, the total number N of particles in the multi-particle model is, for example, 200 to 2000, the average size of the particle radius is, for example, 100 nm, and the particle radius is distributed in a log-normal distribution with a standard deviation of, for example, 80 nm. In the multi-particle model, the center of gravity of the particles forming the LiFePO positive electrode corresponds to the SOC of the lithium iron phosphate ion battery, and the relationship between the SOC according to the center of gravity of the particles and the OCV of the LiFePO positive electrode also has the same characteristics as those shown in FIG. 2, and the voltage difference between the two spinodal points is approximately 20 mV, and the average voltage of the two spinodal points is approximately 3.43 V. Furthermore, in the multi-particle model, since there can be multiple particle distribution states for each SOC, the OCV of the positive electrode has a width of about 20 mV in the high SOC region and the low SOC region. Therefore, even in the multi-particle model, the rate of change in the OCV of the LiFePO positive electrode relative to changes in the SOC of the lithium iron phosphate ion battery is very small over a wide range other than the high and low SOC regions, making it difficult to accurately estimate the SOC of the lithium iron phosphate ion battery from the relationship between SOC and OCV.

Next, the procedure for estimating the SOC of each battery cell 2 included in the battery 1 will be described in detail with reference to Figures 4 and 5. Figure 4 is a flowchart showing an example of an SOC estimation routine executed by an electronic control unit 10 (hereinafter referred to as "ECU 10") mounted on the vehicle V to manage the battery 1. The SOC estimation routine in Figure 4 is repeatedly executed by the ECU 10, for example, every 10-100 ms while the system of the vehicle V is activated.

4, the ECU 10 first sets the variable m, i.e., the number of the battery cells 2 included in the battery 1, to a value of 1 (step S100). The ECU 10 also acquires information necessary for estimating the SOC of the battery cell 2 m, such as a current (present) measured value V(t) of the voltage (terminal voltage) of the m-th battery cell 2 (hereinafter referred to as "battery cell 2 _m ") measured by a voltage sensor (not shown), a current (present) measured value I(t) of the current of the battery cell 2 _m measured by a current sensor (not shown), the salt concentration overvoltage dV _dce , the previous OCV value OCV(t-1) of _{the battery cell 2 m ,} the previous lithium concentration value C _j (t-1) of each particle when it is assumed that the LiFePO positive electrode of the battery cell 2 _{m is composed of multiple particles, and the temperature T of the battery cell 2 m detected} by a temperature sensor (not shown) (step S110). The salt concentration overvoltage dV _dce is calculated separately based on the above formula (5), and the previous value OCV of the battery cell 2 _m , OCV(t−1), and the previous value C _j (t−1) of the lithium concentration of each particle are calculated the previous time the SOC estimation routine was executed.

Next, the ECU 10 calculates the overvoltage ΔV MPM due to the reaction of the LiFePO positive electrode corresponding to the measured voltage and current values V(t), I(t) and the salt concentration overvoltage dV _dce acquired in step S110 according to the above formula (4) (step S120). The ECU 10 also calculates the overvoltage η _j of each of the multiple particles in the multi-particle model using the overvoltage ΔV _MPM calculated in step S120, the previous value C _j (t-1) of the lithium concentration acquired in step S110, and the second map shown in Fig. 5 (step S130). In step S130, the ECU 10 calculates the lithium composition x _j (=C _j (t-1 ₎ /C _max ) of each particle based on the previous value C _j (t-1) of the lithium concentration, and then acquires the reference voltage corresponding to the lithium composition x _j from the second map. The second map shown in Fig. 5 was created by offsetting the relationship between the lithium composition x of lithium iron phosphate ( _LixFePO4 ) shown in Fig. 2 and the OCV of the LiFePO positive electrode so that the average voltage V _AVE becomes 0 volts. In other words, the reference voltage obtained from the second map corresponds to the OCV _j corresponding to the lithium composition x _j based on the previous value C _j (t-1) of the lithium concentration of each particle minus the average voltage V _AVE . Furthermore, in step S130, the ECU 10 calculates the overvoltage η _j of each of the multiple particles in the multi-particle model by subtracting the obtained reference voltage from the overvoltage ΔV _MPM .

