JP2023105965A - Battery status estimating system and battery status estimating method - Google Patents

Abstract

To provide a battery status estimating system and method capable of suppressing an amount of data transmitted from a vehicle.SOLUTION: A battery state estimating system comprises: an extraction section that extracts actual travel information from an actual travel event of a vehicle; a state estimating section that estimates a deterioration state of a secondary battery; and a database that defines a representative travel event in travel events belonging to a category of a travel event feature quantity, based on travel information previously obtained from a plurality of vehicles, and stores a representative travel event feature quantity as a profile. The state estimating section extracts, from the database, a feature quantity which is similar to an actual feature quantity included in the actual travel information, specifies the representative travel event corresponding to the feature quantity, calculates an estimated output of the secondary battery from the representative travel event, calculates an actual output of the secondary battery from the actual travel information, and when a difference between the actual output and the estimated output is equal to or more than an output threshold, adds a correction output corresponding to the difference to the estimated output to correct the output of the secondary battery, and estimates the deterioration state of the secondary battery based on the corrected output.SELECTED DRAWING: Figure 1

Description

The present invention relates to a battery state estimation system and a battery state estimation method for estimating the state of deterioration of a secondary battery.

Conventionally, a circuit having a configuration in which the electromotive force curves of the positive electrode and the negative electrode obtained in advance are relatively changed in the capacity direction and the difference between the electromotive force curves of the two electrodes is fitted to the electromotive force curve of the secondary battery is provided. A capacity degradation estimation device for estimating is known (Patent Document 1).

JP 2015-87344 A

However, the capacity deterioration estimating apparatus described above basically has a configuration in which the positive and negative electromotive force curves are read from a storage area in the circuit while the secondary battery is connected to the circuit. Moreover, although Patent Document 1 states that an arithmetic device having the circuit may be used as a server on the Internet, the information handled is the same, and an improvement in estimation accuracy cannot be expected.

Then, in the capacity deterioration estimation device described in Patent Document 1, in order to improve the accuracy of estimation by mounting an arithmetic device having the circuit on a server on the Internet, for example, time series data is sent to the cloud. It is also conceivable to carry out a simulation. However, there is a problem that the data volume of time-series data becomes large.

A problem to be solved by the present invention is to provide a battery state estimation system and a battery state estimation method capable of suppressing the amount of data transmitted from a vehicle.

The present invention categorizes the characteristic amounts of driving events based on driving information obtained in advance from a plurality of vehicles, defines a representative driving event among the driving events belonging to the category, and defines the characteristic amount of the representative driving event. are stored as profiles, feature values similar to the actual feature values included in the actual travel information are extracted from the database, representative travel events corresponding to the extracted feature values are specified, and two representative travel events are identified. Calculate the estimated output of the secondary battery, calculate the actual output, which is the actual output of the secondary battery, from the actual driving information, and if the difference between the actual output and the estimated output is equal to or greater than the output threshold, it corresponds to the difference The above problem is solved by correcting the output of the secondary battery 3 by adding the corrected output to the estimated output and estimating the state of deterioration of the secondary battery based on the corrected output.

ADVANTAGE OF THE INVENTION According to this invention, the data volume transmitted from a vehicle can be suppressed.

FIG. 1 is a block diagram showing a battery state estimation system according to one embodiment of the present invention. FIG. 2(a) is a table listing feature amounts of actual data for each driving event, and FIG. 2(b) is a table showing a list of categorized actual data of driving events. FIG. 3 is a conceptual diagram of data showing feature amount data of a representative travel event in a coordinate space. FIG. 4 is a flowchart showing the control procedure of the battery deterioration state estimation method executed by the server-side controller of the battery state estimation system. FIG. 5 is a graph (a) showing the characteristics of the capacity deterioration amount of the secondary battery 3 with respect to the event time, and a graph (b) showing the characteristics of the time threshold with respect to the event time.

An embodiment of a battery state estimation system and a battery state estimation method for estimating the state of deterioration of a secondary battery according to the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a battery state estimation system according to one embodiment of the present invention. A battery state estimation system according to the present embodiment is a system that estimates the deterioration state of a secondary battery mounted on a vehicle using a vehicle model and a battery model on a server (cloud). As shown in FIG. 1 , the battery state estimation system includes vehicle 1 and server 100 .

A vehicle 1 includes a vehicle controller 2 and a secondary battery 3 . In addition to the vehicle controller 2 and the secondary battery 3, the vehicle 1 includes sensors for detecting the running state of the vehicle (vehicle speed sensor, accelerator opening sensor, etc.) and sensors for detecting the state of the secondary battery 3 ( voltage/current sensor, temperature sensor, etc.), a communication device for communicating with the server 100, a motor that serves as a driving source of the vehicle, accessories, and the like.

