CN111354989B - Reconfigurable battery pack control method and system and storage medium - Google Patents

Reconfigurable battery pack control method and system and storage medium Download PDF

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CN111354989B
CN111354989B CN202010106723.5A CN202010106723A CN111354989B CN 111354989 B CN111354989 B CN 111354989B CN 202010106723 A CN202010106723 A CN 202010106723A CN 111354989 B CN111354989 B CN 111354989B
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battery
state
health
battery pack
batteries
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CN111354989A (en
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李飞
张莉
刘伟荣
杨迎泽
彭军
黄志武
李恒
张晓勇
陈彬
张瑞
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Central South University
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    • 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
    • H01M10/4207—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells for several batteries or cells simultaneously or sequentially
    • 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
    • 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
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • 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

Abstract

The invention discloses a reconfigurable battery pack control method, a reconfigurable battery pack control system and a storage medium, wherein the method comprises the following steps: acquiring current, voltage and temperature data of each battery in the discharging process; extracting characteristic parameters reflecting battery aging from the acquired current, voltage and temperature data in the discharging process of each battery, and predicting by using a battery health state estimation model trained in advance respectively based on the characteristic parameters reflecting battery aging of each battery to obtain the health state distribution of each battery; and establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision, and reconstructing the battery pack according to the battery pack reconstruction control decision. Through reconfiguring the reconfigurable battery pack, inconsistency among batteries can be reduced, the utilization rate of the batteries is improved, overcharging and overdischarging are avoided, the service life of the battery pack is prolonged, and safe operation of the battery pack can be ensured.

Description

Reconfigurable battery pack control method and system and storage medium
Technical Field
The invention relates to the technical field of batteries, in particular to a reconfigurable battery pack control method, a reconfigurable battery pack control system and a storage medium.
Background
Along with the vigorous development of the economy of China, the problem of environmental pollution is increasingly serious. The increasing living standard of people and the continuous increase of the number of automobiles in cities are contradictory to the increasing shortage of non-renewable energy petroleum, so that environmental and energy problems have been the focus of global attention. At present, green vehicles are researched all over the world, and pure electric vehicles and hybrid electric vehicles are successively proposed to solve the two problems. For example, chevrolet and tesla electric vehicles use hundreds and thousands of batteries, respectively, to construct large battery systems.
The inventors have found that there are several major drawbacks to conventional battery pack designs that are packaged in a battery pack in a secure manner. Since these fixed battery topologies are unable to accommodate the dynamic behavior and manufacturing variations of the battery cells, over time, the hazards of inconsistencies between the batteries are magnified as the battery pack is repeatedly charged and discharged, resulting in inefficient energy conversion. In particular, one or more weak cells will charge or discharge faster than the other cells, while weaker cells will limit the useful life of the entire battery pack and may even cause serious safety problems such as ignition and explosion due to thermal runaway and overheating. The fixed topology also imposes restrictions on management: this can lead to a series of problems including overcharging and overdischarging, due to lack of flexibility in performing proactive management.
Disclosure of Invention
The invention provides a reconfigurable battery pack control method, a reconfigurable battery pack control system and a storage medium, and aims to solve the problems that a fixed packaged battery pack in the prior art is short in service life and has potential safety hazards.
In a first aspect, a reconfigurable battery pack control method is provided, comprising:
acquiring current, voltage and temperature data of each battery in the discharging process;
extracting characteristic parameters reflecting battery aging from the acquired current, voltage and temperature data in the discharging process of each battery, and predicting by using a battery health state estimation model trained in advance respectively based on the characteristic parameters reflecting battery aging of each battery to obtain the health state distribution of each battery;
and establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision, and reconstructing the battery pack according to the battery pack reconstruction control decision.
Further, the characteristic parameters reflecting the aging of the battery comprise average voltage drop MVF, voltage signal fluctuation index VFI, temperature and current;
wherein, the average voltage drop MVF is calculated by the following formula:
Figure BDA0002388714660000021
in the formula, VrAt rated voltage, VjThe voltage value of the jth sampling point in the discharging process is shown, and N is the total number of the sampling points;
the voltage signal fluctuation index VFI is calculated by the following formula:
Figure BDA0002388714660000022
in the formula, yjThe voltage value of the jth sampling point in the discharging process is shown, N is the total number of the sampling points, mu is the average value of the voltage values of all the sampling points, and w is the sampling frequency.
