CN115001053A - V2G optimal frequency modulation method for actively inhibiting battery aging - Google Patents
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Abstract
The invention relates to a V2G optimal frequency modulation method for actively inhibiting battery aging, which belongs to the field of battery optimization and comprises the following steps: s1: the electric automobiles participate in the V2G service, collect the real-time SOC values of all the electric automobiles in the electric automobile centralized manager, preliminarily select the electric automobiles participating in the frequency modulation service, and send control signals to all the electric automobiles; s2: the frequency modulation power calculation module converts the load frequency control signal into a frequency modulation power demand signal and transmits the frequency modulation power demand signal to the MPC control module; s3: the MPC control module optimizes the charge and discharge power of each electric automobile participating in V2G frequency modulation by taking the minimum battery aging and the best tracking effect of the frequency modulation power demand signal as optimization targets based on the frequency modulation power demand signal and according to the collected SOC information of the electric automobile, and sends a power control signal to the intelligent charging module of the electric automobile; s4: the intelligent charging module controls the charging and discharging power of the electric automobile participating in V2G frequency modulation in real time.
Description
Technical Field
The invention belongs to the field of battery optimization, and relates to a V2G optimal frequency modulation method for actively inhibiting battery aging.
Background
Nowadays, with the increasingly prominent energy and environmental problems, renewable energy sources such as hydropower, wind power, photovoltaic and the like are taken as representatives of clean energy sources, and the penetration scale of the renewable energy sources to a power grid is continuously enlarged. However, the renewable energy has the characteristics of large fluctuation, strong intermittency and the like, which brings great pressure to the frequency stability of the power system. The traditional unit is low in climbing rate and low in response speed, so that the influence of uncertainty of renewable energy sources on frequency disturbance of a power system cannot be adjusted in time. With the popularization of electric vehicles, more and more EVs can be used as a mobile energy storage system and a peak shaving device to be connected into a power grid, and the functions of peak shaving, valley filling, frequency regulation and the like of the EVs can greatly improve the reliability and the economical efficiency of the operation of a power system. For this reason, the V2G (Vehicle-to-Grid) technology has attracted widespread attention by researchers at home and abroad.
The participation of the vehicle in the V2G service means that its battery needs to be charged and discharged frequently according to the grid demand, which inevitably exacerbates the aging of the battery. Jafari et al found that if the electric vehicle performs the V2G service every day, the battery life would be significantly shortened. Bishop et al found that V2G service affects power cell degradation to varying degrees at different cell capacities, charge regimes and depths of discharge, with degradation of the electric vehicle battery pack being most sensitive to the charge regime and the depth of discharge of the individual cells. Thingvad et al studied the effect of long-term performance of V2G service on battery capacity degradation of electric vehicles, and found that the available capacity of the battery decreased by 10% on average after two years and by 17.8% after five years. The aging of the battery is increased, so that the maintenance cost of the battery is increased, and the service benefit brought by V2G is reduced. Zheng et al found that at the current cost of the battery, the electric vehicle provided V2G service, which was extremely difficult for the owner to profit from. Gough et al found that the profit made by the V2G service could not offset the cost of battery aging caused by electric vehicles participating in ancillary services. Lunz et al found that V2G provides minimal revenue in the power market for energy exchange as compared to battery aging costs. In addition, battery capacity degradation can also cause the vehicle range to decrease, causing "range anxiety" for the user. Therefore, it is important to research how to inhibit the battery aging caused by the vehicle participating in the V2G service, so as to prolong the battery life.
