CN111180814A - Charging control method of series lithium ion battery pack based on multi-module charger - Google Patents

Charging control method of series lithium ion battery pack based on multi-module charger Download PDF

Info

Publication number
CN111180814A
CN111180814A CN201911155088.3A CN201911155088A CN111180814A CN 111180814 A CN111180814 A CN 111180814A CN 201911155088 A CN201911155088 A CN 201911155088A CN 111180814 A CN111180814 A CN 111180814A
Authority
CN
China
Prior art keywords
battery
charging
soc
model
battery pack
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
CN201911155088.3A
Other languages
Chinese (zh)
Inventor
欧阳权
吴兆香
王志胜
马瑞
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.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
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 Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN201911155088.3A priority Critical patent/CN111180814A/en
Publication of CN111180814A publication Critical patent/CN111180814A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • H01M10/441—Methods for charging or discharging for several batteries or cells simultaneously or sequentially
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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 charging control method of a series lithium ion battery pack based on a multi-module charger, which comprises the following steps: (1) establishing a series battery module model of n modules; (2) determining a hard constraint condition in the charging process of the series battery pack; (3) adding user requirements in the design of a charging protocol; (4) confirming the energy loss of the battery pack; (5) generating an optimal average SOC track on the basis of the steps (1) - (4); (6) enabling the SOC of each battery to track a preset optimal average SOC track by using a distributed charging method; the invention does not use an accurate and complex battery model, only uses a simplified nominal model, can effectively restrain the behavior violating the battery charging constraint due to the online bias compensation, and has stronger robustness and feasibility of practical application.