Further, the battery state of health estimation model is obtained by training through the following method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a mild gradient lifting tree, taking a battery state of health stage as output of the mild gradient lifting tree, and training to obtain a battery state of health estimation model based on the mild gradient lifting tree;
the battery state of health estimation model is used for predicting the state of health of the battery, and the prediction result is the probability that the state of health of the battery is in each state of health, namely the state of health distribution.
By taking the average voltage drop MVF and the voltage signal fluctuation index VFI as characteristic parameters, the characteristics of capacity, internal resistance and the like of the battery under an offline condition are avoided, and the health state of the battery can be accurately reflected, so that an accurate battery health state estimation model can be established and an accurate health state prediction result can be provided.
Further, the establishing of the battery pack optimization model specifically includes:
determine the total number of
Figure BDA0002388714660000023
The battery packs are connected in series, wherein N is the total number of batteries, and N is the number of the batteries contained in each series battery pack;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels from high to low, wherein the i-th battery state of health distribution hi(z) is represented by:
Figure BDA0002388714660000024
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected electrodesGroup of cells andiand sjThe conflict relationship between the two is as follows:
Figure BDA0002388714660000031
Figure BDA0002388714660000032
then the battery group s is connected in seriesjDistribution of health status
Figure BDA0002388714660000033
The calculation formula of (2) is as follows:
Figure BDA0002388714660000034
and finally, establishing a health state-based battery pack optimization model as follows:
Figure BDA0002388714660000035
Figure BDA0002388714660000036
wherein the content of the first and second substances,
Figure BDA00023887146600000317
a structural logic relation operator corresponding to the general generating function; for a given function f, the function f,
Figure BDA0002388714660000037
the calculation formula is as follows:
Figure BDA0002388714660000038
for series batteries sjDistribution of health status
Figure BDA0002388714660000039
The formula for the calculation of (a) is,
Figure BDA00023887146600000310
j in the calculation formulanIndicating the state of health of the nth cell,
Figure BDA00023887146600000311
indicates that the nth cell is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600000312
indicates that the nth cell is at the jthnA stage of individual health status;
with respect to the formula of the state-of-health based battery optimization model h (z),
Figure BDA00023887146600000313
j in the calculation formulanIndicating the state of health of the nth series stack,
Figure BDA00023887146600000314
indicating that the nth series battery is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600000315
indicating that the nth series battery is at the jthnA stage of individual health status;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,sm}. Wherein, the solving can adopt a graph theory method to solve:
let P be the set of feasible cell strings, for each feasible path Pi(series path of a series-connected battery), mapped to the corresponding vertex v in the graph GiThe weight of the top point is the health state of the series battery
Figure BDA00023887146600000316
If and only if the two paths conflict with each other (i.e., conflict (p)i,pj) 1) to connect two vertices v in GiAnd vj；
Finally greedily the vertex v with the maximum valueiAdding the solution into a solution set, and deleting vertexes connected with the solution set at the same time until no vertex is contained any more, wherein the solution set is the m series battery packs { s1,s2,…,sm}。
A reasonable reconfiguration strategy is designed based on the health state of the battery pack under the limited flexibility of the reconfigurable battery pack structure, so that the inconsistency among batteries is reduced, and the service life of the battery pack is prolonged.
In a second aspect, a reconfigurable battery pack control system is provided, comprising an energy storage system circuit and a battery management subsystem;
the energy storage system circuit comprises a plurality of batteries and controllable switch groups which are arranged corresponding to the batteries, and each controllable switch group is used for controlling the corresponding battery to be connected with other batteries in parallel or in series and controlling whether the corresponding battery is connected with a battery pack or not;
the battery management subsystem is used for acquiring current, voltage and temperature data in the discharging process of each battery, extracting characteristic parameters reflecting battery aging from the current, voltage and temperature data, predicting to obtain the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, generating a battery pack reconstruction control decision according to the health state distribution of each battery, and reconstructing the battery pack according to the battery pack reconstruction control decision.
Furthermore, the controllable switch group comprises a series switch, an input switch, an output switch and two parallel switches;
the two parallel switches are respectively connected in series at two ends of the corresponding battery and are used for controlling whether the corresponding battery is connected in parallel with other batteries;
the series switch is connected in parallel at two ends of the corresponding battery and is used for controlling whether the corresponding battery is connected with other batteries in series or not;
the input switch and the output switch are respectively connected with the negative electrode and the positive electrode of the corresponding battery in series and are used for controlling whether the corresponding battery is connected with the battery pack.
