WO2020090949A1 - 蓄電素子評価装置、コンピュータプログラム、蓄電素子評価方法、学習方法及び生成方法 - Google Patents

蓄電素子評価装置、コンピュータプログラム、蓄電素子評価方法、学習方法及び生成方法 Download PDF

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WO2020090949A1
WO2020090949A1 PCT/JP2019/042707 JP2019042707W WO2020090949A1 WO 2020090949 A1 WO2020090949 A1 WO 2020090949A1 JP 2019042707 W JP2019042707 W JP 2019042707W WO 2020090949 A1 WO2020090949 A1 WO 2020090949A1
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storage element
action
state
soh
reward
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French (fr)
Japanese (ja)
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南 鵜久森
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GS Yuasa International Ltd
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GS Yuasa International Ltd
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Priority to JP2020554018A priority Critical patent/JP7380585B2/ja
Priority to CN201980084253.2A priority patent/CN113227810A/zh
Priority to US17/290,191 priority patent/US20220041078A1/en
Priority to EP19878143.7A priority patent/EP3875976A4/en
Publication of WO2020090949A1 publication Critical patent/WO2020090949A1/ja
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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Definitions

  • Deterioration of the power storage element changes depending on the environment in which the power storage element is used (in the case of an electric vehicle, the running state, flight state, and usage environment). If a specific electric vehicle is used excessively, the power storage device mounted on the electric vehicle deteriorates early.
  • the power storage element evaluation device an action including a change of the load state of the power storage element, an action selection unit that selects based on the action evaluation information, and the state of the power storage device when the action selected by the action selection unit is performed.
  • the reward acquisition unit Based on the state acquisition unit to acquire, the reward acquisition unit to acquire the reward when the action selected by the action selection unit, the state acquired by the state acquisition unit and the reward acquired by the reward acquisition unit,
  • An update unit that updates the action evaluation information
  • an evaluation unit that executes an action based on the action evaluation information updated by the update unit and evaluates the state of the power storage element.
  • the power storage element evaluation method selects an action including a change in the load state of the power storage device based on the action evaluation information, acquires the state of the power storage device when the selected action is executed, and selects the selected action.
  • the reward at the time of execution is acquired, the action evaluation information is updated based on the obtained state and the reward, and the action based on the updated action evaluation information is executed to evaluate the state of the power storage element.
  • the learning method selects an action including a change in the load state of the electricity storage device based on the action evaluation information, acquires the state of the electricity storage device when the selected action is executed, and executes the selected action.
  • the reward at that time is acquired, the behavior evaluation information is updated based on the acquired reward, and the behavior corresponding to the state of the power storage element is learned.
  • the computer program causes the computer to select an action including a change in the load state of the electricity storage device based on the action evaluation information, a process of acquiring the state of the electricity storage device when the selected action is executed, and a selection process.
  • the process of acquiring the reward when performing the action, the process of updating the action evaluation information based on the obtained state and the reward, and the action based on the updated action evaluation information are executed, The process of evaluating the state and the process are executed.
  • the action selection unit selects an action including a change in the load state of the power storage element based on the action evaluation information.
  • the action evaluation information is a action value function or a table that defines an evaluation value of an action in a certain environment in reinforcement learning, and means a Q value or a Q function in Q learning.
  • the load state of the power storage element includes physical quantities such as current, voltage, and power when the power storage element is charged or discharged. Further, the load state may include the temperature of the power storage element.
  • the change of the load state is made by changing patterns of current, voltage, power or temperature (including fluctuation range, average value, peak value, etc.), changing the use location of the storage element, changing the use state (for example, use state and storage). Change between states) etc. Considering that individual load states exist in each of the plurality of power storage elements, changing the load state of the power storage elements corresponds to load distribution.
  • the action selection unit corresponds to an agent in reinforcement learning and can select the action with the highest evaluation in the action evaluation information.
  • the update unit updates the behavior evaluation information based on the acquired status and reward. More specifically, the update unit corresponds to an agent in reinforcement learning and updates the action evaluation information in the direction of maximizing the reward for the action. As a result, it is possible to learn the behavior that is expected to have the maximum value in a certain environment.
  • the load of the power storage elements can be optimally distributed in consideration of deterioration of the power storage elements, and the cost can be reduced as a whole.
  • the power storage element evaluation device includes a second reward calculation unit that calculates a reward based on the number of times of switching, and the reward acquisition unit can obtain the reward calculated by the second reward calculation unit.