Currently, researchers extend battery life in two main ways. On the one hand, next generation battery technologies with ultra-long battery life are developed. The latest NMC811/Graphite cells as developed by Dahn et al can have operating lifetimes of up to decades at room temperature. On the other hand, an advanced V2G optimization control model is developed based on current battery technology to suppress battery aging. Reniers et al calculate revenue and measure battery aging based on a physical model of lithium battery aging, making up the gap between technical fidelity and economic dispatch in the energy trading market. Data analysis shows that the method can increase 20% of income and simultaneously reduce 30% of battery aging compared with the traditional heuristic degradation modeling method. Fortenbacher et al propose a novel two-stage centralized Model Predictive Control scheme (MPC) for distributed battery storage, which is composed of a scheduling entity and a real-time Control entity, and researches show that the consumption of a power grid scheduling on a battery can be reduced by 30% by using a detailed battery Model in the real-time Control stage. Patsios et al propose an integrated electro-thermal-chemical modeling method and build an integrated modeling framework for the grid energy storage system from the battery to the grid. Simulations have found that the power system schedule can be reduced by a factor of two with respect to battery drain. Uddin and the like construct a complete battery degradation model, the model comprehensively considers all aging behaviors of the battery, and the result shows that the smart grid can reduce the capacity attenuation of the battery pack of the electric automobile by 9.1% at most. Li et al propose a scheduling system for actively suppressing battery aging, which quantifies battery aging by a Rain-Flow Cycle Counting (RCC) method, and finds that the battery charge-discharge Cycle can be minimized by optimizing the scale of vehicles involved in scheduling and the charge-discharge time of each vehicle, with the minimization of battery aging as one of optimization objectives. Most of the current research focuses on V2G peak clipping and valley filling and energy trading market power scheduling, but V2G fm service also has an important role in maintaining the stability of the power system.
Hashmi et al introduce a distributed random control framework for tracking the frequency modulated power demand signal at the grid end under the condition of satisfying the battery constraint limit, and define different random decision rules for different types of batteries to optimize the standing time and the charging state of a single battery. Rajamand and the like find that the State Of Charge (SOC) Of the electric automobile is fed back to a control structure, and the frequency/voltage regulation Of the micro-grid can be well improved. Kaur et al propose a double-deck hierarchical control scheme to electric automobile participation secondary frequency control, use the demand as the constraint condition with the electric automobile owner, the result shows, this scheme has reduced the electric wire netting frequency deviation. Peng et al propose an intelligent charging algorithm that can coordinate the charging and discharging of an electric vehicle according to a frequency deviation signal, which can improve the load factor and voltage level of a low voltage distribution network. Rehman et al propose an optimal hierarchical bidirectional aggregation algorithm that provides voltage and frequency regulation services to the grid by optimizing electric vehicle charge and discharge power, predicting power demand, and performing day-ahead load scheduling in the smart grid. Although the electric automobile can remarkably improve the stability of a power system by participating in the V2G frequency modulation service, the influence of battery aging on the service benefit of the V2G is also considered. However, there is little research on the impact of actively suppressing the V2G fm service on the aging of batteries of electric vehicles.
Disclosure of Invention
In view of this, the present invention is directed to suppressing the influence of the V2G frequency modulation on the battery aging of the electric vehicle, improving the stability of the whole V2G system, and providing a V2G optimal frequency modulation control method aiming at suppressing the battery aging and tracking the power required by the frequency modulation at the power grid end.
In order to achieve the purpose, the invention provides the following technical scheme:
a V2G optimal frequency modulation method for actively inhibiting battery aging comprises the following steps:
s1: the electric automobiles participate in the V2G service, the real-time SOC values of all the electric automobiles in the electric automobile centralized manager are collected through the judgment module, the electric automobiles participating in the frequency modulation service are preliminarily selected based on the SOC values, and control signals are sent to all the electric automobiles;
s2: the frequency modulation power calculation module converts a load frequency control signal transmitted by the power grid control center into a frequency modulation power demand signal and transmits the frequency modulation power demand signal to the MPC control module;
s3: the MPC control module optimizes the charge and discharge power of each electric automobile participating in V2G frequency modulation by taking the minimum battery aging and the best tracking effect of the frequency modulation power demand signal as optimization targets based on the frequency modulation power demand signal and according to the collected SOC information of the electric automobile, and then sends a power control signal to the intelligent charging module of the electric automobile;
s4: based on the power control signal, the electric automobile intelligent charging module controls the charging and discharging power of the electric automobile participating in V2G frequency modulation in real time, so that the battery aging is restrained while the frequency stability of a power system is maintained.
Further, in step S1, the selection condition is determined as:
wherein λ is n The value of the control signal determines whether the nth EV is qualified to participate in the V2G frequency modulation service; lambda n 1 means that the nth vehicle EV qualifies, λ n 0 means that the nth EV is not qualified; SOC n (t) represents the SOC value, SOC, of the nth EV at time t min 、SOC max Respectively representing the minimum and maximum SOC values allowed by the battery of the electric automobile.