The vehicle controller 2 is a control device that controls the entire vehicle, and is connected to in-vehicle sensors and the like via a CAN communication network. Note that the controller (processor) mounted on the vehicle 1 is composed of a plurality of ECUs, but in the present embodiment, one controller is used for ease of explanation. The vehicle controller 2 acquires the travel information of the vehicle 1 as time-series data for each travel event. The driving event is a period from when the main switch (ignition switch) of the vehicle 1 is turned on until it is turned off, and/or a period from the start of charging to the end of charging/discharging when the secondary battery 3 is externally charged. , which shows the running state of the vehicle in time-series data. The vehicle controller 2 manages actual travel information of the vehicle 1 for each travel event. The actual travel information includes vehicle state information indicating the state of the vehicle 1 and battery state information of the secondary battery 3 . The vehicle state information corresponds to feature amounts that express vehicle travel with parameters, such as travel distance, vehicle speed, travel time, elevation difference, average vehicle speed, average load, and maximum load. The battery state information is information used for deterioration calculation of the secondary battery 3, and includes, for example, battery voltage/current, SOC, and battery temperature. The vehicle controller 2 encodes the actual driving information and uploads it to the server 100 . The vehicle controller 2, for example, calculates an average vehicle speed for a vehicle speed profile acquired at intervals of one second, and encodes the calculated average vehicle speed as a representative statistic to encode actual travel information. Note that the actual travel information to be encoded is not limited to vehicle speed information, and may be other information.
Since the vehicle controller 2 transmits the actual driving information to the server 100 for each driving event, it is possible to reduce the amount of transmission data.

Secondary battery 3 is, for example, a lithium ion secondary battery and is mounted on vehicle 1 . The secondary battery 3 of this type uses, as a negative electrode active material, an active material having a plurality of charge/discharge regions in which the charge/discharge potential changes stepwise with the insertion/extraction of lithium ions. can be done. A graphite-based active material containing a graphite structure is suitable as an active material having a plurality of charge/discharge regions in which the charge/discharge potential changes stepwise with the insertion/extraction of lithium ions. The secondary battery 3 is not limited to the liquid electrolyte lithium ion battery, and may be an all-solid lithium ion secondary battery. Moreover, the secondary battery 3 is not limited to a lithium ion battery, and may be other types of batteries such as a lead battery.

Server 100 comprises controller 10 and database 20 . The controller 10 receives data including actual driving information from the vehicle 1, decodes it into a profile (time-series data), and estimates the state of deterioration of the secondary battery 3 by simulation. The data stored in the database 20, the profile of the actual driving information obtained by decoding, and the vehicle/battery model are used for the simulation. Decoding is not limited to decoding of data acquired from the vehicle 1, and may include statistical processing by calculation (statistical calculation processing). The database 20 categorizes the characteristic amounts of the driving events based on the driving information obtained in advance from a plurality of other vehicles, defines a representative driving event among the driving events belonging to the category, and defines the representative driving event. Features are stored as profiles. That is, the database 20 is constructed from data of other vehicles and past data of the vehicle.

The controller 10 extracts actual travel information from an actual travel event of the vehicle 1 and extracts from the database 20 feature amounts similar to the actual feature amounts included in the actual travel information. The controller 10 then identifies a representative travel event corresponding to the extracted feature quantity. A representative travel event is represented by a profile (representative travel profile) of characteristic amounts indicating representative vehicle travel among the categorized travel events. In other words, acquiring a representative travel event corresponds to virtually acquiring a profile of past travel events by adopting data with a high degree of similarity based on feature amounts representing travel. After acquiring the representative travel event, the controller 10 simulates the load input to the secondary battery 3 and estimates the deterioration state (state quantity) of the secondary battery 3 from the simulation result.

The controller 10 corrects the profile of the representative travel event in order to absorb the deviation between the vehicle travel represented by the representative travel event and the actual travel, and then calculates the deterioration state (state quantity) of the secondary battery 3. presume.

The controller 10 has an extractor 11 and a state estimator 12 as functional blocks for estimating the deterioration state of the secondary battery 3 . The extraction unit 11 extracts actual travel information from an actual travel event of the vehicle 1 . The state estimation unit 12 estimates the deterioration state of the secondary battery 3 based on the actual running information extracted by the extraction unit 11, the profile stored in the database 20, and the like.

