Secondary battery management device, secondary battery management method, and program

Abstract

To reduce a battery charging time of an electric vehicle for delivery, and optimize a battery loadage by suppressing deterioration of a battery.SOLUTION: A secondary battery management device 100A comprises: a power calculation unit 112 that calculates a predicted power W2 which is predicted to be required for a mobile vehicle to move along a designated planned moving route Rt, based on a power database 142 for recording a required power corresponding to a moving route of and a moving date and time or an ambient temperature when moving of the mobile vehicle driven by a secondary battery; and a travel-starting-state determination unit 114 that calculates a used SOC area of the secondary battery which can secure the predicted power W2 or a used voltage area according to a traveling condition, by referring to a battery deterioration database 144 which records a deterioration state of the secondary battery or records the past used state of the secondary battery along with data which shows a correlation between the used state and the deterioration state of the secondary battery, and determines conditions of SOCs and Vs at the start of traveling based on the calculated used SOC area or used voltage area.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a secondary battery management device, a secondary battery management method, and a program.

Background Art

[0002] Environmental problems have been brought into sharp focus, and in order to prevent global warming, reduction of carbon dioxide emissions is required in all scenarios. Against this backdrop, for automobiles using gasoline engines, which are one of the major sources of carbon dioxide emissions, alternatives such as hybrid electric vehicles and electric vehicles are being promoted. Since the performance of in-vehicle batteries, which serve as the power sources for electric vehicles and hybrid electric vehicles, affects the driving performance, in recent years, high-performance and lightweight lithium-ion batteries have been used. The price of the driving battery is high, and the price of electric vehicles is also higher than that of gasoline vehicles in the same class. From the user side, it is desired that the vehicle can be operated for a long time, and from the manufacturer side, there is a demand to suppress costs with an optimal battery capacity. Generally, the capacity of a secondary battery decreases with charge and discharge. In particular, in a lithium secondary battery in which the positive electrode material is a lithium-containing transition metal oxide such as LiCoO 2 ,LiMn x Ni y Co (1-x-y) O 2 (x + y>1) and the negative electrode is a combination of graphite and an amorphous carbon material, it is known that deterioration tends to progress in a fully charged state.

[0003] As the background art in this technical field, the summary of Patent Document 1 below states that "a remaining battery amount detection unit 15b that detects the remaining battery amount of an electric vehicle battery, a next trip information detection unit 15e that detects the departure time of the next trip when using the electric vehicle and the required power consumption in the trip as next trip information, and a charging control unit 15c that determines and charges the charging speed and the charging amount for charging the required power consumption by the next trip departure time based on the remaining battery amount and the next trip information."

[0004] In the charging control device for an electric vehicle described in Patent Document 1, from the perspective of preventing the deterioration from progressing when the mounted battery is charged to full charge every time, when charging a plurality of co - utilized electric vehicles until the next trip, charging control is performed taking into account the battery deterioration cost, which is the degree of influence on battery deterioration. Based on the remaining amount of the battery, the detected departure time of the next trip, and the required power consumption, the charging speed and the charging amount for charging the required power consumption by the departure time of the next trip are determined and charged, thereby performing charging control for a plurality of vehicles that optimizes the charging speed and the charging amount.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In recent years, the online shopping market has expanded, and the volume of goods flow such as delivery and distribution has increased. There is a demand for improving delivery efficiency, and as shown in the background art, there is also a demand for reducing carbon dioxide emissions. Against this background, it is considered that vehicles powered by large secondary batteries such as electric vehicles and plug - in hybrid vehicles will increase for delivery and distribution vehicles. For the power supply of electric vehicles and plug - in hybrid electric vehicles for delivery and distribution, large - capacity secondary batteries are required. In the case of electric vehicles, as the vehicle size increases, the required energy amount also increases, and the battery weight increases, resulting in a trade - off problem between the loading capacity and the battery cost. The weight of the vehicle body and the performance of the battery affect driving. For reducing logistics costs and responding to the environment, it is important for the in - vehicle battery to operate efficiently with an optimal battery amount.

[0007] However, secondary batteries such as lithium-ion batteries deteriorate with each charge-discharge cycle, resulting in a decrease in capacity and an increase in internal resistance. This causes fluctuations in the output of the secondary battery. The degree of deterioration progression in a 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.

[0008] As described above, in the technology applying the content of Patent Document 1, the charging time of an electric vehicle can be shortened. However, what is mainly considered is the deterioration effect due to rapid charging. For this reason, since the way of using the battery when the electric vehicle will be used for delivery driving in the future is not considered, it is impossible to avoid the possibility of accelerating the deterioration of the battery.

[0009] For example, regarding delivery and delivery trucks, it is difficult to say that they are always in an environment where they can avoid direct sunlight and the reflection of the road surface during operation and parking during the day. Especially in summer, the temperature is high, so the higher the SOC (state of charge; charge rate or state of charge) is maintained, the more the deterioration is accelerated. The characteristics of a secondary battery are greatly affected by temperature. In particular, characteristics such as capacity and resistance change. Referring to FIG. 1, the content will be described.

[0010] FIG. 1 is a diagram showing various characteristic examples of a secondary battery (for example, a lithium-ion battery). First, graph G2 is a graph showing the resistance value ratio DCR of the internal resistance with respect to the temperature of the secondary battery. In graph G2, the horizontal axis is the temperature of the secondary battery, and the vertical axis is the resistance value ratio DCR. Here, the resistance value ratio DCR represents the ratio at each temperature with the resistance value at 25°C being set to "1". The lower the temperature, the higher the resistance and the smaller the output.

[0011] Graph G4 is a graph showing the capacity retention rate of a secondary battery with respect to the SOC and temperature of the secondary battery. In graph G4, the horizontal axis represents the SOC of the secondary battery, and the vertical axis represents the capacity retention rate when the secondary battery is stored for a predetermined period. Also, characteristics G4-25, G4-50, G4-65, and G4-80 are the characteristics at battery temperatures of 25°C, 50°C, 65°C, and 80°C, respectively. As shown in graph G4, the higher the SOC and the higher the battery temperature, the lower the capacity retention rate.

[0012] Graph G6 is a diagram showing the rate of increase in internal resistance when a secondary battery is stored at various temperatures for 6 months. As shown in the figure, the higher the temperature, the higher the rate of increase in resistance and the greater the deterioration. Graph G8 is a diagram showing the rate of increase in resistance when a secondary battery is stored at 25°C for 190 days at various SOCs. As shown in the figure, the higher the SOC, the higher the rate of increase in resistance and the greater the deterioration. The examples shown here are just examples, and the deterioration characteristics vary depending on the type and specifications of the battery material, and the deterioration varies depending on the usage method.

[0013] As described above, in Patent Document 1, the consideration is the deterioration effect due to rapid charging. Since the usage of the battery when the electric vehicle is running after charging is not considered, it is impossible to avoid the possibility that the deterioration of the battery is accelerated depending on the state during running. Therefore, in a vehicle that uses a battery as a power source for delivery and distribution, it would be preferable to provide an optimal charging navigation system that reflects the degree of deterioration of the secondary battery module and is linked with a delivery and distribution system that efficiently charges the battery and effectively utilizes the battery in a short time. This invention has been made in view of the above circumstances, and an object thereof is to provide a secondary battery management device, a secondary battery management method, and a program that suppress the deterioration rate of a secondary battery by reflecting the state during running and enable stable running.

Means for Solving the Problems

[0014] To solve the above problems, the secondary battery management device of the present invention calculates a predicted power consumption amount predicted to be required for the moving body to move along a designated planned travel route based on a power consumption database that stores the required power consumption amount corresponding to the travel date and time or the outside air temperature during travel and the travel route of the moving body driven by the secondary battery. Refer to a battery degradation database that records the past usage status of the secondary battery together with data indicating the correlation between the usage status and the degradation status of the secondary battery. According to the Degradation state of the secondary battery, it calculates the usable SOC range or usable voltage range of the secondary battery that can secure the predicted power consumption amount, and based on the calculated usable SOC range or usable voltage range, determines the travel start condition, which is the voltage or SOC of the secondary battery in the fully charged state, by a travel start state determination unit. It is characterized by comprising these components.

[0015] Specifically, based on a power consumption database that stores the required power consumption amount corresponding to the travel date and time or the outside air temperature during travel and the travel route of the moving body driven by the secondary battery, it calculates a predicted power consumption amount predicted to be required for the moving body to move along a designated planned travel route. It also records the deterioration state of the secondary battery or refers to a battery deterioration database that records the past usage state of the secondary battery together with data indicating the correlation between the usage state and the deterioration state of the secondary battery. According to the deterioration state, it calculates the usable SOC range or usable voltage range of the secondary battery that can secure the predicted power consumption amount, and based on the calculated usable SOC range or usable voltage range, determines the recommended voltage or recommended SOC of the secondary battery at the start of travel, and determines the travel start condition, which is the voltage or SOC of the secondary battery in the fully charged state, by a travel start state determination unit. It is characterized by comprising these components. In the preferred embodiment described below, in a vehicle that uses a battery as a power source for delivery, the degree of deterioration of the secondary battery module is reflected to charge efficiently, and the battery is effectively utilized. Further, in the preferred embodiment, an optimal charging navigation system linked to the delivery and distribution system is provided. Therefore, in the secondary battery management device according to the preferred embodiment, the predicted power consumption considered necessary is calculated based on the previously accumulated driving route history during delivery, the current deterioration state of the secondary battery in the vehicle, the environmental temperature, the planned delivery route, and the load.

[0016] And in the preferred embodiment, based on the predicted power consumption, the planned delivery route, the parking time on the planned delivery route, and the battery temperature or air temperature, the SOC region for actual application is selected from at least two or more different SOC regions, and the starting charge rate SOCs, which is the SOC at the start of driving or at the completion of charging, is determined.

Advantages of the Invention

[0017] According to the present invention, the power required for driving can be appropriately determined, the waiting time due to charging during vehicle operation can be shortened, the charge state of the secondary battery can be appropriately determined, and by using the battery under conditions where it is less likely to deteriorate, the optimization of the required battery capacity and the cost performance of the vehicle can be improved.

Brief Description of the Drawings

[0018]

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Embodiments for Carrying Out the Invention

[0019] In the storage of a secondary battery, etc., it is preferable to keep the SOC at which the secondary battery stays as low as possible. Therefore, in the preferred embodiment described below, in a vehicle that uses a battery as a power source for delivery, the degree of deterioration of the secondary battery module is reflected, charging is efficiently performed in a short time, and the battery is effectively utilized. Further, in the preferred embodiment, an optimal charging navigation system linked to a delivery / dispatch system is provided. Therefore, in the secondary battery management device according to the preferred embodiment, the predicted power consumption considered necessary is calculated based on the previously accumulated driving route history during delivery and dispatch, the current deterioration state of the secondary battery in the vehicle, the environmental temperature, the planned delivery route, and the load.

[0020] Then, in the preferred embodiment, based on the predicted power consumption, the planned delivery route, the parking time on the planned delivery route, and the battery temperature or the air temperature, the SOC region for actual application is selected from at least two or more different SOC regions, and the starting charge rate SOCs, which is the SOC at the start of driving or at the completion of charging, is determined. Thus, according to the preferred embodiment, it is possible to ensure the driving amount (predicted power consumption) in the region with the least deterioration, enable short-time charging, appropriately manage the predicted power consumption, efficiently use the battery, minimize the battery installation amount, and improve its cost performance.