Description

Charging control method of series lithium ion battery pack based on multi-module charger
Technical Field
The invention belongs to the technical field of energy systems, and particularly relates to a charging control method of a series lithium ion battery pack based on a multi-module charger.
Background
The lithium ion battery has the advantages of high energy density, low self-discharge and the like, and has certain market share in a plurality of application fields such as electric vehicles and the like. However, improving the performance of the lithium ion battery needs to further solve some problems, such as safety guarantee, battery equalization, and improving the energy efficiency of the battery. Of these issues, how to achieve reliable charge management for batteries remains a critical and challenging issue.
In practical application, the well-designed charging method can not only prevent the battery from being overcharged or overheated so as to ensure the safety of the battery, but also improve the performance of the battery and reduce energy loss. Therefore, the study of suitable charging methods is crucial for the reliable management of lithium ion batteries.
In recent years, many model-based approaches have been proposed to achieve reasonable charge management for lithium ion batteries. Charging methods can be classified into two broad categories, based on Electrochemical Models (EMs) and Equivalent Circuit Models (ECMs), depending on the type of charging model employed. However, most charging methods only consider designing a charging method for a single battery, and do not simultaneously consider the inconsistency of different batteries in a group. It is noted that in real automotive applications, hundreds of lithium ion batteries are connected in series or in parallel in order to provide sufficient power and energy to the electric vehicle, thus requiring a suitable charging method to ensure that all batteries are charged under reliable conditions.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a charging control method of a series lithium ion battery pack based on a multi-module charger, which gives consideration to user requirements and energy loss.
The technical scheme is as follows: the invention relates to a charging control method of a series lithium ion battery pack based on a multi-module charger, which comprises the following steps:
(1) establishing an n-module series battery module model, and confirming a state space expression of the n-module series battery module model;
(2) determining hard constraint conditions in the charging process of the series battery pack, wherein the hard constraint conditions comprise the charging current, SOC (state of charge) and terminal voltage of each battery in the battery pack;
(3) adding user requirements in the design of a charging protocol, setting a target SOC and battery charging duration of a user in a charging control method, and self-adjusting the charging current according to the selection of the user;
(4) confirming the energy loss of the battery pack, and establishing a cost function of the energy loss in the charging process;
(5) generating an optimal average SOC track on the basis of the steps (1) - (4);
(6) the SOC of each battery is made to track a predetermined optimal average SOC trajectory using a distributed charging method.
Further, the battery pack model established in the step (1) specifically comprises:
for an n-module series lithium ion battery pack, as shown in fig. 1, each cell is independently charged by a small charging module, which allows the battery pack to be fully charged without the risk of overcharging the cell; for a battery pack with n series-connected modules, the dynamic characteristics of each battery can be represented by an internal resistance Equivalent Circuit Model (ECM), which balances the computational complexity and the model accuracy, and it should be noted that the charge control method can be generalized to any ECMs of lithium ion batteries, as shown in fig. 1, such ECM is composed of a voltage source for simulating energy storage and a series internal resistance for representing charge and discharge energy loss, and the model of the ith (1 ≦ i ≦ n) battery can be expressed as:
Figure RE-GDA0002448262280000021
wherein
Figure RE-GDA0002448262280000022
SOCi(k)，
Figure RE-GDA0002448262280000028
Respectively refer to SOC, charging current, and terminal voltage of the ith battery; qiIndicates the capacity of the ith battery in units ofA·h；η0Representing coulombic efficiency; t represents a sampling period;
Figure RE-GDA0002448262280000023
the open circuit voltage and the internal resistance of the ith cell, which are nonlinear functions with respect to the SOC, can be expressed as:
Figure RE-GDA0002448262280000024
then, the model of the n-module battery pack can be represented by the following state space expression:
Figure RE-GDA0002448262280000025
for a series battery, there is usually an energy imbalance between the cells, resulting in the lowest SOC cell limiting the available capacity of the battery, which has a bucket effect, and the SOC of the battery can be reflected by the lowest SOC, i.e.:
xp(k)＝min{xi(k),i＝1,2,…n} (4)
wherein x isp(k) E R represents the SOC of the entire battery pack, since each battery is charged independently by the existing multi-module charger, the maximum SOC does not limit the performance of the battery pack. SOC Difference x of Batteryd(k) E R can be calculated from the formula:
Figure RE-GDA0002448262280000026
where | l | · | represents a norm,
Figure RE-GDA0002448262280000027
average vector representing battery SOC, 1nRepresenting an n-dimensional column vector;
further, the charging hard constraints in step (2) include the charging current, SOC and terminal voltage of each battery in the battery pack,