Further, in step S3, the MPC control module evaluates the tracking effect on the fm power demand signal based on the error Ψ, and the expression is as follows:
wherein w is T total /Δt，T total The total time length of frequency modulation is delta t is a sampling time interval, and the closer psi approaches to 0, the better the tracking effect is shown; n is the number of vehicles participating in frequency modulationi represents a vehicle number of a vehicle,
the charging and discharging power of the ith vehicle in the kth sampling period,
and the grid needs frequency modulation power for the kth sampling period.
Further, the battery charge-discharge model is as follows: the charge-discharge characteristics of the battery are described by applying a battery equivalent internal resistance model, and the expression is as follows:
wherein t represents time, I i (t) represents the battery current of the ith vehicle at time t, R i Indicates the internal resistance of the battery of the i-th vehicle,
indicating the battery open circuit voltage of the ith vehicle at time t,
representing the battery line end voltage of the ith vehicle at time t,
the charging and discharging rate of the battery side of the ith vehicle at the time t is represented, wherein the charging is positive, and the discharging is negative;
the battery SOC is defined as a ratio of its remaining battery capacity to its rated capacity, and is expressed as follows:
wherein Z is i (t) represents the battery SOC, Q of the ith vehicle at time t i (t) represents the battery available capacity of the ith vehicle at time t,
indicating the battery rated capacity of the ith vehicle. The time is derived based on equation (5):
discretizing equation (7) using euler's formula:
wherein the subscript t k Denotes the kth time, then t k+1 ＝t k +Δt。
Is the amount of change in the k-th period SOC.
Further, the battery capacity degradation model is: the battery aging is described by using a battery active material loss model, which is as follows:
wherein:
wherein phi is (T) Is a temperature influencing factor; k is a radical of AM Is an active substance loss index factor; e AM Activation energy for active substances; r is an ideal gas constant; t is the ambient temperature;
the capacity fade of the battery in the kth Δ T period is:
the formula is as follows:
substituting equation (11) yields:
using the trapezoidal rule, we have:
further, the constraint conditions for the electric automobile to participate in the V2G frequency modulation include:
battery SOC constraint, namely the SOC of each electric vehicle battery participating in frequency modulation is not lower than the minimum SOC value and not higher than the maximum SOC value within the frequency modulation time:
Z min ≤Z i (t)≤Z max ,i＝1,...,N (15)
constraint of battery charge and discharge power, namely, the charge and discharge power of each electric vehicle participating in frequency modulation is not lower than the minimum discharge power and is not higher than the maximum charge power:
objective function J MD Comprises the following steps:
and alpha and beta are penalty factors, and the battery aging inhibition rate and the tracking effect of the total vehicle charge and discharge power on the power demand of the power grid are controlled by adjusting the alpha and beta values.
Further, in the MPC control module, in the process that the electric vehicle participates in the V2G frequency modulation, an optimization model for suppressing battery aging in the previous M time periods is constructed as follows:
Z min ≤Z i (t)≤Z max i＝1，...，N，k＝1，...M
the invention has the beneficial effects that: the invention establishes an optimization model aiming at inhibiting the battery aging by introducing a battery aging model based on a mechanism. Based on a model prediction control theory, a brand-new optimization controller is developed, and the real-time efficient control of the charging and discharging power of the electric automobile is realized. The simulation result shows that when the benchmark optimization control strategy is adopted, the battery capacity fading caused by the V2G to the battery with the smaller SOC value is minimum; when the optimal control strategy for suppressing the battery aging is adopted, V2G is more likely to make the electric vehicle with a larger SOC value respond to the frequency modulation signal with a smaller charge/discharge power, and vice versa. When the foresight period is 30, the controller has the best comprehensive performance, and the battery aging can be reduced by up to 22.34%. Additional studies have found that battery aging can exacerbate the depletion of V2G in battery performance.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic diagram of energy flow and information flow between an electric vehicle and a power grid;
FIG. 2 is an optimized control schematic diagram for actively suppressing battery aging;
fig. 3 is a plot of fm power demand control signals, where (a) is data for a 12 hour period of a day and (b) is a portion of the data from the screenshot of (a).
