JP7097336B2 - Secondary battery module temperature estimation method, deterioration state estimation method and life prediction method, secondary battery module temperature estimation device, deterioration state estimation device and life prediction device, and charging device - Google Patents

Description

The present invention relates to a secondary battery module temperature estimation method, a deterioration state estimation method and a life prediction method, a secondary battery module temperature estimation device, a deterioration state estimation device and a life prediction device, and a charging device.

At present, global environmental problems are getting a lot of attention, and in order to prevent global warming, it is required to reduce carbon dioxide emissions in every situation. Against this background, automobiles that use gasoline engines, which are one of the major sources of carbon dioxide emissions, are being replaced by hybrid electric vehicles and electric vehicles. Large secondary batteries represented by hybrid electric vehicles and power sources for electric vehicles need to have high output and large capacity. Therefore, the storage battery module constituting such a large secondary battery is generally configured by connecting a plurality of battery cells in series and parallel.

On the other hand, recently, the number of households that generate their own power by generating solar power or wind power is increasing, and if there is surplus power, it is sold to an electric power company. However, when the amount of power sold to the grid increases, the grid voltage becomes unstable. Therefore, it is considered to level the load by storing surplus power in the power storage system and supplying it from the power storage system when there is a shortage. It has also been proposed to use an electric vehicle for this load leveling.

As the in-vehicle battery, a high-performance and lightweight lithium-ion battery is generally used because the performance of the battery affects the running. Therefore, the price of a traveling battery is generally high, and the price of an electric vehicle is also higher than that of a gasoline-powered vehicle of the same class. Therefore, there is a demand from users that they want to continue using and running electric vehicles for a longer period of time. Therefore, it is important to more appropriately diagnose the degree of deterioration of the battery and diagnose the remaining life.

In order to know the deterioration of the in-vehicle battery more accurately, it is desirable to calculate the resistance and capacity of the battery by a charge / discharge test at a constant current. However, once it is attached to the vehicle, it takes time to diagnose deterioration, such as removing the battery during maintenance and inspection by the manufacturer.

A secondary battery such as a lithium-ion battery deteriorates with each repetition of charging and discharging, and its capacity decreases and its internal resistance increases. This causes fluctuations in the output of the secondary battery. The degree of deterioration of the secondary battery varies depending on the usage history of the secondary battery, such as the environment and method in which the secondary battery has been used up to now. Therefore, there is a demand for a technique for accurately estimating the deterioration state according to the usage history of the secondary battery.

In addition, with the spread of electric vehicles, it is important to utilize used batteries that are widely available. At this time, it is important to understand the deteriorated state of the battery in order to promote the proper use of the battery.

In Patent Document 1, the amount of current flowing through the secondary battery is integrated, a predetermined deterioration coefficient is calculated, and the integrated current value is obtained in accordance with each of a plurality of usage conditions that affect the capacity deterioration of the secondary battery. A battery deterioration estimation method for estimating the level of capacity deterioration of a secondary battery by correcting such factors with a deterioration coefficient is disclosed.

Patent Document 2 describes the current and terminal voltage of a battery for the purpose of suppressing the cost of directly attaching a temperature detector for detecting the temperature of the secondary batteries constituting the assembled battery to each secondary battery. Battery temperature estimation provided with a means for calculating the internal resistance value of the battery based on the above and estimating the battery temperature based on the ratio between the internal resistance value and the initial internal resistance value of the battery at the reference temperature. The system is disclosed.

Japanese Patent No. 5466564 Japanese Unexamined Patent Publication No. 2015-118022

The characteristics of the secondary battery, such as capacity and resistance, change depending on the temperature.

Under low temperature conditions, the resistance of the battery increases significantly. At sub-zero temperatures, the resistance of the battery may be several times higher than at room temperature.

In addition, the deterioration rate of the battery varies depending on the temperature at which the battery is stored. When the battery is stored at a temperature higher than normal temperature, the deterioration of the battery using the resistance value as an index tends to progress easily. In order to evaluate the degree of deterioration of the battery, it is important to consider the temperature condition of the battery.

The battery deterioration estimation method described in Patent Document 1 estimates the level of deterioration under specific usage conditions, and is limited to capacity estimation. When combining a plurality of used battery packs having different standards for each manufacturer, it is not always possible to make a sufficient estimation simply by correcting with the deterioration coefficient as in the battery deterioration estimation method described in Patent Document 1. There is room for improvement.

In the past, in order to accurately grasp the deterioration state of the battery, measures such as removing the battery individually and re-measuring it were taken. However, such a method is time-consuming and labor-intensive.