After the process of step S130, the ECU 10 calculates the current density i j of each of the particles in the multi-particle model based on the overvoltage η _j acquired in step S110, the previous lithium concentration value C _j (t-1), and the temperature T in accordance with the above formula (2) (step S140). Furthermore, the ECU 10 calculates the estimated battery current I _app of the battery cell 2 _m in accordance with the current density i _j of each particle calculated in step S140 and the surface area S _j of each particle in accordance with the above formula (3) (step S150). Then, the ECU 10 calculates the current value SOC(t) of the SOC of the battery cell 2 m by adding a value obtained by dividing the calculated estimated battery current I _app by the battery capacity C to the previous value SOC(t _{-1), which is the SOC of the battery cell 2 m calculated} when the SOC estimation routine was executed the previous time (step S160).

The ECU 10 also obtains the current value OCV(t) of the OCV of the battery cell 2 _m corresponding to the current value SOC(t) of the SOC of the battery cell 2 _m from the first map shown in Fig. 3(c) (step S170). Furthermore, the ECU 10 calculates the amount of change ∂C _j /∂t in the lithium concentration of each particle based on the current density i _j calculated in step S140 according to the following formula (6), and calculates the current value C _j (t) of the lithium concentration of each particle by adding the calculated amount of change ∂C _j /∂t to the previous value C _j (t-1) of the lithium concentration calculated at the previous execution of the SOC estimation routine (step S180). Note that in formula (6), "V _j " is the volume (= 4/3 x π x r _j ³ ) of the j-th particle. The current OCV value OCV(t) of battery cell _2m calculated in step S170 is used as the previous OCV value OCV(t-1) the next time the SOC estimation routine is executed, and the current lithium concentration value C _j (t) of each particle calculated in step S180 is used as the previous lithium concentration value C _j (t-1) the next time the SOC estimation routine is executed.

After calculating the current value C _j (t) of the lithium concentration of each particle in step S180, the ECU 10 increments the variable m (step S190) and determines whether or not the variable m exceeds the total number M of the battery cells 2 in the battery 1 (step S200). If the ECU 10 determines that the variable m is equal to or smaller than the total number M (step S200: NO), it executes the processes of steps S110-S190 again. If the ECU 10 determines that the variable m exceeds the total number M (step S200: YES), it temporarily ends the routine of FIG. 4 at that point.

As described above, when estimating the SOC of each battery cell 2 of the battery 1, which is a lithium iron phosphate ion battery, a multi-particle model is used, which assumes that the LiFePO positive electrode formed of lithium iron phosphate (LiFePO ₄ ) is composed of multiple particles containing lithium. In addition, when estimating the SOC of each battery cell 2, an overvoltage ΔV MPM due to a reaction of the LiFePO positive electrode is calculated (step S120 ₎ based on at least the measured voltage value V(t) of the battery cell 2 _m and the previous OCV value OCV(t-1) of the battery cell 2 _m obtained from a first map that is predetermined to define the relationship between the _SOC and OCV of the lithium iron phosphate ion battery (battery cell 2 m ). Furthermore, the overvoltage ηj of each of the particles constituting the LiFePO positive electrode is calculated ( _{step S130} ) using the overvoltage ΔV _MPM due to the reaction of the LiFePO positive electrode and a second map shown in Fig. 5 that is determined in advance based on the relationship between the lithium composition x of lithium iron phosphate (LixFePO 4 ) and the OCV of the LiFePO positive electrode shown in Fig. 2. Then, the current density _ij of each of the particles is calculated from the overvoltage _ηj of each of the particles (step S140), and the estimated battery current I _app of the battery cell 2 _m is calculated _from the current density _ij of each of the particles (step S150).

More specifically, the overvoltage ΔV _MPM due to the reaction of the positive electrode is calculated by subtracting the previous value OCV(t-1) of the OCV corresponding to at least the previous value SOC(t-1) of the SOC obtained from the first map from the measured value V(t) of the voltage of the battery cell 2 m (step S120). In addition, the overvoltage η _j of each of the plurality of particles is calculated from the overvoltage ΔV _MPM due to the reaction of the positive _electrode and the reference voltage of each of the plurality of particles obtained from the second map (step S130). Furthermore, the current value SOC(t) of the SOC of the battery cell 2 _m is calculated (estimated) from the previous value SOC(t-1), the estimated battery current I _app , and the battery capacity C (steps S140-S160), and the current value OCV(t) of the OCV corresponding to the current value SOC(t) is obtained from the first map (step S170).