  • the power information acquisition unit acquires load power information of the storage element.
  • the load power information is information indicating a transition of load power over a predetermined period, and includes charge power when the storage element is charged and discharge power when the storage element is discharged.
  • the predetermined period may be a period of one day, one week, one month, spring, summer, fall, winter, or one year.
  • the coefficient K1 is a deterioration coefficient, and the correspondence between the SOC and the temperature T and the coefficient K1 may be calculated, or may be stored in a table format.
  • the number K2 is similar to the coefficient K1.
  • FIG. 12 is a schematic diagram showing an example of the evaluation values of the evaluation value table 64.
  • the areas are C1, C2, C3, C4, and C5.
  • the state SOHA before action is SOHA ⁇ 100, 90, 100, 98, 99 ⁇ . That is, the SOHs of the power storage elements arranged in the areas C1, C2, C3, C4, and C5 before the action are 100, 90, 100, 98, and 99, respectively.
  • the SOH (90) of the storage element in the region C2 is lower than the SOH of other storage elements, as in the state SOHA, unless the region is switched. Becomes
  • the processing unit 60 executes the action including the change of the load state of the storage element based on the updated evaluation value table 64 (that is, the learned evaluation value table 27) to evaluate the state of the storage element including the SOH. be able to.
  • the updated evaluation value table 64 that is, the learned evaluation value table 27
  • FIG. 16 is a schematic diagram showing an example of replacement of a storage element.
  • FIG. 16 shows a change in the load state of a power storage element mounted on an electric vehicle based on a command output by control unit 51.
  • the replacement information that is, the switching information between the mounted state and the stored state includes information such as a switching date, a state, a period, and the number of times of switching for each power storage element (electric vehicle).
  • the period is a period in the mounted state when the state is “loaded”, and a period in the stored state when the state is “stored”.
  • the SOH of the storage element can be evaluated as a result of the switching between the mounted state and the stored state.
  • the load of the power storage elements can be optimally distributed in consideration of deterioration of the power storage elements, and the cost can be reduced as a whole.
  • the action selection unit 63 updates the evaluation value table 64 on the basis of the acquired state s t + 1 and the reward r t + 1 . More specifically, the action selection unit 63 updates the evaluation value table 64 in the direction of maximizing the reward for the action. As a result, it is possible to learn the behavior that is expected to have the maximum value in a certain state of the environment.
  • the evaluation value table 64 that can maximize the reward can be learned.
  • FIG. 21 is a schematic diagram showing an example of the transition of SOH according to the operation method obtained by the reinforcement learning when the SOH estimation unit 61 is used before the operation is started.
  • FIG. 21 shows a case where the SOH estimating unit 61 is used from the start of operation.
  • SOH represents SOH of all power storage elements.
  • the expected life is 10 years.
  • a graph indicated by "a large number of switches (SOH priority)" shows a case where the system is operated so that the average SOH of the power storage elements in the entire system including a plurality of power storage elements can be maintained high.
  • the parameters to be set are the coefficient K1 and the coefficient K2, which are represented by the SOC function.
  • the SOH simulator may be generated in a development environment different from that of the storage element evaluation server 50.
  • FIG. 24 is a flowchart showing an example of the processing procedure of reinforcement learning according to the present embodiment.
  • the processing unit 60 sets the evaluation value (Q value) of the evaluation value table 64 to the initial value (S11). For the setting of the initial value, for example, a random number can be used.
  • Processing unit 60 obtains the state s t (S12), selects and executes an action a t that can be taken in the state s t (S13).
  • the processing unit 60 acquires the state s t + 1 obtained as a result of the action a t (S14), and acquires the reward r t + 1 (S15).
  • the reward may be 0 (no reward).
  • the processing unit 60 updates the evaluation value of the evaluation value table 64 by using the above formula (2) or formula (3) (S16), and determines whether or not the operation result of the storage element is obtained (S17). ). If operational result of the storage element has not been obtained (NO in S17), the processing unit 60, the state s t + 1 the state s t (S18), step S13 continues the subsequent processing. When the operation result of the power storage element is obtained (YES in S17), processing unit 60 outputs the evaluation result of the power storage element (S19), and ends the process.
  • the processing unit 60 is, for example, hardware such as a CPU (for example, a multi-processor having a plurality of processor cores mounted), a GPU (Graphics Processing Units), a DSP (Digital Signal Processors), and an FPGA (Field-Programmable Gate Arrays). Can be configured by combining.
  • the processing unit 60 may be configured by a virtual machine, a quantum computer, or the like.
  • the agent is a virtual machine existing on the computer, and the state of the agent is changed by parameters or the like.
  • the behavior including the change of the load state (for example, the charging / discharging algorithm) with respect to the state including the SOH of the power storage element used in the EMS is performed by the reinforcement learning.
  • the obtained SOH of the storage element can be evaluated as a result of the action including the change of the load state.

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