The data structure of the representative travel profile stored in the database 20 will be described with reference to FIGS. 2 and 3. FIG. The controller 10 acquires in advance the actual data of the travel information of the vehicle 1 and constructs the database 20 . The actual data is not limited to data of travel information when the vehicle 1 actually travels, but may be past data of the user of the vehicle 1 or other users, or data acquired in advance experimentally. The travel information indicates a feature quantity representing travel of the vehicle. In the following description, the feature quantity includes at least "running distance (km)" and "maximum speed km/h". Note that the "travel distance (km)" and "maximum speed km/h" are merely examples of the index of the feature amount, and "travel time" and the like may be included.

FIG. 2(a) is a table listing feature amounts of actual data for each traveling event. The actual data is given an index (Idx) for each running event. In the example of FIG. 2(a), feature amounts of driving events with Idx "1" to "3" are shown. The controller 10 categorizes the actual data according to the feature amount, and aggregates the data for each traveling event. FIG. 2(b) is a table representing a list of categorized actual data of travel events. For example, the controller 10 categorizes the distance traveled in 5km increments and the top speed in bins of 10km increments. FIG. 2(b) represents the actual data of the travel event included in the bin of travel distance 10-15 km and maximum speed 30-40 km/h. That is, among the actual data listed in FIG. 2(a), the data corresponding to the mileage range of 10 to 15 km and the maximum speed of 30 to 40 km/h (in FIG. 2(b), Idx "6, 661, 894 ” data) are aggregated.

The controller 10 calculates, for each category, the center of gravity of the data categorized by the predetermined feature bins. The center of gravity of the data corresponds to the average of various indexes of the feature amount, the category center, or the median value of the feature amount. The controller 10 takes the actual data closest to the center of the categorized actual data of the driving event in FIG. 2(b) as the representative data of the category. For example, in the example of FIG. 2(b), assuming that the center of gravity of the data has a travel distance of 11.1 km and a maximum speed of 38.9 km/h, the controller 10 determines that the data with Idx "661" has a travel distance of 10 to 15 km. and representative data (representative value) for the category of maximum speed of 30 to 40 km/h. Then, the controller 10 defines the driving event of the data of Idx "661" as the representative driving event. As a result, the controller 10 defines a representative travel event among the travel events belonging to the categories of travel distances of 10 to 15 km and maximum speeds of 30 to 40 km/h. Further, the controller 10 uses the time-series data of Idx "661" as the profile of the feature amount in the representative travel event. The controller 10 similarly defines representative travel events for other categories.

After deleting data other than the actual data of the representative travel event, the controller 10 stores the feature amount of the representative travel event in the database as a profile. In the example of FIG. 2B, the time-series data with Idx "661" is stored in the database, and the time-series data with Ids "6" and "894" are deleted. The controller 10 similarly defines representative travel events for other categories, and deletes time-series data that do not correspond to the representative travel events. FIG. 3 is a conceptual diagram of data showing feature amount data of a representative travel event in a coordinate space. In the example of FIG. 3, the horizontal axis and the vertical axis represent the travel distance and the maximum speed, and the circles represent the profiles of representative travel events for each category. That is, the database 20 stores profiles corresponding to circles in FIG. In this way, the controller 10 selects (clusters) the profiles of traveling events obtained in advance through experiments or the like to be stored in the database 20, and then constructs a database of representative traveling events. Note that the profile of the representative driving event is not necessarily limited to actual data, and may be data calculated by a time-series clustering process capable of general similarity calculation, or normalized data.

Further, the controller 10 acquires actual driving information (profile) of the driving event from the vehicle 1 in order to estimate the deterioration state of the secondary battery 3, as will be described later. If the vehicle does not belong to each category defined by the database 20, the database 20 may be updated by storing the data of the actual travel information in the database 20. FIG. The updating of the database 20 will be described below.

Data that do not belong to each category defined by the database 20 are outlier data (specific data) that deviate from the data indicated by the circles in FIG. Data P indicated by squares in FIG. 3 are outlier data. When the controller 10 acquires the actual travel information of the travel event from the vehicle 1, the controller 10 refers to the database 20 to specify the feature amount in the representative travel event that is closest to the feature amount. In the example of FIG. 3, data Q is the closest data. Then, the controller 10 calculates the distance from the coordinate point of the outlier data P to the coordinate point of the data Q (the distance in the feature amount space). This distance may be the Euclidean distance or the Mahalanobis distance.