Furthermore, the battery management subsystem comprises a sensing signal acquisition module, a communication module, a state decision module and a switch driving module which are connected in sequence;
the sensing signal acquisition module is used for acquiring voltage, current and temperature data of each battery;
the communication module is used for transmitting the data acquired by the sensing signal acquisition module to the state decision module;
the state decision module is used for receiving the data transmitted by the communication module, extracting characteristic parameters reflecting battery aging from the data, predicting and obtaining the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, obtaining a battery pack reconstruction control decision according to the health state distribution of each battery, generating a switch control signal according to the battery pack reconstruction control decision and sending the switch control signal to a switch driving signal;
the switch driving signal is used for receiving the switch control signal sent by the state decision module and controlling the on-off of the switches in the controllable switch group according to the switch control signal.
Further, the state decision module obtains the state of health distribution of each battery through a preset battery state of health estimation model prediction, wherein the battery state of health estimation model is obtained through the following training method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a mild gradient lifting tree, taking a battery state of health stage as output of the mild gradient lifting tree, and training to obtain a battery state of health estimation model based on the mild gradient lifting tree;
the characteristic parameters reflecting the aging of the battery comprise average voltage drop MVF, voltage signal fluctuation index VFI, temperature and current; wherein, the average voltage drop MVF is calculated by the following formula:
Figure BDA0002388714660000051
in the formula, VrAt rated voltage, VjThe voltage value of the jth sampling point in the discharging process is shown, and N is the total number of the sampling points;
the voltage signal fluctuation index VFI is calculated by the following formula:
Figure BDA0002388714660000052
in the formula, yjThe voltage value of the jth sampling point in the discharging process is shown, N is the total number of the sampling points, mu is the average value of the voltage values of all the sampling points, and w is the sampling frequency;
the battery state of health estimation model is used for predicting the state of health of the battery, and the prediction result is the probability that the state of health of the battery is in each state of health, namely the state of health distribution.
By taking the average voltage drop MVF and the voltage signal fluctuation index VFI as characteristic parameters, the characteristics of capacity, internal resistance and the like of the battery under an offline condition are avoided, and the health state of the battery can be accurately reflected, so that an accurate battery health state estimation model can be established and an accurate health state prediction result can be provided.
Further, the state decision module obtains a battery pack reconfiguration control decision according to the state of health distribution of each battery, and specifically includes:
establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision; the establishing of the battery pack optimization model specifically comprises the following steps:
determine the total number of
Figure BDA0002388714660000053
The battery packs are connected in series, wherein N is the total number of batteries, and N is the number of the batteries contained in each series battery pack;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels from high to low, wherein the i-th battery state of health distribution hi(z) is represented by:
Figure BDA0002388714660000054
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected battery packs andiand sjThe conflict relationship between the two is as follows:
Figure BDA0002388714660000061
Figure BDA0002388714660000062
then the battery group s is connected in seriesjDistribution of health status
Figure BDA0002388714660000063
The calculation formula of (2) is as follows:
Figure BDA0002388714660000064
and finally, establishing a health state-based battery pack optimization model as follows:
Figure BDA0002388714660000065
Figure BDA0002388714660000066
wherein the content of the first and second substances,
Figure BDA00023887146600000617
a structural logic relation operator corresponding to the general generating function; for a given function f, the function f,
Figure BDA0002388714660000067
the calculation formula is as follows:
Figure BDA0002388714660000068
for series batteries sjDistribution of health status
Figure BDA0002388714660000069
The formula for the calculation of (a) is,
Figure BDA00023887146600000610
j in the calculation formulanIndicating the state of health of the nth cell,
Figure BDA00023887146600000611
indicates that the nth cell is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600000612
indicates that the nth cell is at the jthnA stage of individual health status;
is directed to based onThe formula of the state of health battery optimization model h (z),
Figure BDA00023887146600000613
j in the calculation formulanIndicating the state of health of the nth series stack,
Figure BDA00023887146600000614
indicating that the nth series battery is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600000615
indicating that the nth series battery is at the jthnA stage of individual health status;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,sm}. Wherein, the solving can adopt a graph theory method to solve:
let P be the set of feasible cell strings, for each feasible path Pi(series path of a series-connected battery), mapped to the corresponding vertex v in the graph GiThe weight of the top point is the health state of the series battery
Figure BDA00023887146600000616
If and only if the two paths conflict with each other (i.e., conflict (p)i,pj) 1) to connect two vertices v in GiAnd vj；
Finally greedily the vertex v with the maximum valueiAdding the solution into a solution set, and deleting vertexes connected with the solution set at the same time until no vertex is contained any more, wherein the solution set is the m series battery packs { s1,s2,…,sm}。
In a third aspect, there is provided a computer readable storage medium comprising stored program instructions adapted to be loaded by a processor and to perform the reconfigurable battery pack control method as described above.