It is possible to acquire the current and voltage at the time of energization without directly taking it out of the vehicle when charging an electric vehicle (EV) or a plug-in hybrid vehicle with a charger. However, when using the current quick charger, regarding the information obtained from the quick charger, the temperature information is obtained separately because there is no information on the temperature of the in-vehicle battery in the communication standard at the time of charging such as CHAdeMO (registered trademark). There is a problem that it is difficult to accurately detect the degree of deterioration of the secondary battery whose characteristics depend on the temperature.

In the battery temperature estimation system described in Patent Document 2, the battery temperature can be estimated, but when the full charge capacity is calculated for continuous energization, an error occurs due to the temperature change due to energization. Conceivable.

An object of the present invention is to estimate the temperature of a secondary battery, determine the degree of deterioration of the battery, and estimate the life even when the temperature information of the secondary battery constituting the secondary battery module cannot be obtained. ..

The method for estimating the temperature of the secondary battery module of the present invention includes a step of acquiring charging characteristic data including the current and voltage supplied to the secondary battery module at the time of charging and charging time, charging characteristic data, and a database. A process of collating the existing charge characteristic data with the correlation data of the temperature, estimating the temperature of the secondary battery constituting the secondary battery module, determining the degree of deterioration of the battery, and estimating the life. include.

According to the present invention, the temperature of the secondary battery can be estimated even when the temperature information of the secondary battery constituting the secondary battery module cannot be obtained.

It is a graph which shows the temperature dependence of the resistance of a battery. It is a graph which shows the influence of the storage temperature of a battery on the resistance of a battery. It is a figure for demonstrating the temperature rise of a battery at the time of charging. It is a schematic diagram which shows the conventional battery management system. It is an overall block diagram which shows the life prediction device of the secondary battery module of this invention, and the device connected to this. It is a schematic diagram which shows an example of the structure for estimating the deterioration state of the secondary battery module which concerns on this invention. It is a schematic diagram which shows an example of the battery temperature estimation calculation unit which concerns on this invention. It is a flow diagram which shows an example of the life prediction method of the secondary battery module of this invention. This is an example of the method for calculating the degree of deterioration in the present invention. It is an overall block diagram which shows the basic structure of the battery system of the vehicle which is the object of charging by a charger. It is a block diagram which shows an example of the life prediction apparatus of the secondary battery module of this invention.

The present invention estimates the temperature of the secondary battery constituting the secondary battery module, calculates the deterioration state of the secondary battery, and predicts the life of the secondary battery.

First, the subject of the present invention will be described in detail.

The characteristics of the secondary battery, such as capacity and resistance, change depending on the temperature.

Under low temperature conditions, the resistance of the battery increases significantly. At sub-zero temperatures, the resistance of the battery may be several times higher than at room temperature.

FIG. 1 is a graph showing an example of the temperature dependence of the resistance of a lithium ion secondary battery. The horizontal axis is the temperature, and the vertical axis is the standardized resistance value (DC resistance). Here, the resistance at 25 ° C. is set to 1.

As shown in this figure, the resistance becomes very large at a temperature of 0 ° C. or lower. At -20 ° C, it is about 7 times higher than the value at 25 ° C.

FIG. 2 is a graph showing an example of the effect of the storage temperature of the battery on the resistance of the battery. The horizontal axis is the temperature set when the battery is stored, and the vertical axis is the standardized resistance value (DC resistance). The ratio of the resistance value measured after storage under a constant storage voltage condition to the initial resistance is shown.

As shown in this figure, the higher the storage temperature, the larger the resistance value, which increases by 20% at 60 ° C and 40% at 80 ° C. As described above, the rate of resistance deterioration of the lithium ion secondary battery differs depending on the temperature, and the condition of the battery temperature is important for evaluating the degree of deterioration of the battery.

FIG. 3 is a diagram for explaining the temperature rise of the battery during charging.

In this figure, four graphs are shown together, and the time on the horizontal axis has the same scale in all the graphs. In the four graphs, the vertical axis is the charging current I, the battery voltage V, the battery temperature T _cell , and the temperature rise ΔT.

In this figure, the curve a is a case of charging from a normal temperature state, and the curve b is a case of charging from a low temperature state with a constant current. The temperature rise ΔT of the battery has a curve a for charging from a normal temperature state, whereas a curve b for charging from a low temperature state has a large internal resistance, so that the amount of heat generated is larger than the normal temperature. Therefore, it behaves like a curve b. If there is no heat radiation to the outside of the battery, it is considered that the temperature rises as shown by curves a'and b'.

