US20210190868A1 - Active battery management method - Google Patents

Active battery management method Download PDF

Info

Publication number
US20210190868A1
US20210190868A1 US17/272,824 US201917272824A US2021190868A1 US 20210190868 A1 US20210190868 A1 US 20210190868A1 US 201917272824 A US201917272824 A US 201917272824A US 2021190868 A1 US2021190868 A1 US 2021190868A1
Authority
US
United States
Prior art keywords
battery
discharging
charging
signal injections
signals
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
US17/272,824
Other languages
English (en)
Inventor
Gilles J. Benoit
Catherine A. Leatherdale
Don Vincent West
Vincent J.L. Chevrier
Brandon A. Bartling
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.)
3M Innovative Properties Co
Original Assignee
3M Innovative Properties Co
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 3M Innovative Properties Co filed Critical 3M Innovative Properties Co
Priority to US17/272,824 priority Critical patent/US20210190868A1/en
Assigned to 3M INNOVATIVE PROPERTIES COMPANY reassignment 3M INNOVATIVE PROPERTIES COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BARTLING, BRANDON A., CHEVRIER, VINCENT J.L., BENOIT, GILLES J., LEATHERDALE, CATHERINE A., WEST, DON VINCENT
Publication of US20210190868A1 publication Critical patent/US20210190868A1/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • H—ELECTRICITY
    • 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
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • 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
    • H01M10/44—Methods for charging or discharging
    • H01M10/443—Methods for charging or discharging in response to temperature
    • H—ELECTRICITY
    • 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
    • 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
    • H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
    • H02J7/0047—
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/02—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries for charging batteries from AC mains by converters
    • H02J7/04—Regulation of charging current or voltage
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/40—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries characterised by the exchange of charge or discharge related data
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/80—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries including monitoring or indicating arrangements
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/80—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries including monitoring or indicating arrangements
    • H02J7/82—Control of state of charge [SOC]
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/80—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries including monitoring or indicating arrangements
    • H02J7/84—Control of state of health [SOH]
    • 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
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/90—Regulation of charging or discharging current or voltage
    • H—ELECTRICITY
    • 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
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • Battery management is of critical importance to a number of commercial applications ranging from consumer electronics to automotive electrification and grid-level energy storage. Battery management is also a key component of the value proposition of any battery system whose utility depends on delivering a minimum amount of energy reliably and safely over an extended period of time, from a couple of years for consumer electronics to a decade for grid installations.
  • a first method for active battery management includes injecting randomized controlled signals in charging or discharging of a battery and ensuring the signal injections occur within normal operational ranges and constraints. The method also includes monitoring performance of the battery in response to the controlled signals, computing confidence intervals about the causal relationships between the battery performance and the controlled signals, and selecting optimal signals for the charging or discharging of the battery based on the computed confidence intervals.
  • a second method for active battery management includes providing signal injections for charging or discharging of a battery and receiving response signals corresponding with the signal injections. The method also includes measuring a utility of the response signals, accessing data relating to charging or discharging of the battery, and modifying the data based upon the utility of the response signals.
  • FIG. 1 is a diagram illustrating a system for implementing an active battery management method
  • FIG. 2 is a flow chart of a search space method for the system
  • FIG. 3 is a flow chart of a signal injection method for the system
  • FIG. 4 is a flow chart of a continuous learning method for the system
  • FIG. 5 is a flow chart of a memory management method for the system
  • FIGS. 6A-6D illustrate the search space of all possible charge profiles for the Examples
  • FIGS. 7A-7C illustrate that the algorithm in the Examples identified distinct effect sizes for old cells versus new cells.
  • FIGS. 8A-8C illustrate voltage versus capacity for a charge profile assigned by the algorithm in the Examples.
  • Embodiments of the invention include a method for improving battery management by implementing random experiments on the charge and discharge variables and inferring their causal effects on utility metrics such as energy capacity, power, fade rate, charge time, internal resistance, state of health, cell imbalance, temperature, cell swelling, electricity cost, and more. Linear combinations of any of the above utility metrics may also be defined to give the figure of merit which balances competing requirements.
  • This battery management can be used in, for example, electric or hybrid vehicles, electric bicycles, consumer electronic devices, grid storage systems and other vehicles and devices using batteries.
  • FIG. 1 is a diagram illustrating a system for implementing an active battery management method.
  • the system includes a processor 10 electrically coupled with a power source 12 , a load 20 , and a data storage 22 .
  • Power source 12 provides power for charging one or more batteries 14 , 16 , and 18 , and the batteries provide power to load 20 .
  • Data storage 22 such as an electronic memory, stores profiles and parameters 24 , external data 26 , and results 28 . Results can include, for example, time series of current and voltage, energy capacity, temperature and more for each cell or string of cells.
  • processor 10 injects signals to power source 12 using profiles and parameters 24 and possibly external data 26 in order to evaluate the performance of batteries 14 , 16 , and 18 , for example how the batteries perform for charging and discharging of them.