[0021] [First Embodiment] FIG. 2 is a block diagram of a secondary battery management system S1 according to a preferred first embodiment. The secondary battery management system S1 includes a secondary battery management device 100A (computer), a power consumption database 142, and a battery deterioration database 144. The secondary battery management device 100A includes hardware such as a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), and HDD (Hard Disk Drive), which are components of a general computer. The SSD or HDD stores an OS (Operating System), application programs, various data, etc. The OS and application programs are loaded into the RAM and executed by the CPU. In FIG. 2, the internal components of the secondary battery management device 100A are shown as blocks representing functions realized by application programs and the like.

[0022] That is, the secondary battery management device 100A includes a power amount calculation unit 112 (power amount calculation means, power amount calculation process) and a travel start state determination unit 114 (charging condition determination means, charging condition determination process). The secondary battery management device 100A manages a secondary battery (not shown) mounted as a power source in a vehicle. Here, the secondary battery management device 100A may be a computer mounted on the vehicle or a separate computer from the vehicle.

[0023] The vehicle delivers goods and the like along various travel routes. The power amount database 142 stores, in association with each other, history information indicating the usage history of the vehicle and the standard required power amount. Here, the history information includes at least the travel date and time or the outside air temperature during travel and route information indicating the travel route. When, for example, planned route information Rt (planned travel route) for specifying a planned travel route is input by the user, the power amount calculation unit 112 calculates predicted power amounts W1 and W2 that are predicted to be required for the vehicle to travel along the planned travel route based on the power amount database 142. Here, the predicted power amount W1 is the predicted power amount calculated based on the planned route information Rt, and the predicted power amount W2 is the result of correcting the predicted power amount W1 based on the degradation state of the secondary battery and the like.

[0024] The battery degradation database 144 is configured to include correlation data that can estimate the usage conditions of the battery and the changes in the capacity and resistance of the battery. However, instead of the current degradation state, past usage states (for example, the transition of the environmental temperature, the amount of power output, etc.) and degradation influence data may be stored. Here, the degradation influence data is data that describes the correlation between the usage conditions of the secondary battery and the degradation state. Based on the battery degradation database 144, the driving start state determination unit 114 calculates the usage voltage range or the usage SOC range of the secondary battery that can ensure the predicted power amount W2.

[0025] Here, the usage voltage range is the range from the voltage at the start of discharge to the voltage at the end of discharge of the secondary battery when the vehicle travels on the planned travel route indicated by the planned route information Rt. Also, the usage SOC range is the range from the SOC at the start of discharge to the SOC at the end of discharge. The driving start state determination unit 114 preferably determines the voltage range to be used so that the degradation of the battery is not accelerated.

[0026] The driving start state determination unit 114 refers to the battery degradation database 144 and calculates the usage voltage range appropriate for operation. Then, based on the usage voltage range, as the charging completion condition, the driving start voltage Vs (driving start condition), which is the voltage of the secondary battery in the fully charged state, or the driving start charging rate SOCs (driving start condition), which is the SOC of the secondary battery in the fully charged state, is determined and output.

[0027] Here, the actual driving start voltage Vs may be the upper limit value of the above-described usage voltage range, or a voltage obtained by adding a slight margin to the upper limit value. Similarly, the driving start charging rate SOCs may be the SOC corresponding to the upper limit value of the usage voltage range, or the SOC corresponding to a voltage obtained by adding a slight margin to the upper limit value. Hereinafter, Vs and / or SOCs may be collectively referred to as "driving start condition (SOCs, Vs)".

[0028] When the ECU of the vehicle recognizes the driving start condition (SOCs, Vs) output by the driving start state determination unit 114, it sets the driving start condition (SOCs, Vs) in a charging device (not shown). As a result, the charging device starts charging the secondary battery. Then, when the SOC or the output voltage of the secondary battery reaches the driving start condition (SOCs, Vs), the charging device ends the charging operation. However, when the SOC or the battery voltage of the secondary battery 11 is equal to or higher than the driving start condition (SOCs, Vs), the vehicle can skip the charging operation and start driving.

[0029] [Second Embodiment] FIG. 3 is a block diagram of a secondary battery management system S2 according to a preferred second embodiment. In the following description, parts corresponding to those of the first embodiment described above may be denoted by the same reference numerals, and the description thereof may be omitted. The secondary battery management system S2 includes a charging device 200, a secondary battery management device 100B (computer), a power amount database 142, and a battery degradation database 144.

[0030] The hardware configuration of the secondary battery management device 100B is the same as that of the secondary battery management device 100A of the first embodiment. And the secondary battery management device 100B includes a power amount calculation unit 112 and a driving start state determination unit 114, similarly to the secondary battery management device 100A. Further, the secondary battery management device 100B includes a database update unit 118 and an analysis unit 120.

[0031] In addition, this embodiment manages a plurality of vehicles 10 (mobile bodies). However, only one vehicle 10 is illustrated in FIG. 3. Each vehicle 10 is provided with a secondary battery 11 as a power source and a data processing unit 12. The data processing unit 12 measures the position information of the vehicle 10 and the state (voltage, current, etc.) of the secondary battery 11 at a predetermined sampling period, accumulates a certain amount of the results as time-series data D10, and outputs them. Then, the data processing unit 12 transmits the time-series data D10 to the analysis unit 120 of the secondary battery management device 100B wirelessly or by wire. The transmission timing may be real-time, but it is transmitted at a suitable timing according to the communication load and the amount of data that can be accumulated. For example, it is transmitted at the timing of parking, after stopping the vehicle, or starting the starter during one driving.

[0032] In addition, the analysis unit 120 analyzes the time-series data D10 received from the plurality of vehicles 10, and generates data to be stored in the power amount database 142 and the battery deterioration database 144 based on the content thereof. The database update unit 118 updates the power amount database 142 and the battery deterioration database 144 based on the data generated by the analysis unit 120.

[0033] Similar to that in the first embodiment, the power amount database 142 in this embodiment stores the history information in association with the standard required power amount. In addition, the history information in this embodiment includes the moving date and time, the outside air temperature during movement, route information such as the delivery route, the position information of the vehicle 10, the usage time, the vehicle type of the vehicle 10, the load of the vehicle 10, the vehicle ID for identifying the vehicle 10, and the driver ID for identifying the driver.

[0034] The required power consumption of the vehicle 10 varies depending on the vehicle weight and load of the vehicle 10, even on the same route, and the electricity cost also fluctuates. Therefore, by accumulating and utilizing the relationship between the vehicle weight / load and the required power consumption, the predicted power consumptions W1 and W2 can be estimated more accurately. Also, it is preferable to memorize the vehicle weight and load of the vehicle 10 in conjunction with a truck scale. Furthermore, the vehicle weight and load may be values obtained by arithmetic summation from vehicle type data, the sum of the weights of the loaded goods, the driver's weight, etc. The power consumption calculation unit 112 calculates the predicted power consumption W1 for the vehicle 10 to travel in the future by referring to this power consumption database 142.

[0035] Furthermore, the power consumption calculation unit 112 acquires the deterioration state of the secondary battery 11 based on the degree of deterioration SOH detected by the in-vehicle BMS or charger, and corrects the predicted power consumption W1 based on the current deterioration state of the secondary battery 11 and the assumed temperature Tass, and outputs the correction result as the predicted power consumption W2. Here, the assumed temperature Tass may be, for example, the temperature acquired from a temperature sensor (not shown) attached to the secondary battery 11, or the air temperature in the delivery time zone forecasted by weather forecast or the like.

[0036] Also, it is preferable that the travel start state determination unit 114 determines the charge completion condition, that is, the travel start condition (SOCs, Vs) from the usage conditions by an artificial intelligence that has performed learning processing by supervised learning with respect to the battery deterioration database 144.

[0037] Here, the travel start state determination unit 114 may store the relationship between the deterioration condition of the secondary battery 11 and the optimal travel start condition (SOCs, Vs) as data in the form of a map (table), and determine the travel start condition (SOCs, Vs) by referring to this map-form data. Also, the relationship between the usage conditions of the secondary battery 11 and the optimal travel start condition (SOCs, Vs) may be stored in the form of a map. Furthermore, the relationship between the battery usage conditions, the power consumption, and the optimal travel start condition (SOCs, Vs) may be stored in the form of a map.

[0038] The charge control unit 124 determines a charging request (output, time, and other information) based on the above-described traveling start conditions (SOCs, Vs) and the duty ratio Dtr, and controls the charging device 200 according to the determined charging request. The function of the traveling start state determination unit 114 is the same as that in the first embodiment. However, the traveling start state determination unit 114 in this embodiment receives an assumed temperature Tass, which is an assumed value of the ambient temperature of the secondary battery 11 targeted.

[0039] Then, the traveling start state determination unit 114 sets SOCs or Vs such that the traveling start conditions (SOCs, Vs) for the same predicted power amount W2 become lower as the assumed temperature Tass becomes higher. Here, the traveling start state determination unit 114 may store the relationship between the deterioration conditions of the secondary battery 11 and the traveling start conditions (SOCs, Vs) as a two-dimensional map (table). Also, the traveling start state determination unit 114 may implement the relationship between the usage conditions of the secondary battery 11 and the optimal traveling start voltage Vs as a map. Furthermore, the traveling start state determination unit 114 may map and implement the relationship between the usage conditions of the secondary battery 11, the predicted power amount W2, and the optimal traveling start voltage Vs.

[0040] Here, the operation of the traveling start state determination unit 114 will be described in more detail. Referring to FIG. 4, an example of the operation of determining the usage SOC region by referring to the current SOC of the secondary battery 11 will be described. FIG. 4 is a diagram showing the relationship between the SOC and the voltage of the secondary battery 11. The horizontal axis in FIG. 4 is the SOC, with the left end being 100% and the right end being 0%. The open-circuit voltage OCV and the closed-circuit voltage CCV when a predetermined current flows through the secondary battery 11 decrease as the SOC decreases, as shown in the figure. The hatched regions EA1 and EA2 are examples of voltage regions (or SOC regions) where the above-described predicted power amount W2 can be ensured, and these become candidates for the usage voltage region or the usage SOC region.

[0041] In addition to those shown in the figures, various voltage regions or SOC regions that can be candidates for the operating voltage region or the operating SOC region can be considered. The SOC of the region EA1 on the low-voltage side is in the range from the lower limit value SOC1L to the upper limit value SOC1H, and the difference between the two (SOC1H - SOC1L) is called the SOC width ΔSOC1. Similarly, the SOC of the region EA2 on the high-voltage side is in the range from the lower limit value SOC2L to the upper limit value SOC2H, and the difference between the two (SOC2H - SOC2L) is called the SOC width ΔSOC2.

[0042] The upper limit values SOC1H and SOC2H shown in FIG. 4 are candidate values for the starting charging rate SOCs described above. By the way, as shown in the graphs G6 and G8 of FIG. 1 above, the deterioration rate of the lithium-ion battery tends to increase as the storage temperature increases and as the SOC during storage increases. Therefore, based on these deterioration characteristics, the running start state determination unit 114 takes into account the temperature during operation and sets a use SOC region in which the secondary battery 11 is less likely to deteriorate, and outputs the upper limit value (for example, SOC1H, SOC2H) of the use SOC region as the running start charging rate SOCs. Alternatively, instead of this, the running start state determination unit 114 may output the open circuit voltage OCV corresponding to the upper limit value of the use SOC region as the running start voltage Vs.