In the voltage behavior when charging the battery from a low temperature (curve b), the voltage at the initial stage of charging rises at once because the resistance at the low temperature is large. After that, the battery temperature rises due to heat generated by the internal resistance, and accordingly, the resistance becomes smaller and the voltage rise due to the resistance and the current value becomes smaller.

Capacity measurement measures the amount of electricity between the upper and lower voltage limits. In the measurement at low temperature, the temperature inside the battery changes, so the capacity also changes when the temperature changes. The degree of deterioration of a battery is indexed by capacity and resistance. In order to compare and evaluate the degree of deterioration of batteries, it is necessary to evaluate the capacity and resistance by measurement under the same conditions.

FIG. 4 is a schematic diagram showing a deterioration detection operation in a conventional battery management system.

As shown in this figure, in the conventional battery management system (BMS), the SOH of the battery is calculated by the SOH calculation unit using the voltage V, the current I, and the temperature T of the battery. That is, the temperature T is also used for the calculation of SOH. Here, SOH is an index showing the degree of deterioration (deterioration state) of the battery, and is calculated based on resistance or capacity. In addition, BMS is also called a battery controller.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 5 shows an overall configuration (life prediction device for a secondary battery module) according to an embodiment of the present invention.

In this figure, the secondary battery module 11 (storage battery module) installed in the vehicle 1 (battery system) is the target of the life diagnosis.

The charger 2 (charging device) transfers information such as the state of charge (SOC) obtained from the communication protocol in the charging cable, the output current I from the charger, and the output voltage V to the server 4 when charging. It is configured to be able to transmit by wireless or wired communication.

The server 4 is a life prediction device for the secondary battery module 11, and has a resistance / capacity calculation unit 5, a deterioration estimation unit 6, and a life calculation unit 7. The resistance / capacitance calculation unit 5, the deterioration estimation unit 6, and the life calculation unit 7 can cooperate with each other and share information. The server 4 may be outside the vehicle 1 and the charger 2, or may have a configuration in which a part thereof is built in the charger 2.

Further, the present invention can be applied not only to a vehicle but also to a moving body such as a traveling robot or a flying robot powered by a secondary battery.

When the vehicle 1 is connected to the charger 2, the secondary battery module 11 is charged. That is, the current I is supplied to the secondary battery module 11 by the current command from the vehicle. The current I and the voltage V at this time are notified to the vehicle, and the vehicle notifies the SOC according to the charge amount. Corresponds to the arrow between the vehicle 1 and the charger 2 in the figure. At this time, the charger 2 acquires information (charging information) such as the SOC of the secondary battery constituting the secondary battery module 11, the output current I of the charger, and the output voltage V from the vehicle 1. The charger 2 transmits the acquired charging information to the resistance / capacity calculation unit 5. The output voltage of the charger is assumed to be equal to the closed circuit voltage (CCV) of the secondary battery module 11.

The resistance / capacity calculation unit 5 calculates the current capacity and resistance value of the secondary battery from the acquired information, and calculates the degree of deterioration by comparing with the initial value. The deterioration estimation unit 6 has a deterioration prediction formula (also referred to simply as a “prediction formula”) and predicts future deterioration transitions. The life calculation unit 7 calculates the life from the prediction result calculated by the deterioration estimation unit 6 using the standard values of the capacity and resistance of the life standard of the vehicle 1. Further, the life calculation unit 7 can transmit information such as the degree of deterioration and the remaining life to an arbitrary terminal (not shown).

The "current" capacitance and resistance data may be, for example, data several days ago, but it is desirable that the data is the latest data (real-time data).

In the above description, the server 4 may have at least the life calculation unit 7 of the resistance / capacity calculation unit 5, the deterioration estimation unit 6, and the life calculation unit 7. In that case, the resistance / capacitance calculation unit 5 and the deterioration estimation unit 6 may be arranged in the charger 2. Further, when the server 4 has the deterioration estimation unit 6 and the life calculation unit 7 among the resistance / capacity calculation unit 5, the deterioration estimation unit 6 and the life calculation unit 7, the resistance / capacity calculation unit 5 may be used. It may be arranged in the charger 2.

A plurality of vehicle information acquired from the charger 2 can be stored in one server 4. By accumulating the measured deterioration data, the database can be enriched. Then, by referring to the deterioration information of the same model from the database, it becomes easy to create a deterioration estimation formula that refers to the initial battery characteristic value and the driving method even when the new vehicle starts to be used, and the battery deterioration and remaining life. It becomes possible to predict the estimation of the above more accurately in a short time.

It is desirable that the database is provided on the server 4. Further, it is desirable that the battery temperature estimation unit is provided in the resistance / capacity calculation unit 5.