This makes it possible to estimate the SOC of the battery cell 2 _m based on the estimated battery current I _app calculated to offset the difference between the previous value OCV(t-1) of the OCV of the battery cell 2 _m acquired from the first map and the actual value. That is, when the previous value OCV(t-1) acquired from the first map is smaller than the actual OCV, the overvoltage ΔV _MPM calculated in step S120 is increased, so that the estimated battery current I _app is calculated to be large so as to offset the difference in the OCV, and when the previous value OCV(t-1) acquired from the first map is larger than the actual OCV, the overvoltage ΔV _MPM calculated in step S120 is decreased, so that the estimated battery current I _app is calculated to be small so as to offset the difference in the OCV. Thus, by repeatedly estimating the SOC of the battery cell 2 _m , the estimated value of the SOC, i.e., the current value SOC(t), can be converged to the actual value. As a result, it becomes possible to accurately estimate the SOC of each battery cell 2 including a LiFePO positive electrode formed from lithium iron phosphate (LiFePO ₄ ), which exhibits peculiar behaviors such as memory effect, path dependency, and voltage hysteresis.

Furthermore, in the above embodiment, the first map is created by subtracting the OCV of the negative electrode of the lithium iron phosphate battery from the average voltage V _AVE of the two spinodal points of the LiFePO positive electrode for each SOC, and the second map is created by offsetting the relationship between the lithium composition x of lithium iron phosphate (LixFePO ₄ ) and the OCV of the positive electrode so that the average voltage V _AVE becomes 0 V. This makes it possible to more appropriately calculate the overvoltage η _j of each of the multiple particles by using the overvoltage ΔV _MPM due to the reaction of the positive electrode and the second map.

In the above embodiment, the change amount ∂C _j /∂t in the lithium concentration of each particle is calculated based on the current density i _j of each of the plurality of particles, and the current value C _j (t) of the lithium concentration is calculated from the previous value C _j (t-1) of the lithium concentration and the change amount ∂C _j /∂t (step S180), and in step S130 of Fig. 4, the reference voltage of each of the plurality of particles corresponding to the lithium composition x _j based on the previous value C _j (t-1) of the lithium concentration is obtained from the second map. This makes it possible to more appropriately calculate the reference voltage of each of the plurality of particles, and therefore the overvoltage η _j .

2, the overvoltage ΔV _MPM due to the reaction of the LiFePO positive electrode is calculated by subtracting the previous OCV value OCV(t-1), the salt concentration overvoltage dV _dce , and the product of the measured current I(t) and the internal resistance Rd of the battery cell 2 _m from the measured voltage V(t) of the battery cell 2 _m . This makes it possible to more appropriately calculate the overvoltage ΔV _MPM due to the reaction of the positive electrode while reducing the calculation load. However, in step S120 of FIG. 2, the sum ΔV _MPM +IR of the overvoltage ΔV _MPM due to the reaction of the positive electrode and the overvoltage due to the internal resistance Rd may be calculated by subtracting the previous OCV value OCV(t-1) and the salt concentration overvoltage dV _dce from the measured voltage V( _t ) of the battery cell 2 _{m without using the measured current I} (t). In this case, the overvoltage ΔV _MPM and the estimated battery current I _app that minimize |ΔV _MPM+IR - (ΔV _MPM +I _app · Rd)| may be derived by iteration.

The battery 1, which includes multiple battery cells 2 that are lithium iron phosphate batteries, is not limited to being a high-voltage battery that exchanges power with the motor generator MG. In other words, the battery 1 may be configured as a low-voltage battery such as an auxiliary battery (12 V battery).

Furthermore, the invention of this disclosure is in no way limited to the above embodiment, and it goes without saying that various modifications can be made within the scope of the present disclosure. Furthermore, the above embodiment is merely one specific form of the invention described in the Summary of the Invention section, and does not limit the elements of the invention described in the Summary of the Invention section.

The disclosed invention can be used in fields such as the manufacturing of lithium iron phosphate batteries.

1 battery, 2 battery cell, 10 electronic control unit (ECU), MG motor generator, V vehicle.

Claims (1)

1. A method for estimating an SOC of a lithium iron phosphate ion battery including a positive electrode formed of lithium iron phosphate, comprising:
calculating an overvoltage caused by a reaction of the positive electrode based on at least a measured value of the voltage of the lithium iron phosphate battery and an OCV obtained from a first map that is predetermined to define a relationship between an SOC and an OCV of the lithium iron phosphate battery;
calculating an overvoltage of each particle on the assumption that the positive electrode is composed of a plurality of particles each containing lithium, using an overvoltage due to a reaction of the positive electrode and a second map that is determined in advance based on a relationship between the lithium composition of the lithium iron phosphate and an OCV of the positive electrode;
Calculating a current density of each of the particles based on the overpotential of each of the particles;
Calculating an estimated battery current of the lithium iron phosphate battery based on the current density of each of the particles;
An SOC estimation method for estimating an SOC of the lithium iron phosphate battery based on the estimated battery current.

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