[0043] Thereby, it is possible to ensure the necessary predicted power amount W2 and operate the secondary battery 11 while suppressing deterioration. Furthermore, the charging time can be shortened compared to the case where the secondary battery 11 is always fully charged, and the vehicle 10 can be operated efficiently. For example, in summer, the temperature is high and the secondary battery 11 is likely to become high temperature due to radiant heat and waste heat, and the deterioration of the secondary battery 11 tends to progress. Therefore, in summer, it is preferable to set the use SOC region to the low SOC side (for example, the region EA1 in FIG. 4) to shorten the charging time. In this case, the upper limit value SOC1H is selected as the running start charging rate SOCs.

[0044] Conversely, in winter when the temperature is low, as shown in graph G2 of FIG. 1, since the resistance ratio DCR increases, the closed-circuit voltage CCV during discharge tends to be low with respect to the open-circuit voltage OCV. As a result, the battery capacity that can be actually used becomes smaller compared to the actual SOC (generally highly dependent on temperature characteristics and rate characteristics). On the other hand, as shown in graph G6, at low temperatures of 20°C or lower, the rate of deterioration progression slows down. Therefore, in winter, it is preferable to prioritize ensuring the amount of electric power and set the used SOC region to the high-SOC side (for example, region EA2 in FIG. 4). In this case, as the starting charging rate SOCs, the upper limit value SOC2H is selected.

[0045] The deterioration state of a secondary battery, particularly a lithium-ion battery, varies in the rate of deterioration progression depending on the range of SOC used, the operating temperature, the energizing current, the operating voltage range, etc. Therefore, the driving start state determination unit 114 selects an optimal used SOC region from relationships such as those stored in a database in the form of equations or maps.

[0046] [Third Embodiment] Next, a preferred third embodiment will be described. In the following description, parts corresponding to those of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. FIG. 5 is a diagram showing the structure of time-series data D10 in the third embodiment. The configuration of the third embodiment is the same as that of the second embodiment (FIG. 3), but the time-series data D10 output by the data processing unit 12 of each vehicle 10 is configured as shown in FIG. 5.

[0047] Each row in FIG. 5 is a record of the time-series data D10, and the database update unit 118 (see FIG. 3) creates one record, for example, at every predetermined sampling period during the driving of the vehicle 10. Each record includes a date D11, a time D12, a vehicle longitude D13, a vehicle latitude D14, a battery temperature D15, a battery voltage D16, a battery current D17, and an electric energy amount D18.

[0048] The date D11 and time D12 are the date and time of the sampling timing. Also, the vehicle longitude D13 and vehicle latitude D14 are the longitude and latitude of the vehicle 10 at the sampling timing. The battery temperature D15, battery voltage D16, and battery current D17 are the temperature, output voltage, and output current of the secondary battery 11 at the sampling timing. Also, the power amount D18 is the amount of power output from the secondary battery 11 after the secondary battery 11 was last charged until the sampling timing.

[0049] The data processing unit 12 of each vehicle 10 wirelessly transmits the time-series data D10 to the analysis unit 120 in real time for each sampling timing. Thereby, the analysis unit 120 generates in real time the data to be stored in the power amount database 142 and the battery degradation database 144. Then, the database update unit 118 updates the power amount database 142 and the battery degradation database 144 in real time based on the data generated by the analysis unit 120.

[0050] However, the data processing unit 12 of each vehicle 10 does not necessarily need to transmit the time-series data D10 to the analysis unit 120 in real time. For example, the data processing unit 12 may start accumulating the time-series data D10 after the charging of the secondary battery 11 is completed, and then supply the accumulated time-series data D10 to the analysis unit 120 when the vehicle 10 is connected to the charging device 200 again for charging. In this case, the database update unit 118 will update the power amount database 142 and the battery degradation database 144 during the period when the vehicle 10 is connected to the charging device 200.

[0051] Further, based on the time-series data D10, the analysis unit 120 accumulates time for each class width of SOC in relation to time. Then, it detects the time during which the SOC stays within a predetermined range where the SOC is substantially constant for a predetermined time or more in the time-series data. The time detected here is a time when the SOC does not fluctuate, that is, the battery usage is low, for example, the "parking period". Even during the parking period, the vehicle 10 supplies power to auxiliary equipment and the like, so it can be considered that the SOC fluctuates slightly but is substantially constant. The SOC during this parking time is called the parked interval staying SOC. The analysis unit 120 also extracts this parked interval staying SOC at this time together with the staying time. For example, in the section Tr10 from time t14 to t20 in FIG. 5, the vehicle longitude D13 and the vehicle latitude D14 have constant values (L13 and Lo13). Therefore, the analysis unit 120 identifies the period Tr10 as a parking section.

[0052] It is more preferable that the driving start state determination unit 114 has a function of receiving the assumed temperature Tass of the secondary battery 11, and a function of determining the driving start conditions (SOCs, Vs) so that the parked interval staying SOC of the secondary battery 11 becomes lower when the received assumed temperature Tass is higher than the temperature of the threshold value of the allowable degradation behavior.

[0053] In addition, the analysis unit 120 calculates the relationship between the standard power consumption and temperature during driving based on the pattern obtained by analyzing the driving period and the staying period of the vehicle 10. Further, the analysis unit 120 divides the route information such as the delivery route into a plurality of partial routes, and analyzes the relationships such as the average value of the battery current D17, the variation range of the SOC (ΔSOC), and the central SOC (the median value of the SOC in the partial route) in each partial route.

[0054] Then, the analysis unit 120 stores the analysis result in the power consumption database 142 via the database update unit 118 as the "detailed route driving database". Then, the driving start state determination unit 114 predicts the deterioration state of the secondary battery 11 in the divided partial route based on the relationship between this detailed route driving database and the battery voltage D16. Thereby, the driving start state determination unit 114 can determine the SOC region for use in which deterioration is suppressed based on the predicted value of the deterioration state.

[0055] Also, the analysis unit 120 extracts the necessary data from the power consumption database 142 and updates the power consumption database 142 via the database update unit 118 based on the extracted data. Also, it is preferable to link the route information with the time-priority and shortest-distance route recommended by a navigation system (not shown). Further, it is preferable that the navigation system can propose a one-stroke route that reflects traffic regulations such as one-way traffic and considers an efficient delivery order for each time zone, and it is preferable to link the route information with this one-stroke route. Thereby, the typical predicted power consumption Wrec during delivery can be calculated.

[0056] As the history of route delivery accumulates, the power consumption database 142 is enriched, and thereby the calculation accuracy of the predicted power consumption W2 in a typical route is further improved. At the time of this history accumulation, the analysis unit 120 calculates the degree of deterioration SOHQ (deterioration state) regarding the capacity and the degree of deterioration SOHR (deterioration state) regarding the resistance, and records these calculation results in the battery deterioration database 144. However, the degrees of deterioration SOHQ and SOHR may be calculated not only by the BMS or the secondary battery management device 100A inside the vehicle but also by the charging device 200. For example, the degrees of deterioration SOHQ and SOHR may be detected based on the voltage, current, temperature, etc. of the secondary battery 11 when the charging device 200 charges the secondary battery 11.

[0057] The battery degradation database 144 stores the usage conditions of the secondary battery 11, the internal resistance of the secondary battery 11, and correlation data that can estimate changes in capacity. Also, as described above, the usage conditions of the secondary battery 11 include at least a plurality of temperature, current, usage voltage range, usage SOC region, usage electricity amount, and rest ratio. Here, the degradation calculation unit may adopt any of time series, daily unit, cycle unit, and number of times unit.

[0058] Furthermore, by using the analysis log of the SOC·temperature residence time of the secondary battery 11 in the vehicle 10, for the operating region of the secondary battery 11, the degradation degree can be calculated by multiplying the degradation coefficient by the weighting coefficient γ, and degradation can be considered in a simple manner. The degradation estimation in this embodiment can be applied not only to the above-described method but also to those using correlation data between the usage range of the SOC of the secondary battery 11, temperature, and degradation.

[0059] Also, the travel start state determination unit 114 may determine the travel start charge rate SOCs of the secondary battery 11 as follows based on the predicted power amount W2 and the current SOC. First, in the discharge curve reflecting the current degradation of the secondary battery 11, an upper limit SOC value at which the predicted power amount W2 can be secured is calculated from the SOC of the output lower limit value (for example, 10%) toward the higher SOC side. This SOC is called SOCreq.

[0060] Next, the travel start state determination unit 114 calculates the lower limit SOC when the predicted power amount W2 is output from the current SOC, and when the calculated SOC is higher than the SOC of the output lower limit value described above, the current SOC pre may be set as the travel start charge rate SOCs. On the other hand, when the SOC calculated from the current SOC is equal to or lower than the SOC of the output lower limit value, SOCreq may be set as the travel start charge rate SOCs. pre

[0061] [Fourth Embodiment] FIG. 6 is a block diagram of a secondary battery management system S4 according to a preferred fourth embodiment. In the following description, parts corresponding to those of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. The secondary battery management system S4 includes a charging device 200, a secondary battery management device 100D (computer), a power amount database 142, a battery degradation database 144, and an operation schedule planning unit 146.

[0062] Here, the operation schedule planning unit 146 plans the operation schedule of each vehicle 10 according to the weight, dimensions, delivery destination location, etc. of the goods to be delivered by the plurality of vehicles 10. Further, the secondary battery management device 100D has the same configuration as the secondary battery management device 100B (see FIG. 3) of the second embodiment, and further includes a charging schedule planning unit 122. The charging schedule planning unit 122 plans the charging schedule of each vehicle 10, that is, the charging start time, charging end time, etc., based on the operation schedule output by the operation schedule planning unit 146.

[0063] The secondary battery management device 100D in the present embodiment may have a duty ratio determination unit 116. The duty ratio determination unit 116 determines a duty ratio Dtr, which is the ratio of the energized state during charging until completion. Here, the energized state is a state in which current is supplied to the secondary battery 11. Also, a state in which no current is supplied to the secondary battery 11 or a current smaller than the threshold current is supplied is defined as a rest state.

[0064] Then, the duty ratio determination unit 116 determines the degradation state of the secondary battery 11 based on the battery degradation database 144, and has a function of decreasing the duty ratio Dtr as the degradation of the secondary battery 11 progresses. The charge control unit 124 controls the completion time of the charging device 200 based on the above-described travel start conditions (SOCs, Vs) and the duty ratio Dtr. By controlling the rest time after charging according to the degree of battery degradation, it becomes possible to further suppress battery degradation. Note that the secondary battery management device 100D may be configured without the duty ratio determination unit 116.

[0065] This embodiment is a preferred embodiment applicable when an electric vehicle is adopted as the vehicle 10, particularly in a commercial last-mile delivery system. The operation schedule planning unit 146 sets the optimal standard route RS(t) for all-house deliveries by time zone in the delivery area. Here, t is the delivery start time or the delivery time zone.

[0066] When the charging device 200 is arranged only at the business office, the power amount calculation unit 112 cooperates with the delivery or logistics system, and the operation schedule planning unit 146 sorts the delivery goods for each time zone and calculates the predicted power amount W1 required for each route in the delivery area based on the power amount database 142. Here, since the predicted power amount W1 is also a function of the delivery start time t, it may be hereinafter referred to as "predicted power amount W1(t)".