FIG. 6 shows a configuration for performing an SOH calculation according to the present invention. In the following description, the configuration shown in FIG. 5 will be described with reference to the reference numerals.

The battery temperature estimation calculation unit 111 (battery temperature estimation unit), the SOH calculation unit 112, and the database 113 shown in FIG. 6 are built in the server 4 of FIG. In addition, all or a part of these configurations may be provided in other than the server 4. For example, it may be provided in the charging device (charging device side database).

The battery temperature estimation calculation unit 111 is provided in the resistance / capacity calculation unit 5 shown in FIG. 5, and the SOH calculation unit 112 is provided in the deterioration estimation unit 6 shown in FIG.

In FIG. 6, information such as the output current I and the output voltage V acquired from the charger 2 is input to the battery temperature estimation calculation unit 111 and the SOH calculation unit 112. The battery temperature estimation calculation unit 111 includes curve data (existing charging characteristic data stored in the database 113) showing the relationship between the temperature (battery temperature or ambient temperature) stored in the database 113 and the charging voltage or charging current. (Correlation data between and temperature) is acquired, and the corresponding battery temperature T _cell is estimated. In this case, if possible, it is desirable to obtain the initial temperature T ₀ of the battery. As the initial temperature T ₀ of the battery, the ambient temperature of the battery or the charger 2 may be substituted.

The SOH calculation unit 112 calculates the current SOH using the battery temperature T _cell estimated by the battery temperature estimation calculation unit 111 and the input charging current and charging voltage. In this case, the calculation may be performed using the SOC obtained from the charger 2.

FIG. 7 shows a battery temperature estimation calculation unit according to the present invention.

In this figure, the temperature estimation calculation unit 150 performs a primary search in the database 151 using the input voltage, current, SOC, initial temperature T ₀ , etc., and refers to the relationship between the corresponding voltage profile and temperature. Then, the estimated value of the battery temperature is output. By inputting the initial temperature T ₀ and the SOC, it is possible to determine the initial value of the search start area of the start SOC and the initial temperature, and to shorten the search time from a huge database.

The temperature estimation calculation unit 150 corresponds to the battery temperature estimation calculation unit 111 of FIG.

In this case, the database 151 stores data of current, environmental temperature, SOC, voltage, and battery temperature change with respect to the time when the battery whose internal resistance is known in advance is energized. Alternatively, it has a plurality of maps in which the feature amount is extracted from the current, the environmental temperature, the voltage, and the battery temperature with respect to the above-mentioned energization time. The battery temperature can be specified by adopting the temperature under the conditions closest to the current and voltage changes of the stored data. Alternatively, the battery temperature is output in chronological order according to the initial temperature, SOC, current, and voltage input by the estimation module that has learned the feature amount of the accumulated data. The temperature data output from the battery temperature estimation calculation block is used, and the SOH calculation block outputs the internal resistance value at this time by referring to the database or from the internal resistance estimation module whose feature amount has been learned. The capacity is corrected from the measured resistance deterioration degree, estimated battery temperature, etc., and the full charge capacity value at the same temperature as the measured temperature in the specifications displayed in the catalog etc. is estimated. .. This makes it possible to detect capacity deterioration without being affected by the increase or decrease in capacity due to temperature. Here, the "catalog, etc." means the material provided by the manufacturer or the seller.

FIG. 8 shows an example of the life prediction method of the secondary battery module of the present invention.

In this figure, the steps of resistance and capacitance detection, deterioration degree calculation, and life prediction, which are surrounded by a broken line, are shown.

First, the voltage, current, time, etc. are acquired from the charger (S1).

Next, the battery temperature with respect to time is estimated by collating with the previously acquired charge curve learned (S2). In other words, the data regarding the charging voltage or charging current stored in the database and the change over time of the temperature are acquired, and the battery temperature with respect to time is estimated. The steps up to this point correspond to the processing in the temperature estimation calculation unit 150 of FIG. 7.

Next, the relationship between the time and the current value is converted into the relationship between the SOC and the open circuit voltage (OCV) (S3).

The relationship between the SOC and OCV of the corresponding battery module is searched from the database (S4). Then, the resistance value R is calculated by dividing the voltage V and the OCV at the same SOC by the current value I (S5). Note that step S3 will be described later with reference to FIG.

For the calculated resistance value R, a temperature coefficient is calculated using the correlation between the SOC, temperature, and internal resistance stored in the database and the battery temperature estimated in S2, and the temperature coefficient R is calculated using this temperature coefficient. Correct (S6). The steps up to this point correspond to the processing in the resistance / capacitance calculation unit 5 of FIG.