and (3) limiting the charging current: the threshold value of the charging current plays an important role in the safety of the battery, since an excessively high current may affect the performance of the battery and even cause a fire during charging. Therefore, the battery charging current should be kept within the following range:
0n≤u(k)≤uM1n(6)
wherein is uMThe maximum allowable charging current of the E R battery can be obtained by the recommendation of a battery manufacturer; 0nIs an n-dimensional zero-column vector.
SOC limitation: the SOC of the battery is not allowed to exceed its upper limit to avoid overcharging, in the range
x(k+1)≤xM1n(7)
Wherein xMThe expression is the upper limit value of the battery SOC;
and limiting the terminal voltage: at the end of each sampling interval, the battery terminal voltage and the designed charging current should be prevented from exceeding the allowed voltage limits to avoid damage, which should be satisfied:
f(x(k+1))+h(x(k+1))u(k)≤yM1n(8)
wherein y isME R represents the maximum allowable terminal voltage of the battery;
furthermore, the user requirement in step (3) means that in practical application, most of the existing charging methods focus on fast charging with 1C current to fully charge the battery in a short time, however, large-current charging without a preset charging period is harmful to the safety of the battery, especially in high-power application, the user requirement is considered in the charging protocol design, the charging current can be self-adjusted according to the user selection, the charging process is more intelligent, and the safety of the battery pack in the charging process is ensured, so that the user can set the own target SOC and the battery charging duration in the charging control method according to the requirement, which are expressed as
x(N)＝xs1n,Ts＝NT (9)
Wherein xsAnd TsRespectively representing a target SOC and a charging time; n is the number of cycles, and since hard constraints (6) - (8) need to be met throughout the charging process, the user's needs may not always be feasibleFor example, even if charging is continued with the maximum allowed charging current, the battery pack cannot be charged from empty to full with very tight charging time requirements, so we do not use (9) in the charging control design, but try to drive x (N) to the required x (N) while satisfying all charging constraints s1nThus, the cost function J with respect to the user's needsuCan be expressed as:
Ju＝(x(N)-xs1n)T(x(N)-xs1n) (10)
further, another key charging goal in step (4) is to improve the charging efficiency by reducing the energy loss during charging, and a new cost function of the energy loss during charging is established based on equation (3), which is as follows:
Figure RE-GDA0002448262280000031
therefore, according to equations (3), (6) to (11), the total charging task participated by the user can be expressed as a multi-objective constraint optimization problem, taking into account both the user demand and the energy loss:
Figure RE-GDA0002448262280000032
Figure RE-GDA0002448262280000033
where x (0) represents the initial SOC vector of the battery, γ1＞0,γ2> 0 is a weight coefficient; in order to obtain the optimal control current for each battery to complete the user participation task of the corresponding battery pack, equation (12) needs to be solved directly, but also faces the following difficulties:
(a) the number of the optimization variables in equation (12) is N × N, resulting in a large amount of calculation by the controller, that is, it is difficult to directly obtain a charging method for a large-scale battery pack;
(b) model parameters in the nonlinear model (3) are usually difficult to extract accurately, only an approximate nominal model can be obtained in practical application, and to a certain extent, a directly obtained charging method is an open-loop method and cannot compensate deviation of a battery model;
in order to overcome the defects and realize the charging task participated by the user, the patent proposes an innovative double-layer optimal charging control method, as shown in fig. 2, which has a leader-followers framework;
further, the optimal average SOC trajectory in step (5) is specifically:
under the drive of the leader-followers concept of a multi-body system, a distributed average tracking framework is developed to charge the batteries in the battery pack, under the framework, an average charging track leader is designed firstly, then all batteries followers track the track simultaneously, and compared with a direct solution (12), the calculation amount is obviously reduced, therefore, firstly, a battery nominal model is used for generating an optimal average charging track, and the expression is as follows:
Figure RE-GDA0002448262280000041
subject to C(U)≤0 (15)
wherein:
Figure RE-GDA0002448262280000042
Figure RE-GDA0002448262280000043
wherein vector U is an optimization variable; g (U) is belonged to RN*N,F(U)∈RN,G1(U)∈RN*N,YM∈RN,Φ∈R2N
UM∈R2N,M∈RN*N,XC∈RNSatisfy the requirement of
G(U)＝diag{h0(x0(0)),…,h0(x0(0)+b0HN-1U)}
F(U)＝[f0(x0(0)+b0H1U),…,f0(x0(0)+b0HNU)]T
G1(U)＝diag{h0(x0(0)+b0H1U),…,h0(x0(0)+b0HNU)}
YM＝yM1N,Φ＝[IN,-IN]T,
Figure RE-GDA0002448262280000044
Herein INA powerful technique for representing an NxN dimensional identity matrix, a so-called barrier function method, can be used to search for an optimal charging current sequence UrThen, for all 1 ≦ k ≦ N, the optimal average SOC trajectory
Figure RE-GDA0002448262280000046
The following can be set:
Figure RE-GDA0002448262280000045
further, the distributed charging method in step (6) specifically includes:
the goal of a distributed SOC trajectory based on a charging algorithm is to use a distributed framework that independently controls the current of the ith (1 ≦ i ≦ n) battery, thereby bringing it to its SOC, xi(k) Following the previously generated optimal trajectory
Figure RE-GDA0002448262280000057