[0067] Furthermore, the power amount calculation unit 112 corrects the predicted power amount W1(t) based on conditions such as the load in the vehicle 10, the current degradation degree of the secondary battery 11, the temperature, the assumed temperature Tass, and the weather, and determines the predicted power amount W2(t). The travel start state determination unit 114 determines the used SOC region (for example, regions EA1 and EA2 in FIG. 4) and the travel start conditions (SOCs, Vs) based on the determined predicted power amount W2(t) and the assumed temperature Tass.

[0068] At this time, the travel start state determination unit 114 refers to the battery degradation database 144 and determines the upper SOC value so that the predicted power amount W2 can be ensured and the minimum SOC during use does not fall below the SOC lower limit value (for example, 10%). Then, the travel start state determination unit 114 outputs the travel start conditions (SOCs, Vs) based on the calculation result. At this time, if the voltage of the current secondary battery 11 is higher than the travel start voltage Vs, the delivery is started without charging. On the other hand, if the voltage of the current secondary battery 11 is lower than the travel start voltage Vs, it is advisable to charge the secondary battery 11 with the charging device 200. By doing so, the overall charging time can be saved and shortened.

[0069] The amount of power required to deliver a large amount of packages is different between the case of delivering with a single fully charged vehicle 10 and the case of delivering with a plurality of vehicles 10 while dividing time zones. Generally, when there is a large amount of packages, delivering with a plurality of vehicles 10 while dividing time zones can shorten the charging time of the vehicles 10 and also suppress the deterioration of the secondary battery 11. Therefore, the operation schedule planning unit 146 uses a data selection system using machine learning to disperse the packages without arrival time specification along the delivery route.

[0070] Then, the charging schedule planning unit 122 controls the charging schedule so that the secondary battery 11 is charged during the time zone when each vehicle is parked at the business office (near the charging device 200). Thereby, in the secondary battery management system S4, it is possible to efficiently deliver packages without reducing customer satisfaction. Furthermore, according to the secondary battery management system S4, it is possible to suppress the battery deterioration of the vehicle 10 which is an electric vehicle and reduce the excess of the battery mounting amount.

[0071] FIG. 7 is a flowchart of a travel start charge rate calculation program executed by the secondary battery management device 100D of the fourth embodiment. In step S11 of FIG. 7, the power amount calculation unit 112 receives the planned route information Rt of the planned travel route, the vehicle weight of the vehicle 10, the load of the vehicle 10, and the assumed temperature Tass at the time of delivery. Then, the power amount calculation unit 112 accesses the power amount database 142 based on the received information and acquires the predicted power amount W1. As described above, the power amount database 142 stores the history information including the travel date and time, the outside air temperature during travel, etc., in association with the standard required power amount.

[0072] Next, in step S12, the power amount calculation unit 112 acquires the assumed temperature Tass, and at the same time, acquires the current deterioration information (deterioration degrees SOHQ, SOHR, etc.) of the secondary battery 11. Then, based on the acquired information and the battery deterioration database 144, the predicted power amount W2 is calculated by correcting the predicted power amount W1 as necessary.

[0073] The predicted power consumption W2 can be obtained, for example, based on the correction formula "W2 = α × W1 + βd". Here, α is a correction coefficient, and βd is an auxiliary power margin or the like. Alternatively, for example, based on the correction formula "W2 = α × W1 × βdd", the predicted power consumption W2 may be calculated by correcting the predicted power consumption W1 by a multiple. Here, βdd is an auxiliary power margin or the like based on a multiple. Further, the predicted power consumption W2 may be calculated by increasing or decreasing a fixed value with respect to the predicted power consumption W1.

[0074] Next, in step S13, the travel start state determination unit 114 determines whether it is possible to output the predicted power consumption W2 at the current SOC of the secondary battery 11. That is, it is determined whether the predicted power consumption W2 can be obtained in the SOC region from the current SOC to the SOC lower limit value (for example, 10%) when the current SOC is set as the maximum SOC. If it is determined that the predicted power consumption W2 can be output, it is determined as "Yes", and the process proceeds to step S18. In step S18, the current SOC is output as the travel start charge rate SOCs. On the other hand, if the predicted power consumption W2 cannot be output at the current SOC of the secondary battery 11, it is determined as "No" in step S13, and the process proceeds to step S14.

[0075] In step S14, the travel start state determination unit 114 calculates the SOC region, that is, the SOC region EA01 (not shown), in which the predicted power consumption W2 can be obtained with the SOC lower limit value as the minimum value. Next, when the process proceeds to step S15, the travel start state determination unit 114 calculates the SOC region, that is, the SOC region EA02 (not shown), in which the predicted power consumption W2 can be obtained with SOC50%, which is a region with less deterioration, as the center.

[0076] Next, when the process proceeds to step S16, the travel start state determination unit 114 calculates a SOC region, that is, a SOC region EA03 (not shown), in which the predicted power consumption W2 can be obtained with the upper limit value of the SOC (for example, 100%) as the maximum value. Next, when the process proceeds to step S17, the travel start state determination unit 114 refers to the battery degradation database 144 and selects, as the used SOC region, the one among the SOC regions EA01, EA02, and EA03 in which the degradation of the secondary battery 11 is predicted to be the smallest. Next, when the process proceeds to step S19, the travel start state determination unit 114 outputs the maximum value of the used SOC region as the travel start charging rate SOCs.

[0077] In the above-described example, the travel start conditions (SOCs, Vs) are determined by calculating three SOC regions EA01, EA02, and EA03 and determining the used SOC region from among them. However, the method for determining the travel start conditions (SOCs, Vs) is not limited to this. For example, a plurality of SOCs that are candidates for the travel start charging rate SOCs may be determined in advance, and for each of them, a SOC region in which the predicted power consumption W2 can be secured may be calculated, and the used SOC region may be determined by referring to the battery degradation database 144 for each of the calculated SOC regions. If there are any SOC regions among the calculated plurality of SOC regions that have the same degree of degradation, it is preferable to select the SOC region on the higher SOC side where the travelable section is the longest.

[0078] [Fifth Embodiment] FIG. 8 is a block diagram of a charging navigation system S5 according to a preferable fifth embodiment. In the following description, parts corresponding to the respective parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. The charging navigation system S5 includes a secondary battery management device 100E (computer) and a vehicle 10. The charging navigation system S5 is also connected to a weather forecast system 131, a delivery navigation system 132, and a truck scale system 133.

[0079] Here, the weather forecasting system 131 distributes weather forecasts such as the current temperature, weather, and future temperature and weather. Also, the delivery navigation system 132 determines the luggage to be loaded on each vehicle 10 and the transport route based on the destinations, weights, dimensions, etc. of the multiple pieces of luggage to be transported. Further, the truck scale system 133 stores and manages the vehicle ID of each vehicle 10, the vehicle weight, and the load weight.

[0080] The vehicle 10 includes a secondary battery 11 as a power source, a data processing unit 12, a vehicle BMS (Battery Management System) 16, and an operation history recording unit 18. The vehicle BMS 16 detects the state of the secondary battery 11. The operation history recording unit 18 records the operation history of the vehicle 10. The operation history recording unit 18 may be arranged inside the vehicle BMS 16.

[0081] FIG. 9 is a block diagram of the battery deterioration detection unit of the vehicle BMS 16 in the fifth embodiment. The vehicle BMS 16 includes an SOH calculation unit 16a. The SOH calculation unit 16a calculates the degree of deterioration SOH based on the voltage, current, and temperature of the secondary battery 11. Note that the degree of deterioration SOH is a general term for the degrees of deterioration SOHQ, SOHR, etc. described above.

[0082] Returning to FIG. 8, the hardware configuration of the secondary battery management device 100E is the same as that of the secondary battery management device 100A in the first embodiment. The secondary battery management device 100E includes a battery operation area determination selection unit 103, a delivery scheduled vehicle condition input unit 104, a predicted power amount calculation unit 105 (power amount calculation unit), and a power amount database 142. Also, the battery operation area determination selection unit 103 includes a predicted power amount correction unit 106 (power amount calculation unit), a travel start state determination unit 114, a data analysis unit 109, and a battery deterioration database 144.

[0083] The delivery-scheduled vehicle condition input unit 104 in the secondary battery management device 100E acquires the battery degradation degrees SOHQ and SOHR, the cell temperature Tcell, and the current vehicle SOC from the vehicle BMS 16 via the data processing unit 12. Further, the delivery-scheduled vehicle condition input unit 104 acquires the environmental temperature Ta such as the air temperature and weather information from the weather forecast system 131, and acquires the planned route information Rt of the planned route for delivery and distribution from the delivery navigation system 132 used by a logistics company or the like. Furthermore, the delivery-scheduled vehicle condition input unit 104 acquires the vehicle ID, vehicle weight, and load in conjunction from the truck scale system 133 owned by the logistics company.

[0084] The predicted power amount calculation unit 105 extracts necessary reference conditions to refer to the power amount database 142 and calculates the predicted power amount W1. When there is a difference between the current driving conditions and the reference conditions with respect to the predicted power amount W1, the battery operation area determination selection unit 103 corrects the predicted power amount W1 in the predicted power amount correction unit 106 to obtain the predicted power amount W2. As described above, the predicted power amount W2 may be calculated based on, for example, "W2 = α × W1 + βd" or "W2 = α × W1 × βdd", or the predicted power amount W2 may be calculated by increasing or decreasing a fixed value with respect to the predicted power amount W1. Hereinafter, with reference to FIG. 10, an example of obtaining the predicted power amount W2 by correcting the predicted power amount W1 by a multiple will be described.

[0085] FIG. 10 is a schematic diagram of the power amount correction data D30 included in the power amount database 142 of the fifth embodiment. Each row in FIG. 10 is a record of the power amount correction data D30, and each record includes a route D31, a power amount D32, a winter coefficient D33, a summer coefficient D34, and load coefficients D35 and D36 for 50% and 80% of the load. The power amount D32 is the power amount for traveling each route D31 when the non-degraded secondary battery 11 at 20°C is applied and the load is 100%.

[0086] The winter coefficient D33 is the coefficient to be multiplied by the power consumption D32 in winter, and the summer coefficient D34 is the coefficient to be multiplied by the power consumption D32 in summer. Also, the load coefficients D35 and D36 are the coefficients to be multiplied by the power consumption D32 when the loads are 50% and 80% respectively. When the vehicle 10 passes through Route A and Route B, the predicted power consumption W1 can be obtained as the sum of the power consumptions D32 of both, that is, "W1 = Wa1 + Wa2". When the driving schedule condition is in summer, the amount of load is 50%, and the non-deteriorated secondary battery 11 is applied, for the predicted power consumption W1, the respective summer coefficient D34 and load coefficient D35 are applied, and the predicted power consumption W2 can be obtained as "W2 = Wa1 × α1 × β1 + Wa2 × α1 × β2".

[0087] Also, in FIG. 8, the predicted power consumption correction unit 106 also refers to the battery deterioration database 144 to determine the predicted power consumption W2. The driving start state determination unit 114 calculates the SOC region for using the battery using the deterioration information of the secondary battery 11, the battery temperature, and the current SOC in the same manner as in the first to fourth embodiments. That is, the driving start state determination unit 114 calculates the SOC region for use so that the predicted power consumption W2 can be ensured, the charging time is short, and the deterioration of the secondary battery 11 during use is small.