Advantageous effects
The invention provides a reconfigurable battery pack control method, a reconfigurable battery pack control system and a reconfigurable battery pack control storage medium, wherein before each use, the health state of each battery is predicted, then the connection mode between the batteries is dynamically adjusted according to the health state of each battery, a mode of first series connection and then parallel connection is adopted, the batteries with similar health states are connected in series to form a series battery pack, the batteries with similar health states are connected in series, and because the internal resistance, the temperature and the terminal voltage of the batteries in the charge and discharge processes are very similar, the inconsistent diffusion in the charge and discharge processes is avoided, and then all the series battery packs are connected in parallel. The reconfigurable battery pack is reconstructed by modeling and solving the reconfigurable battery pack, so that the inconsistency among batteries can be reduced, the utilization rate of the batteries is improved, overcharging and overdischarging are avoided, the service life of the battery pack is prolonged, and the safe operation of the battery pack can be ensured.
Drawings
FIG. 1 is a circuit schematic of a reconfigurable battery pack control system provided by an embodiment of the present invention;
FIG. 2 is a block schematic diagram of a reconfigurable battery pack control system provided by an embodiment of the present invention;
fig. 3 is a flowchart of a reconfigurable battery pack control method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Example 1
The present embodiment provides a reconfigurable battery pack control system, as shown in fig. 1, including an energy storage system circuit 1 and a battery management subsystem;
the energy storage system circuit comprises a plurality of batteries 11 and controllable switch groups which are arranged corresponding to the batteries, wherein each controllable switch group is used for controlling the corresponding battery to be connected with other batteries in parallel or in series and controlling whether the corresponding battery is connected with a battery pack;
the battery management subsystem is used for acquiring current, voltage and temperature data in the discharging process of each battery, extracting characteristic parameters reflecting battery aging from the current, voltage and temperature data, predicting to obtain the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, generating a battery pack reconstruction control decision according to the health state distribution of each battery, and reconstructing the battery pack according to the battery pack reconstruction control decision.
Specifically, as shown in fig. 1 and fig. 2, the controllable switch group includes a series switch 13, an input switch 14, an output switch 15, and two parallel switches 12;
the two parallel switches 12 are respectively connected in series at two ends of the corresponding battery 11 and are used for controlling whether the corresponding battery is connected in parallel with other batteries;
the series switch 13 is connected in parallel to two ends of the corresponding battery 11 and is used for controlling whether the corresponding battery is connected in series with other batteries;
the input switch 14 and the output switch 15 are respectively connected in series with the negative electrode and the positive electrode of the corresponding battery 11, and are used for controlling whether the corresponding battery is connected to the battery pack.
The battery management subsystem comprises a sensing signal acquisition module 2, a communication module 3, a state decision module 4 and a switch driving module 5 which are sequentially connected;
the sensing signal acquisition module 2 is used for acquiring voltage, current and temperature data of each battery;
the communication module 3 is used for transmitting the data acquired by the sensing signal acquisition module 2 to the state decision module 4;
the state decision module 4 is used for receiving the data transmitted by the communication module 3, extracting characteristic parameters reflecting battery aging from the data, predicting and obtaining the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, obtaining a battery pack reconfiguration control decision according to the health state distribution of each battery, generating a switch control signal according to the battery pack reconfiguration control decision, and sending the switch control signal to the switch driving signal 5;
the switch driving signal 5 is used for receiving the switch control signal sent by the state decision module 4 and controlling the on-off of the switches in the controllable switch group according to the switch control signal.
In specific implementation, as shown in fig. 2, the sensing signal acquisition module 2 includes a sensor for acquiring a signal, a signal conditioning channel for amplifying and filtering a signal of the sensor, an a/D conversion circuit for converting an analog signal into a digital signal, and a dual-port RAM for storing data, which are sequentially connected, and each battery is correspondingly configured with a sensor for acquiring a signal, specifically including a voltage sensor, a current sensor, and a temperature sensor. The communication module adopts a CAN communication protocol, and is respectively connected with the double-port RAM and the state decision module. The state decision module can adopt a DSP2812 singlechip, and is connected with the switch driving module through a GPIO interface. The switch driving module outputs a control signal to the controllable switch group to carry out on-off control, wherein the control signal is a Pulse Width Modulation (PWM) signal; it is clear that for the series and parallel switches controlling the same battery, only one switch can be switched on at a time, if the battery is connected in series with the following battery, the series switch is closed, and if the battery is connected in parallel with the following battery, the two parallel switches are closed.