Further, it is troublesome to directly extract model parameters of all cells in the battery pack, because the open-circuit voltage and the internal resistance value of each cell need to be determined by a fitting method based on a previous complex experiment, so that, in the charging method based on SOC tracking, which we propose, only using an approximate and simplified nominal model of the cell, the previous complex experiment for extracting all cell model parameters can be avoided, and thus a more effective method can be obtained in practical applications, based on the idea that the ith cell model can be rewritten by its known nominal model plus an unknown offset term as:
Figure RE-GDA0002448262280000051
considering the model bias term wi(k) Unknown, the charging method must dynamically correct the corresponding model of the ith battery to avoid possible violations of the battery's terminal voltage limit.
Further, an observer is used for estimating the deviation of the battery model on line, FIG. 3 shows a block diagram of the charging control method of the ith (i is more than or equal to 1 and less than or equal to n) battery based on SOC tracking,
(a) observer of model bias: based on (17), an observer is proposed for the unknown model bias estimation of the ith cell:
Figure RE-GDA0002448262280000052
wherein the content of the first and second substances,
Figure RE-GDA0002448262280000053
Figure RE-GDA0002448262280000054
is to estimate the model bias, liDesigning the gain of an observer;
(b) the SOC tracking-based bias compensation charging method with the model comprises the following steps: when the relevant safety restrictions (6), (7), (8) are satisfied, in order to make the SOC of the i-th battery, i.e., xi(k) (i is more than or equal to 1 and less than or equal to n) tracking the pre-generated optimal track, and providing an online compensation charging algorithm with model deviation based on SOC tracking:
Figure RE-GDA0002448262280000055
Figure RE-GDA0002448262280000056
wherein, γ3＞0,γ4> 0 is a weight coefficient. The battery tracks the previously generated optimal trace, the charging current u, by solving a constraint optimization problem (19) at each stepi(k) Can be obtained.
Further, the nominal model of the battery generates an optimal average charging trajectory, and the derivation process of equation (15) is as follows:
firstly, the expression of the battery nominal model for generating the optimal average charging track is
Figure RE-GDA0002448262280000067
Wherein x0(k)∈R,y0(k)∈R,u0(k) E, R represents the state, output and input of a battery nominal model; b0,f0(·),h0(. represents b respectivelyi,fi(·),hi(. 1. ltoreq. i.ltoreq.n) nominal value, x0(k) Is equal to the average initial SOC of the battery, such as:
Figure RE-GDA0002448262280000061
the nominal model of the battery selects a simplified and approximate form of the actual model of the battery; similar to equation (12), based on the battery nominal model (13), the SOC optimal trajectory based on user participation in the charging process can be generated by solving the following optimization problem:
Figure RE-GDA0002448262280000062
Figure RE-GDA0002448262280000063
definition of U ═ U0(0)，…，u0(N-1)]T∈RN,
Figure RE-GDA0002448262280000064
Then (13) can be expressed in a new form: x is the number of0(k)＝x0(0)+b0HkU, will x0(k) By x0(0)+b0HkU instead, the optimization problem (14) can be further written as equation (15).
For our proposed packet charging control strategy, a larger sampling period T reduces the computational complexity of optimal charging trajectory generation, mainly because for a fixed T in equation (9), with a given charging time TsThe sampling step length N will become larger; however, TsThe increase of (2) may also cause the discretization of the battery model to be large, the adjustment speed of the SOC tracking control becomes slow, which is not favorable for the improvement of the charging performance, in order to obtain proper balance between the calculation cost and the charging performance, two layers of the charging control strategy need to be provided with different sampling periods, and the optimal average charging trajectory scheduling algorithm (14) adopts a large sampling period T ═ T1The charging control method based on SOC tracking (19) adopts a small sampling period T ═ T2And then, a linear interpolation method is adopted to enable the sampling period of the preset SOC track to be consistent with the sampling period of the charging algorithm based on SOC tracking. Through the process, different sampling periods can be set, and the efficiency of the double-layer battery pack charging control strategy is improved.
Further, the barrier function method searches for an optimal charging current sequence UrThe algorithm of (1) is as follows:
(a) setting an iteration index t to be 0 and initially optimizing a variable U(0)The intermediate variable U being equal to U(0)Initial parameter
Figure RE-GDA0002448262280000065
Gain c > 1, precision epsilon1＞0,ε2＞0；
(b) Order to
Figure RE-GDA0002448262280000066
Wherein C isj(U) is the jth element of vector C (U), and the Newton order and decrement are calculated:
Figure RE-GDA0002448262280000071
and is
Figure RE-GDA0002448262280000072
(c) Selecting backtracking line search[24]In (1)
Figure RE-GDA0002448262280000076
Updating as step size
Figure RE-GDA0002448262280000077
(d) If it is not
Figure RE-GDA0002448262280000073
Then U is(t)U; otherwise, returning to the step (b);
(e) if it is not
Figure RE-GDA0002448262280000074
Then stop and output Ur＝U(t)(ii) a Otherwise, let t be t +1,
Figure RE-GDA0002448262280000075
and returning to the step (b).
