CN113283166B - Retired power battery residual value optimization method - Google Patents
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Abstract
The invention discloses a retired power battery residual value optimization method, which comprises the steps of predicting a decay rule of a power battery during echelon utilization according to a combination of a gray model and a least square support vector machine; converting capacity decay of the battery into damage cost, taking the highest daily charge and discharge benefits of the retired battery as an objective function, and taking power balance constraint, energy storage safety and retired battery scheduling as constraint conditions; importing micro-grid data, solving the gray model by utilizing a particle swarm algorithm, formulating a retired battery operation scheme with highest benefit, and collecting use data of retired batteries under the retired battery operation scheme; and feeding back the real data used after the battery is used in a gradient way to the least square support vector machine to serve as a training set of the least square support vector machine, so that rolling prediction of the fading speed is completed. The invention provides data support for the subsequent establishment of the running scheme, and effectively solves the problem of inconsistency of the battery decay law.
Description
Technical Field
The invention relates to the technical field of echelon utilization of retired power batteries, in particular to a method for optimizing the residual value of retired power batteries.
Background
With the recent rise in heat of new energy automobiles, with the decommissioning of power batteries, it is expected that the decommissioning of chinese power lithium batteries exceeds 73 ten thousand tons in 2025, 70% of which can be used in steps, and the market size exceeds 200 billions. In the month of 2020, the industrial information department solicits comments from the new energy automobile power storage battery echelon utilization management method, encourages the echelon utilization enterprises to cooperate with enterprise protocols such as new energy automobile production, power storage battery production, scrapped motor vehicle recovery and disassembly and the like, and strengthens information sharing.
However, the existing retired battery cascade utilization as energy storage has the following problems: the degradation of the battery is extremely irregular and nonlinear, and the degradation rules of the batteries of different manufacturers or different batches have inconsistency, so that the problem is caused for the subsequent establishment of an operation scheme; at present, enterprises generally adopt a deep charging and discharging mode, the discharging depth is controlled to be 70% -80%, and the damage cost caused by battery degradation is not considered. In conclusion, the historical information of the battery is fully mined, the degradation rule of the battery is predicted, and the accuracy of a degradation model can be improved, so that the residual value of the retired battery is improved.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a method for optimizing the residual value of the retired power battery, which can solve the problem of inconsistency of the battery decay law.
In order to solve the technical problems, the invention provides the following technical scheme: predicting a declination rule of a power battery in the echelon utilization period according to the combination of a gray model and a least square support vector machine; converting capacity decay of the battery into damage cost, taking the highest daily charge and discharge benefits of the retired battery as an objective function, and taking power balance constraint, energy storage safety and retired battery scheduling as constraint conditions; importing micro-grid data, solving the gray model by utilizing a particle swarm algorithm, formulating a retired battery operation scheme with highest benefit, and collecting use data of retired batteries under the retired battery operation scheme; and feeding back the real data used after the battery is used in a gradient way to the least square support vector machine to serve as a training set of the least square support vector machine, so that rolling prediction of the fading speed is completed.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: before the prediction is carried out, selecting the discharge depth of the battery as a main factor influencing the degradation of the battery, and establishing m groups of gray models according to m groups of different discharge depths; collecting historical use data of a power battery of the electric automobile, and importing the historical use data into the gray model; and taking the prediction result of the gray model as input, taking actual data as output, and training the least square support vector machine.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the least squares support vector machine includes, training using a regression model, as follows,
wherein w is T And b is offset for weight vector.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the constraint is established using a risk minimization principle, including,
wherein e i As error, γ is a regularization parameter.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the kernel function of the regression model selects the radial basis function, as follows,
where s is the kernel width, and the learning and generalization ability of the LSSVM model is greatly affected by g and s.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the objective function may comprise a function of the object,
wherein F is the net daily gain of the retired battery pack; p (P) t Charging and discharging power for the retired battery pack, wherein the charging is negative and the discharging is positive; lambda (lambda) t The electricity price at the time t is the photovoltaic online electricity price represented by the absorption photovoltaic time; η represents the charge-discharge efficiency of the retired battery; c is the energy storage cost converted into daily.