The capacitance value calculated from the current and time and the resistance value R acquired in S6 are converted into the acquired values under the conditions such as the reference temperature (for example, 25 ° C.), current, time, etc. such as the measured temperature in the catalog display specifications. Is corrected (S7).

The full charge capacity is calculated using the converted capacity value, the ratio of the corrected resistance value and capacity value to the initial value or the catalog value is obtained, and the deterioration degrees SOHR and SOHQ are calculated (S8). Steps S7 to S8 correspond to the processing in the deterioration estimation unit 6 of FIG.

The SOHQ and SOHR obtained in S8 are sequentially accumulated for each identification number (ID) of the battery module, changes in time series are acquired, and the life (years, distances) is predicted based on the accumulated data (S9). Step S9 corresponds to the process in the life calculation unit 7 of FIG.

With the above flow, it is possible to accurately detect the degree of deterioration of the battery even when the battery temperature cannot be directly acquired.

In the method shown in this figure, since the constant current data at the time of charging the vehicle is used, it is possible to estimate the capacity and resistance more accurately than before. Further, when the charging is quick charging, the deterioration degree SOHQ and SOHR of the battery can be calculated in a shorter time. The degree of deterioration is also referred to as "deterioration state".

Here, the deteriorated state of the battery is SOHR calculated using the internal resistance of the battery (hereinafter, also simply referred to as “resistance”) or SOHQ calculated using the full charge capacity of the battery. SOHR is SOH obtained based on the internal resistance of the battery, represents the rate of increase in the internal resistance of the battery that increases with the deterioration of the battery, and is defined by the following equation (1).

SOHR = 100 × R ₁ (SOC, T) / R ₀ (SOC, T)… (1)
In the equation, R ₁ (SOC, T) represents the current (deteriorated) internal resistance [Ω] of the cell. R ₀ (SOC, T) represents the internal resistance [Ω] of the cell when it is new.

The SOHQ calculated using the capacity is the current capacity divided by the initial capacity. The SOHR of the above formula (1) is expressed as a percentage, but the current ratio to the initial ratio is not necessarily limited to the percentage.

As described above, by correcting with the measured value, it is possible to estimate the remaining life by accurate prediction.

In the method shown in FIG. 8, it is possible to detect even if the temperature information is not known at all, but a sensor function to acquire the ambient temperature is added to the charger, and the ambient temperature is used as the input of the initial temperature T ₀ , and the current in the database is used. By searching the voltage profile, it is possible to shorten the time from the search to the calculation output, and it is possible to calculate the life in a shorter time. Further, by inputting the start SOC (SOC _ini ), the database search time can be further shortened.

Further, the temperature rise ΔT is calculated from the temperature profile acquired in step S2, that is, the change over time of the temperature, and the calorific value Q is calculated by using ΔT and the heat capacities Cp and _cell of the battery module. Then, the resistance value R'is back-calculated from the calorific value Q and the current.

Q (t) = ΔT (t) × C _{p, cell} + q'(t)… (2)
R'(t) = Q (t) / (I ² t) ... (3)
Here, q'(t) is the amount of heat dissipated to the outside of the battery. q'(t) can be calculated using the heat dissipation area A, the heat transfer coefficient h, the surface temperature T _cell , and the refrigerant temperature _TC . Compared with the resistance value R obtained in S6, if there is a deviation, further correction is performed and the result is accumulated in the database. This makes it possible to obtain a more appropriate resistance value. By repeating the above steps and accumulating data, the database is further enriched, and the more data is added to the already acquired database, the higher the detection accuracy is.

Furthermore, based on the resistance value of the battery output using the voltage curve, the battery temperature is corrected by the correction coefficient, and the capacity at the time of large current energization at the current value I observed at the time of measurement, which is stored in the database, is calculated. Convert to the capacity under the same standard conditions as the nominal capacity acquired with the low current value in the catalog etc.

The deterioration degrees SOHQ and SOHR of the reference values obtained as described above can be obtained with fair standards for the battery modules of any vehicle, and can be compared. By utilizing the degree of deterioration of these standards, it is possible to set the residual value of used batteries and to use them stably in a system using multiple modules for other purposes. In particular, it is possible to shorten the time required for battery deterioration evaluation and performance measurement during reuse, which is effective in utilizing used batteries. Utilization of used batteries also leads to a reduction in the amount of waste batteries and can contribute to the realization of a sustainable society.

Here, an example of a method for calculating the degree of deterioration corresponding to the above step S3 will be described with reference to FIG. 9.