Has the advantages that: the invention provides a user participation charging control method considering user requirements and energy loss, and the charging method can be more intelligent due to user participation, and has positive influence on user satisfaction; a new charging control framework based on leader-followers is provided, firstly, an optimal average charging track (leader) is designed, all batteries (followers) track the track simultaneously, and thus compared with the mode that an optimal charging sequence is arranged for each battery independently, the calculation workload is obviously reduced; in the proposed charge control method, no accurate and complex battery model is used, only a simplified form of nominal model is used; due to online bias compensation, behaviors violating battery charging constraints can be effectively inhibited; in addition, the proposed charge control method has greater robustness and feasibility of practical application than a charge control method that relies on an accurate and complex battery model.
Drawings
FIG. 1 is a diagram of a multi-module charger for a battery pack;
FIG. 2 is a schematic diagram of a user engagement optimal charging control method;
FIG. 3 is a block diagram of a method for controlling charging of an ith battery based on SOC tracking;
fig. 4(a) is an open circuit voltage diagram of a battery and (b) is an internal resistance diagram of a battery pack;
FIG. 5(a) is a battery SOC diagram, (b) is a battery charging current diagram, (c) is a battery pack SOC diagram, (d) is a battery SOC difference diagram, and (e) is a battery SOC difference diagram at xs＝100％,TsEnergy loss at 120 min;
fig. 6(a) is a graph showing the comparison result of the charging control with or without the model offset compensation of the terminal voltage of the 5 th battery, and (b) is a graph showing the comparison result of the charging control with or without the model offset compensation of the charging current of the 5 th battery.
Detailed Description
For a further understanding of the present invention, reference will now be made in detail to the embodiments illustrated in the drawings.
In order to study the effectiveness of the proposed optimal charge control method with user involvement, this example tested a battery pack of 10 series cells. The capacity and initial SOC of each cell were randomly selected in consideration of the unbalance of the cells in the battery pack, as shown in table 1.
TABLE 1
Figure RE-GDA0002448262280000081
The initial SOC of the battery pack, the average initial SOC of the battery, and the initial SOC are 3%, 7.5%, and 9.08%, respectively, and fig. 4(a) and 4(b) show the relationship between the maps from the battery SOCs to the open-circuit voltage f (-) and the SOCs and the internal resistance h (-) respectively. Their nominal form in the charge control method may be chosen as:
Figure RE-GDA0002448262280000082
wherein c is1＝3.61,d1＝3.13,c2＝1.21,d2＝3.2,c3＝0.8,d3＝3.282,c4＝-0.46,d4＝0.057, d5＝0.034,c6＝2.06,d6-1.923, the maximum allowed charging current and the terminal voltage of the battery are set to 3C rate and 4.2V, respectively, and the weight factor of the cost function is γ1＝104,γ2＝0.1,γ3＝104,γ4＝10-3The sampling periods are respectively selected as T1＝300s,T2＝1s。
Take SOC and charging time specified by user as an example, set xs＝100％,TsAfter the charging method of the present patent is adopted, the charging results of the SOC and the charging current of the battery are respectively shown in fig. 5(a) and fig. 5(b), and it is obvious that even if a simple battery nominal model is used in the designed charging control method, the hard constraint in the charging process can be well ensured; fig. 5(c), 5(d) and 5(e) show the SOC of the corresponding battery pack, the SOC difference of the battery and the energy loss, respectively, and it can be seen that the actual state of charge of the battery pack is 98.48%, the predetermined average charging trajectory is well tracked, and the state of charge desired by the user is approached at the end of charging, and the SOC difference between the batteries is reduced from 9.08% to 0.64%, verifying the effectiveness of the multi-module charger and the designed charging control method; note that when the batteries are nearly fully charged, the SOC variation between the batteries increases, mainly because the region requires a constant voltage phase to satisfy the terminal voltage constraint, resulting in inconsistent charging current for each battery.
To further prove the advantages of the proposed SOC tracking based charging method with model bias compensation, taking the 5 th battery with and without model bias compensation as an example, the detailed terminal voltage and charging current are compared, as shown in fig. 6(a) and 6(b), without considering the battery model bias, the terminal voltage of the battery will exceed its upper limit of 4.2V during charging, in contrast, by compensating the battery model bias on-line, when the battery SOC approaches the fully charged state, a similar constant voltage phase will be generated, further suppressing the voltage rise, and protecting the battery safety.
It is worth noting that the leader-follower charging control method proposed by the inventor can still achieve satisfactory performance in the battery charging field only by using an approximate nominal model with a piecewise linear mapping form (20); this is another advantage of our proposed charge control method, enabling bias compensation of the model; since accurate identification of model parameters throughout the charging process is a very important task, our proposed charging control method is more suitable for practical battery charging applications than those requiring an accurate battery model.
The simulation result shows the effectiveness of the charging method, which is the known first battery pack charging application combining offline scheduling and online closed-loop regulation, can reduce the calculation burden and improve the robustness, thereby inhibiting the negative influence of the deviation of the battery model, and the proposed leader-follower-based charging control method can be easily popularized to the serial charging of other battery types without any mechanism knowledge.