As decommissioning power according to the inventionA preferred embodiment of the battery residual value optimization method, wherein: maintenance and repair costs C E Cost of power loss C l Cost of breaking C f Comprising the steps of (a) a step of,
C f ＝L(t)C PV
wherein k is m Maintenance coefficients for retired batteries; r is (r) i The number of times the battery is cycled for retirement; r is R i The total cycle number of the retired battery is; k (k) i Loss coefficient of retired battery; mu (mu) t As a state variable, 1 corresponds to charge-1 corresponds to discharge.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the energy storage state constraint includes that,
wherein, I (t) is charge-discharge current; v is the rated voltage of the battery; SOC (t), SOC (t+1) respectively represents charge states before and after charge and discharge; e (E) b Representing the rated capacity of the battery; SOC (State of Charge) max And SOC (System on chip) min Representing the upper and lower limits of the state of charge of the energy storage system, respectively.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the energy balance constraint includes that the energy balance constraint includes,
P load ＝P Gird +P ESB +P DG
wherein P is load Representing the total load of the micro-grid; p (P) Gird Representing micro-grid purchase power; p (P) ESB Representing the charge and discharge power of the storage battery; p (P) DG Representing distributed energy power.
As a preferable scheme of the retired power battery residual value optimizing method, the invention comprises the following steps: the scheduling period constraint includes that,
SOC 0 ＝SOC H
wherein SOC is 0 Representing the retired battery pack SOC at the beginning of a schedule; SOC (State of Charge) H Indicating the retired battery pack SOC after the end of the schedule.
The invention has the beneficial effects that: the invention utilizes the combined prediction algorithm of the gray model and the least square support vector machine to mine historical use data of the battery, predicts the instant decay speed of the battery, provides data support for the subsequent establishment of an operation scheme, and effectively solves the problem of inconsistency of the decay rule of the battery; according to the invention, the particle swarm optimization is adopted to optimize the optimal scheduling model, the highest profit of the retired battery is taken as a target, the damage cost of the battery is considered, different operation schemes are formulated under different health states of the retired battery, and the residual value of the retired battery is furthest mined; according to the invention, battery data after echelon utilization is fed back in real time, the degradation speed of the battery is predicted in a rolling way, and the fine prediction of the battery degradation rule is realized, so that the benefit of echelon utilization of the power battery is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method for optimizing the residual value of a retired power battery according to one embodiment of the invention;
FIG. 2 is a schematic diagram of a flow chart for predicting capacity fade rate of a retired battery in a method for optimizing residual value of a retired power battery according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of a main wiring form of an optical storage micro-grid of a method for optimizing the residual value of a retired power battery according to an embodiment of the invention;
fig. 4 is a schematic view illustrating a photovoltaic-load characteristic analysis of an optical storage micro-grid according to a method for optimizing the residual value of a retired power battery according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a speed of instantaneous capacity decay of a retired battery of an optical storage micro-grid according to a method for optimizing a residual value of a retired power battery according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a prediction result of an instantaneous degradation speed of a retired battery of an optical storage micro-grid according to a method for optimizing a residual value of a retired power battery according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a retired battery optimizing result of an optical storage micro-grid according to a retired power battery residual value optimizing method according to an embodiment of the present invention;
fig. 8 is a schematic diagram comparing the results before and after optimizing the optical storage micro-grid according to the method for optimizing the residual value of the retired power battery according to one embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 5, for a first embodiment of the present invention, there is provided a method for optimizing a remaining value of a retired battery, including:
s1: historical usage data of the power battery of the electric automobile is collected and imported into a gray model. The following are to be described:
in this embodiment, a cyclic charge and discharge test is performed on 18650-type power lithium battery, the actual measured data is used as basic data to predict the next instant decay rate, the battery charge and discharge test is completed by a blue-ray battery test system CT2001A, various parameters of a new single battery are shown in table 1, and the cyclic test steps are shown in table 2.
Table 1:18650 lithium-ion power battery parameter table.
| Project | Specification of specification |
| Nominal voltage | 3.2V |
| Nominal capacity | 1.15Ah |
| Internal resistance of | 10mΩ |
| Cut-off voltage for charging | 3.65±0.05V |
| Cut-off voltage for charging | 2.5±0.05V |
Table 2: and (3) circulating experimental steps.