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
The V2G technology can realize information and energy interaction between the electric automobile and the power grid, and provides possibility for the electric automobile to participate in power grid auxiliary service and optimize power system operation. Fig. 1 is a schematic diagram of energy flow and information flow between an electric vehicle and a power grid, wherein a centralized manager is composed of a data management system and a control system, and realizes unified management and control of a plurality of electric vehicles. The data management system is responsible for collecting real-time charging and discharging power and SOC information of the electric automobile, real-time frequency modulation power demand data and related historical data and the like sent by the power grid dispatching center and reporting the data to the power grid dispatching center; and the control system analyzes the frequency modulation power demand data and the scheduling instruction, optimizes the vehicle charging and discharging strategy and sends the control instruction to the charging pile. For example, when the grid frequency is disturbed, the dispatching center sends a power demand instruction to the centralized manager, and the centralized manager controls the charging pile through the control system to change the charging and discharging power of the electric vehicle, so that the total power provided by the V2G meets the frequency modulation demand.
Fig. 2 is a schematic diagram of an optimal control for actively suppressing battery aging according to the present invention, wherein the schematic frame mainly includes four modules: the device comprises a judgment module, a frequency modulation power calculation module, an MPC control module and an EV intelligent charging module.
In fig. 2, the electric vehicle participation V2G service determination module is responsible for collecting real-time SOC values of each electric vehicle in the electric vehicle centralized manager, preliminarily selecting electric vehicles participating in the frequency modulation service based on the real-time SOC values, and sending control signals to each electric vehicle, where the determination and selection conditions are as follows:
wherein λ n The value of the control signal determines whether the nth EV is eligible to participate in the V2G fm service. Lambda n 1 means that the nth vehicle EV qualifies, λ n 0 means that the nth EV is not qualified. Wherein the SOC n (t) represents the SOC value, SOC, of the nth EV at time t min 、SOC max Respectively representing the minimum and maximum SOC values allowed to be reached by the battery of the electric vehicle.
The frequency modulation power calculation module converts a load frequency control signal transmitted by a power grid control center into a frequency modulation power demand signal and transmits the frequency modulation power demand signal to the MPC control module, and the MPC control module optimizes the charge and discharge power of each electric vehicle participating in V2G frequency modulation by taking the battery aging minimum and the frequency modulation power demand signal tracking effect as an optimization target based on the frequency modulation power demand signal and according to the collected SOC information of the electric vehicle, and further transmits the power control signal to the intelligent charging module of the electric vehicle. Wherein the controller evaluates the tracking effect of the frequency modulation power demand signal based on the error psi, and the expression is as follows:
wherein w ═ T total /Δt，T total For the total duration of the frequency modulation, Δ t is the sampling interval, and the closer Ψ is to 0, the better the tracking effect. N is the number of vehicles participating in frequency modulation, the subscript i represents the vehicle number,
the charging and discharging power of the ith vehicle in the kth sampling period,
and the grid needs frequency modulation power for the kth sampling period.
Based on the control signal, the intelligent charging pile controls the charging and discharging power of the electric automobile participating in the frequency modulation of V2G in real time, so that the aging of the battery is restrained while the frequency stability of a power system is maintained.
The invention uses the battery equivalent internal resistance model to describe the charge-discharge characteristics, and the expression is as follows:
wherein t represents time, I i (t) represents the battery current of the ith vehicle at time t, R i Indicates the internal resistance of the battery of the i-th vehicle,
indicating the battery open circuit voltage of the ith vehicle at time t,
representing the battery line end voltage of the ith vehicle at time t,
the charge/discharge rate of the battery side of the i-th vehicle at time t is represented, and the charge is positive and the discharge is negative.
A battery (SOC) is defined as a ratio of its remaining battery capacity to its rated capacity, and is expressed as follows:
wherein Z is i (t) represents the battery SOC, Q of the ith vehicle at time t i (t) represents the battery available capacity of the ith vehicle at time t,
indicating the battery rated capacity of the i-th vehicle. Further time derivation can be derived based on equation (5):
discretizing equation (7) by using Euler's formula can obtain:
wherein the subscript t k Denotes the kth time, then t k+1 ＝t k +Δt。
Is the amount of change in the k-th period SOC.