Claims (10)

1. A charging control method of a series lithium ion battery pack based on a multi-module charger is characterized by comprising the following steps:
(1) establishing a series battery module model of n modules;
(2) determining hard constraint conditions in the charging process of the series battery pack, wherein the hard constraint conditions comprise the charging current, the SOC and the terminal voltage of each battery in a battery pack model;
(3) adding user requirements in the design of a charging protocol, setting a target SOC and battery charging duration of a user in a charging control method, and self-adjusting the charging current according to the selection of the user;
(4) confirming the energy loss of the battery pack, and establishing a cost function of the energy loss in the charging process;
(5) generating an optimal average SOC track on the basis of the steps (1) - (4);
(6) the SOC of each battery is made to track a predetermined optimal average SOC trajectory using a distributed charging method.
2. The method of claim 1, wherein the charging control of the series lithium ion battery pack based on the multi-module charger,
the battery pack model in the step (1) is specifically as follows: n groups of modules connected in series, the model of the ith (1 ≦ i ≦ n) cell may be expressed as:
Figure FDA0002284584610000011
where k denotes the time of the kth instant,
Figure FDA0002284584610000012
SOCi(k),
Figure FDA0002284584610000013
respectively refer to SOC, charging current, and terminal voltage of the ith battery; qithe capacity of the ith cell is expressed in units of A.h, eta0Representing the coulombic efficiency, T representing the sampling period,
Figure FDA0002284584610000014
Figure FDA0002284584610000015
the open circuit voltage and the internal resistance of the ith cell, which are nonlinear functions with respect to the SOC, can be expressed as:
Figure FDA0002284584610000016
then, the model of the n-module battery pack can be represented by the following state space expression:
Figure FDA0002284584610000017
for a series battery, the SOC of the battery may be reflected by the lowest SOC, i.e.:
xp(k)＝min{xi(k),i＝1,2,…n} (4)
wherein x isp(k) E is R represents the SOC of the whole battery pack; since each battery is independently charged by the existing multi-module charger, the SOC difference x of the batteryd(k) E R can be calculated from the formula:
Figure FDA0002284584610000018
where | l | · | represents a norm,
Figure FDA0002284584610000019
average vector representing battery SOC, 1nRepresenting an n-dimensional column vector.
3. The method of claim 1, wherein the charging current in step (2) is kept within the following range:
0n≤u(k)≤uM1n(6)
wherein is uMMaximum allowable charging current of ∈ R battery, 0nIs an n-dimensional zero-column vector;
the SOC range is
x(k+1)≤xM1n(7)
Wherein xMThe expression is the upper limit value of the battery SOC;
the termination voltage should satisfy:
f(x(k+1))+h(x(k+1))u(k)≤yM1n(8)
wherein y isME R represents the maximum allowable terminal voltage of the battery.
4. The method of claim 1, wherein the target SOC and the battery charging duration in step (3) are expressed as SOC and battery charging duration
x(N)＝xs1n,Ts＝NT (9)
Wherein xsAnd TsRespectively representing a target SOC and a charging time, wherein N is the number of cycles;
trying to drive x (N) to the desired x with all charging constraints mets1n(ii) a Thus, the cost function J against the user's demanduCan be expressed as:
Ju＝(x(N)-xs1n)T(x(N)-xs1n) (10)
the energy loss of the battery pack in the step (4) is a new cost function of the energy loss in the charging process established on the basis of the formula (3), wherein the formula is as follows:
Figure FDA0002284584610000021
therefore, according to equations (3), (6) to (11), the total charging task in which the user participates is expressed as a multi-objective constrained optimization problem:
Figure FDA0002284584610000022
where x (0) represents the initial SOC vector of the battery, γ1＞0,γ2> 0 is a weight coefficient.
5. The method for controlling charging of a series lithium ion battery pack based on a multi-module charger according to claim 1, wherein the optimal average SOC trajectory in step (5) is specifically:
constructing a leader-followers framework, firstly determining an average charging track leader, then simultaneously tracking the track by all the battery followers, and generating an optimal average charging track by using a battery nominal model, wherein the expression is as follows:
Figure FDA0002284584610000023
subject to C(U)≤0 (15)
wherein:
Figure FDA0002284584610000031
Figure FDA0002284584610000032
wherein vector U is an optimization variable; g (U) is belonged to RN*N,F(U)∈RN,G1(U)∈RN*N,YM∈RN,Φ∈R2NUM∈R2N,M∈RN*N,XC∈RNSatisfy the requirement of
G(U)＝diag{h0(x0(0)),…,h0(x0(0)+b0HN-1U)}
F(U)＝[f0(x0(0)+b0H1U),…,f0(x0(0)+b0HNU)]T
G1(U)＝diag{h0(x0(0)+b0H1U),…,h0(x0(0)+b0HNU)}
YM＝yM1N,Φ＝[IN,-IN]T,