S2: and selecting the discharge depth of the battery as a main factor influencing the degradation of the battery, and establishing m groups of gray models according to m groups of different discharge depths. The step needs to be described as follows:
the degradation speed of the battery is affected by different discharge depths of the battery under different residual capacities, and the degradation of the battery is essentially the accumulation of the discharge loss L (t), namely:
L(t)＝f(R c ,Dod)
capacity fade rate Q of battery loss Can be expressed as:
the gray model generates some more regular data through approximate, non-unique discrete and random data deduction, thereby establishing a related differential equation model to describe the change rule of the data, and the modeling process is as follows:
(1) Accumulating and generating; by known discrete historical data formation sequences: x is X (0) ＝{X (0) (1)，X (0) (2)，···X (0) (n)}，X (0) Performing one-time accumulation generation to obtain a generation sequence X (1) ＝{X (1) (1)，X (1) (2)，···X (1) (n) }, wherein:
(2) Modeling; from X (1) Constructing a background value sequence: z is Z (1) ＝{Z (1) (2),Z (1) (3),···Z (1) (n) }, wherein: z is Z (1) (k)＝aX (1) (k-1)+(1-a)X (1) (k) K=2, 3, the terms, n, let a=0.5 assume X in general (1) With an approximate exponential change law, the whitening equation is:
discretizing the above formula, differentiating, and obtaining GM (1, 1) ash differential equation as follows:
X (0) (k)+aZ (1) (k)＝μ
(3) Solving parameters a and m; with minimumSolving parameters a and m in the formula by a square method, wherein a is a reflection sequence X (0) A growth coefficient of the growth rate of (2); m is called the ash action amount (endogenous variable), and the total amount of the reaction is large.
(4) Establishing a prediction model; x is X (1) The predictive formula is:
wherein k=0, 1,2, ·; x is X (0) The predictive formula of (2) is:
wherein k=1, 2;
s3: and taking the gray model prediction result as input, and taking actual data as output to train a least square support vector machine model. Among them, it is also to be noted that:
for a given training sample s= { (x 1 ,y 1 ),(x 2 ,y 2 ),···,(x n ,y n ) And nonlinear mapping j (·), defining its LSSVM regression model as:
wherein w is T Is a weight vector; b is the bias.
Establishing constraint problems through a risk minimization principle:
wherein e i Is an error; g is a regularization parameter.
The lagrange multiplier a is introduced and converted into:
according to KKT conditions, w, b, e are respectively i ,a i Partial differentiation is carried out, and finally the following steps are obtained:
the matrix form is:
obtaining an LSSVM regression function:
wherein K (x i X) is a kernel function, K (x) i ,x)＝j(x) T ·j(x)。
In this embodiment, the radial basis function is selected as a kernel function of the LSSVM regression model, and the expression is:
wherein s is the kernel width; the learning and generalization ability of the LSSVM model is largely affected by g and s.
S4: and predicting a fading rule during echelon utilization of the power battery according to the combination of the gray model and the least square support vector machine. Referring to fig. 2, it should also be noted that:
in order to analyze the influence of four different discharge depth working conditions on the battery decay speed, grey prediction models are respectively built, and the prediction results are shown in figure 3.
Referring to fig. 3, at the beginning of battery use, the rate of battery decay at each depth of discharge tends to decrease, which indicates that the battery tends to self-stabilize, and then the battery exhibits signs of accelerated decay, where the rate of battery decay increases most rapidly with full charge, and at 60% depth of discharge, the gap in decay rate is reduced compared to 80% depth of discharge, and the gap in decay rate is enlarged compared to 40% depth of discharge.
And predicting the degradation rule of the battery in the healthy state of 65-80% by the combination of the data and the GM-LSSVM, and intercepting three sections for analysis, wherein the result is shown in figure 4.
Referring to fig. 4, as the state of health of the battery decreases, the degradation speed of the battery increases accordingly, the degradation speed difference between the full charge and shallow discharge of the battery further increases, and in deep charge and deep discharge (dod=60% -80%), the degradation speed of the battery is substantially flat, i.e., the effect of the depth of discharge on the degradation of the battery is negligible.