The upper graph of FIG. 9 shows an example of the change of voltage with time in quick charge and low speed charge. Time is on the horizontal axis and voltage is on the vertical axis.

The curve (a) is for fast charging and the curve (b) is for OCV and slow charging. The voltage curve at low speed charge, for example at 0.2 C rate, is almost the same as the OCV curve when the internal resistance of the battery is low. The discharge rate that matches the OCV differs depending on the battery. The amount of electricity charged by the _end of charging in the curve (a) is defined as Q _ch .

The graph at the bottom of FIG. 9 is a conversion of the graph at the top of FIG. 9 into an SOC-V curve, and shows an example of a charge voltage curve (quick charge curve) and an OCV curve. The horizontal axis is SOC and the vertical axis is voltage.

The curve (c) is for quick charging, and the curve (d) is the OCV of the battery. In the upper graph and the lower graph, the range of electric energy Q _ch corresponds. The difference between the charge voltage curve and the OCV curve is ΔV (SOC).

The OCV of the battery is obtained by searching the database for the specifications of the battery used in the vehicle 1 from the information of the vehicle 1 in FIG.

The open circuit voltage (OCV) at the start of charging is converted into SOC _ini , and the voltage OCV _ini , which is the start OCV, is obtained. Alternatively, the SOC at the start of charging notified is set as the SOC _ini , and the OCV _ini , which is the start OCV, is obtained.

As shown in the charge curve (c), the charge voltage for the SOC is calculated from the notified SOC _ini to the SOC _end , and the difference ΔV (SOC) between the charge voltage and the OCV at the same SOC is divided by the energization current value. The resistance value R can be obtained. In particular, immediately after the start of energization, it is before the SOC fluctuates, and it is suitable because the influence of the reaction resistance is small.

In a lithium ion battery, the resistance value R calculated as described above is known to be dependent on SOC.

The relationship between R and SOC shown in this figure can be expressed as a function or a table.

Assuming that the voltage and current at the end of charging / discharging of the battery are V _last and I _last , respectively, and the open circuit voltage of the battery detected after a predetermined time from the end of charging / discharging is OCV, the resistance value R at the end of energization is shown in the resistance curve. Is obtained by the following equation (4).

R = | OCV-V _last | / I _last ... (4)
Alternatively, if the current value I of the battery being charged and discharged is constant and the closed circuit voltage of the battery is CCV, the resistance value R can also be obtained by the following equation (5).

R = | OCV-CCV | / I ... (5)
Further, the resistance value R can also be obtained from the coefficient A in the linear approximation formula Ax + C obtained by the relationship between the plurality of currents and the closed circuit voltage of the battery according to the current value x at a constant SOC. When the constant C is OCV, the coefficient A becomes the internal resistance DCR.

Further, the capacity Q can be obtained by the following equation (6) from the difference between the SOC _ini which is the initial SOC and the SOC _end which is the SOC at the end, that is, the fluctuation range of the SOC and the electric energy Q _ch .

Q = Q _ch / (SOC _end -SOC _ini ) × 100… (6)
The obtained Q is a value at the battery temperature T _cell , and the characteristics at the T _cell are converted into the capacity obtained at the reference temperature. The conversion method uses the coefficient of the ratio of the temperature T _cell to the reference temperature (for example, 25 ° C.) capacity, or uses the obtained resistance value and the estimated temperature at that time, and uses the initial reference temperature and the resistance at each temperature. Applying the value ratio, the obtained resistance value is converted to the resistance value at the reference temperature, the voltage curve is recalculated, and the value between the upper limit voltage and the lower limit voltage, which is the definition of the battery capacity, is set as the reference value capacity.

The steps up to this point correspond to steps S4 to S7 in FIG.

SOHQ and SOHR are calculated by comparing the resistance value converted to the reference temperature and the capacitance with the initial value (corresponding to step S8 in the figure). The obtained SOHQ and SOHR are stored and stored in time series, and the life is estimated from the stored data using a prediction deterioration formula or a deterioration prediction relational expression by learning. The deterioration prediction formula is updated by accumulating data (corresponding to step S9 in the figure).

The data acquired during charging is sequentially accumulated in the database, improving the accuracy of temperature estimation. In addition, SOH is also accumulated in the sequential database (for deterioration estimation), and the deterioration prediction formula is updated.

In order to build a highly accurate prediction model for battery deterioration prediction, it is necessary to have a large amount of data.