[0088] Then, the driving start state determination unit 114 notifies the vehicle 10 and the charging device 200 (see FIG. 3) of the maximum value of the SOC region for use as the driving start charging rate SOCs. The charging device 200 does not charge when the current SOC value exceeds the driving start charging rate SOCs, and charges up to the driving start charging rate SOCs when the current SOC is less than the driving start charging rate SOCs.

[0089] The operation history recording unit 18 of the vehicle 10 acquires and accumulates the driving date and time, driving time, position information, battery voltage, battery current, battery temperature, power consumption, etc. in time series, and communicates the accumulation result to the secondary battery management device 100E via the data processing unit 12 by wireless or wired communication. Then, the battery operation area determination selection unit 103 accumulates the communicated data. The data analysis unit 109 creates data for the power amount database 142 and adds it to the power amount database 142. Further, the data analysis unit 109 analyzes and extracts items to be accumulated in the battery degradation database 144, such as the degradation degrees SOHQ and SOHR and driving conditions, and adds the results to the battery degradation database 144.

[0090] As a result, for each driving of the vehicle 10, various data are further accumulated, and the predicted power amount W2 can be selected more appropriately. As described above, it is possible to drive by charging only the power amount corresponding to the required predicted power amount W2 to the secondary battery 11, and by using the degradation suppression area, it is possible to perform efficient delivery and distribution while suppressing the degradation of the secondary battery 11. In addition, efficient battery utilization becomes possible, the margin can be made smaller than the conventional mounted battery amount, and the vehicle can have an appropriate battery amount, thereby improving the cost performance.

[0091] [Sixth Embodiment] Next, a preferred sixth embodiment will be described. In the following description, parts corresponding to those of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. Further, since the configuration of the sixth embodiment is the same as that of the fifth embodiment (see FIG. 8), the illustration is omitted.

[0092] Generally, when a battery continuously charges and discharges at a large current, its degradation tends to accelerate. To suppress this, in this embodiment, the relationship between the degradation of the secondary battery 11 and the rest time ratio of the battery is obtained in advance. And in the battery degradation database 144, information about the rest time Tr (not shown) during which the secondary battery 11 is not energized and the energization time Tc (not shown) during which the secondary battery 11 is energized is accumulated. Using this accumulated information, it is preferable to arrange the route so that the location with a long rest time Tr is on the low SOC side in the latter half of the route. Thereby, the degradation caused by the secondary battery 11 being held at a high SOC can be suppressed.

[0093] Also, generally, when the secondary battery 11 is rapidly charged, since the current is large, the amount of heat generation increases and the internal temperature rises, and thus the degradation tends to progress. Therefore, it is preferable to provide a certain rest time before delivery without using the secondary battery 11 after rapid charging. The energization ratio Rduty of the secondary battery 11 can be obtained by "Rduty = tc / (tc + tr)". It is advisable to create a map of the current value and the threshold value Rdth of the energization ratio that promotes degradation so that this energization ratio Rduty is below the threshold value Rdth that promotes degradation. Then, referring to this map, after setting the starting charging rate SOCs, the recommended delivery start time is displayed on a display (not shown), and scheduling is performed so that delivery starts after the end of the rest time after charging. Also, if the charging time until the end of the charging command to the charger is set as the total time after reaching the full charge voltage and passing through the rest time, and the rest time is included in the full charge time, delivery can start simultaneously with the completion of charging. By applying this embodiment, further degradation of the battery can be suppressed, and efficient delivery and distribution become possible.

[0094] As described above, the travel start state determination unit 114 may store the relationship between the deterioration conditions of the secondary battery 11 and the optimal travel start conditions (SOCs, Vs) as data in the form of a map (table), and determine the travel start conditions (SOCs, Vs) by referring to this map-form data. Also, the relationship between the usage conditions of the secondary battery 11 and the optimal travel start conditions (SOCs, Vs) may be stored in map form. Further, the relationship between the battery usage conditions, the amount of electric power, and the optimal travel start conditions (SOCs, Vs) may be stored in map form. By implementing the relationships of various parameters in a mapped manner in this way, it becomes possible to reduce the amount of data at the time of implementation and reduce the amount of calculation, enabling high-speed calculation with a margin in the system storage. That is, the travel start charge rate SOCs can be calculated faster, and it becomes possible to construct a system with an inexpensive device.

[0095] Furthermore, the battery operation area determination selection unit 103 may update the power amount database 142 in real time. That is, by communication from the vehicle 10, it is advisable to record the time-series latitude, longitude, SOC of the secondary battery 11, battery voltage, battery current, power amount, battery temperature, and environmental temperature together with the driver ID or vehicle ID. Then, referring to the load weight of the vehicle 10 and the delivery completion record, it is advisable to construct a weight change correspondence database that describes the relationship between the weight change and the required power amount in the power amount database 142. Learning data can be extracted from the created weight change correspondence database, and the extracted data can be used for supervised learning to output the predicted power amount W2. Alternatively, the predicted power amount W2 can be output by selecting the condition that the Euclidean distance or Mahalanobis distance of the specified item is small from the weight change correspondence database.

[0096] FIG. 11 is a schematic diagram of the travel performance data D40 included in the power amount database 142 of the present embodiment. Each row in FIG. 11 is a record of the travel performance data D40, and each record includes a record number D42, a route condition parameter section D44, a deterioration information section D46, and a required power amount section D48.

[0097] The record number D42 is a numerical value that identifies the record and is a uniquely determined numerical value within the range of "1" to "N". Also, the root condition parameter section D44 has m characteristic parameters k1 to km. Here, the characteristic parameters k1 to km include the driving date and time, vehicle ID, driver ID, temperature, delivery route, load, vehicle weight, and location information (area) in the record, and may further include battery temperature, weather, and delivery time zone information. The deterioration information section D46 includes the deterioration degrees SOHQ and SOHR at the time of acquisition of the record. Also, the required power amount section D48 is the consumed power amounts Wc1 to Wcn consumed by the vehicle 10 during the driving related to each record. These consumed power amounts Wc1 to Wcn become the target variables when performing correlation analysis and learning processing.

[0098] In the present embodiment, the secondary battery management device 100E calculates the predicted power amount W2 on the planned driving route based on this driving performance data D40. First, the delivery planned vehicle condition input section 104 receives the planned route information Rt, temperature, weather, vehicle ID, driver ID, load, vehicle weight, location information of the vehicle 10, and battery deterioration degree of the secondary battery 11 for the delivery plan. The predicted power amount calculation section 105 divides each value by the average value of the driving performance data D40 for the items input to the delivery planned vehicle condition input section 104, normalizes them, and calculates the difference from the input conditions for each item. Further, the differences are multiplied by the weight coefficients for each item and added together, and the square root of the result is calculated. Specifically, the operation shown in the following formula (1) is executed.

[0099]

Equation

[0100] In Equation (1), z1 is the value obtained by normalizing the value of the input item, and z is the value obtained by normalizing the value of the corresponding item in the driving performance data D40. Also, d is the least squares error. The predicted power amount calculation unit 105 searches for driving performance information under the condition that this least squares error d is closest to 0, that is, the Euclidean distance is close, and calculates the power in the searched driving performance information as the predicted power amount W1. Further, the predicted power amount calculation unit 105 may output the predicted power amount W1 when the least squares error d is less than a predetermined threshold value.

[0101] The predicted power amount correction unit 106 selects the information that is farthest away from the obtained predicted power amount W1 and the selected driving performance information. Then, the predicted power amount correction unit 106 selects a coefficient from the power amount correction data D30 shown in FIG. 10 for the items related to the difference, and corrects the predicted power amount W1 to calculate the predicted power amount W2. Also, the driving start state determination unit 114 selects an SOC region in which the degree of deterioration estimated from the parameters during battery use is small, and determines the driving start charge rate SOCs.

[0102] Furthermore, when the difference between the driving start charge rate SOCs and the current SOC value is larger than the power amount that can be charged with a normal charging current within the allowable threshold value Ctime_th (not shown) as the charging time, the battery operation region determination selection unit 103 commands the charging device 200 (see FIG. 3) to perform rapid charging, thereby shortening the charging time. As a result, the secondary battery management device 100E can formulate a charging schedule so as not to interfere with route driving, and thus can efficiently deliver and distribute goods.

[0103] [Seventh Embodiment] FIG. 12 is a block diagram of a secondary battery deterioration degree detection system S7 according to a preferred seventh embodiment. In the following description, parts corresponding to those of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. The secondary battery degradation degree detection system S7 includes a charging device 200 and a server unit 210. Also, the vehicle 10 targeted in this embodiment is equipped with a secondary battery 11 and a vehicle BMS 16, similar to that in the fifth embodiment (see FIG. 8). This secondary battery 11 is the object of degradation detection.

[0104] The charging device 200 is configured to be able to transmit information such as the state of charge (SOC), output current I, output voltage V, etc., obtained from the communication protocol in the charging cable during charging, to the server unit 210 by wireless or wired communication. The server unit 210 is a degradation diagnosis device for the secondary battery 11, and its hardware configuration is the same as that of the secondary battery management device 100A (see FIG. 2) in the first embodiment.

[0105] In FIG. 12, the internal of the server unit 210 shows the functions realized by an application program, etc., as blocks. That is, the server unit 210 includes a resistance-capacitance calculation unit 212 and a degradation detection unit 214. The resistance-capacitance calculation unit 212 and the degradation detection unit 214 cooperate with each other and can share information. Note that the server unit 210 may be outside the charging device 200, or a part or all of the server unit 210 may be built into the charging device 200.

[0106] When the vehicle 10 is connected to the charging device 200, the secondary battery 11 is charged up to the starting charge rate SOCs for driving. At this time, the charging device 200 acquires information such as the SOC, output current I, output voltage V, etc., of the secondary battery 11 from the vehicle 10. The charging device 200 transmits the acquired charging information to the resistance-capacitance calculation unit 212. The resistance-capacitance calculation unit 212 calculates the current capacity and resistance values of the secondary battery from the acquired information, and the degradation detection unit 214 calculates the degradation degrees SOHQ and SOHR by comparing with the initial values.

[0107] The degradation detection unit 214 can transmit information on the degree of degradation to the terminal 220. Also, information on a plurality of vehicles 10 can be stored in one server device 210. As a result, the actual measurement degradation database can be enriched. Then, by referring to the degradation information of the same vehicle type from the database, it becomes possible to more accurately predict the degradation trend of the battery.

[0108] FIG. 13 is a flowchart of a degradation detection routine executed by the server device 210. First, in step S31, the resistance-capacitance calculation unit 212 acquires a voltage, a current, and a charging time from the charging device 200. Here, the "charging time" is the elapsed time since the start of charging. Next, in step S32, the resistance-capacitance calculation unit 212 estimates the battery temperature with respect to the charging time based on a charging curve acquired in advance by learning.

[0109] Next, when the process proceeds to step S33, the resistance-capacitance calculation unit 212 obtains the relationship between SOC and voltage as shown in graphs G11 and G12 of FIG. 14 (details will be described later) based on the acquired voltage value, charging time, current value, and charging rate SOC. Next, when the process proceeds to step S34, the resistance-capacitance calculation unit 212 searches for the relationship between the SOC and the open circuit voltage OCV of this secondary battery 11 based on the database stored in the resistance-capacitance calculation unit 212.

[0110] Next, when the process proceeds to step S35, the resistance-capacitance calculation unit 212 calculates a provisional resistance value R0 by dividing the difference between the charging voltage V and the open circuit voltage OCV at the same SOC by the charging current I as shown in graph G12 of FIG. 14. Here, the provisional resistance value R0 is a provisional value of the resistance value R that is the internal resistance of the secondary battery 11.