It should be noted that the state decision module obtains the state of health distribution of each battery through a preset battery state of health estimation model prediction, wherein the battery state of health estimation model is obtained through the following training method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value, and the value of K can be 5 in specific implementation;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a Light Gradient Boosting Machine (Light Gradient Boosting Machine), taking a battery state of health as output of the Light Gradient Boosting Machine, and training to obtain a battery state of health estimation model based on the Light Gradient Boosting tree;
the characteristic parameters reflecting the aging of the battery comprise average voltage drop MVF, voltage signal fluctuation index VFI, temperature and current; wherein, the average voltage drop MVF is calculated by the following formula:
Figure BDA0002388714660000091
in the formula, VrAt rated voltage, VjThe voltage value of the jth sampling point in the discharging process is shown, and N is the total number of the sampling points;
the voltage signal fluctuation index VFI is calculated by the following formula:
Figure BDA0002388714660000092
in the formula, yjThe voltage value of the jth sampling point in the discharging process is shown, N is the total number of the sampling points, mu is the average value of the voltage values of all the sampling points, and w is the sampling frequency;
the battery state of health estimation model is used for predicting the state of health of the battery, and the prediction result is the probability that the state of health of the battery is in each state of health, namely the state of health distribution.
By taking the average voltage drop MVF and the voltage signal fluctuation index VFI as characteristic parameters, the characteristics of capacity, internal resistance and the like of the battery under an offline condition are avoided, and the health state of the battery can be accurately reflected, so that an accurate battery health state estimation model can be established and an accurate health state prediction result can be provided.
The state decision module obtains a battery pack reconfiguration control decision according to the health state distribution of each battery, and specifically comprises:
establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision; the establishing of the battery pack optimization model specifically comprises the following steps:
determine the total number of
Figure BDA0002388714660000093
A non-intersecting series battery s1,s2,…,smN is the total number of batteries, N is the number of batteries contained in each series battery pack, and N is determined by the load when the reconfigurable battery pack is used;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels in order from high to low; specifically, the higher the capacity of the battery is, the higher the level of the state of health stage is, firstly, according to the state of health stage with the maximum probability corresponding to each battery, performing primary ranking from high to low according to the level, and then, for the batteries with the same state of health stage with the maximum probability, performing fine ranking according to the state of health stage with the maximum probability of each battery and the size after the probability corresponding to all the state of health stages higher than the state of health stage;
wherein the distribution h of the state of health of the ith celli(z) is represented by:
Figure BDA0002388714660000094
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected battery packs andiand sjThe conflict relationship between the two is as follows:
Figure BDA0002388714660000101
Figure BDA0002388714660000102
then the battery group s is connected in seriesjDistribution of health status
Figure BDA0002388714660000103
The calculation formula of (2) is as follows:
Figure BDA0002388714660000104
and finally, establishing a health state-based battery pack optimization model as follows:
Figure BDA0002388714660000105
Figure BDA0002388714660000106
wherein the content of the first and second substances,
Figure BDA00023887146600001017
a structural logic relation operator corresponding to the general generating function; for a given function f, the function f,
Figure BDA0002388714660000107
the calculation formula is as follows:
Figure BDA0002388714660000108
for series batteries sjDistribution of health status
Figure BDA0002388714660000109
The formula for the calculation of (a) is,
Figure BDA00023887146600001010
j in the calculation formulanIndicating the state of health of the nth cell,
Figure BDA00023887146600001011
indicates that the nth cell is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600001012
indicates that the nth cell is at the jthnA stage of individual health status;
with respect to the formula of the state-of-health based battery optimization model h (z),
Figure BDA00023887146600001013
j in the calculation formulanIndicating the state of health of the nth series stack,
Figure BDA00023887146600001014
indicating that the nth series battery is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600001015
indicating that the nth series battery is at the jthnA stage of individual health status;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,smAnd the state decision module generates a PWM wave switch control signal corresponding to the battery pack reconfiguration control decision conversion according to the battery pack reconfiguration control decision conversion, and outputs the PWM wave switch control signal to the switch driving module. Wherein, the solving can adopt a graph theory method to solve:
let P be the set of feasible cell strings, for each feasible path Pi(series path of a series-connected battery), mapped to the corresponding vertex v in the graph GiThe weight of the top point is the health state of the series battery
Figure BDA00023887146600001016
If and only if the two paths conflict with each other (i.e., conflict (p)i,pj) 1) to connect two vertices v in GiAnd vj；
Finally greedily the vertex v with the maximum valueiAdding to solution setIn the method, the vertexes connected with the battery packs are deleted at the same time until no vertex is contained any more, and the solution set is the m series battery packs { s1,s2,…,sm}。
Example 2
As shown in fig. 3, the present embodiment provides a reconfigurable battery pack control method including:
acquiring current, voltage and temperature data of each battery in the discharging process;
extracting characteristic parameters reflecting battery aging from the acquired current, voltage and temperature data in the discharging process of each battery, and predicting by using a battery health state estimation model trained in advance respectively based on the characteristic parameters reflecting battery aging of each battery to obtain the health state distribution of each battery;
and establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision, and reconstructing the battery pack according to the battery pack reconstruction control decision.
