Classifications

• • 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
  • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
  • B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
  • B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
• • 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
  • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
  • B60L3/12—Recording operating variables ; Monitoring of operating variables
• • 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]
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
• • 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
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/40—Drive Train control parameters
  • B60L2240/54—Drive Train control parameters related to batteries
  • B60L2240/545—Temperature
• • 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
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/40—Drive Train control parameters
  • B60L2240/54—Drive Train control parameters related to batteries
  • B60L2240/547—Voltage
• • 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
  • B60L2260/00—Operating Modes
  • B60L2260/40—Control modes
  • B60L2260/46—Control modes by self learning
• • 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
  • B60L2260/00—Operating Modes
  • B60L2260/40—Control modes
  • B60L2260/50—Control modes by future state prediction
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
  • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
  • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements

Definitions

• the present invention relates to a fault detection system of a battery, in particular a fault detection system powered by artificial intelligence.
• Electric vehicles are vehicles in which only an electric motor is utilized as a driving part, and hybrid vehicles are vehicles both have an internal combustion engine and an electric motor in drivetrain.
• a battery pack with a plurality of battery cells is provided on the underside of an electric vehicle and a hybrid vehicle.
• the lifetime of the battery used in an electric vehicle or a hybrid electric vehicle is reduced depending on the number of charges, depth of discharge, charge and discharge current, charge environment (temperature change) or similar environmental conditions. This situation also depends on the driving behaviour of the user and charging temperatures, fast charging frequency, amount of charge with regenerative brake, timing of charge with regenerative brake and similar conditions may cause a reduction in lifetime. Anomalies may occur in the battery utilized in an electric vehicle or hybrid electric vehicle due to production faults, deterioration over time or similar reasons.
• US2020278398A1 discloses an anomaly detection system for a secondary battery which detects the remaining capacity of the secondary battery on an electric vehicle, cautions against the secondary battery with anomalous characteristics, stops using the secondary battery, changes the secondary battery, or changes charging conditions of the secondary battery is provided.
• the anomaly detection system is provided; the system compares a value obtained by estimating internal resistance or SOC of a secondary battery based on the measured value of a current or a voltage of the secondary battery with the use of a nonlinear Kalman filter and a value input to an anomaly detection system (network) of Al to predict a change in the internal resistance; the system regards a case where the difference is large as an anomaly; and the system detects an anomaly.
• the object of the invention is to early detection of the cell fault of the cells in the batteries.
• the invention is a fault detection method for a battery comprising creating a battery pack virtual model providing a voltage threshold value indicating voltage distribution values of at least one battery cell at the battery and usability of predefined a battery cell by means of a virtual data generator in a control unit; collecting a first data part and at least a second data part from the created battery pack virtual model by means of a data collector; generating a training dataset by merging the first and second data parts by a controller; training an artificial intelligence module to detect and distinguish the cells that behave differently from distribution values by a controller detecting the cell voltage distribution values according to the changing parameters by utilizing the training data and process as the input data.
• the fault detection method comprising the steps of determining the cell voltage distributions of the battery cell and generating an input data by a neuroprocessor and analytics module connected with the artificial intelligence module in a signal transmitting manner; determining whether the battery cell has a cell fault condition, by means of artificial intelligence module utilizing the input data detecting and distinguishing the battery cells that behave differently from the distribution values.
• the fault detection method works actively in the background while the vehicle is running, and the cell fault situation that may occur in the battery cell during use can be detected in advance. In this way, problems that may occur during the use of batteries in the vehicle are prevented and malfunctions that may occur in the vehicle are prevented.
• the artificial intelligence module comprises the process step of classification of the cell fault status as a low severity level, average severity level and high severity level. Sorting by severity allows to determine which cell is in the most risky state.
• the classification can be based on present, not present flags.
• the degree of fault can be calculated as a % value.
• a preferred application comprising the step of early detection of cell fault by detecting and distinguishing the cells with different behavior from these distribution values by means of a neural network provided in the artificial intelligence module taking the cell voltage distribution values as the input data and assigning the severity level of the cell fault depending on the cell voltage distribution.