  • Performance metrics can include, for example, delivered power, energy capacity, and fade rate.
  • Processor 10 stores as results 28 the response to the signal injections, and those responses can be used to optimize performance of the batteries.
  • the processing for the battery management can occur locally with dedicated firmware on the battery charging system, on standalone PC, or be cloud based and occur remotely from the batteries.
  • Profiles and parameters 24 includes possible charging and discharging rates, charging and discharging profiles, and profile endpoints.
  • the charging and discharging profiles include the shape of such profiles and possibly time at a particular state of charge or voltage.
  • the charging endpoints include the percentage of charge in the batteries at which to start and stop charging charging the batteries, and the discharging endpoints include the percentage of charge in the batteries at which to start and stop discharging the batteries.
  • the profiles and parameters can be stored in look-up tables, for example.
  • External data 26 can include, for example, environmental conditions or factors such as temperature, humidity, airflow around the batteries, time of day or year, and time since turning on the device or vehicle using the batteries.
  • external data can also include the following with respect to such devices: usage of applications, user settings, a scheduled event or alarm, a power consumption pattern, a time of day, or the location of the device.
  • external data can include time of day or time next scheduled use, typical driving patterns for the vehicle, electricity cost versus time (i.e., for example to avoid peak pricing), predicted weather conditions, planned travel route, or traffic conditions.
  • the batteries can include a single physical battery or multiple physical batteries that collectively provide power. In the case of multiple physical batteries, the batteries may have the same or different construction or electrochemistry.
  • the process of injecting signals for charging and discharging of the batteries seeks to optimize charging and discharging profiles for a particular battery or a pack of batteries.
  • the pack of batteries can be considered as a single battery where the pack collectively operates together, or the pack of batteries can be considered multiple physical batteries driven one-by-one. Examples of types of batteries include lithium ion, reflow, lead acid, and others.
  • FIGS. 2-5 are flow charts of methods for active battery management to optimize charging and discharging profiles and parameters. These methods can be implemented in, for example, software modules for execution by processor 10 .
  • FIG. 2 is a flow chart of a search space method.
  • the search space method includes the following steps: receive control information (including costs) 30 ; construct multidimensional space of all possible control states 32 ; constrain space of potential control spaces 34 ; determine normal/baseline sampling distribution 36 ; determine highest utility sampling distribution 38 ; and automated control selection within constrained space 40 .
  • FIG. 3 is a flow chart of a signal injection method.
  • the signal injection method includes the following steps: receive set of potential signal injections 42 ; compute spatial and temporal reaches of signal injections 44 ; coordinate signal injections in space and time 46 ; implement signal injections 48 ; collect response data 50 ; and associate response data with signal injections 52 .
  • the signal injections are changes in charging and discharging profiles and parameters for battery management.
  • the responses to signal injection are typically battery performance resulting from or related to the changes in profiles and parameters from the signal injections.
  • the algorithm can perturb values in a look-up table representing charging and discharging profiles and parameters, and then monitor and store the corresponding battery performance response.
  • the temporal and spatial reaches of signal injections relate to, respectively, when and where to measure the response signals to those signal injections that are used for computing causal relationships.
  • the cost of signal injection typically relates to how the signal injection affects battery performance, for example signal injection can result in lower battery performance, and is controlled by the specified experimental range.
  • the queue for signal injection involves the order and priority of signal injections and relies on blocking and randomization to guarantee high internal validity at all times, even when optimizing utility.
  • the utility of responses to signal injection involves the effectiveness of the signal injections or other measures of utility.
  • FIG. 4 is a flow chart of a continuous learning method.
  • the continuous learning method includes the following steps: receive set of potential signal injections 54 ; receive current belief states 56 ; compute learning values for signal injections 58 ; receive costs for signal injections 60 ; select and coordinate signal injections 62 ; implement signal injections 64 ; collect response data 66 ; and update belief states 68 .
  • the belief states are a set of different models of battery performance in response to charging and discharging. These belief states may have attached uncertainty values reflecting the likelihood that they are accurate given the current set of trials and knowledge that may tend to confirm or falsify these different models, and the information that can further confirm or falsify the models may be included in this data or derived from the basic characteristics of the particular model and the physics of the underlying system.
  • the learning value is a measure of the value that knowledge generated as a result of the signal injection may provide to subsequent decision-making by a system, such as determining that a particular charging or discharging profile is more likely to be optimal.
  • the learning value may be computed through, for example, predicting the raw number of belief states that may be falsified according to the predictions of a Partially Observable Markov Decision Process (POMDP) or other statistical model, predicted impacts of the signal injection on the uncertainty levels in the belief states in such models, or experimental power analyses computing the reduction in uncertainty and narrowing of confidence intervals based on increasing to the current sample size.
  • POMDP Partially Observable Markov Decision Process
  • FIG. 5 is a flow chart of a memory management method.
  • the memory management method includes the following steps: receive set of historical clusters 70 ;