The characteristic parameters reflecting the aging of the battery comprise average voltage drop MVF, voltage signal fluctuation index VFI, temperature and current;
wherein, the average voltage drop MVF is calculated by the following formula:
Figure BDA0002388714660000111
in the formula, VrAt rated voltage, VjThe voltage value of the jth sampling point in the discharging process is shown, and N is the total number of the sampling points;
the voltage signal fluctuation index VFI is calculated by the following formula:
Figure BDA0002388714660000112
in the formula, yjIs the voltage value of the jth sampling point in the discharging process, N is the total number of the sampling points, mu is the average value of the voltage values of all the sampling points,w is the sampling frequency.
By taking the average voltage drop MVF and the voltage signal fluctuation index VFI as characteristic parameters, the characteristics of capacity, internal resistance and the like of the battery under an offline condition are avoided, and the health state of the battery can be accurately reflected, so that an accurate battery health state estimation model can be established and an accurate health state prediction result can be provided.
The battery state of health estimation model is obtained by training through the following method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a mild gradient lifting tree, taking a battery state of health stage as output of the mild gradient lifting tree, and training to obtain a battery state of health estimation model based on the mild gradient lifting tree;
the battery state of health estimation model is used for predicting the state of health of the battery, and the prediction result is the probability that the state of health of the battery is in each state of health, namely the state of health distribution.
The establishing of the battery pack optimization model specifically comprises the following steps:
determine the total number of
Figure BDA0002388714660000121
A non-intersecting series battery s1,s2,…,smN is the total number of batteries, N is the number of batteries contained in each series battery pack, and N is determined by the load when the reconfigurable battery pack is used;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels from high to low, wherein the i-th battery state of health distribution hi(z) is represented by:
Figure BDA0002388714660000122
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected battery packs andiand sjThe conflict relationship between the two is as follows:
Figure BDA0002388714660000123
Figure BDA0002388714660000124
then the battery group s is connected in seriesjDistribution of health status
Figure BDA0002388714660000125
The calculation formula of (2) is as follows:
Figure BDA0002388714660000126
and finally, establishing a health state-based battery pack optimization model as follows:
Figure BDA0002388714660000127
Figure BDA0002388714660000128
wherein the content of the first and second substances,
Figure BDA00023887146600001216
a structural logic relation operator corresponding to the general generating function; for a given function f, the function f,
Figure BDA0002388714660000129
the calculation formula is as follows:
Figure BDA00023887146600001210
for series batteries sjDistribution of health status
Figure BDA00023887146600001211
The formula for the calculation of (a) is,
Figure BDA00023887146600001212
j in the calculation formulanIndicating the state of health of the nth cell,
Figure BDA00023887146600001213
indicates that the nth cell is at the jthnThe probability of the phase of the individual health state,
Figure BDA00023887146600001214
indicates that the nth cell is at the jthnA stage of individual health status;
with respect to the formula of the state-of-health based battery optimization model h (z),
Figure BDA00023887146600001215
j in the calculation formulanIndicating the state of health of the nth series stack,
Figure BDA0002388714660000131
indicating that the nth series battery is at the jthnAt a stage of health stateThe probability of the occurrence of the event,
Figure BDA0002388714660000132
indicating that the nth series battery is at the jthnA stage of individual health status;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,smAnd converting the reconfiguration control decision of the battery pack to generate a PWM wave switch control signal corresponding to the reconfiguration control decision of the battery pack, and outputting the PWM wave switch control signal to the switch driving module. Wherein, the solving can adopt a graph theory method to solve:
let P be the set of feasible cell strings, for each feasible path Pi(series path of a series-connected battery), mapped to the corresponding vertex v in the graph GiThe weight of the top point is the health state of the series battery
Figure BDA0002388714660000133
If and only if the two paths conflict with each other (i.e., conflict (p)i,pj) 1) to connect two vertices v in GiAnd vj；
Finally greedily the vertex v with the maximum valueiAdding the solution into a solution set, and deleting vertexes connected with the solution set at the same time until no vertex is contained any more, wherein the solution set is the m series battery packs { s1,s2,…,sm}。
Example 3
The present embodiment provides a computer-readable storage medium comprising stored program instructions adapted to be loaded by a processor and to perform the reconfigurable battery pack control method according to embodiment 2.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The invention provides a reconfigurable battery pack control method, a reconfigurable battery pack control system and a reconfigurable battery pack control storage medium, wherein before each use, the health state of each battery is predicted, then the connection mode between the batteries is dynamically adjusted according to the health state of each battery, a mode of first series connection and then parallel connection is adopted, the batteries with similar health states are connected in series to form a series battery pack, the batteries with similar health states are connected in series, and because the internal resistance, the temperature and the terminal voltage of the batteries in the charge and discharge processes are very similar, the inconsistent diffusion in the charge and discharge processes is avoided, and then all the series battery packs are connected in parallel. The reconfigurable battery pack is reconstructed by modeling and solving the reconfigurable battery pack, so that the inconsistency among batteries can be reduced, the utilization rate of the batteries is improved, overcharging and overdischarging are avoided, the service life of the battery pack is prolonged, and the safe operation of the battery pack can be ensured.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A reconfigurable battery pack control method, comprising:
acquiring current, voltage and temperature data of each battery in the discharging process;
extracting characteristic parameters reflecting battery aging from the acquired current, voltage and temperature data in the discharging process of each battery, and predicting by using a battery health state estimation model trained in advance respectively based on the characteristic parameters reflecting battery aging of each battery to obtain the health state distribution of each battery; the battery state of health estimation model is obtained by training through the following method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a mild gradient lifting tree, taking a battery state of health stage as output of the mild gradient lifting tree, and training to obtain a battery state of health estimation model based on the mild gradient lifting tree;
the battery health state estimation model is used for predicting the health state of the battery, and the prediction result is the probability that the health state of the battery is in each health state stage, namely the health state distribution;
and establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision, and reconstructing the battery pack according to the battery pack reconstruction control decision.
2. The reconfigurable battery pack control method according to claim 1, wherein the characteristic parameters reflecting battery aging include an average voltage drop MVF, a voltage signal fluctuation index VFI, a temperature, and a current;
wherein, the average voltage drop MVF is calculated by the following formula:
Figure FDA0003105433050000011
in the formula, VrAt rated voltage, VjThe voltage value of the jth sampling point in the discharging process is shown, and N is the total number of the sampling points;
the voltage signal fluctuation index VFI is calculated by the following formula:
Figure FDA0003105433050000012
in the formula, yjThe voltage value of the jth sampling point in the discharging process is shown, N is the total number of the sampling points, mu is the average value of the voltage values of all the sampling points, and w is the sampling frequency.
3. The reconfigurable battery pack control method according to any one of claims 1 to 2, wherein the establishing of the battery pack optimization model specifically includes:
determine the total number of
Figure FDA0003105433050000021
The battery packs are connected in series, wherein N is the total number of batteries, and N is the number of the batteries contained in each series battery pack;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels from high to low, wherein the i-th battery state of health distribution hi(z) is represented by:
Figure FDA0003105433050000022
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected battery packs andiand sjThe conflict relationship between the two is as follows:
Figure FDA0003105433050000023
Figure FDA0003105433050000024
then the battery group s is connected in seriesjDistribution of health status
Figure FDA0003105433050000025
The calculation formula of (2) is as follows:
Figure FDA0003105433050000026
and finally, establishing a health state-based battery pack optimization model as follows:
Figure FDA0003105433050000027
Figure FDA0003105433050000028
wherein the content of the first and second substances,
Figure FDA0003105433050000029
a structural logic relation operator corresponding to the general generating function;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,sm}。
4. A reconfigurable battery pack control system comprising an energy storage system circuit and a battery management subsystem;
the energy storage system circuit comprises a plurality of batteries and controllable switch groups which are arranged corresponding to the batteries, and each controllable switch group is used for controlling the corresponding battery to be connected with other batteries in parallel or in series and controlling whether the corresponding battery is connected with a battery pack or not;
the battery management subsystem is used for acquiring current, voltage and temperature data in the discharging process of each battery, extracting characteristic parameters reflecting battery aging from the current, voltage and temperature data, predicting to obtain the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, generating a battery pack reconstruction control decision according to the health state distribution of each battery, and reconstructing the battery pack according to the battery pack reconstruction control decision;
the state decision module predicts the state of health distribution of each battery through a preset battery state of health estimation model, wherein the battery state of health estimation model is obtained through training by the following method:
dividing the health state of the battery into K health state stages according to the capacity of the battery, wherein K is a preset value;
acquiring voltage, current and temperature data of a plurality of batteries in the discharging process;
extracting characteristic parameters reflecting battery aging from the obtained voltage, current and temperature data of a plurality of batteries in the discharging process, and constructing a training sample set by taking the characteristic parameters reflecting battery aging of each battery and the health state stage of the battery as a sample;
based on a training sample set, taking characteristic parameters reflecting battery aging as input of a mild gradient lifting tree, taking a battery state of health stage as output of the mild gradient lifting tree, and training to obtain a battery state of health estimation model based on the mild gradient lifting tree;
the battery state of health estimation model is used for predicting the state of health of the battery, and the prediction result is the probability that the state of health of the battery is in each state of health, namely the state of health distribution.