The controller 10 stores a profile corresponding to the outlier data in the database 20 when the calculated distance is equal to or greater than the predetermined distance. On the other hand, the controller 10 does not store the profile corresponding to the outlier data in the database 20 when the calculated distance is less than the predetermined distance. Accordingly, when actual data based on a running event that does not correspond to a representative running event is uploaded to the server 100 , the controller 10 adds the outlier actual data to the database 20 . Whether or not the database 20 needs to be updated based on the outlier data may be determined based on the index of the feature amount that has a high degree of influence on the deterioration calculation of the secondary battery 3, in addition to the distance in the feature amount space. . For example, outlier data may be added to the database 20 if the distance traveled is long, or if the ambient temperature is significantly different from the temperature in the representative travel event.

When the server 100 determines that the actual data transmitted to the server 100 corresponds to outlier data, the vehicle 1 may upload the profile temporarily stored in the vehicle controller 2 to the server. Thereby, the communication load between the vehicle 1 and the server 100 can be suppressed while suppressing the storage capacity of the vehicle controller 2 .

Next, a processing flow for estimating the deterioration state of the secondary battery 3 by the controller 10 will be described with reference to FIG. FIG. 4 is a flow chart showing the control procedure of the method for estimating the state of deterioration of the battery executed by the controller 10. As shown in FIG. The control flow for estimating the state of deterioration of the battery shown in FIG. 4 is triggered by the fact that the vehicle finishes the actual driving event, transmits a signal containing the actual driving information to the server 100, and the server 100 receives the signal. be done.

In step S<b>1 , the extraction unit 11 acquires actual travel information by extracting actual travel information from an actual travel event of the vehicle 1 . In step S<b>2 , the state estimation unit 12 acquires from the database 20 a similar feature amount similar to the actual feature amount included in the actual travel information, among the feature amounts of the representative travel event included in the database 20 . Specifically, for example, in the examples of FIGS. 2 and 3, the state estimating unit 12 identifies the data of the feature amount closest to the coordinate point of the actual feature amount in the feature amount space, and uses the identified data The feature amount of the representative travel event shown is acquired as a similar feature amount.

In step S<b>3 , the state estimation unit 12 identifies a representative travel event corresponding to the similar feature amount on the database 20 . In step S4, the state estimator 12 calculates an estimated output of the secondary battery 3 from the identified representative travel event. The state estimating unit 12 has a vehicle model that receives a travel event profile and outputs a battery load profile. The vehicle model is represented by an arithmetic expression, and models the load applied to the secondary battery when the vehicle is driven by an input driving event. The input of the battery model is the profile of the representative travel event identified in step S3 (representative travel profile), which corresponds to the profile of the actual feature quantity. The output of the battery model is time-series data representing the power consumption of the secondary battery 3 when the vehicle is driven in the representative driving event. The power consumption of the secondary battery 3 corresponds to the estimated output of the secondary battery 3, and the power consumption profile corresponds to the battery load profile. For example, when the profile of the representative travel event is time-series data of vehicle speed (an example of the actual feature value), the state estimation unit 12 detects the secondary battery when the vehicle 1 travels along the vehicle speed of the time-series data. 3 calculates the power consumption profile (battery load profile) using the battery model. Thereby, the state estimating unit 12 calculates a battery load profile from the profile of the actual feature amount using the vehicle model. Furthermore, the state estimator 12 calculates an estimated output of the secondary battery 3 from the battery load profile. The estimated power corresponds to the power or amount of power consumed by the secondary battery 3 in the running event. When the estimated power is used as the power amount, it can be calculated by, for example, integrating the power consumption indicated by the battery load profile with the time element of the profile. When the estimated power is used as the power, the average value, the median value, or the like of the power consumption indicated by the battery load profile may be used.

In step S5, the state estimator 12 calculates the actual output of the secondary battery 3 from the actual running information. The actual output corresponds to the actual power consumption or amount of power consumed by the secondary battery 3 in the running event, and is the power or amount of power consumed to operate the auxiliary equipment (auxiliary equipment power consumption) and the driving power. It corresponds to power obtained by adding the power consumed as energy or the amount of power (driving power consumption). On the other hand, the estimated output (initial value) of the secondary battery 3 calculated using the vehicle model described above is the drive power consumption calculated by the simulation, and does not include the accessory power consumption.