For the graphite cathode lithium ion battery, the performance degradation is mainly caused by the growth of a Solid-liquid Interface (SEI) and the loss of active substances. The SEI film growth is mainly caused by side reactions of the electrolyte on the electrode surface, and the active material loss is mainly the active material cracking and detachment caused by the mechanical stress induced by lithium ion intercalation and deintercalation into and out of the electrode. The invention can minimize the battery capacity decline by optimizing the electric automobile current. To simplify the model, the present invention uses a battery active material loss model to describe battery aging.
The loss model of the battery active material under the constant temperature condition is as follows:
wherein:
wherein phi (T) Is a temperature-affecting factor; k is a radical of AM Is an active substance loss index factor; e AM Activation energy for active substances; r is an ideal gas constant; t is the ambient temperature. Equation (9) shows that the battery, whether charged or discharged, causes its capacity to decline.
The capacity fade of the battery in the kth Δ T period is:
the formula is as follows:
substituting equation (11) yields:
further using the trapezoidal rule, we get:
the participation of the electric automobile in the V2G frequency modulation mainly comprises two constraints.
1) And (4) battery SOC constraint, namely the SOC of each electric vehicle battery participating in frequency modulation is not lower than the minimum SOC value or higher than the maximum SOC value within the frequency modulation time.
Z min ≤Z i (t)≤Z max ，i＝1，...，N (15)
2) And (4) constraint of battery charging and discharging power, namely that the charging and discharging power of each electric automobile participating in frequency modulation is not lower than the minimum discharging power and is not higher than the maximum charging power.
In order to minimize battery aging of an electric vehicle participating in V2G frequency modulation in M time periods in the future and ensure good frequency modulation power tracking effect, an objective function J is constructed MD ：
Where α and β are penalty factors. The battery aging inhibition rate and the tracking effect of the total charge and discharge power of the vehicle on the power demand of the power grid can be controlled by adjusting the values of alpha and beta.
In the V2G frequency modulation process of the electric automobile, an optimization model for inhibiting battery aging in the previous M time periods is constructed as follows:
S.t. 
Z min ≤Z i (t)≤Z max i＝1，...，N，k＝1，...M
fig. 3 is a diagram of a fm power demand control signal. The fm power demand control signal is selected from data of a 12 hour day time period in a certain power market, as shown in fig. 3 (a). FIG. 3(b) shows a portion of the data taken from FIG. 3(a) with a total duration of 1950s, which will be the main simulation experimental data of the present invention, with a sampling time interval of 15 s. In the experiment, 10 electric automobiles of three different types are selected to participate in frequency modulation service, wherein the electric automobiles comprise NIO EC6, BYDhan and Tesla model S, the battery capacities corresponding to the three automobile models are 214.29Ah, 135Ah and 211.44Ah respectively, and in the experiment, the initial SOC value of the battery of the automobile is set from low to high in a constraint range. Without being particularly stated, the SOH of the battery of the invention is defined as 92.9%, namely the battery capacity is attenuated by 7.1%. In order to simplify the calculation process, the invention assumes that the battery packs of the three types of electric vehicles all adopt Li (NiCoAl) O 2 The rated capacity and the rated voltage of the single battery are respectively 3.0Ah and 3.6V. In this study, the Open Circuit Voltage (OCV) and the Internal Resistance (R) of the cell were measured i ) And respectively carrying out proportional amplification to obtain corresponding parameters of the corresponding vehicle battery pack.
Parameter k in a battery aging model (7) AM 1.368l/Ah and E AM 39500J/mol. The temperature of the battery pack is assumed to be 25 ℃, so T298.15K. Other relevant parameter settings are shown in table 1. The invention adopts a power average distribution strategy as a benchmark optimization control strategy to verify the high performance of the developed model.