5. The reconfigurable battery pack control system according to claim 4, wherein the controllable switch set comprises a series switch, an input switch, an output switch and two parallel switches;
the two parallel switches are respectively connected in series at two ends of the corresponding battery and are used for controlling whether the corresponding battery is connected in parallel with other batteries;
the series switch is connected in parallel at two ends of the corresponding battery and is used for controlling whether the corresponding battery is connected with other batteries in series or not;
the input switch and the output switch are respectively connected with the negative electrode and the positive electrode of the corresponding battery in series and are used for controlling whether the corresponding battery is connected with the battery pack.
6. The reconfigurable battery pack control system according to claim 4 or 5, wherein the battery management subsystem comprises a sensing signal acquisition module, a communication module, a state decision module and a switch driving module which are connected in sequence;
the sensing signal acquisition module is used for acquiring voltage, current and temperature data of each battery;
the communication module is used for transmitting the data acquired by the sensing signal acquisition module to the state decision module;
the state decision module is used for receiving the data transmitted by the communication module, extracting characteristic parameters reflecting battery aging from the data, predicting and obtaining the health state distribution of each battery based on the characteristic parameters reflecting battery aging of each battery, obtaining a battery pack reconstruction control decision according to the health state distribution of each battery, generating a switch control signal according to the battery pack reconstruction control decision and sending the switch control signal to a switch driving signal;
the switch driving signal is used for receiving the switch control signal sent by the state decision module and controlling the on-off of the switches in the controllable switch group according to the switch control signal.
7. The reconfigurable battery pack control system according to claim 6, wherein the state decision module obtains the battery pack reconfiguration control decision according to the state of health distribution of each battery, and specifically comprises:
establishing a battery pack optimization model and solving according to the health state distribution of each battery to generate a battery pack reconstruction control decision; the establishing of the battery pack optimization model specifically comprises the following steps:
determine the total number of
Figure FDA0003105433050000041
A non-intersecting series battery s1,s2,…,smN is the total number of batteries, and N is the number of batteries contained in each series battery pack;
n batteries { b1,b2,…,bNThe health status distribution is denoted as h1(z),h2(z),…,hN(z), then ranking the battery state of health stage levels from high to low, wherein the i-th battery state of health distribution hi(z) is represented by:
Figure FDA0003105433050000042
wherein K is the total number of healthy state phases, zkIndicating that the battery is in the k-th state of health phase, zkCoefficient p ofkIs the probability that the battery is in the kth state of health stage;
defining an index variable ai,jAnd two series-connected battery packs andiand sjThe conflict relationship between the two is as follows:
Figure FDA0003105433050000043
Figure FDA0003105433050000044
then the battery group s is connected in seriesjDistribution of health status
Figure FDA0003105433050000045
The calculation formula of (2) is as follows:
Figure FDA0003105433050000046
and finally, establishing a health state-based battery pack optimization model as follows:
Figure FDA0003105433050000047
Figure FDA0003105433050000048
wherein the content of the first and second substances,
Figure FDA0003105433050000049
a structural logic relation operator corresponding to the general generating function;
solving the battery pack optimization model based on the health state to obtain a battery pack reconstruction control decision { s1,s2,…,sm}。
8. A computer readable storage medium, characterized in that the storage medium comprises stored program instructions adapted to be loaded by a processor and to perform the reconfigurable battery pack control method according to any of claims 1 to 3.
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