Figure FDA0002284584610000033
Herein INExpressing an NxN dimensional identity matrix, and searching for an optimal charging current sequence U by using a barrier function methodrThen, for all 1 ≦ k ≦ N, the optimal average SOC trajectory
Figure FDA0002284584610000034
The following can be set:
Figure FDA0002284584610000035
6. the charging control method for the series lithium ion battery pack based on the multi-module charger of claim 1, wherein the distributed charging method in the step (6) is specifically: the goal of a distributed SOC trajectory based on a charging algorithm is to use a distributed framework that independently controls the current of the ith (1 ≦ i ≦ n) battery, thereby bringing it to its SOC, xi(k) Following the previously generated optimal trajectory
Figure FDA0002284584610000036
7. The method of claim 6, wherein only approximate and simplified nominal models of batteries are used in the distributed charging method, and the ith battery model can be rewritten from its known nominal model plus an unknown bias term as:
Figure FDA0002284584610000037
model bias term wi(k) Unknown, the distributed charging method dynamically corrects the corresponding model for the ith battery to avoid possible violations of the battery's terminal voltage limit.
8. The method for controlling charging of a series lithium ion battery pack based on a multi-module charger according to claim 7, wherein the corresponding model of the ith battery is dynamically corrected on line by using an observer, and the method comprises the following steps:
(a) observer of model bias: based on equation (17), for the unknown model bias estimation of the ith cell, an observer is proposed:
Figure RE-FDA0002448262270000041
wherein the content of the first and second substances,
Figure RE-FDA0002448262270000042
Figure RE-FDA0002448262270000043
is to estimate the model bias, liDesigning the gain of an observer;
(b) the SOC tracking-based bias compensation charging method with the model comprises the following steps: when the relevant safety restrictions (6), (7), (8) are satisfied, in order to make the SOC of the i-th battery, i.e., xi(k) (i is more than or equal to 1 and less than or equal to n) tracking the pre-generated optimal track, and providing an online compensation charging algorithm with model deviation based on SOC tracking:
Figure RE-FDA0002448262270000044
wherein, γ3＞0,γ4> 0 is a weight coefficient, which allows the battery to track the optimal trace, charging current u, generated before it by solving a constraint optimization problem (19) at each stepi(k) Can be obtained.
9. The method of claim 5, wherein the nominal model of the battery generates an optimal average charging trajectory, and the expression thereof is first:
Figure FDA0002284584610000045
wherein x0(k)∈R,y0(k)∈R,u0(k) e.R represents the state, output and input of the nominal model of the battery, b0,f0(·),h0(. represents b respectivelyi,fi(·),hi(. 1. ltoreq. i.ltoreq.n) nominal value, x0(k) Is equal to the average initial SOC of the battery, such as:
Figure FDA0002284584610000046
the nominal model of the battery selects a simplified and approximate form of the actual model of the battery; similar to equation (12), based on the battery nominal model (13), the SOC optimal trajectory based on user participation in the charging process is generated by solving the following optimization problem:
Figure FDA0002284584610000047
definition of U ═ U0(0)，…，u0(N-1)]T∈RN,
Figure FDA0002284584610000048
Then (13) can be expressed in a new form: x is the number of0(k)＝x0(0)+b0HkU, will x0(k) By x0(0)+b0HkU instead, the optimization problem (14) can be further written as equation (15).
10. The method of claim 5, wherein the barrier function method searches for an optimal charging current sequence UrThe algorithm of (1) is as follows:
(a) setting an iteration index t to be 0 and initially optimizing a variable U(0)The intermediate variable U being equal to U(0)Initial parameter
Figure FDA0002284584610000051
Gain c > 1, precision epsilon1＞0,ε2＞0；
(b) Order to
Figure FDA0002284584610000052
Wherein C isj(U) is the jth element of vector C (U), and the Newton order and decrement are calculated:
Figure FDA0002284584610000053
and is
Figure FDA0002284584610000054
(c) Selecting in backtracking search
Figure FDA0002284584610000055
Updating as step size
Figure FDA0002284584610000056
(d) If it is not
Figure FDA0002284584610000057
Then U is(t)U; otherwise, returning to the step (b);
(e) if it is not
Figure FDA0002284584610000058
Then stop and output Ur＝U(t)(ii) a Otherwise, let t be t +1,
Figure FDA0002284584610000059
and returning to the step (b).
CN201911155088.3A 2019-11-22 2019-11-22 Charging control method of series lithium ion battery pack based on multi-module charger Pending CN111180814A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911155088.3A CN111180814A (en) 2019-11-22 2019-11-22 Charging control method of series lithium ion battery pack based on multi-module charger