[0111] Here, the resistance-capacitance calculation unit 212 stores a database of the correlation relationship among the SOC, the battery temperature, and the resistance value R. Next, when the process proceeds to step S36, the resistance-capacitance calculation unit 212 corrects the provisional resistance value R0 based on this correlation database and the temperature coefficient based on the battery temperature estimated in step S32, and calculates a provisional resistance value R1.

[0112] Next, when the process proceeds to step S37, the resistance-capacitance calculation unit 212 calculates a provisional capacitance value Q0 based on the charging current I and the charging time. Here, the provisional capacitance value Q0 is a provisional value of the current capacitance value Q of the secondary battery 11. Further, the resistance-capacitance calculation unit 212 corrects the provisional capacitance value Q0 and the provisional resistance value R1 to calculate the capacitance value Q and the resistance value R. Specifically, the capacitance value Q and the resistance value R are obtained by converting them into the acquired values under the reference temperature, current, and time conditions such as the measurement temperature in the catalog display specification.

[0113] Next, when the process proceeds to step S38, the degradation detection unit 214 obtains the ratio of the acquired capacitance value Q and resistance value R to the capacitance value Q and resistance value R of the secondary battery 11 when not in use, and thereby calculates the degradation degrees SOHQ and SOHR. That is, the server unit 210 outputs these degradation degrees SOHQ and SOHR as the degradation information of the secondary battery 11.

[0114] Here, the degradation information of the secondary battery 11 will be further described. The degradation state of the secondary battery 11 is SOHR calculated using the internal resistance of the battery (hereinafter also simply referred to as "resistance") and / or SOHQ calculated using the full charge capacity of the battery. SOHR is the degradation degree obtained based on the internal resistance of the battery, represents the increase rate of the internal resistance of the battery that increases with the degradation of the battery, and is defined by the following formula (2). SOHR = 100×R1(SOC,T) / R0(SOC,T) …(2)

[0115] In Equation (2), R1(SOC,T) represents the internal resistance [Ω] of the secondary battery 11 at present (after deterioration). R0(SOC,T) represents the internal resistance [Ω] of the secondary battery 11 when new. SOHQ calculated using the capacity is the current capacity divided by the initial capacity. Although SOHR in the above Equation (1) is expressed as a percentage, it is not necessarily limited to a percentage as long as it is the current ratio to the initial value. Here, an example of a method for calculating the degree of deterioration diagnosis due to deterioration will be shown using the graphs G11 and G12 in FIG. 14.

[0116] FIG. 14 is a diagram showing the voltage characteristics of the secondary battery 11. First, the graph G11 in FIG. 14 shows an example of the voltage - charging time characteristics during charging. In the graph G11, the horizontal axis is the elapsed time after the start of charging, and the vertical axis is the voltage of the secondary battery 11. The characteristic G110 in the figure is a voltage curve that can be regarded as the open - circuit voltage OCV or the charging voltage at a low rate and is approximately OCV. The characteristic G112 is the voltage change during rapid charging. Let the open - circuit voltage (OCV) at the start of rapid charging be OCV ini At the charging time until the elapsed time tend, the amount of electricity Qch is charged. In any characteristic, the open - circuit voltage OCV increases with the passage of charging time. However, according to the characteristic G112 of rapid charging, the amount of electricity Qch corresponding to full charge is supplied to the secondary battery 11 before a relatively short elapsed time tend elapses.

[0117] Also, the graph G12 in FIG. 14 shows the relationship between the SOC and the voltage of the secondary battery 11. In the graph G12, the characteristic G120 shows the relationship between the SOC and the open - circuit voltage OCV, and the characteristic G122 shows the relationship between the SOC and the charging voltage Vc. G122 is the voltage curve G112 of the graph G11 shown corresponding to the SOC. The open - circuit voltage OCV at the start of charging of G112 ini is converted from the relationship between the SOC and OCV to the SOC, and the corresponding SOC value is SOC ini is obtained. SOC iniThe amount of electricity Qch of the charged graph G11 is converted into SOC, and SOCend is obtained. Here, the charging voltage Vc is the voltage that appears in the secondary battery 11 when a predetermined charging current is supplied to the secondary battery 11. As shown in the figure, for the same SOC value, the charging voltage Vc is higher than the open circuit voltage OCV. The difference between the two is called the voltage difference ΔV(socx). The voltage difference ΔV(socx) is mainly the voltage drop in the internal resistance of the secondary battery 11. When the SOC of the secondary battery 11 is charged from SOCini to SOCend, the amount of electricity Qch to be charged is equal to the amount of electricity Qch shown in the graph G11.

[0118] The resistance-capacitance calculation unit 212 (see FIG. 13) can acquire the specifications of the secondary battery 11 applied to the vehicle 10 from the database inside the resistance-capacitance calculation unit 212 based on the information regarding the vehicle 10. Among those specifications, an OCV curve corresponding to the characteristic G120 is included. Therefore, the resistance-capacitance calculation unit 212 acquires SOCini, which is the SOC at the start of charging, based on the open circuit voltage OCV at the start of charging and the characteristic G120.

[0119] Also, the resistance-capacitance calculation unit 212 measures the charging voltage Vc and the open circuit voltage OCV at the same SOC, and calculates the voltage difference ΔV(socx) based on both. Further, the resistance-capacitance calculation unit 212 divides this voltage difference ΔV(socx) by the charging current I to obtain the resistance value R of the internal resistance of the secondary battery 11. In particular, immediately after the secondary battery 11 is energized, before the SOC fluctuates and the influence of the reaction resistance is small, it is preferable to obtain the resistance value R at that time. When the secondary battery 11 is a lithium-ion battery, it is known that the resistance value R calculated as described above depends on the SOC.

[0120] Therefore, the relationship between the resistance value R and the SOC can be expressed as a function or a table. For example, when the secondary battery 11 is charged from a certain SOCa (not shown) to another SOCb (not shown), the respective resistance values R may be applied to perform a correction calculation. When the voltage and current at the end of charge and discharge of the secondary battery 11 are Vlast and Ilast respectively, and the open circuit voltage of the battery detected after a predetermined time from the end of charge and discharge is OCV, the resistance value R in the resistance curve is obtained by the following formula (3). R = (|OCV - Vlast|) / Ilast …(3)

[0121] Also, when the charging current I is constant, if the closed circuit voltage of the battery is CCV, the resistance value R can also be obtained by the following formula (4). R = (OCV - CCV) / I …(4)

[0122] Also, the resistance value R can be obtained by other methods. For example, based on the current value x at a certain SOC and the closed circuit voltage CCV, a linear approximation formula "CCV = Hx + J" can be obtained. The resistance value R can be obtained based on the coefficient H in this approximation formula. Here, when the constant J is equal to the open circuit voltage OCV, the coefficient H can be considered to be equal to the resistance value R.

[0123] Also, the capacitance value Q can be obtained by the following formula (5) based on the difference between the SOCini which is the SOC at the start of charging and the SOCend which is the SOC at the end of charging, that is, the variation range of SOC, and the charge amount Qch. Q = Qch / (SOCend - SOCini)×100 …(5)

[0124] [Eighth Embodiment] FIG. 15 shows an example of a vehicle battery system according to a preferred eighth embodiment. In the following description, parts corresponding to those of the above-described other embodiments may be denoted by the same reference numerals, and the description thereof may be omitted. The vehicle battery system S8 is installed inside the vehicle 10 and includes a measurement value detection unit 22, a battery control unit 24, a load control unit 26, and a host control unit 28.

[0125] Further, the secondary battery 11 is configured by connecting a plurality of single batteries 11a, 11b, etc., each having a positive electrode and a negative electrode, in series. The secondary battery 11 is connected to a load device (not shown) and supplies power to the load device. The measurement value detection unit 22 detects various information regarding the state of the secondary battery 11, for example, data such as the total current, total voltage, ambient temperature, maximum temperature, average temperature, minimum temperature of the secondary battery 11, and the respective temperatures and voltages of the single batteries 11a, 11b. The data detected by the measurement value detection unit 22 is input to the battery control unit 24. Note that the measurement value detection unit 22 preferably also detects the respective types or models of the single batteries 11a, 11b, in other words, the individual characteristics of the single batteries 11a, 11b.

[0126] Based on the data input from the measurement value detection unit 22, the battery control unit 24 calculates the current state of charge (such as SOC) of the secondary battery 11, and executes processes such as detection of an abnormal state, calculation of the available input / output power, and generation of a temperature control command. Each signal output from the battery control unit 24 is supplied to the load control unit 26 and the host control unit 28. Based on the supplied signals, the host control unit 28 outputs a control command to the load control unit 26. This control command specifies, for example, the maximum power and maximum energy that the load device can consume. The load control unit 26 executes control of the load device based on the control command input from the host control unit 28 and the information input from the battery control unit 24.

[0127] [Embodiment 9] FIG. 16 is a block diagram of a secondary battery management system S9 according to a preferred Embodiment 9. In the following description, parts corresponding to those of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. The secondary battery management system S9 includes a detection unit 30 and a battery operation region determination selection unit 40.

[0128] The hardware configuration of the battery operation area determination selection unit 40 is the same as that of the secondary battery management device 100A (see FIG. 2) in the first embodiment. In FIG. 16, the functions realized by an application program or the like inside the battery operation area determination selection unit 40 are shown as blocks. That is, the battery operation area determination selection unit 40 includes a data storage unit 41, a data selection unit 42, a degradation calculation unit 43, an operation parameter calculation unit 45, a degradation prediction unit 46, and a travel start state determination unit 114.

[0129] The detection unit 30 detects data such as the voltage, current, and temperature of the secondary battery 11 (see FIG. 15) together with time information such as the date and time, and outputs the detection results to the battery operation area determination selection unit 40. At this time, the SOC information notified to and calculated by the charging device 200 (see FIG. 12) is also input to the battery operation area determination selection unit 40. The function of the travel start state determination unit 114 is the same as that in the fifth embodiment (see FIG. 8). The data selection unit 42 selects data (including data related to battery temperature and travel history) acquired by the detection unit 30 or the measurement value detection unit 22 (see FIG. 15) in the vehicle 10 (see FIG. 3) and data output from the detection unit 30 that meet preset conditions, and outputs them to the data storage unit 41.

[0130] Note that the battery temperature and travel history may be obtained via an OBD (On-board diagnostics) terminal or an arbitrary terminal (not shown) using the measurement values of the measurement value detection unit 22 shown in FIG. 15. The data storage unit 41 includes an SOC-OCV data storage unit 41a, an SOC-R data storage unit 41b, an operation parameter data storage unit 41c, and a database 44.

[0131] The data selection unit 42 acquires the open circuit voltage of the secondary battery 11 (see FIG. 15) for the SOC that meets the conditions at regular intervals or a certain number of times, and accumulates it in the SOC-OCV data storage unit 41a. Similarly, the data selection unit 42 acquires data on the resistance value of the secondary battery 11 for the SOC and accumulates it in the SOC-R data storage unit 41b. Further, the data selection unit 42 obtains, based on the data output from the measurement value detection unit 22 (see FIG. 15), data such as current, energization time, and SOC fluctuation for a short period, for example, several minutes, as usage history data corresponding to the usage time of the secondary battery 11, and accumulates it in the operation parameter data storage unit 41c.