In step S6, the state estimation unit 12 estimates the deterioration state of the secondary battery 3 from the battery load profile and the previous value. The previous value is a parameter of deterioration information given from the information obtained in the calculation flow for estimating the deterioration state of the battery by the controller 10 or the calculation result, and represents a parameter relating to the deterioration state of the secondary battery that has progressed so far. . The previous values are, for example, the amount of SEI formed, battery capacity, battery temperature, battery voltage/current, SOC, and the like. The state estimator 12 calculates the deterioration state of the secondary battery 3 from the load applied to the secondary battery 3 . The state estimating unit 12 has a battery model that receives the load applied to the secondary battery 3 as an input and outputs the deterioration state of the secondary battery 3 . The battery model is represented by an arithmetic expression, and indicates the progression of the deterioration state of the secondary battery 3 when the secondary battery 3 consumes power indicated by the battery load profile with respect to the current state of the secondary battery 3. It is a model of the condition. In other words, the battery load profile represents the load applied to the secondary battery 3 . The current state of the secondary battery 3 is indicated by the previous value. Then, the state estimator 12 uses the battery model to estimate the deterioration state of the secondary battery 3 based on the previous value and the battery load profile. The deterioration state of the secondary battery 3 is indicated by the state quantity of the secondary battery 3, the degree of deterioration, and the like. Thereby, the state estimator 12 estimates the deterioration state of the secondary battery 3 through a simulation using the profile (representative running profile and battery load profile).

In step S7, the state estimator 12 determines whether the difference between the actual output and the estimated output is equal to or greater than the output threshold. Specifically, the state estimator 12 subtracts the estimated output from the actual output to obtain an absolute value. The difference between the actual output and the estimated output may be either the difference in power consumption or the difference in power consumption. The output threshold value is a threshold value for determining whether or not the estimated output estimated from the simulation needs to be corrected, and is a value that is determined in advance according to the required deterioration state estimation accuracy, the computing power of the controller 10, and the like. If the difference between the actual output and the estimated output is greater than or equal to the output threshold, the controller 10 executes the control flow of step S8. The estimated output indicates the load applied to the secondary battery 3 when the vehicle is driven in the representative driving event, and differs from the load applied by the secondary battery 3 during actual driving. In addition, since the actual output includes the power used for the operation of the accessories, the power also appears in the difference between the actual output and the estimated output. Therefore, in the present embodiment, when the difference between the actual output and the estimated output is equal to or greater than the output threshold, the estimated output of the secondary battery 3 is corrected in a step described later, and the secondary battery 3 is corrected based on the corrected estimated output. The state of deterioration of the battery 3 is estimated. On the other hand, when the difference between the actual output and the estimated output is less than the output threshold, the controller 10 terminates the control flow for estimating the state of deterioration of the battery. The controller also outputs the state of deterioration of the secondary battery 3 estimated in the control flow of step S6 as a simulation result.

When the difference between the actual output and the estimated output is equal to or greater than the output threshold, the state estimator 12 determines whether the event time is equal to or greater than the time threshold in step S8. The event time is the duration of the travel event, and corresponds to, for example, the time from when the main switch of the vehicle 1 is turned on until it is turned off. The controller 10 extracts the event time from the actual travel information. The time threshold is a threshold that is set so as to correct the estimated power, which will be described later, only in driving scenes that affect the estimation of the state of deterioration of the secondary battery 3 . Then, when the event time is equal to or longer than the time threshold, the output of the secondary battery 3 is corrected in step S9 to be described later. On the other hand, if the event time is less than the time threshold, the control flow for estimating the state of deterioration of the battery is terminated without correcting the output of the secondary battery 3 .

The state estimator 12 also sets a time threshold according to the event time. FIG. 5 is a graph (a) showing the characteristics of the capacity deterioration amount of the secondary battery 3 with respect to the event time, and a graph (b) showing the characteristics of the time threshold with respect to the event time. As shown in FIG. 5(a), the longer the event time, the larger the capacity deterioration amount (battery deterioration rate). As a characteristic of the secondary battery 3, it is known that deterioration progresses according to the rule of passage of time. Therefore, when the event time is short, for example, when the elapsed time is less than one minute, the progress of deterioration is slight, and the influence on the deterioration state of the secondary battery 3 is small. Therefore, as shown in FIG. 5B, when the event time is short, the state estimating unit 12 sets the time threshold to a high value, and when the event time of the driving event is short, the secondary battery 3 Disable output correction. On the other hand, the state estimating unit 12 sets the time threshold to a lower value as the event time becomes longer, and corrects the output of the secondary battery 3 when the event time of the driving event is short.