In the present invention, since the database is sequentially updated and the deterioration prediction formula is also updated, the measured data increases even after the prediction formula with a certain amount of data is created and implemented, and the deterioration prediction formula can be updated. can. Therefore, the error can be reduced as the amount of measurement data increases. In addition, since the battery temperature can be estimated and converted into characteristic values (capacity, resistance) at the reference temperature to detect the degree of deterioration, it is possible to compare the performance of multiple modules acquired under different conditions with the same reference. , It will be easier to evaluate the value of batteries and utilize used batteries.

As described above, in the present invention, since the deterioration prediction formula is updated even after mounting, it is possible to reduce the man-hours for building the model until mounting. In other words, the man-hours for the battery deterioration test for constructing the battery deterioration estimation formula can be reduced, and the remaining life diagnosis with high accuracy becomes possible in a short time.

FIG. 10 shows the basic configuration of the battery system of the vehicle to be charged by the charger.

In this figure, the system configuration includes a secondary battery module 11 (storage battery module) used in the vehicle and a measured value detection unit 21 installed in the vehicle. The secondary battery module 11 is configured by connecting a plurality of cell cells 11a and 11b having a positive electrode and a negative electrode in series. The secondary battery module 11 is connected to a load (not shown) and supplies electric power to the load.

In addition, the battery control unit 30, the upper control unit 60, and the load control unit 70 are also components of the system.

This system controls the secondary battery module 11 by the measured value detection unit 21, the battery control unit 30, the upper control unit 60, and the load control unit 70.

The measured value detection unit 21 detects various information (data) regarding the state of the secondary battery module 11. For example, data such as total current, total voltage, environmental temperature, maximum temperature, average temperature, minimum temperature of the secondary battery module 11, temperature and voltage of each of the cells 11a and 11b are detected. The data detected by the measured value detection unit 21 is input to the battery control unit 30. It is desirable to detect the types or models of the cells 11a and 11b, in other words, the individual characteristics of the cells 11a and 11b.

The battery control unit 30 calculates the current charge state (SOC) of the secondary battery module 11 based on the data input from the measured value detection unit 21, detects an abnormal state, and calculates the power that can be input / output. , Generates temperature control commands, etc. These information obtained by the upper control unit 60 are output to the load control unit 70.

The load control unit 70 executes load control based on the control command input from the host control unit 60 and the information input from the battery control unit 30.

FIG. 11 shows an example of the life prediction device of the secondary battery module of the present invention.

In this figure, the remaining life diagnosis unit 50 has each functional block of a data storage unit 41, a data selection unit 42, a deterioration calculation unit 43, a deterioration prediction unit 46 (deterioration state estimation unit), and a remaining life calculation unit 47. The remaining life diagnosis unit 50 can realize each function corresponding to these functional blocks by, for example, executing a program stored in advance by the CPU.

The detection unit 20 detects data such as battery voltage and current together with time information such as date and time, and outputs the data to the remaining life diagnosis unit 50. At this time, the SOC information notified to the charger and calculated is also input to the remaining life diagnosis unit 50. In addition, the outside air temperature by the outside air temperature measurement sensor provided in the charger is also input. In the remaining life diagnosis unit 50, the data selection unit 42 selects data that matches the preset conditions from the output information of the charger and the data output from the detection unit 20, and outputs the data to the data storage unit 41. It has a temperature estimation unit in the data selection unit and calculates the temperature by referring to the database. The calculated temperature and charge measurement acquisition data are stored in the database.

The data storage unit 41 has an SOC-OCV data storage unit 41a, an SOC-R data storage unit 41b (SOC-resistance data storage unit), and a database 44.

The data selection unit 42 refers to the database and selects the data necessary for temperature estimation by the temperature estimation calculation unit 150. In order to calculate the voltage at the time of energization and the battery temperature calculated by the temperature estimation calculation unit 150 with respect to the SOC, the SOC-OCV data storage unit 41a of the battery with respect to the temperature is provided. Similarly, the SOC-R data storage unit 41b stores the acquired resistance data for the number of seconds of each temperature with respect to the SOC. As a result, the existing charge characteristic data is accumulated.

The database 44 is provided with various data used for deterioration estimation. For example, a prediction formula for estimating deterioration, an initial value of a parameter, a change amount of a parameter, and the like. Using the data of the database 44, the SOC-OCV data storage unit 41a, and the SOC-R data storage unit 41b, the capacity and resistance in the internal state are calculated, and deterioration is detected. The SOC-OCV data storage unit 41a and the SOC-R data storage unit 41b also store the initial data of the battery in advance. The current deterioration state is estimated by the deterioration calculation unit 43 and output to the deterioration prediction unit 46. Further, the calculated deterioration state is added to the database 44 as time-series data and accumulated. The calculated parameters are used by the deterioration prediction unit 46 for predicting deterioration in time series. Further, the remaining life calculation unit 47 calculates and outputs the predicted life or the remaining life using the database 44.