TABLE 1
When the frequency modulation control strategy of the electric vehicle V2G is simulated under the condition that M is 20, compared with the charging and discharging power distribution situation of the electric vehicle adopting the reference optimization control strategy and the charging and discharging power distribution situation of the battery adopting the active battery aging inhibition optimization control strategy, it can be seen that compared with the power average distribution strategy, the charging and discharging power magnitude of the battery in each period of the optimized power distribution strategy is in a negative correlation with the SOC value, namely the charging and discharging power of the battery with the initial SOC value of 0.342 is larger, and the charging and discharging rate of the battery with the initial SOC value of 0.860 is smaller. Comparing the SOC variation trend of the electric automobile adopting the reference optimization control strategy with the battery SOC variation track after the frequency modulation power optimization, the maximum slope of the track curve with the initial SOC value of 0.342 can be obtained, which indicates that the battery always responds to the frequency modulation signal with the maximum charging and discharging power at the moment. The change of the battery SOC curve with the initial SOC value of 0.860 is smooth, namely, the vehicle battery responds to the frequency modulation signal with smaller charge and discharge power in the simulation time. The SOC deviation remains within a very narrow SOC range over a 1 hour simulation time, with a maximum deviation of no more than 1 percentage point. And (3) battery capacity decline of the electric automobile when a power average distribution strategy is adopted. The result indicates that the larger the SOC value of the battery is, the faster the capacity thereof is degraded when the charge and discharge power is the same, which is consistent with the result expressed by equation (14). And (5) the capacity decline condition of the vehicle battery after the frequency modulation strategy is optimized. Compared with the average power distribution strategy, the electric vehicle with the larger battery SOC value has the obviously reduced battery capacity decline caused by the obviously reduced charge and discharge power under the condition of the power optimal distribution strategy, and the optimization result shows that the V2G frequency modulation power optimal control strategy provided by the invention can reduce the overall aging of the battery by 22.15%.
And simulating the participation of the electric automobile in the V2G frequency modulation power optimization under the condition that M is 30, and adopting an active battery aging suppression optimization control strategy to optimize the charge and discharge power distribution condition of the battery. It can be seen that the electric vehicle with the initial SOC value of 0.860 has a small charge/discharge power allocated thereto in most of the time. Compared with the simulation result of M-20, the charging and discharging power of the electric automobile is obviously reduced, and the charging and discharging power of the other two electric automobiles is slightly increased. And modulating the frequency of the battery SOC variation track after power optimization. It can be seen that the SOC curve of the electric vehicle with the initial SOC value of 0.860 becomes more gradual due to the decrease of the charging and discharging power again, and is nearly a horizontal line, while the other two electric vehicles have very small changes in the SOC of the battery due to the very small changes in the charging and discharging power. Compared with the situation that the capacity of the vehicle battery is declined after the frequency modulation strategy is optimized, the capacity attenuation of the electric vehicle with the larger SOC is obviously reduced, and the capacity attenuation changes of the two electric vehicles with the smaller SOC are extremely small. The results show that when the look-ahead period M is 30, the overall aging of the battery is reduced by 22.34% compared to the power-averaging allocation strategy. Compared with the simulation result of M-20, the battery aging inhibition capability of the optimization controller is improved by 0.19%.
Table 2 shows initial values, final values, and variation ranges of the SOCs of the 10 selected electric vehicles participating in the V2G frequency modulation. As can be seen from table 2, under different frequency modulation strategies, the maximum deviations of the final value and the initial value of the battery SOC of the electric vehicle are 0.24%, 0.51%, and 0.70%, respectively, that is, the SOC deviation caused by the electric vehicle participating in the V2G frequency modulation service in a short time is very small, and will not affect the trip of the vehicle owner. In addition, the SOC deviation is small, the net energy supply of the electric automobile to a power grid is small, the price overflow of the electric automobile in the electricity market is large except at the peak of electricity utilization, the price fluctuation of the electricity in most of other periods is small, the possibility that the electric automobile participates in V2G frequency modulation in a short time to obtain profit is extremely low in consideration of the aging cost of the battery, and the main reason that the market price is not considered in the invention and the importance is placed on restraining the V2G frequency modulation aging battery is provided.
TABLE 2
From the performance results of the controller when M is 10, 20, 30 and 40 respectively, it can be concluded that the controller has a good tracking effect on the grid frequency modulation power demand signal in 4 different look-ahead time periods, and the tracking errors Ψ are 0.986%, 0.953%, 0.891% and 0.775% respectively. And as the control period M increases, the tracking effect tends to be slightly improved. The run time of the controller grows exponentially with increasing M. When M is 10, the program run time is only 1.36s, but in contrast, this strategy has the worst effect of suppressing battery aging; and when M is 40, although the strategy is best for restraining the battery capacity degradation, the overall aging of the battery is reduced by 22.47% compared with the power average distribution strategy, but the program running time is 27.45s, which is far beyond the sampling time 15s set by the experiment. Compared with the simulation result of M-20, although the controller operation time is increased when M-30, the controller operation time is still less than the sampling time 15s, and the effect of suppressing the battery aging is improved. Therefore, the look-ahead period M can be set to 30 in consideration of the benefit of each participant of V2G and the control performance of the controller, while satisfying the sampling time. In practice, a reasonable value of M can be determined based on hard conditions.