The state estimator 12 may set the output threshold (threshold used in determining the control flow in step S7) according to the event time. The characteristic of the output threshold with respect to the event time may be the same as the characteristic shown in FIG. 5(b). That is, if the event time is short, the output threshold is set to a high value. Therefore, even if the difference between the actual output and the estimated output is large, the output of the secondary battery 3 is not corrected because the influence on the deterioration state of the secondary battery 3 is small in the driving event input from the vehicle. On the other hand, the longer the event time is, the lower the output threshold is set. Therefore, even if the difference between the actual output and the estimated output is small, the output correction of the secondary battery 3 is performed because the deterioration state of the secondary battery 3 is greatly affected when the event time is long.

As a modification, the state estimating unit 12 sets a distance threshold according to the distance traveled in the travel event instead of the event time, and corrects the output of the secondary battery 3 only when the travel distance is equal to or greater than the distance threshold. may be performed. The characteristic of the distance threshold with respect to the travel distance may be the same as the characteristic shown in FIG. 5(b).

Furthermore, as a modification, the state estimation unit 12 sets an average output threshold according to the average output of the secondary battery 3 instead of the event time and the travel distance, and only when the average output is equal to or higher than the average output threshold, Output correction of the secondary battery 3 may be performed. The average output of the secondary battery 3 is the average value of the power output from the secondary battery 3 during the running event. The average output of the secondary battery 3 may be the average of the actual outputs calculated from the actual running information or the estimated average calculated from the battery load profile. The characteristic of the average output threshold with respect to the average output may be the same as the characteristic shown in FIG. 5(b). As a characteristic of the secondary battery 3, the capacity deterioration amount (battery deterioration rate) progresses as the number of cycles increases. When the average output is low, the number of cycles is also small, so the progress of deterioration is slight. Therefore, in the modified example, when the average output of the secondary battery 3 is low, the state estimation unit 12 sets the average output threshold to a high value so that the output of the secondary battery 3 is not corrected. On the other hand, the state estimation unit 12 sets the average output threshold to a lower value as the average output increases, and corrects the output of the secondary battery 3 when the number of cycles in the running event is large. Note that the state estimator 12 may use the maximum speed, average vehicle speed, average slope, etc. instead of the average output of the secondary battery 3 .

Furthermore, in the above modified example, the state estimator 12 may set the output threshold according to the travel distance, average output, maximum speed, average vehicle speed, or average gradient. The characteristic of the output threshold for each index may be the same as the characteristic shown in FIG. 5(b). Furthermore, the state estimator 12 may determine whether or not to correct the output of the secondary battery 3 by combining indicators such as event time and travel distance.

In step S9, the state estimator 12 corrects the output of the secondary battery 3 by adding a corrected output corresponding to the difference between the actual output and the estimated output to the estimated output. The corrected output corresponds to the power of the secondary battery 3 (corrected power: P _adjust ) and is calculated by the following equations (1) and (2).

After calculating the corrected power in the manner described above, the state estimator 12 corrects the output of the secondary battery 3 by adding the corrected power to the estimated power. According to Equation (1), the corrected power is a value calculated based on the value obtained by subtracting the estimated power amount from the actual power amount, and the corrected power may be a negative value. If the corrected power is a negative value, the estimated power will be subtracted by the corrected power.

Further, according to Equation (1), the correction power is a value corresponding to the difference between the actual output and the estimated output. The difference between the actual output and the estimated output is caused by the difference in auxiliary machine power consumption included in the actual output. That is, the state estimating unit 12 may correct the output of the secondary battery by adding the corrected output to the power consumption of the accessory. The correction of adding the correction output to the auxiliary machine power consumption can be expressed by the following formula (3).

In the control process of step S9, when the output of the secondary battery 3 is corrected, the loop of the control flow returns to step S6, and the state estimating unit 6 performs two operations based on the corrected output of the secondary battery 3. The state of deterioration of the secondary battery 3 is estimated. The output correction of the secondary battery 3 may be reflected in the battery load profile, or may be reflected in the calculation result using the secondary battery model.

As described above, in the present embodiment, the controller 10 calculates the correction output (correction power) according to the difference between the actual power consumption of the secondary battery 3 and the power obtained from the simulation result after executing the simulation. Furthermore, the controller 10 adds the corrected output to the profile and then performs the simulation again to estimate the final state of deterioration of the secondary battery 3 . As a result, the battery load energy that affects the deterioration of the secondary battery 3 can be calculated with high accuracy by performing correction according to the deviation amount.

