EP4291908A1 - Procédé de détection précoce de défaut pour une batterie - Google Patents

Procédé de détection précoce de défaut pour une batterie

Info

Publication number
EP4291908A1
EP4291908A1 EP21925959.5A EP21925959A EP4291908A1 EP 4291908 A1 EP4291908 A1 EP 4291908A1 EP 21925959 A EP21925959 A EP 21925959A EP 4291908 A1 EP4291908 A1 EP 4291908A1
Authority
EP
European Patent Office
Prior art keywords
data
battery
cell
artificial intelligence
fault
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21925959.5A
Other languages
German (de)
English (en)
Other versions
EP4291908A4 (fr
Inventor
Can KURTULUS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Eatron Technologies Ltd
Original Assignee
Eatron Technologies Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eatron Technologies Ltd filed Critical Eatron Technologies Ltd
Publication of EP4291908A1 publication Critical patent/EP4291908A1/fr
Publication of EP4291908A4 publication Critical patent/EP4291908A4/fr
Pending legal-status Critical Current

Links

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.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)

Abstract

L'invention concerne un procédé de détection de défaut pour une batterie, comprenant la création (20) d'un modèle virtuel de bloc-batterie fournissant une valeur de seuil de tension indiquant des valeurs de distribution de tension d'au moins une cellule de batterie (1) au niveau de la batterie et l'aptitude à l'utilisation d'une cellule de batterie prédéfinie (1) au moyen d'un générateur de données virtuelles (12) dans une unité de commande (10) ; la collecte d'une première partie de données et d'au moins une deuxième partie de données à partir du modèle virtuel de bloc-batterie créé au moyen d'un collecteur de données (14) ; la génération d'un ensemble de données d'entraînement par fusion des première et deuxième parties de données par un contrôleur (16) ; l'entraînement d'un module d'intelligence artificielle (30) pour détecter et distinguer les cellules qui se comportent différemment de valeurs de distribution par un contrôleur (16) détectant les valeurs de distribution de tension de cellule en fonction des paramètres de changement en utilisant les données d'entraînement et le processus en tant que données d'entrée ; l'entraînement d'un module d'intelligence artificielle (30) de manière à déterminer (60) si la cellule de batterie (1) présente une condition de défaut de cellule en utilisant les données d'entrée détectant et distinguant les cellules de batterie (1) qui se comportent différemment des valeurs de distribution.
EP21925959.5A 2021-02-11 2021-02-11 Procédé de détection précoce de défaut pour une batterie Pending EP4291908A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/TR2021/050125 WO2022173384A1 (fr) 2021-02-11 2021-02-11 Procédé de détection précoce de défaut pour une batterie

Publications (2)

Publication Number Publication Date
EP4291908A1 true EP4291908A1 (fr) 2023-12-20
EP4291908A4 EP4291908A4 (fr) 2024-11-27

Family

ID=82838560

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21925959.5A Pending EP4291908A4 (fr) 2021-02-11 2021-02-11 Procédé de détection précoce de défaut pour une batterie

Country Status (2)

Country Link
EP (1) EP4291908A4 (fr)
WO (1) WO2022173384A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118884288A (zh) * 2024-09-25 2024-11-01 浙江达航数据技术有限公司 基于bit和bms数据交互的电池安全监测方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
BE1030866B1 (de) * 2022-09-09 2024-04-09 volytica diagnostics GmbH Computerprogramm und Verfahren zur Analyse von Inhomogenitäten sowie Anomaliedetektion und -vorhersage von elektrischen Energiespeichern
CN116577673B (zh) * 2023-07-12 2023-09-12 深圳先进储能材料国家工程研究中心有限公司 一种基于分布式神经网络的储能电站故障诊断方法及系统
CN118219910B (zh) * 2024-05-22 2024-09-06 浙江祥晋汽车零部件股份有限公司 一种换电站及换电站换电方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090112395A1 (en) * 2007-10-31 2009-04-30 Toyota Motor Engineering & Manufacturing North America, Inc.. System for detecting a battery malfunction and performing battery mitigation for an hev
CN104714175A (zh) * 2013-12-12 2015-06-17 北京有色金属研究总院 电池系统故障诊断方法及系统
US11658350B2 (en) * 2019-02-28 2023-05-23 Purdue Research Foundation Smart battery management systems
CN111007401A (zh) * 2019-12-16 2020-04-14 国网江苏省电力有限公司电力科学研究院 一种基于人工智能的电动汽车电池故障诊断方法及设备
CN111873853B (zh) * 2020-07-30 2022-05-10 广州小鹏汽车科技有限公司 电池管理方法、装置、电池管理系统、车辆以及存储介质

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118884288A (zh) * 2024-09-25 2024-11-01 浙江达航数据技术有限公司 基于bit和bms数据交互的电池安全监测方法

Also Published As

Publication number Publication date
WO2022173384A1 (fr) 2022-08-18
EP4291908A4 (fr) 2024-11-27

Similar Documents

Publication Publication Date Title
Shah et al. State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review
Ismail et al. Battery state of charge estimation using an Artificial Neural Network
EP4291908A1 (fr) Procédé de détection précoce de défaut pour une batterie
Zhang et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
Wu et al. A fault detection method of electric vehicle battery through Hausdorff distance and modified Z-score for real-world data
KR101992051B1 (ko) 배터리 잔량 예측 방법 및 배터리 잔량 예측 시스템
CN109116242B (zh) 一种动力电池的数据处理方法和装置
He et al. Voltage abnormality-based fault diagnosis for batteries in electric buses with a self-adapting update model
KR20180055192A (ko) 배터리 상태를 추정하는 방법 및 장치
Zhou et al. Data-driven battery state-of-health estimation and prediction using IC based features and coupled model
CN110568360B (zh) 一种基于模糊逻辑算法的锂电池老化诊断方法
Yao et al. Fault identification of lithium-ion battery pack for electric vehicle based on ga optimized ELM neural network
Zhang et al. Adaptive internal short-circuit fault detection for lithium-ion batteries of electric vehicles
Wang et al. Charging diagnosis of power battery based on adaptive STCKF and BLS for electric vehicles
Chen et al. State of health estimation for lithium-ion battery based on long short term memory networks
Zhang et al. Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach
Ren et al. Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system
CN119749240B (zh) 新能源汽车的动力电池异常诊断方法、装置及存储介质
Huang et al. Safety risk assessment for automotive battery pack based on deviation and outlier analysis of voltage inconsistency
US20230133120A1 (en) Big-data-based battery diagnostic system and control method therefor
Saxena et al. PHM of Li‐ion Batteries
Shete et al. Fault diagnosis of electric vehicle’s battery by deploying neural network
Mehta et al. A Comprehensive study of Machine Learning Techniques used for estimating State of Charge for Li-ion Battery
Cao et al. Multi-Fault Diagnosis for Series-Connected Lithium-Ion Battery Packs Based on Improved Sensor Topology and Correlation Coefficient Method
Virk et al. Fault prediction using artificial neural network and fuzzy logic

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20230817

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20241028

RIC1 Information provided on ipc code assigned before grant

Ipc: B60L 3/12 20060101ALI20241022BHEP

Ipc: G01R 31/367 20190101ALI20241022BHEP

Ipc: B60L 3/00 20190101ALI20241022BHEP

Ipc: G01R 31/36 20200101AFI20241022BHEP