In order to research the performance degradation of batteries participating in V2G frequency modulation under different SOH conditions, the invention constructs battery packs with two other SOH conditions, and the single batteries of the battery packs are Li (NiCoAl) O 2 The open-circuit voltage and the internal resistance of the battery and the battery pack can be obtained by scaling corresponding parameters of the single batteries under the same health state. As can be seen from the simulation results, the OCV-SOC curve has small change along with the aging of the battery, and the internal resistance R i Increasing as the battery ages.
Under the average allocation strategy, the batteries under different situations participate in the capacity fading situation of the V2G frequency modulation, including A, B, C, D, E5 situations. In case a, the SOH of the batteries of the respective vehicles is 92.9%, in case B, the SOH of the batteries of NIO EC6 and Tesla model S is 92.9%, in case B, the SOH of the batteries of BYD han is 87.6%, in case C, the SOH of the batteries of the respective vehicles is 87.6%, in case D, the SOH of the batteries of NIO EC6, BYD han and Tesla model S is 92.9%, 87.6% and 82%, respectively, and in case E, the SOH of the batteries of the respective vehicles is 82%. The results show that the degree of capacity fade of the battery involved in the V2G frequency modulation is positively correlated with its initial SOH, i.e., the battery aged the least in case a and the most in case E.
Based on the optimized frequency modulation strategy and the look-ahead period M of 30, the capacity fade scenario where the battery participates in the frequency modulation of V2G in different cases shows that the battery aging increases by 0.22%, 0.34%, 0.47%, and 0.73% for cases B, C, D and E, respectively, compared to case a. Obviously, as the initial SOH of the battery decreases, the capacity fade of the battery participating in the V2G frequency modulation process gradually increases, i.e., the low SOH battery accelerates the degradation process of its performance. Aging was reduced by 22.34%, 22.21%, 22.19%, 22.17%, and 22.07% for cases A, B, C, D and E, respectively, as compared to the power-averaging strategy. It can be seen that the developed controller plays a good role in inhibiting aging of the batteries in different health states participating in the frequency modulation of V2G, and the inhibiting effect is 22.07% at least.
And (3) changing curves of the vehicle battery pack delta SOC under different initial SOH conditions based on the V2G frequency modulation optimization control strategy (M is 30). It can be observed that the variation of SOC of the battery pack with 18% decay in battery capacity is always greater than the variation of SOC of the battery pack with 7.1% decrease in battery capacity for the same initial SOC of the battery pack at any time. This phenomenon is a major cause of the increase in battery aging, as can be seen in conjunction with equation (13). This phenomenon is mainly caused by the deterioration of the battery capacity and the increase of the internal resistance, as shown in equation (8). Degradation of battery performance can therefore exacerbate battery aging during the V2G frequency modulation process.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (7)
1. A V2G optimal frequency modulation method for actively inhibiting battery aging is characterized in that: the method comprises the following steps:
s1: the electric automobiles participate in the V2G service, the real-time SOC values of all the electric automobiles in the electric automobile centralized manager are collected through the judgment module, the electric automobiles participating in the frequency modulation service are preliminarily selected based on the SOC values, and control signals are sent to all the electric automobiles;
s2: the frequency modulation power calculation module converts a load frequency control signal transmitted by the power grid control center into a frequency modulation power demand signal and transmits the frequency modulation power demand signal to the MPC control module;
s3: the MPC control module optimizes the charge and discharge power of each electric automobile participating in V2G frequency modulation by taking the minimum battery aging and the best tracking effect of the frequency modulation power demand signal as optimization targets based on the frequency modulation power demand signal and according to the collected SOC information of the electric automobile, and then sends a power control signal to the intelligent charging module of the electric automobile;
s4: based on the power control signal, the electric automobile intelligent charging module controls the charging and discharging power of the electric automobile participating in V2G frequency modulation in real time, so that the battery aging is restrained while the frequency stability of a power system is maintained.