As described above, in the present embodiment, the battery state estimation system categorizes the characteristic amounts of the driving events based on the driving information obtained in advance from a plurality of vehicles 1, and selects representative driving events that are representative of the driving events belonging to the category. and stores characteristic amounts of representative travel events as profiles. Then, the controller 10 extracts from the database 20 a feature quantity similar to the actual feature quantity included in the actual travel information, identifies a representative travel event corresponding to the extracted feature quantity, and selects a secondary battery from the representative travel event. Calculate the estimated output of 3, calculate the actual output that is the actual output of the secondary battery from the actual driving information, and if the difference between the actual output and the estimated output is equal to or greater than the output threshold, correction corresponding to the difference By adding the output to the estimated output, the output of the secondary battery 3 is corrected, and the state of deterioration of the secondary battery 3 is estimated based on the corrected output. That is, the controller 10 refers to the profile of the representative travel event from the database 20 via the feature amount, and executes the deterioration state estimation calculation using the profile. Further, the controller 10 calculates the deterioration state by generating a driving event obtained by adding or subtracting a correction output corresponding to the difference between the actual power consumption and the estimated power consumption or the difference between the actual power consumption and the estimated power consumption. This makes it possible to keep the communication information from the vehicle 1 to a minimum (data size corresponding to the feature amount). In addition, it is possible to reproduce the actual vehicle running profile, which is unknown. Furthermore, it is possible to easily correct the deviation between the actual running and the simulation. As a result, the deterioration state of the secondary battery 3 can be estimated with high accuracy while suppressing the amount of data transmitted from the vehicle 1 to the server 100 .

Further, in the present embodiment, the state estimator 12 of the controller 10 corrects the output of the secondary battery by adding the correction output to the accessory power consumption. That is, the state estimator 12 adds the correction output to the accessory power consumption used in the deterioration state estimation calculation. As a result, the calculation load can be reduced by uniformly adding the correction outputs, and the modification scale of the existing calculation model can be minimized.

In the present embodiment, the state estimating unit 12 of the controller 10 detects when at least one of the event time, the travel distance, and the average output of the secondary battery 3 in the actual travel event is equal to or less than a predetermined threshold value. does not execute correction processing of the output of the secondary battery 3 based on the corrected output. That is, correction is performed only in scenes that have an impact on the deterioration estimation amount. Thereby, the calculation load can be reduced.

In the present embodiment, the state estimating unit 12 of the controller 10 calculates a battery load profile from the profile of the actual feature quantity using the vehicle model, corrects the battery load profile based on the corrected output, and corrects the battery load profile using the battery model. The state of deterioration of the secondary battery is estimated from the battery load profile. That is, the calculation at the time of re-simulation based on the corrected output adds a correction component in the offset calculation of the battery load profile. This can reduce the computational load.

As a modification of the present embodiment, the database 20 stores the output of the secondary battery 3 when the vehicle 1 is run in the representative travel event as an estimated output, and the state estimation unit 12 stores the output of the secondary battery 3 from the representative travel event. Instead of computing the estimated output of 3, the estimated output stored in the database 20 may be used. In this embodiment, the profile of the representative driving event is input to the vehicle model to calculate the battery load profile, and the estimated output of the secondary battery 3 is calculated from the calculation result. Considering the specifications of the secondary battery 3 and driving events under typical environmental conditions, the estimated electric power of the secondary battery 3 at each Idx of the representative driving event is calculated in advance, and stored in the database 20 in association with each Idx. .

For example, in the example of FIG. 2, the estimated power calculation value is stored for the data of Idx “661” stored in the database 20 . The estimated power corresponds to power consumption or power consumption consumed by the secondary battery 3 when the vehicle is driven in the representative driving event of Idx "661". The estimated power may be calculated using a vehicle model as described above. Similarly, for the other representative travel events stored in the database 20, the estimated power corresponding to each representative travel event is calculated in advance, and the estimated power is calculated for the Idx data of each representative travel event. Stores the computed value. Then, the state estimator 12 extracts from the database 20 an estimated output corresponding to the specified representative travel event, instead of the arithmetic processing in the control flow of step S3. Thereby, the calculation load can be reduced.

As a modification of this embodiment, the database 20 may store the estimated output of the secondary battery 3 as data for each user. The representative travel event defined by the database 20 is a travel event statistically representative of the user's travel event. In the modified example, the estimated output of the secondary battery 3 is estimated for the obtained driving event while obtaining the actual driving event for each individual user through the above simulation. Then, in this modified example, the data of the representative travel event is updated for each user. The updating for each user is not limited to the feature amount stored in the database 20, but may be performed on the calculated value of the estimated power stored for the data of each representative travel event in the above modified example. Data may be updated, for example, for each driving event. In updating the data, the parameter of the feature amount or the estimated power may be updated so as to become the average value, the final value, or the median value of the feature amount. In addition, the parameter of the feature amount or the estimated power may be updated with a representative value among a plurality of users classified by age, gender, region, etc., not limited to individual users. In addition, when data is updated with a representative value among a plurality of users, the distribution may be held. As a result, it is possible to use the corrected output reflecting the usage (history) of each person as the initial value in the simulation, thus avoiding re-simulation. As a result, the calculation accuracy can be improved while reducing the calculation load.