  • the memory management method executes the following steps: receive set of historical external factor states 78 ; compute stability of signal injections versus external factors states 80 ; select two states to split cluster across 82 only if there is enough variance across the two states and enough data within each state (after splitting) to be able to drive decisions in each state (i.e., compute confidence intervals); and update set of historical clusters 84 .
  • a cluster is a group of experimental units that are statistically equivalent with respect to the measure causal effects. Within a cluster, effects are measured free of bias and/or confounding effects from external factors, which guarantees that we are measuring causation and not just correlations/associations. Distribution of measured effects within each cluster are approximately normally distributed.
  • Table 1 provides an algorithm of an embodiment for automatically generating and applying causal knowledge for active battery management. This algorithm can be implemented in software or firmware for execution by processor 10 .
  • TABLE 1 inject randomized controlled signals into battery charging and dis- charging based upon changes in charging and discharging profiles and related parameters 2 ensure signal injections occur within normal operational ranges and constraints 3 monitor battery performance in response to the signal injections 4 compute causal knowledge about the relationship between signal in- jections and monitored battery performance 5 select optimal signals for the battery performance based on current causalknowledge and possibly external data
  • Power characterizes the amount of energy per unit time. Maximizing power is a balancing act of minimizing charge time while minimizing the loss in energy capacity with each cycle. In electric vehicle applications, increased power enables faster acceleration and greater performance. In grid applications such as peak shaving where a typical charge/discharge cycle is 24 hours, increased power translates to longer life-time and/or smaller installations.
  • the 4 independent variables consisted of the two coordinates (cutoff voltage: V, and constant current: I) of the two control points of the cubic Bezier function. Each independent variable was discretized into 8 levels, resulting in a total of 4096 possible combinations. Once fully charged, the cells were discharged under a fixed discharge profile (constant current at 250 mA to 3V).
  • the Figure Of Merit was defined as delivered power calculated as the discharge energy divided by cycle time (i.e., charge time+discharge time). Prior to any significant aging of the cells, both discharge energy and discharge time were nearly constant across cells (or cycles) and the FOM was driven primarily by charge time. Additional dependent variables were recorded with each cycle including charge energy and charge time. External variables were also recorded with each cycle to explore their possible effect of the dependent variables and find clustering opportunities.
  • ID cell identification
  • FIGS. 6A-6D show the search space of all possible charge profiles for the described experiment.
  • the optimum charge profile is shown as the dashed line with its end points and control handle points shown as the dot-dashed line.
  • the chosen FOM total delivered power
  • the power values associated with the optimum charge profile are shown as the dark gray circles.
  • the FOM and the exploit frequency are also plotted as a function of time.
  • FIG. 6A shows the search space of all possible charge profiles.
  • FIG. 6B shows the FOM versus discharge energy.
  • FIG. 6C shows the FOM versus time.
  • FIG. 6D shows the exploit frequency.
  • FIGS. 7A-7C show that, over time, the algorithm identified distinct effect sizes for old cells versus new cells. Once clustering is initiated, the algorithm exploits charge profiles that are optimum for each distinct cluster, which may or may not be the same across clusters.
  • FIGS. 7A and 7B illustrate the Example of method for maximizing delivered power, where E-Cap refers to energy capacity.
  • FIG. 7C illustrates the Example of method for maximizing delivered power under environmental conditions.
  • FIG. 8A shows voltage versus capacity for a charge profile assigned by the algorithm (solid line) versus reference C/20 charge profile (dashed line) illustrating the overpotential AV.
  • FIG. 8B shows SOC and overpotential versus current.
  • FIG. 8C shows internal resistance versus SOC.
  • Knowledge of in situ internal resistance over time can be used to improve the performance and safety of the battery management system by eliminating charge profiles in the search space that could lead to significant over- and under-voltage, heating and degradation of the cells.
  • Internal resistance maps can be built from subgroups within the data, and examples of possible subgroups include but are not limited to: cell age, number of cycles, temperature exposure, cumulative discharged energy, average discharge current, average charge current, maximum charge current, average voltage, manufacturing batch. Changes in internal resistance can also be used to detect anomalous conditions such as onset of a short circuit in a battery pack.
  • SOH state of health
  • internal resistance across cells While cell aging tends to be fairly uniform at the beginning of battery life (within manufacturing tolerances), it becomes increasingly more heterogeneous and unpredictable with each additional charge/discharge cycle. This is an important consideration when repackaging used cells into packs for new applications as a way to extent their life time in less demanding applications.
  • One example is reusing EV batteries for grid-level energy storage.
  • the development of smart battery management systems that are capable of balancing cell ageing is critical to ensure safe, reliable and durable operations and make the application economically viable. Accurate determination of the internal resistance of each cell as well as clustering across homogeneous cell groups is an effective mechanism to quantitatively implement cell balancing in practice.
  • hybrid vehicles can use systems that combine different types of cells with different performance and ageing characteristics.
  • the power grid can use systems that combine high power and high energy storage. This translates to greater variance in the data, resulting in greater difficulty to apply standard data analytics techniques for battery management.
  • the algorithm disclosed herein can address this type of problem by automatically identifying the minimum set of homogeneous clusters that can be used for reliable causal inference over time.
  • the algorithm disclosed herein can also be used to implement experiments on batteries across different vehicles.
  • the cubic Bezier curve can be defined as an affine combination of two quadratic Bezier curves:

Landscapes

  • Engineering & Computer Science (AREA)
  • General Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
US17/272,824 2018-09-11 2019-09-03 Active battery management method Pending US20210190868A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/272,824 US20210190868A1 (en) 2018-09-11 2019-09-03 Active battery management method

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201862729598P 2018-09-11 2018-09-11
US17/272,824 US20210190868A1 (en) 2018-09-11 2019-09-03 Active battery management method
PCT/IB2019/057409 WO2020053702A1 (en) 2018-09-11 2019-09-03 Active battery management method

Publications (1)

Publication Number Publication Date
US20210190868A1 true US20210190868A1 (en) 2021-06-24

Family

ID=69777670

Family Applications (3)

Application Number Title Priority Date Filing Date
US17/272,824 Pending US20210190868A1 (en) 2018-09-11 2019-09-03 Active battery management method
US17/274,307 Active 2041-11-26 US12092694B2 (en) 2018-09-11 2019-09-10 Active battery management method for economic optimization
US18/824,176 Pending US20240426921A1 (en) 2018-09-11 2024-09-04 Active battery management method for economic optimization

Family Applications After (2)

Application Number Title Priority Date Filing Date
US17/274,307 Active 2041-11-26 US12092694B2 (en) 2018-09-11 2019-09-10 Active battery management method for economic optimization
US18/824,176 Pending US20240426921A1 (en) 2018-09-11 2024-09-04 Active battery management method for economic optimization