[0132] The database 44 is provided with various data used for deterioration estimation. These are, for example, prediction formulas for estimating deterioration, initial values of parameters, amounts of change in parameters, and the like. The deterioration calculation unit 43 uses the data in the database 44, the SOC-OCV data storage unit 41a, and the SOC-R data storage unit 41b to calculate the capacity and resistance value, which are the internal states of the secondary battery 11, and calculates the deterioration state of the secondary battery 11. Here, the SOC-OCV data storage unit 41a and the SOC-R data storage unit 41b also store the initial data of the secondary battery 11 in advance.

[0133] The deterioration calculation unit 43 estimates the current deterioration state of the secondary battery 11 and outputs the result to the upper control unit 28 (see FIG. 15) and the deterioration prediction unit 46. The deterioration prediction unit 46 performs time-series deterioration prediction using the parameters calculated by the operation parameter calculation unit 45 and outputs the result as deterioration prediction data. The travel start state determination unit 114 determines the usage SOC range and outputs the travel start condition (SOCs, Vs).

[0134] Here, the operation of the operation parameter calculation unit 45 will be described in detail. The operation parameter calculation unit 45 calculates various operation parameters related to the operation conditions of the secondary battery 11 based on the data selected by the data selection unit 42 and the usage history data stored in the operation parameter data storage unit 41c in the data storage unit 41. The operation parameters of the secondary battery 11 are, for example, the amount of electric power, the rest time ratio, the moving distance, the operating center SOC during operation, the operating voltage range, the operating upper limit voltage, the operating lower limit voltage, the average current, the maximum and minimum currents, the battery temperature, the environmental temperature, the heat generation factor, the effective current, the average operating electric quantity, the charging capacity, the discharging capacity, the SOC fluctuation range, the energization polarity ratio per unit time, the rest time ratio per unit time, and so on.

[0135] Note that the operation parameters are not limited to these. The operation parameter calculation unit 45 can use parameters that can be derived from various operation conditions as the operation parameters of the secondary battery 11. Also, the data selection unit 42 selects the data within a predetermined range detected by the measurement value detection unit 22 as the data to be stored in the data storage unit 41 and used for calculating the parameters related to the use of the secondary battery 11.

[0136] In vehicles such as electric vehicles, the input and output of the battery are involved in acceleration and deceleration during driving. When decelerating, the battery accepts the regenerative current, and discharges during driving and when stopped. FIG. 17 is a diagram showing an example of the temporal changes in the battery voltage V, charge and discharge current I, and SOC of the secondary battery 11 detected by the vehicle. Graph G21 shows the battery voltage V of the secondary battery 11, graph G22 shows the charge and discharge current I, and graph G23 shows the change in SOC. The charge and discharge current I has a positive value for the charging current and a negative value for the discharging current. Hereinafter, an example of data selection executed by the data selection unit 42 will be described with reference to FIG. 17.

[0137] In the vehicle system, due to sensor errors, accessories, and energization of the control circuit, when the battery is used as driving power, even if there is a rest time, the detected value of the energization current value may not exactly match 0. An interval within a certain current width that can be regarded as the rest time of the battery is regarded as the rest time, and the current within this certain current width is described as the current value "0". At time t in FIG. 17 0 ~t 01During this period, a discharge current flows through the secondary battery 11, and at time t 01 the charge-discharge current I is "0". Then, at time t c1 a discharge current starts to flow through the secondary battery 11 again. Thus, during the period from time t 0 to t ch the secondary battery 11 is intermittently discharged. Also, during the period from time t ch to t 05 the secondary battery 11 is charged and its voltage is rising.

[0138] And until time t 05 to t c a period continues during which the charge-discharge current I is 0 (except for the error of the measuring instrument). This period is longer than a predetermined rest threshold period Tr th . Thus, when the elapsed time from the end of energization exceeds the rest threshold period Tr th the data selection unit 42 records the battery voltage V (the same, V tp1 ) at the timing immediately before the start of energization again (in the illustrated example, immediately before time t c ) as the open-circuit voltage OCV. Further, the data selection unit 42 also records the SOC (the same, SOC tp1 ) at that timing. tp1 ) at that timing.

[0139] Similarly, until time t 06 to t cp the charge-discharge current I is 0, and this period is longer than the rest threshold period Tr th . Therefore, the data selection unit 42 records the battery voltage V (the same, V tp2 ) at the timing immediately before the start of energization again as the open-circuit voltage OCV, and also records the SOC (the same, SOC tp2 ) at that timing. In this way, as the relationship between the SOC and the open-circuit voltage OCV is accumulated, the SOC-OCV characteristics can be approximately obtained.

[0140] Also, the data selection unit 42 detects the timing at which the absolute value |I| of the charge-discharge current I changes steeply (within a predetermined time) from 0 to a predetermined current threshold I j or more. In the example of FIG. 17, at time tcn corresponds to the timing. The data selection unit 42 records the battery voltage V cn at the detected timing (time t cn ), the state of charge SOC cn , and the charge / discharge current I cn . Further, the data selection unit 42 records the battery voltage V cn-1 (not shown) and the state of charge SOC cn-1 (not shown) at the time t cn-1 (not shown) immediately before the secondary battery 11 enters the closed circuit state.

[0141] Then, the data selection unit 42 calculates the resistance value R of the secondary battery 11 by first approximation based on the battery voltages V cn , V cn-1 , the states of charge SOC cn , SOC cn-1 , and the charge / discharge current I cn . Also, the data selection unit 42 detects the energization end time when the absolute value |I| of the charge / discharge current I changes to 0. In the illustrated example, times t 01 , t 02 , t 03 , t 04 , t 05 , etc. correspond to this. These are collectively referred to as the energization end time t 0x hereinafter.

[0142] In addition, the data selection unit 42 identifies the time when the energization of the secondary battery 11 starts after the energization end time t 0x . In the illustrated example, times t c1 , t c2 , t c3 , etc. correspond to this. These are collectively referred to as the energization start time t cx hereinafter. The data selection unit 42 records the battery voltages V and SOCs at these energization end times t 0x and energization start times t cx as the voltages and SOCs before and after the charge / discharge of the secondary battery 11, respectively.

[0143] For example, the data selection unit 42 selects times t 01 , t 02 , t03 Record the SOC in [it] as the state of charge SOCa, SOCb, SOCc, and record the battery voltages V 01 , V 02 (not shown), V 03 (not shown) as the open circuit voltage. Also, the data selection unit 42, for example, at time t c1 , t c2 , t c3 Record the state of charge SOC, the charge and discharge current I, and the battery voltages V c1 , V c2 (not shown), V c3 (not shown).

[0144] Also, the data selection unit 42 records the time from a certain power - off time t 0x to the immediately subsequent power - on time t cx as the rest time Tr (not shown) (for example, the time from time t 01 to time t c1 ). And the data selection unit 42 records the time from a certain power - on time t cx to the immediately subsequent power - off time t 0x as the energization time Tc (not shown) (for example, the time from time t c1 to time t 02 ). Then, the data selection unit 42 obtains the integrated value of the charge and discharge current I at each energization time Tc by calculation, and thereby records the time - series data of the capacitance value Q (not shown).

[0145] Also, the data selection unit 42 calculates the input / output power based on the battery voltage V and the charge and discharge current I at each energization time Tc, and calculates and records the input / output energy amount by integrating the input / output power. Further, the data selection unit 42 obtains the total value ΣTr of the rest times Tr within a certain period and the total value ΣTc of the energization times Tc, and extracts ΣTc / (ΣTc + ΣTr) as the energization ratio.

[0146] Further, the data selection unit 42 integrates the charge and discharge current I for each current polarity over a certain period, divides the result by each energization time, calculates the average charge current and the average discharge current, and records them together with the battery temperature. Further, the data selection unit 42 also records the amount of electricity that has changed during energization and the energization time based on the charge and discharge current I accumulated as described above.

[0147] In FIG. 16, the operation parameter calculation unit 45 can analyze a variety of operation parameters. Although not particularly illustrated, these parameters include, for example, the start voltage Vini, the end voltage Vlast, the maximum voltage Vmax, the minimum voltage Vmin, the cell temperature Tcell, the ambient temperature Ta, the charge capacity Qc, the discharge capacity Qd, the amount of electricity fluctuation ΔQ per unit time, the maximum current Imax, the minimum current Imin, the average current Iave, the effective current Ie, the operating center voltage Vcenter, the residence time ratio Ratet of each voltage region, the energization polarity ratio tp per unit time, the energization time ratio ts, the rest time ratio tr, the upper and lower limit SOC (SOCmax, SOCmin), the upper and lower limit voltages (Vmax, Vmin), the SOC fluctuation width ΔSOC, the total energization time ttotal, etc. Note that the energization polarity ratio tp is an index indicating how many times the polarity of charging and discharging has changed within a certain energization time.

[0148] As shown in FIG. 17, in the secondary battery 11, the SOC also changes with the change in the charge and discharge current I. The maximum value of the SOC at this time is defined as the maximum SOC, the minimum value of the SOC is defined as the minimum SOC, and the SOC fluctuation width ΔSOC is obtained from the difference between the two. The data selection unit 42 extracts, for example, the amount of electricity ΔQc in the portion where charging continues and the amount of electricity ΔQd in the portion where discharging continues from the current waveform. Then, the data selection unit 42 calculates the amount of electricity fluctuation ΔQ per unit time by integrating these values within a unit time and dividing by the average value.

[0149] Further, the data selection unit 42 also defines, as an index of heat generation, the integrated value I of the heat generation factor 2Using parameters such as t and the change in battery temperature ΔT, the operating parameters can be analyzed. Note that the number of data used for analyzing the operating parameters can be arbitrarily determined.

[0150] In addition, the operating parameter calculation unit 45 analyzes the operating parameters for several hours to several tens of days corresponding to the storage period of the data stored in the data storage unit 41, and approximates these parameters to patterns corresponding to the operating state. Further, the calculated deterioration state of the secondary battery 11 and the operating parameters at that time may be stored as a history set in time series, and the conditions, capacity, and resistance changes when the operating method changes may be recorded. Thereby, there is an effect that abnormalities of the same secondary battery 11 can be easily detected.

[0151] It is also conceivable that temperature data during use of the secondary battery 11 is not output via an OBD terminal (not shown) of the vehicle 10. In such a case, the ambient temperature and battery temperature when the vehicle is running and stopped may be recorded in the terminal 220 (see FIG. 12) and used as the temperature history of the secondary battery 11. As described above, according to the present embodiment, information regarding the weight of the vehicle 10 and the deterioration state of the secondary battery 11 can be appropriately detected, the capacity, resistance, etc. of the secondary battery 11 can be obtained, and by using highly accurate deterioration prediction information, it becomes possible to specify appropriate driving start conditions (SOCs, Vs).

[0152] [Embodiment 10] FIG. 18 is a block diagram of a secondary battery management system S10 according to a preferred Embodiment 10. In the following description, parts corresponding to the respective parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted. The secondary battery management system S10 includes a charging device 200, a secondary battery management device 100F (computer), a power amount database 142, and a battery deterioration database 144.

[0153] The hardware configuration of the secondary battery management device 100F is the same as that of the secondary battery management device 100D (see FIG. 6) in the fourth embodiment. And the secondary battery management device 100F, similar to the secondary battery management device 100D, includes a power amount calculation unit 112, a travel start state determination unit 114, a duty ratio determination unit 116, a database update unit 118, an analysis unit 120, and a charge control unit 124. These functions are also the same as those in the fourth embodiment. According to the present embodiment, since the charge control unit 124 in the secondary battery management device 100F controls the charging device 200 based on the duty ratio Dtr, the configuration in each vehicle 10 can be simplified.