In this embodiment, it is not necessary to execute all of the control flows (the flow shown in FIG. 4) indicating the control procedure of the method for estimating the state of deterioration of the battery, and for example, the control flow of step S8 may be omitted. Also, the execution order of each control flow is not limited to the order shown in FIG.

Although the embodiments of the present invention have been described above, these embodiments are described to facilitate understanding of the present invention, and are not described to limit the present invention. Therefore, each element disclosed in the above embodiments is meant to include all design changes and equivalents that fall within the technical scope of the present invention.

DESCRIPTION OF SYMBOLS 1... Vehicle 2... Vehicle controller 3... Secondary battery 10... Controller 11... Extraction part 12... State estimation part 20... Database 100... Server

Claims (7)

A battery state estimation system for estimating the deterioration state of a secondary battery mounted on a vehicle,
an extraction unit that extracts actual travel information from an actual travel event of the vehicle;
a state estimation unit that estimates the deterioration state of the secondary battery;
Categorize the characteristic amounts of the driving events based on the driving information obtained in advance from a plurality of vehicles, define a representative driving event among the driving events belonging to the category, and use the characteristic amount of the representative driving event as a profile. a database for storing;
The state estimation unit
extracting from the database a feature quantity similar to the actual feature quantity included in the actual travel information;
identifying the representative running event corresponding to the extracted feature quantity;
calculating an estimated output of the secondary battery from the representative running event;
calculating the actual output, which is the actual output of the secondary battery, from the actual running information;
when the difference between the actual output and the estimated output is equal to or greater than the output threshold, correcting the output of the secondary battery by adding a corrected output corresponding to the difference to the estimated output;
A battery state estimation system for estimating the deterioration state of the secondary battery based on the corrected output. In the battery state estimation system according to claim 1,
The output of the secondary battery includes drive power consumed as drive energy for the vehicle and accessory consumption power consumed to operate accessories of the vehicle,
The battery state estimation system, wherein the state estimation unit corrects the output of the secondary battery by adding the correction output to the power consumption of the accessory. In the battery state estimation system according to claim 1 or 2,
When at least one value of the event time, the traveled distance, or the average output of the secondary battery in the actual running event is equal to or less than a predetermined threshold value, the output of the secondary battery based on the corrected output A battery state estimation system that does not perform correction processing. In the battery state estimation system according to any one of claims 1 to 3,
The database is
storing the output of the secondary battery when the vehicle is run in the representative travel event as the estimated output;
The state estimation unit
A battery state estimation system that uses the estimated output stored in the database instead of calculating the estimated output of the secondary battery from the representative running event. In the battery state estimation system according to claim 4,
The battery state estimation system, wherein the database stores the estimated output as data for each user. In the battery state estimation system according to any one of claims 1 to 5,
The state estimation unit
The battery load profile is calculated from the profile of the actual feature quantity by a vehicle model that receives the profile of the representative driving event as an input and outputs a battery load profile that is a profile of the load applied to the secondary battery during the driving event. ,
correcting the battery load profile based on the corrected output;
A battery state estimation system for estimating the state of deterioration of the secondary battery from the corrected battery load profile using a battery model having the load applied to the secondary battery as an input and the state of deterioration of the secondary battery as an output. In a battery state estimation method for estimating the state of deterioration of a secondary battery mounted on a vehicle, executed by a controller,
extracting actual driving information from the actual driving event of the vehicle;
The feature values of the travel events are categorized based on the travel information obtained in advance from a plurality of vehicles, a representative travel event is specified among the travel events belonging to the category, and the feature values of the representative travel events are defined as a profile. extracting a feature quantity similar to the actual feature quantity included in the actual travel information from the stored database;
identifying the representative running event corresponding to the extracted feature quantity;
calculating an estimated output of the secondary battery from the representative running event;
calculating the actual output, which is the actual output of the secondary battery, from the actual running information;
when the difference between the actual output and the estimated output is equal to or greater than the output threshold, correcting the output of the secondary battery by adding a corrected output corresponding to the difference to the estimated output;
A battery state estimation method for estimating the state of deterioration of the secondary battery based on the corrected output.

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