• the mathematical calculations made by the neural network provide reliable results.
• cell fault detection for battery cells can be accurately done.
• a preferred application comprises the step of training the neural network by supervised learning by utilizing training data comprising an input layer, at least one middle layer and an output layer assigning current value of the battery cell according to the changing parameters detecting early fault occurrence by determining and distinguishing cells with different behavior among the cell value distribution. It is ensured that the input layer, middle layer and output layer in the neural network learn all the dynamics of the battery cell.
• a preferred application comprising the step of obtaining the first and second data parts with a data collector by simultaneous and repeatedly measuring current value distribution of the battery cell at the virtual model of the battery pack and matching the data and corresponding various parameters at each point of measurement. Simultaneous and repeated measurement facilitates for the artificial intelligence module to learn battery cell voltage distribution values under various parameters.
• a preferred application comprises the step of obtaining a training data by merging the first and the second data parts and battery cell voltage distribution value at the point of measurement corresponding to the parameters selected from the group comprising the maximum usable battery charge, discharge interval and temperature change.
• the training data is used to train the artificial intelligence module.
• the artificial intelligence is trained with the training data until it reaches the desired performance level. Thus, safety is ensured for application of artificial intelligence to the batteries on the vehicle.
• a preferred application comprises the step of transforming the information classified by the artificial intelligence module into a result data.
• the information obtained by the fault detection method can be converted into a report and suitable for storage.
• a preferred application of the invention the step of saving the result data in a memory unit by means of the artificial intelligence module.
• the loss of result data is prevented and the output data generated by other test results can be accessed at any time.
• Figure 1 is a schematic representation of the control unit and the virtual model of the battery pack created by the control unit of the subject matter fault detection method.
• Figure 2 is a schematic illustration of the artificial intelligence module and signal processor and analytics module of the inventive fault detection method.
• Figure 3 is a schematic representation of the process steps of the inventive fault detection method.
• FIG. 1 shows schematically the control unit of the inventive fault detection method and the battery model created with the control unit.
• a control unit (10) operates according to the working principle of the fault detection method used for batteries.
• a virtual data generator (12) in the control unit (12) visually transforms the battery pack virtual model (20) indicating the cell voltage distribution values and voltage threshold value of the batteries corresponding to a certain temperature change, a certain maximum battery charge and discharge interval parameter indicating the usable status of the battery cells (1) into a virtual space.
• the virtual model of the battery pack (20) is created by the virtual data generator (12).
• the virtual model of the battery pack (20) is arranged to indicate the cell voltage distribution values and voltage threshold value of the batteries corresponding to a certain temperature change, a certain maximum battery charge and discharge interval parameter.
• the virtual modal of the battery pack (20) arranged such that indicate the states of the battery cells (1 ) with different predetermined voltage values under various parameters.
• multiple data parts of the cell voltage distribution value and voltage threshold value of the battery are collected with a data collector (14) arranged in the control unit (10).
• the data parts obtained by the data collector (14) are formed by the simultaneous and repeated measurement of the battery cell voltage distribution in the battery pack virtual model (20) and matching the data parts at each corresponding measurement point.
• the obtained data parts are combined into a training data by means of a controller (16) located in the control unit (10).
• the control unit (10) is having a memory unit (18) that stores the results obtained by the test method.
• FIG. 2 shows schematically the artificial intelligence module and signal processor and analytical module of the test method of the subject matter invention.
• a signal processor and analytical module (40) that is trained to determine the battery cell voltage distribution and cell voltage threshold value according to the changing parameters of the cells in the batteries.
• the signal processor and analytics module (40) calculates the voltage distribution value of the battery cell (1) and creates input data (40), and by using the input data, the artificial intelligence module (30) is a multi-layered neural network (32) detects and distinguishes cells that behave differently from these distribution values.
• the neural network (32) comprises multiple neurons configured to take the cell voltage distribution values as input data and apply an algorithm that detects and distinguishes cells that behave differently from these distribution values.
• the algorithm taking the cell voltage distribution values as input data and using the input data, detects and distinguishes cells that behave differently from these distribution values is trained with the supervised learning method.
• the training data consists of inputs paired with correct corresponding outputs.
• the algorithm models patterns in the data associated with the desired outputs.