Country Status (5)

Country Link
US (3) US20210190868A1 (enExample)
EP (2) EP3850385A4 (enExample)
JP (2) JP7361122B2 (enExample)
CN (3) CN112703412A (enExample)
WO (2) WO2020053702A1 (enExample)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230138942A1 (en) * 2021-10-30 2023-05-04 Beta Air, Llc Systems and methods for battery management for a vehicle
WO2023102265A1 (en) * 2021-12-03 2023-06-08 Sustainable Energy Technologies, Inc. System and method for energy management of a power pack for an electric vehicle including photovoltaic charging
EP4276486A1 (en) * 2022-05-12 2023-11-15 CALB Co., Ltd. Battery device, detection method thereof, and screening method and device of battery unit
US12179608B2 (en) 2021-12-03 2024-12-31 Sustainable Energy Technologies, Inc. Monitoring and managing temperature of power packs
US12194886B2 (en) 2021-12-06 2025-01-14 Sustainable Energy Technologies, Inc. System and method for analyzing temperature changes in supercapacitor battery storage for electric vehicle
US12296721B2 (en) 2021-12-10 2025-05-13 Sustainable Energy Technologies, Inc. Method and systems to trickle charge electric vehicle's supercapacitors using solar energy

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210190868A1 (en) 2018-09-11 2021-06-24 3M Innovative Properties Company Active battery management method
JP6896793B2 (ja) * 2019-05-27 2021-06-30 本田技研工業株式会社 情報処理装置
DE102019216015A1 (de) * 2019-10-17 2021-04-22 Robert Bosch Gmbh Verfahren zum Anpassen einer Anodenüberspannung einer Lithium-Ionen-Batterie, Verfahren zum Verbessern eines Kapazitätsalterungszustands der Lithium-Ionen-Batterie
DE102020212236A1 (de) * 2020-09-29 2022-03-31 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren und Vorrichtung zum Betreiben eines Systems zum Bereitstellen von Alterungszuständen von elektrischen Energiespeichern für eine Vielzahl von Geräten mithilfe von maschinellen Lernverfahren
CN115556610A (zh) * 2021-07-01 2023-01-03 沃尔沃汽车公司 车对车充电系统和方法
KR102769183B1 (ko) * 2022-06-13 2025-02-14 연세대학교 산학협력단 이차전지 수명 분석방법 및 장치
WO2024187166A1 (en) * 2023-03-08 2024-09-12 Lifetime Energy Inc. System and method of determining economic decisions based on lifetime performance of a battery by a real-time battery management system

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6181100B1 (en) * 1998-04-01 2001-01-30 Toyo System Co., Ltd. Power supply apparatus for charging and discharging battery with high energy efficiency
US6374251B1 (en) * 1998-03-17 2002-04-16 Microsoft Corporation Scalable system for clustering of large databases
US20020107858A1 (en) * 2000-07-05 2002-08-08 Lundahl David S. Method and system for the dynamic analysis of data
US20020196027A1 (en) * 2001-06-26 2002-12-26 Tate Edward Dean State-of-charge detection device for a battery
US20030212520A1 (en) * 2002-05-10 2003-11-13 Campos Marcos M. Enhanced K-means clustering
US20090034159A1 (en) * 2006-02-07 2009-02-05 Bayerische Motoren Werke Aktiengesellschaft Method of Monitoring and/or Controlling or Automatically Controlling the Voltage of at Least One Group of Cells in a Compound of Cells of an Energy Storage Device
US20130073490A1 (en) * 2011-09-16 2013-03-21 International Business Machines Corporation Choosing pattern recognition algorithms and data features
US20140361743A1 (en) * 2013-06-11 2014-12-11 Ford Global Technologies, Llc Detection of Imbalance Across Multiple Battery Cells Measured by the Same Voltage Sensor
WO2016115002A1 (en) * 2015-01-16 2016-07-21 3M Innovative Properties Company Systems and methods for selecting grid actions to improve grid outcomes
US20170018096A1 (en) * 2014-03-31 2017-01-19 Ingrain, Inc. Representative Elementary Volume Determination Via Clustering-based Statistics
US20170229881A1 (en) * 2016-02-05 2017-08-10 Nec Laboratories America, Inc. Resilient battery charging strategies to reduce battery degradation and self-discharging
US20190115778A1 (en) * 2017-10-17 2019-04-18 The Board Of Trustees Of The Leland Stanford Junior University Autonomous screening and optimization of battery formation and cycling procedures
US20200164763A1 (en) * 2017-07-21 2020-05-28 Quantumscape Corporation Predictive model for estimating battery states