S5: and converting the capacity decay of the battery into a breaking cost, taking the highest daily charge and discharge income of the retired battery as an objective function, and taking the maintenance cost and the power consumption cost of the battery into consideration, wherein the constraint conditions comprise power balance constraint, energy storage safety constraint and retired battery scheduling constraint. The step also needs to be described as follows:
the retired battery is used for storing energy in a gradient manner, and the main function is to perform arbitrage in cooperation with low time-of-use electricity price and high power generation, absorb partial redundancy photovoltaics when necessary, and establish the objective function with the highest net benefit of energy storage as shown in the following formula:
wherein F is the net daily gain of the retired battery pack; p (P) t Charging and discharging power for the retired battery pack, wherein the charging is negative and the discharging is positive; l (L) t The electricity price at the time t is the photovoltaic online electricity price represented by the absorption photovoltaic time; h represents the charge-discharge efficiency of the retired battery; c is the energy storage cost which is converted into daily, including the maintenance and overhaul cost C E Cost of power loss C l Cost of breaking C f 。
C f ＝L(t)C PV
Wherein k is m Maintenance coefficients for retired batteries; r is (r) i The number of times the battery is cycled for retirement; r is R i The total cycle number of the retired battery is; k (k) i Loss coefficient of retired battery; m is m t As a state variable, 1 corresponds to charge-1 corresponds to discharge. C (C) PV Representing the initial investment cost of the battery.
The constraint conditions are as follows:
(1) Energy storage state constraints
Wherein, I (t) is charge-discharge current; v is the rated voltage of the battery; SOC (t), SOC (t+1) respectively represents charge states before and after charge and discharge; e (E) b Representing the rated capacity of the battery; SOC (State of Charge) max And SOC (System on chip) min Representing the upper and lower limits of the state of charge of the energy storage system, respectively.
(2) Energy balance constraint
P load ＝P Gird +P ESB +P DG
Wherein P is load Representing the total load of the micro-grid; p (P) Gird Representing micro-grid purchase power; p (P) ESB Representing the charge and discharge power of the storage battery; p (P) DG Representing distributed energy power.
(3) Battery power constraint
Wherein P is c,max And P c,min Representing upper and lower limits of the charging power of the retired battery; p (P) d,max And P d,min Indicating upper and lower limits of the discharge power of the retired battery.
(4) Scheduling period constraints
SOC 0 ＝SOC H
Wherein SOC is 0 Representing the retired battery pack SOC at the beginning of a schedule; SOC (State of Charge) H Indicating the retired battery pack SOC after the end of the schedule.
S6: and importing micro-grid data, solving the model by adopting a particle swarm algorithm, making a retired battery operation scheme with highest benefit, and collecting use data of the retired battery under the scheme.
S7: and feeding back the true data used after echelon utilization to the least square support vector machine to serve as a training set of the least square support vector machine, so that rolling prediction of the fading speed is realized.
Example 2
Referring to fig. 5 to 8, a second embodiment of the present invention, which is different from the first embodiment, provides a verification comparison test of a method for optimizing the residual value of a retired power battery, specifically including:
in this embodiment, taking a general mass public transportation demonstration station integrated with some light charge and storage in Zhejiang as an example, the demonstration station is equipped with an 800kW photovoltaic system, a 500kW/2MWh energy storage system, and the battery is a retired lithium iron phosphate battery, and the structure of the battery is shown in fig. 5.
Referring to fig. 6, in the case of electricity load of a bus station and power output of a photovoltaic system in one day, the amount of charging body of the bus station is huge, and the generated energy of the photovoltaic system can be basically absorbed by the bus station; the charging load of the bus is influenced by the travel rule of the bus, and the bus adopts a charging mode of supplementing electricity in the daytime and intensively charging at night, so that the charging load of the bus station is concentrated from night to early morning.
The peak-valley time-of-use electricity price mechanism is implemented in Zhejiang province, the distributed photovoltaic system built by the bus station is implemented for a self-power-consumption allowance internet-surfing type distributed photovoltaic power generation project, the subsidy is a national-level and provincial photovoltaic power generation project degree electricity subsidy, the time-of-use electricity price, the internet-surfing electricity price and the subsidy are shown in table 3, and the retired battery parameters are shown in table 4.