• a supervised learning algorithm will take new invisible inputs and determine what result the new inputs will achieve, based on previous training data.
• the object of the supervised learning model is to predict the correct outcome for newly presented input data.
• the supervised learning method can be preferred as a classification or regression model.
• the classification model provides data points with a category assigned to a classification algorithm during training.
• the task of a classification algorithm is then to take an input value and assign it a class or category to which it is appropriate based on the training data provided.
• an input layer (322), at least one middle layer (324) and an output layer (326) in the neural network (32) are used to input the cell voltage distribution values of the battery cell (1 ) according to the changing parameters and trained through supervised training to detect and distinguish cells that behave differently from these distribution values.
• the cell voltage distribution value is calculated by the signal processor and analytics module (30) and created as input data (50) and transmitted to the artificial intelligence module (30).
• the neurons of the input layer (322) in the neural network (32) located in the artificial intelligence module (30) distinguish the cells that give signals that there is a problem by displaying different behavior among these distribution values.
• Cell fault status is classified by the output layer (326) as low severity, medium severity, and high severity (70).
• the obtained information is converted into a result data (80) by the artificial intelligence module (30) and recorded in the memory unit (18).
• the virtual data generator (12) in the control unit (10) creates the virtual model (20) of the battery pack.
• the voltage distribution values of the battery according to the changing parameters and the voltage threshold values according to the changing parameters are collected by the data collector (14) as data parts.
• a collected first piece of data, a second piece of data, and a third piece of data are formed according to the varying maximum battery charge and discharge interval and the measurement point corresponding to the temperature change.
• the data parts are combined into training data by the controller (16) in the control unit (10).
• the control unit (10) trains the artificial intelligence module (30). With the neuroprocessor and analytical module (40), cell voltage distribution values are generated as input data (50).
• the artificial intelligence module (30) is trained with data parts from the battery pack virtual model (20) until it learns completely to detect and distinguish cells that give early signals about problem formation by taking the battery cell voltage distribution values as input according to the changing parameters of the batteries and displaying different behavior from these distribution values.
• Training the artificial intelligence module (30) using training data could be, for example, a supervised learning model using a recurrent neural network (RNN) model or the like.
• RNN recurrent neural network
• the voltage distribution value of the battery cell (1 ) is transmitted by the signal processor and analytical module (40) as input data by the artificial intelligence module (30), and the value of the cell voltage distribution is used by the artificial intelligence module (30) as input data, and different behavior is observed among these distribution values and detecting cells that give early signals of problem formation, it is determined whether the battery cell (1) has a cell fault condition (60). Depending on the severity of the cell fault, the artificial intelligence module (30) classifies the cell fault (70). The classified cell fault is converted into a result data (80) and stored in the memory unit. In an exemplary application, LiNiMnCo02 (NMC), were chosen as the batteries utilized for an electric vehicle.
• NMC LiNiMnCo02
• a battery early fault detection algorithm is run by the artificial intelligence module (30) to detect whether the battery cells (1 ) have a cell fault.
• the voltage distribution values of the battery cells (1) examined with the battery early fault diagnosis algorithm are determined between -30 °C and 45 °C while the electric vehicle is moving and the maximum discharge power of 200 kW is not exceeded. While the maximum discharge power of 200 kW at 25 °C temperature was not exceeded, when the voltage distribution values were examined, it was determined that there was no cell fault condition. While the maximum discharge power of 200 kW at 35 °C temperature was not exceeded, when the voltage distribution values were examined, it was determined that it had a cell fault condition.
• the detected voltage distribution values are processed with the artificial intelligence algorithm neural network (32) to obtain information whether there is an early signal of cell fault formation or not. If the cell has a fault, it is classified as low severity, average severity, or high severity cell fault, depending on the severity of the fault. In this way, whether the battery cell (1 ) used for the vehicle has a cell fault or not, the faults of the battery cell (1) are actively detected in the background while the vehicle is running, and it can be determined that whether it is not suitable for the use. If a battery with a cell fault can be made suitable for an active ride or a new battery can be used is determined by considering the severity of the cell fault.

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• Engineering & Computer Science (AREA)
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• Sustainable Energy (AREA)
• Transportation (AREA)
• Mechanical Engineering (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Secondary Cells (AREA)

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• 2021
  • 2021-02-11 WO PCT/TR2021/050125 patent/WO2022173384A1/en not_active Ceased
  • 2021-02-11 EP EP21925959.5A patent/EP4291908A4/de active Pending

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