As described above, according to the present embodiment, information on the deterioration state of the secondary battery module can be appropriately detected without preparing a special discharging means, and the internal state of the secondary battery module and the secondary battery module can be detected. The capacity and resistance of the module can be obtained, and the remaining life of the system can be calculated accurately.

The present invention is not limited to the above embodiment. Other aspects conceivable within the scope of the technical idea of the invention are also included within the scope of the invention.

1: Vehicle 2: Charger, 4: Server, 5: Resistance / capacity calculation unit, 6: Deterioration estimation unit, 7: Life calculation unit, 11: Secondary battery module, 20: Detection unit, 21: Measured value detection Unit, 30: Battery control unit, 41: Data storage unit, 41a: SOC-OCV data storage unit, 41b: SOC-R data storage unit, 42: Data selection unit, 43: Deterioration calculation unit, 44: Database, 46: Deterioration prediction unit, 47: Remaining life calculation unit, 50: Remaining life diagnosis unit, 60: Upper control unit, 70: Load control unit, 111: Battery temperature estimation calculation unit, 112: SOH calculation unit, 113: Database, 140: SOH calculation unit, 150: temperature estimation calculation unit, 151: database.

Claims (11)

The process of acquiring charge characteristic data including the current and voltage supplied to the secondary battery module during charging and the charging time, and
A step of collating the charging characteristic data with the existing charging characteristic data stored in the database and the correlation data with the temperature to estimate the temperature of the secondary battery constituting the secondary battery module. A method for estimating the temperature of a secondary battery module, including. The method for estimating the temperature of the secondary battery module according to claim 1 is included.
A method for estimating a deterioration state of a secondary battery module, further comprising a step of calculating a deterioration state of the secondary battery using the temperature of the secondary battery. The method for estimating the deterioration state of the secondary battery module according to claim 2 is included.
A method for predicting the life of a secondary battery module, further comprising a step of predicting the life of the secondary battery. A database that stores the correlation data between existing charge characteristic data and temperature,
The current charging characteristic data including the current and voltage supplied to the secondary battery module at the time of charging and the charging time are acquired, and the current charging characteristic data is collated with the correlation data stored in the database. , A battery temperature estimation unit for estimating the temperature of the secondary battery constituting the secondary battery module, and a temperature estimation device for the secondary battery module. The temperature estimation device for the secondary battery module according to claim 4 is included.
A deterioration state estimation device for a secondary battery module, further including a deterioration estimation unit that calculates a deterioration state of the secondary battery using the temperature of the secondary battery. The device for estimating the deterioration state of the secondary battery module according to claim 5 is included.
A life prediction device for a secondary battery module, further including a life calculation unit for predicting the life of the secondary battery. The database has existing SOC-OCV curve data and
The current charging characteristic data is converted into SOC-V curve data, and based on the existing SOC-OCV curve data and the temperature of the secondary battery estimated by the battery temperature estimation unit. Estimate the internal resistance of the secondary battery and
The deterioration state estimation device for a secondary battery module according to claim 5, which has a configuration for calculating the deterioration state of the secondary battery at a reference temperature using the estimated temperature and the internal resistance of the secondary battery. The deterioration estimation unit uses the temperature data stored in the database and the measured integrated capacity value of the secondary battery module to obtain an estimated value of the full charge capacity of the secondary battery module. The deterioration state estimation device for a secondary battery module according to claim 5, which calculates and calculates the deterioration state of the secondary battery. Further equipped with an environmental temperature sensor that measures the ambient temperature during charging,
The temperature estimation device for a secondary battery module according to claim 4, wherein the database has a configuration for accumulating correlation data of the ambient temperature, charging current, charging voltage, and the temperature of the secondary battery. The temperature difference between the start and end of charging calculated from the temperature of the secondary battery at the time of charging, the calorific value estimated from the charging current and the charging voltage, the heat capacity of the secondary battery, and the above two. By taking the ratio between the converted value and the initial value using the change over time of the internal resistance of the secondary battery, the reference temperature of the secondary battery, the deterioration state at the reference temperature, and the internal resistance of the secondary battery. The method for estimating the deterioration state of the secondary battery module according to claim 2, wherein the deterioration state of the secondary battery under the reference conditions is estimated. The secondary battery according to claim 2, wherein the temperature of the secondary battery is corrected based on the internal resistance of the secondary battery, and the deterioration state of the secondary battery under reference conditions is estimated from the capacity when a large current is applied. How to estimate the deterioration state of the module.

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