Family Cites Families (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3379298B2 (ja) * 1995-08-30 2003-02-24 新神戸電機株式会社 シール鉛蓄電池の寿命判定装置
CN1264471A (zh) * 1997-06-19 2000-08-23 斯耐普昂仪器有限公司 电池试验和分类
KR100317598B1 (ko) 1999-03-13 2001-12-22 박찬구 라플라스 변환 임피던스 측정방법 및 측정장치
US6555991B1 (en) 2002-02-05 2003-04-29 Andrew Michael Zettel Battery operating condition dependent method and apparatus for controlling energy transfer between an energy bus and a system of batteries
CN1862279B (zh) * 2005-05-11 2010-04-28 苏州润源电气技术有限公司 电池组老化率估算、故障检测方法和电池组管理监测装置
CN1975444A (zh) * 2005-11-28 2007-06-06 孙斌 蓄电池内阻及劣化状态在线监测方法及系统
JP5363740B2 (ja) * 2008-01-31 2013-12-11 パナソニック株式会社 充電制御回路、電池パック、及び充電システム
JP5098912B2 (ja) * 2008-07-11 2012-12-12 ソニー株式会社 バッテリパックおよび充電制御システム
KR101288652B1 (ko) * 2009-10-09 2013-07-22 주식회사 엘지화학 사용자 패턴별 충전 설정시스템 및 충전방법
US8564246B2 (en) 2010-03-30 2013-10-22 Grrreen, Inc. Battery charging system and method
CA2815218C (en) * 2010-10-22 2018-10-09 Nucleus Scientific, Inc. Apparatus and method for rapidly charging batteries
CN102545280A (zh) * 2010-12-16 2012-07-04 鸿富锦精密工业（深圳）有限公司 充电电路
RU2635101C2 (ru) * 2012-04-12 2017-11-09 Ист Пенн Мэньюфэкчуринг Ко. Управление емкостью аккумуляторной батареи
US8963494B2 (en) * 2012-05-18 2015-02-24 Tesla Motors, Inc. Charge rate optimization
US20140097787A1 (en) 2012-10-09 2014-04-10 Yi-Ming Lin Active battery management system for a battery pack
KR20140094362A (ko) * 2013-01-22 2014-07-30 한국전자통신연구원 전기차 에너지 관리 장치 및 방법
US9476946B2 (en) 2013-05-08 2016-10-25 GM Global Technology Operations LLC SOC determination by evaluation of effects invoked by dimensional changes of battery cells
US20150372279A1 (en) 2014-06-24 2015-12-24 Turboroto Inc. Active battery stack system and method
CN105005005B (zh) 2015-08-10 2017-10-13 东南大学 一种蓄电池组劣化单体电池检测方法
US20170045902A1 (en) 2015-08-14 2017-02-16 Nec Laboratories America, Inc. Control strategies for grid scale storage operation in frequency regulation markets considering battery health factors
JP6841278B2 (ja) 2016-04-22 2021-03-10 株式会社村田製作所 充放電制御装置、充放電制御方法、電池パック、電子機器、電動車両、電動工具および電力貯蔵システム
CN107528337A (zh) 2016-11-21 2017-12-29 六安市科宇专利技术开发服务有限公司 一种带储能系统的电动汽车充电站
US10193369B2 (en) 2017-01-05 2019-01-29 Gbatteries Energy Canada Inc. Active battery management system
CN107453408B (zh) 2017-07-18 2020-05-08 广东双新电气科技有限公司 一种考虑不确定性的微电网能量优化调度方法
US20210190868A1 (en) 2018-09-11 2021-06-24 3M Innovative Properties Company Active battery management method
EP3938974A4 (en) 2019-03-15 2023-01-04 3M Innovative Properties Company OPERATION OF A SUPPLY CHAIN USING CAUSED MODELS