Table 3: electricity-saving price, internet-surfing electricity price and patch electricity price list in Zhejiang.
Table 4: retired battery parameter table.
| Name of the name | Parameters (parameters) |
| Retired battery rated capacity/MWh | 2 |
| Charging and discharging efficiency of retired battery | 0.85 |
| Retired battery unit capacity price/(Yuan kWh- 1 ) | 0.6 |
| Loss coefficient k of retired battery i | 0.3 |
| Maintenance coefficient k of retired battery m | 0.15 |
| Old retired batteryCoefficient of |
1 |
The net income of the retired battery is influenced by the self loss on one hand, and the electricity price difference of low storage and high generation on the other hand, and the net income can be divided into two scenes according to the electricity price difference:
scene one: the battery pack is charged at the electricity price valley, and the electricity price peak performs the peak-valley arbitrage of discharging.
Scene II: the battery pack is charged at the low electricity price valley, and the peak of the electricity price peak is discharged-Gu Taoli.
The optimization method provided by the embodiment and the optimization method based on the traditional model are respectively solved based on the Matlab platform.
The particle swarm parameters were: particle number 300, maximum iteration number 500, maximum inertial weight 0.8, and optimization result shown in fig. 7.
Referring to fig. 7, in the initial stage of battery cascade utilization, the battery can bear more charge and discharge tasks, and as the retired battery is used, the retired battery needs to reduce the depth of charge and discharge for the purpose of maximizing the benefit; the traditional model only considers the relation between the depth of discharge and the cycle times in the whole life cycle, so that fine prediction cannot be realized, and the optimization result is that the depth of discharge is linearly adjusted downwards; in the initial stage of retired battery use, the most important cost is to split the construction cost at the breaking cost of each charge and discharge, and as the battery is used, the increasing maintenance cost has more and more influence on the operation scheme.
Referring to fig. 8, two optimization results are compared according to two charges and two discharges a day, and according to the schematic diagram of fig. 8, the retired battery optimizing method provided by the embodiment can improve the residual value of the retired battery by approximately 10% compared with the conventional model method.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (6)
1. A method for optimizing the residual value of a retired power battery is characterized by comprising the following steps: comprising the steps of (a) a step of,
selecting the discharge depth of the battery as a main factor influencing the degradation of the battery, and establishing m groups of gray models according to m groups of different discharge depths; collecting historical use data of a power battery of an electric automobile, and importing the historical use data into the gray model; taking the prediction result of the gray model as input, taking actual data as output, and training a least square support vector machine; predicting a declination rule of the power battery during echelon utilization according to the combination of the gray model and the least square support vector machine;
converting capacity decay of the battery into damage cost, constructing energy storage state constraint by taking the highest daily charge and discharge benefits of the retired battery as an objective function, wherein the energy storage state constraint comprises the following steps:
the energy balance constraint is:
P load ＝P Gird +P ESB +P DG
the battery power constraint is:
wherein mu t Is a state variable;
the scheduling period constraint is:
SOC 0 ＝SOC H
wherein I (t) is a charge-discharge current; v is the rated voltage of the battery; SOC (t), SOC (t+1) respectively represents charge states before and after charge and discharge; e (E) b Representing the rated capacity of the battery; SOC (State of Charge) max And SOC (System on chip) min Respectively represent storageUpper and lower limits of the state of charge of the energy system; p (P) load Representing the total load of the micro-grid; p (P) Gird Representing micro-grid purchase power; p (P) ESB Representing the charge and discharge power of the storage battery; p (P) DG Representing distributed energy power; p (P) c,max And P c,min Representing upper and lower limits of the charging power of the retired battery; p (P) d,max And P d,min Representing upper and lower limits of the discharge power of the retired battery; SOC (State of Charge) 0 Representing the retired battery pack SOC at the beginning of a schedule; SOC (State of Charge) H Representing the SOC of the retired battery pack after the dispatching is finished;
importing micro-grid data, solving the gray model by utilizing a particle swarm algorithm, formulating a retired battery operation scheme with highest benefit, and collecting use data of retired batteries under the retired battery operation scheme;
and feeding back the real data used after the battery is used in a gradient way to the least square support vector machine to serve as a training set of the least square support vector machine, so that rolling prediction of the fading speed is completed.