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911155088.3A CN111180814A (en) 2019-11-22 2019-11-22 Charging control method of series lithium ion battery pack based on multi-module charger

Publications (1)

Publication Number Publication Date
CN111180814A true CN111180814A (en) 2020-05-19

Family

ID=70648764

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911155088.3A Pending CN111180814A (en) 2019-11-22 2019-11-22 Charging control method of series lithium ion battery pack based on multi-module charger

Country Status (1)

Country Link
CN (1) CN111180814A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111668894A (en) * 2020-05-21 2020-09-15 南京航空航天大学 Lithium battery pack rapid charging control method based on charging and equalization combination optimization
CN111987377A (en) * 2020-07-31 2020-11-24 蜂巢能源科技有限公司 Battery quick-charging method, battery management system and battery quick-charging device
CN113644328A (en) * 2021-08-20 2021-11-12 北京宝龙德科技有限责任公司 Flexible control method and device for dynamically reconstructing battery module

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111668894A (en) * 2020-05-21 2020-09-15 南京航空航天大学 Lithium battery pack rapid charging control method based on charging and equalization combination optimization
CN111668894B (en) * 2020-05-21 2023-06-20 南京航空航天大学 Lithium battery pack rapid charge control method based on charge and equalization combination optimization
CN111987377A (en) * 2020-07-31 2020-11-24 蜂巢能源科技有限公司 Battery quick-charging method, battery management system and battery quick-charging device
CN111987377B (en) * 2020-07-31 2021-10-26 蜂巢能源科技有限公司 Battery quick-charging method, battery management system and battery quick-charging device
CN113644328A (en) * 2021-08-20 2021-11-12 北京宝龙德科技有限责任公司 Flexible control method and device for dynamically reconstructing battery module
CN113644328B (en) * 2021-08-20 2023-10-24 刘铭新 Flexible control method and device for dynamic reconfiguration battery module

Similar Documents

Publication Publication Date Title
Ouyang et al. Optimal charging control for lithium-ion battery packs: A distributed average tracking approach
Saji et al. SoC estimation of lithium ion battery using combined coulomb counting and fuzzy logic method
Qays et al. Recent progress and future trends on the state of charge estimation methods to improve battery-storage efficiency: A review
Kim Nonlinear state of charge estimator for hybrid electric vehicle battery
Pei et al. Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles
CN111180814A (en) Charging control method of series lithium ion battery pack based on multi-module charger
US11292360B2 (en) Battery equalization method and system, vehicle, storage medium, and electronic device
Azis et al. State of charge (SoC) and state of health (SoH) estimation of lithium-ion battery using dual extended kalman filter based on polynomial battery model
CN110247451B (en) Lithium ion power battery pack full-life cycle balance control method
Chaoui et al. Adaptive state of charge estimation of lithium-ion batteries with parameter and thermal uncertainties
CN102769155B (en) Ordered electric automobile charging method orientated to active intelligent distribution network
CN111668894B (en) Lithium battery pack rapid charge control method based on charge and equalization combination optimization
CN113394840B (en) Intelligent balance control method and system for electric quantity of energy storage battery
CN110474400A (en) A kind of battery pack equilibrium method and device
Qays et al. Active cell balancing control strategy for parallelly connected LiFePO 4 batteries
Taylor et al. Optimal operation of grid-tied energy storage systems considering detailed device-level battery models
Lv et al. Influence of equalization on LiFePO4 battery inconsistency
CN110783987B (en) Layered optimal control method for battery equalization of battery pack
Liu et al. An online SOH estimation method based on the fusion of improved ICA and LSTM
Jia et al. Real-time model predictive control for battery-supercapacitor hybrid energy storage systems using linear parameter-varying models
Ouyang et al. Fast charging control for battery packs with combined optimization of charger and equalizers
Sun et al. A remaining charging electric quantity based pack available capacity optimization method considering aging inconsistency
Hoekstra et al. Range maximisation of electric vehicles through active cell balancing using reachability analysis
Bruen et al. Model based design of balancing systems for electric vehicle battery packs
Fan et al. A fast active balancing strategy based on model predictive control for lithium-ion battery packs

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20200519

RJ01 Rejection of invention patent application after publication