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6374251B1 (en) * 1998-03-17 2002-04-16 Microsoft Corporation Scalable system for clustering of large databases
US6181100B1 (en) * 1998-04-01 2001-01-30 Toyo System Co., Ltd. Power supply apparatus for charging and discharging battery with high energy efficiency
US20020107858A1 (en) * 2000-07-05 2002-08-08 Lundahl David S. Method and system for the dynamic analysis of data
US20020196027A1 (en) * 2001-06-26 2002-12-26 Tate Edward Dean State-of-charge detection device for a battery
US20030212520A1 (en) * 2002-05-10 2003-11-13 Campos Marcos M. Enhanced K-means clustering
US20090034159A1 (en) * 2006-02-07 2009-02-05 Bayerische Motoren Werke Aktiengesellschaft Method of Monitoring and/or Controlling or Automatically Controlling the Voltage of at Least One Group of Cells in a Compound of Cells of an Energy Storage Device
US20130073490A1 (en) * 2011-09-16 2013-03-21 International Business Machines Corporation Choosing pattern recognition algorithms and data features
US20140361743A1 (en) * 2013-06-11 2014-12-11 Ford Global Technologies, Llc Detection of Imbalance Across Multiple Battery Cells Measured by the Same Voltage Sensor
US20170018096A1 (en) * 2014-03-31 2017-01-19 Ingrain, Inc. Representative Elementary Volume Determination Via Clustering-based Statistics
WO2016115002A1 (en) * 2015-01-16 2016-07-21 3M Innovative Properties Company Systems and methods for selecting grid actions to improve grid outcomes
US20180013320A1 (en) * 2015-01-16 2018-01-11 3M Innovative Properties Company Systems and methods for selecting grid actions to improve grid outcomes
US20170229881A1 (en) * 2016-02-05 2017-08-10 Nec Laboratories America, Inc. Resilient battery charging strategies to reduce battery degradation and self-discharging
US20200164763A1 (en) * 2017-07-21 2020-05-28 Quantumscape Corporation Predictive model for estimating battery states
US20190115778A1 (en) * 2017-10-17 2019-04-18 The Board Of Trustees Of The Leland Stanford Junior University Autonomous screening and optimization of battery formation and cycling procedures

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Park et al., "Optimal Input Design for Parameter Identification in an Electrochemical Li-ion Battery Model", June 27–29 2018, 2018 Annual American Control Conference, p2300-2305 (Year: 2018) *
Petchjatuporn et al., "Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries", 14-16 August 2006, IPEMC 2006 (Year: 2006) *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230138942A1 (en) * 2021-10-30 2023-05-04 Beta Air, Llc Systems and methods for battery management for a vehicle
US11983971B2 (en) * 2021-10-30 2024-05-14 Beta Air, Llc Systems and methods for battery management for a vehicle
WO2023102265A1 (en) * 2021-12-03 2023-06-08 Sustainable Energy Technologies, Inc. System and method for energy management of a power pack for an electric vehicle including photovoltaic charging
US12179608B2 (en) 2021-12-03 2024-12-31 Sustainable Energy Technologies, Inc. Monitoring and managing temperature of power packs
US12227108B2 (en) * 2021-12-03 2025-02-18 Sustainable Energy Technologies, Inc. System and method for energy management of a power pack for an electric vehicle including photovoltaic charging
US12194886B2 (en) 2021-12-06 2025-01-14 Sustainable Energy Technologies, Inc. System and method for analyzing temperature changes in supercapacitor battery storage for electric vehicle
US12296721B2 (en) 2021-12-10 2025-05-13 Sustainable Energy Technologies, Inc. Method and systems to trickle charge electric vehicle's supercapacitors using solar energy
EP4276486A1 (en) * 2022-05-12 2023-11-15 CALB Co., Ltd. Battery device, detection method thereof, and screening method and device of battery unit
US12130336B2 (en) 2022-05-12 2024-10-29 Calb Co., Ltd. Battery device, detection method thereof, and screening method and device of battery unit