[0154] [Effects of the Embodiment] As described above, the secondary battery management devices 100A, 100B, 100D, 100E, and 100F according to the preferred embodiments include a power amount calculation unit (105, 106, 112) that calculates a predicted power amount W2 predicted to be required for the moving body (10) driven by the secondary battery 11 to move along a designated planned travel route (Rt) based on a power amount database 142 that stores the required power amount corresponding to the moving date and time or the outside air temperature during movement and the travel route of the moving body (10); a battery degradation database 144 that records the degradation state (SOHQ, SOHR) of the secondary battery 11 or records the past usage state of the secondary battery 11 together with data indicating the correlation between the usage state and the degradation state (SOHQ, SOHR) of the secondary battery 11; and a travel start state determination unit 114 that refers to the battery degradation database 144, calculates a usage SOC region or a usage voltage region of the secondary battery 11 in which the predicted power amount W2 can be ensured according to the degradation state (SOHQ, SOHR), and determines a travel start condition (SOCs, Vs), which is the voltage or SOC of the secondary battery 11 in the fully charged state, based on the calculated usage SOC region or usage voltage region. Thereby, based on the degradation state (SOHQ, SOHR) of the secondary battery 11, the charge state of the secondary battery 11 can be appropriately determined.

[0155] In addition, the power consumption database 142 stores the required power consumption corresponding to the moving date and time or the outside air temperature during movement, the moving route, and in addition, the weight of the moving body (10). It is more preferable that the power consumption calculation units (105, 106, 112) calculate the predicted power consumption W2 based on the weight of the moving body (10) and the planned moving route (Rt). Thereby, the charging state of the secondary battery 11 can be determined more appropriately corresponding to various planned moving routes (Rt) of the moving body (10) having various weights.

[0156] In addition, the secondary battery 11 can be charged while alternately repeating the energized state and the rest state, and it is more preferable to further include a duty ratio determination unit 116 that determines the duty ratio so as to lower the duty ratio of the energized state as the deterioration of the secondary battery 11 progresses based on the battery deterioration database 144. Thereby, the duty ratio determination unit 116 can determine an appropriate duty ratio according to the deterioration state of the secondary battery 11.

[0157] In addition, it is more preferable that the travel start state determination unit 114 stores the relationship between the deterioration state (SOHQ, SOHR) and the travel start conditions (SOCs, Vs) as data in map format, and determines the travel start conditions (SOCs, Vs) by referring to the data in map format. Thereby, it becomes possible to reduce the data amount at the time of implementation and reduce the calculation amount, and it becomes possible to perform high-speed calculation with a margin in the system storage.

[0158] In addition, the power consumption database 142 stores the required power consumption corresponding to the moving date and time or the outside air temperature during movement, the moving route, the weight of the moving body, and in addition, the driver identification information for identifying the driver of the moving body. The travel start state determination unit 114 determines the travel start conditions by an artificial intelligence that has performed learning processing by supervised learning, and it is more preferable to further include a database update unit 118 that updates the power consumption database 142 and the battery deterioration database 144 in real time according to the moving state of the moving body. Thereby, the power consumption database 142 and the battery deterioration database 144 can be updated in real time according to the actual operation state of the moving body (10).

[0159] Further, it is more preferable that the travel start state determination unit 114 has a function of receiving the assumed temperature Tass of the secondary battery 11, and a function of determining the travel start conditions (SOCs, Vs) such that the stay SOC, which is the SOC at which the secondary battery 11 stays for a predetermined time or longer, becomes lower as the received assumed temperature Tass becomes higher. Thereby, appropriate travel start conditions (SOCs, Vs) corresponding to the assumed temperature Tass can be determined.

[0160] Moreover, it is more preferable to further include a charge control unit 124 that controls a charging device 200 that charges the secondary battery 11 based on the travel start conditions (SOCs, Vs) supplied from the travel start state determination unit 114 and the duty ratio Dtr determined by the duty ratio determination unit 116, such as in the secondary battery management devices 100B and 100D. Thereby, the secondary battery 11 can be charged with an appropriate duty ratio Dtr.

[0161] Moreover, it is more preferable to further include a charge schedule planning unit 122 that communicates with an operation schedule planning unit 146 that plans an operation schedule of the moving body (10) and plans a charge schedule of the moving body, such as in the secondary battery management device 100D. Thereby, a charge schedule that conforms to the operation schedule of the moving body (10) can be planned.

[0162] [Modification Example] The present invention is not limited to the above-described embodiments, and various modifications are possible. The above-described embodiments are examples for easy understanding and explanation of the present invention, and are not necessarily limited to those having all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, a part of the configuration of each embodiment can be deleted, or other configurations can be added or replaced. Also, the control lines and information lines shown in the drawings indicate those considered necessary for explanation, and do not necessarily show all the control lines and information lines required on the product. In practice, it may be considered that almost all the configurations are interconnected. Possible modifications to the above embodiments are, for example, as follows.

[0163] (1) The vehicle 10 described above can be applied not only to delivery and distribution trucks, but also to electric buses and road battery vehicles with a fixed route. It is possible to effectively utilize the secondary battery by optimizing the battery level while suppressing deterioration and balancing the usage period and cost. Furthermore, the moving body in the present invention is not limited to the vehicle 10, and also includes traveling robots, flying robots, flying objects, ships, etc. that use a secondary battery as a power source.

[0164] (2) Since the hardware of the secondary battery management devices 100A, 100B, 100D, and 100E in the above embodiments can be realized by a general computer, a flowchart shown in FIGS. 7 and 13, and other programs for executing various processes described above may be stored in a storage medium or distributed via a transmission path.

[0165] (3) The processes shown in FIGS. 7 and 13, and other processes described above were described as software processes using programs in the above embodiments, but a part or all of them may be replaced with hardware processes using ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0166] (4) In each of the above embodiments, the various processes executed may be executed by a server computer via a network not shown, and the various data stored in the above embodiments may also be stored in the server computer.

Description of Signs

[0167] 10 Vehicle (Moving Body) 11 Secondary Battery 100A, 100B, 100D, 100E Secondary Battery Management Device (Computer) 105 Predicted Electric Quantity Calculation Unit (Electric Quantity Calculation Unit) 106 Predicted Electric Quantity Correction Unit (Electric Quantity Calculation Unit) 112 Electric Quantity Calculation Unit (Electric Quantity Calculation Means, Electric Quantity Calculation Process) 114 Travel Start State Determination Unit (Charging Condition Determination Means, Charging Condition Determination Process) 116 Duty Ratio Determination Unit 118 Database Update Unit 122 Charging Schedule Planning Unit 124 Charging Control Unit 142 Electric Quantity Database 144 Battery Deterioration Database 146 Operation Schedule Planning Unit 200 Charging Device Rt Planned Route Information (Planned Travel Route) Vs Travel Start Voltage (Travel Start Condition) Dtr Duty Ratio SOCs Travel Start Charging Rate (Travel Start Condition) Tass Assumed Temperature W2 Predicted Electric Quantity SOHQ, SOHR Degradation Degree (Degradation State)

Claims

1. Based on a power consumption database that stores the required power consumption corresponding to the moving date and time or the outside air temperature during movement and the movement route of a moving body driven by a secondary battery, a power consumption calculation unit that calculates a predicted power consumption predicted to be required for the moving body to move along a specified planned movement route; With reference to a battery degradation database that records the past usage status of the secondary battery together with data indicating the correlation between the usage status and the degradation status of the secondary battery, and according to the degradation status of the secondary battery, calculates a usage SOC region or a usage voltage region of the secondary battery in which the predicted power consumption can be ensured, and based on the calculated usage SOC region or the usage voltage region, determines a travel start condition that is the voltage or SOC of the secondary battery in a fully charged state. A travel start state determination unit; A secondary battery management device characterized by the above.

2. The power consumption database stores the required power consumption corresponding to the weight of the moving body in addition to the moving date and time or the outside air temperature during movement and the movement route; The power consumption calculation unit calculates the predicted power consumption based on the weight of the moving body and the planned movement route. The secondary battery management device according to claim 1, characterized by the above.

3. The secondary battery can be charged while alternately repeating an energized state and a rest state; Based on the battery degradation database, using information on battery degradation and the conduction ratio, a duty ratio determination unit that determines the ratio of the conduction time until charging is completed to the rest time after conduction controls the rest time after charging. The secondary battery management device according to claim 2, characterized by the above.

4. The travel start state determination unit stores the relationship between the usage conditions and the travel start conditions as data in a map format, and determines the travel start conditions by referring to the data in the map format. The secondary battery management device according to any one of claims 1 to 3, characterized by the above.

5. The power consumption database stores the required power consumption corresponding to driver identification information that identifies the driver of the moving body in addition to the moving date and time or the outside air temperature during movement, the movement route, and the weight of the moving body; The travel start state determination unit determines the travel start conditions by an artificial intelligence that has performed learning processing by supervised learning. Further comprising a database update unit that updates the power amount database and the battery degradation database in real time according to the movement status of the mobile body. The secondary battery management device according to claim 3, characterized in that.

6. The travel start state determination unit includes a function of receiving the assumed temperature of the secondary battery, and a function of determining the travel start condition such that the stay SOC, which is the SOC at which the secondary battery stays for a predetermined time or more, decreases as the received assumed temperature increases. The secondary battery management device according to claim 1, characterized in that.

7. Further comprising a charge control unit that controls a charging device for charging the secondary battery based on the travel start condition supplied from the travel start state determination unit and the ratio determined by the duty ratio determination unit. The secondary battery management device according to claim 3, characterized in that.

8. Further comprising a charging schedule planning unit that communicates with an operation schedule planning unit that plans an operation schedule of the mobile body and plans a charging schedule of the mobile body. The secondary battery management device according to claim 3, characterized in that.

9. Based on a power amount database that stores the required power amount corresponding to the moving date and time or the outside air temperature during movement and the movement route of a mobile body driven by a secondary battery, a power amount calculation process for calculating the predicted power amount predicted to be required for the mobile body to move along the designated planned movement route, Recording the degradation state of the secondary battery, or referring to a battery degradation database that records the past usage state of the secondary battery together with data indicating the correlation between the usage state and the degradation state of the secondary battery, and calculating the usage SOC region or the usage voltage region of the secondary battery that can ensure the predicted power amount according to the degradation state, and determining a travel start condition that is the voltage or SOC of the secondary battery in the fully charged state based on the calculated usage SOC region or the usage voltage region. The secondary battery management method is characterized in that.

10. A computer, Based on a power amount database that stores the required power amount corresponding to the moving date and time or the outside air temperature during movement and the movement route of a mobile body driven by a secondary battery, a power amount calculation means for calculating the predicted power amount predicted to be required for the mobile body to move along the designated planned movement route, A battery degradation database that records the degradation state of the secondary battery or records the past usage state of the secondary battery together with data indicating the correlation between the usage state and the degradation state of the secondary battery is referred to, and according to the degradation state, a usage SOC region or a usage voltage region of the secondary battery capable of securing the predicted amount of power is calculated, and based on the calculated usage SOC region or the usage voltage region, charging condition determination means for determining a traveling start condition that is the voltage or SOC of the secondary battery in a fully charged state. A program for causing it to function as.

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