2. The retired power battery residual value optimization method according to claim 1, wherein: the least squares support vector machine includes,
training was performed using a regression model, as follows,
wherein w is T And b is offset for weight vector.
3. The retired power battery residual value optimization method according to claim 2, characterized in that: constraints are established using risk minimization principles, including,
wherein e i Is error, gamma is positiveThe parameters are then quantized.
4. A retired power battery residual value optimization method according to claim 3, characterized in that: the kernel function of the regression model selects the radial basis function, as follows,
5. the retired power battery residual value optimization method according to claim 4, wherein: the objective function may comprise a function of the object,
wherein F is the net daily gain of the retired battery pack; p (P) t Charging and discharging power for the retired battery pack, wherein the charging is negative and the discharging is positive; lambda (lambda) t The electricity price at the time t is the photovoltaic online electricity price represented by the absorption photovoltaic time; η represents the charge-discharge efficiency of the retired battery; c is the energy storage cost converted into daily.
6. The retired power battery residual value optimization method according to claim 5, wherein: maintenance and repair costs C E Cost of power loss C l Cost of breaking C f Comprising the steps of (a) a step of,
C f ＝L(t)C PV
wherein k is m Maintenance coefficients for retired batteries; r is (r) i The number of times the battery is cycled for retirement; r is R i The total cycle number of the retired battery is; k (k) i Loss coefficient of retired battery; mu (mu) t As a state variable, 1 corresponds to charge, -1 corresponds to discharge; l (t) represents each discharge loss, and CPV represents the initial investment cost of the battery.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110504501A (en) * | 2019-08-19 | 2019-11-26 | 国网河北省电力有限公司石家庄供电分公司 | Retired battery Gradient utilization method and system |
| CN110554321A (en) * | 2019-09-26 | 2019-12-10 | 长沙理工大学 | method for detecting SOC (state of charge) of retired power battery in real time |
| CN110808598A (en) * | 2019-10-22 | 2020-02-18 | 云南恒协科技有限公司 | Energy storage system for graded-utilization retired power lithium battery |
| CN110932300A (en) * | 2019-12-18 | 2020-03-27 | 国网福建省电力有限公司 | Power distribution network optimal scheduling method considering echelon utilization energy storage |
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| CN111260185A (en) * | 2020-01-08 | 2020-06-09 | 重庆大学 | Method for evaluating operation reliability of power generation and transmission system with retired electric vehicle battery as large-scale energy storage |
| CN111537899A (en) * | 2020-04-01 | 2020-08-14 | 国网江西省电力有限公司电力科学研究院 | Method for evaluating safety of power battery by gradient utilization |
| CN111709186B (en) * | 2020-06-16 | 2023-04-07 | 四川大学 | Integrated estimation method for health state of retired power lithium battery |
| CN111812536A (en) * | 2020-07-06 | 2020-10-23 | 安徽恒明工程技术有限公司 | Rapid evaluation method for retired power battery residual value |
| CN112103946B (en) * | 2020-08-20 | 2022-04-22 | 西安理工大学 | Particle swarm algorithm-based microgrid energy storage optimization configuration method |
| CN112765772B (en) * | 2020-12-25 | 2022-11-04 | 武汉理工大学 | Power battery residual life prediction method based on data driving |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110504501A (en) * | 2019-08-19 | 2019-11-26 | 国网河北省电力有限公司石家庄供电分公司 | Retired battery Gradient utilization method and system |
| CN110554321A (en) * | 2019-09-26 | 2019-12-10 | 长沙理工大学 | method for detecting SOC (state of charge) of retired power battery in real time |
| CN110808598A (en) * | 2019-10-22 | 2020-02-18 | 云南恒协科技有限公司 | Energy storage system for graded-utilization retired power lithium battery |
| CN110932300A (en) * | 2019-12-18 | 2020-03-27 | 国网福建省电力有限公司 | Power distribution network optimal scheduling method considering echelon utilization energy storage |
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