Also Published As

Publication number Publication date
US20240426921A1 (en) 2024-12-26
JP7361122B2 (ja) 2023-10-13
WO2020055778A1 (en) 2020-03-19
JP7731286B2 (ja) 2025-08-29
JP2022500995A (ja) 2022-01-04
US20210336471A1 (en) 2021-10-28
CN112673509B (zh) 2025-02-18
US12092694B2 (en) 2024-09-17
CN112703412A (zh) 2021-04-23
EP3850700A1 (en) 2021-07-21
EP3850385A1 (en) 2021-07-21
EP3850385A4 (en) 2022-06-29
EP3850700A4 (en) 2022-07-27
JP2022500994A (ja) 2022-01-04
CN119944132A (zh) 2025-05-06
WO2020053702A1 (en) 2020-03-19
CN112673509A (zh) 2021-04-16

Similar Documents

Publication Publication Date Title
US20240426921A1 (en) Active battery management method for economic optimization
US11598818B2 (en) Determination of the aging of an electric storage system
US9846886B2 (en) Strategic modeling for economic optimization of grid-tied energy assets
US12241942B2 (en) Method for estimating aging state of battery and apparatus for performing method therefor
US9411019B2 (en) Method and system for estimating state of health of battery set
EP3988952A1 (en) Method for detecting abnormal battery cell
US20140032141A1 (en) System and Method for Posteriori Adaptation of a State of Charge Model in a Battery
CN107329094A (zh) 动力电池健康状态估算方法及装置
CN117388737A (zh) 电池健康状态的评估方法、装置、设备及存储介质
US20220099752A1 (en) Method and Apparatus for Machine-Individual Improvement of the Lifetime of a Battery in a Battery-Operated Machine
Namdari et al. An entropy-based approach for modeling lithium-ion battery capacity fade
US20230184838A1 (en) Device and method for diagnosing battery
Maheshwari et al. Electric Vehicle Battery Health Monitoring System
CN118944238B (zh) 一种新能源储能锂电池控制系统及控制方法
US20220416548A1 (en) Operational planning for battery-based energy storage systems considering battery aging
CN115825755B (zh) 一种储能电池电芯电压一致性评估方法
Mauri et al. Electro-thermal aging model of Li-ion batteries for vehicle-to-grid services
Romero et al. Fast charge of Li-ion batteries using a two-layer distributed MPC with electro-chemical and thermal constraints
Donadee et al. Estimating the rate of battery degradation under a stationary Markov operating policy
EP4545989A1 (en) Method and system for calculating battery state of charge
US20240086588A1 (en) Systems and Methods for Managing Energy Storage Devices
US20240377464A1 (en) Lead-Acid Battery System And Lead-Acid Battery Life Estimation Method
Ye et al. Quantifying the impact of charging strategy on degradation of battery energy storage system
Seferi Design of a machine learning pipeline for data-driven li-ion degradation modeling
Al-Hilo Aging Characterization of Li-ion Batteries

Legal Events

Date Code Title Description
AS Assignment

Owner name: 3M INNOVATIVE PROPERTIES COMPANY, MINNESOTA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BENOIT, GILLES J.;LEATHERDALE, CATHERINE A.;WEST, DON VINCENT;AND OTHERS;SIGNING DATES FROM 20210122 TO 20210203;REEL/FRAME:055460/0947

STPP Information on status: patent application and granting procedure in general

Free format text: APPLICATION DISPATCHED FROM PREEXAM, NOT YET DOCKETED

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: NON FINAL ACTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION MAILED

STCV Information on status: appeal procedure

Free format text: NOTICE OF APPEAL FILED

STCV Information on status: appeal procedure

Free format text: APPEAL BRIEF (OR SUPPLEMENTAL BRIEF) ENTERED AND FORWARDED TO EXAMINER

STCV Information on status: appeal procedure

Free format text: EXAMINER'S ANSWER TO APPEAL BRIEF MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: NON FINAL ACTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION COUNTED, NOT YET MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE AFTER FINAL ACTION FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: ADVISORY ACTION COUNTED, NOT YET MAILED

STCV Information on status: appeal procedure

Free format text: NOTICE OF APPEAL FILED

STCV Information on status: appeal procedure

Free format text: APPEAL BRIEF (OR SUPPLEMENTAL BRIEF) ENTERED AND FORWARDED TO EXAMINER