2. The optimal frequency modulation method for actively suppressing V2G battery aging according to claim 1, wherein: in step S1, the selection condition is determined as follows:
wherein λ n The value of the control signal determines whether the nth EV is qualified to participate in the V2G frequency modulation service; lambda [ alpha ] n 1 means that the nth vehicle EV qualifies, λ n 0 means that the nth EV is not qualified; SOC n (t) represents the SOC value, SOC, of the nth EV at time t min 、SOC max Respectively representing the minimum and maximum SOC values allowed to be reached by the battery of the electric vehicle.
3. The V2G optimal frequency modulation method for actively suppressing battery aging according to claim 2, wherein: in step S3, the MPC control module evaluates the tracking effect of the fm power demand signal based on the error Ψ, and the expression is as follows:
wherein w ═ T total /Δt，T total For adjustingThe total frequency duration, delta t is a sampling time interval, and the closer psi is to 0, the better the tracking effect is shown; n is the number of vehicles participating in frequency modulation, the subscript i represents the vehicle number,
the charging and discharging power of the ith vehicle in the kth sampling period,
and the grid needs frequency modulation power for the kth sampling period.
4. The optimal frequency modulation method for actively suppressing V2G battery aging according to claim 3, wherein: the battery charging and discharging model is as follows: the charge-discharge characteristics of the battery are described by applying a battery equivalent internal resistance model, and the expression is as follows:
wherein t represents time, I i (t) represents the battery current of the ith vehicle at time t, R i Indicates the internal resistance of the battery of the i-th vehicle,
indicating the battery open circuit voltage of the ith vehicle at time t,
representing the battery line end voltage of the ith vehicle at time t,
the charging and discharging rate of the battery side of the ith vehicle at the time t is represented, wherein the charging is positive, and the discharging is negative;
the battery SOC is defined as a ratio of its remaining battery capacity to its rated capacity, and is expressed as follows:
wherein Z is i (t) represents the battery SOC, Q of the ith vehicle at time t i (t) represents the battery available capacity of the ith vehicle at time t,
indicating the battery rated capacity of the ith vehicle. Derived over time based on equation (5):
discretizing equation (7) using euler's formula:
wherein the subscript t k Denotes the kth time, then t k+1 ＝t k +Δt。
Is the amount of change in the k-th period SOC.
5. The V2G optimal frequency modulation method for actively suppressing battery aging according to claim 4, wherein: the battery capacity fade model is: the battery active material loss model is used for describing the battery aging, and the battery active material loss model under the constant temperature condition is as follows:
wherein:
wherein phi (T) Is a temperature influencing factor; k is a radical of AM Is an active substance loss index factor; e AM Activation energy for active substances; r is an ideal gas constant; t is the ambient temperature;
the capacity fade of the battery in the kth Δ T period is:
the formula is as follows:
substituting equation (11) yields:
using the trapezoidal rule, we have:
6. the V2G optimal frequency modulation method for actively suppressing battery aging as claimed in claim 5, wherein: the constraint conditions for the electric automobile to participate in V2G frequency modulation include:
battery SOC constraint, namely the SOC of each electric vehicle battery participating in frequency modulation is not lower than the minimum SOC value and not higher than the maximum SOC value within the frequency modulation time:
Z min ≤Z i (t)≤Z max ,i＝1,...,N (15)
constraint of battery charge and discharge power, namely, the charge and discharge power of each electric vehicle participating in frequency modulation is not lower than the minimum discharge power and is not higher than the maximum charge power:
objective function J MD Comprises the following steps:
and alpha and beta are penalty factors, and the battery aging inhibition rate and the tracking effect of the total vehicle charge and discharge power on the power demand of the power grid are controlled by adjusting the alpha and beta values.
7. The V2G optimal frequency modulation method for actively suppressing battery aging according to claim 6, wherein: in the MPC control module, an optimization model for inhibiting battery aging in the previous M time periods is constructed as follows when the electric automobile participates in the V2G frequency modulation process:
Z min ≤Z i (t)≤Z max i＝1,...,N,k＝1,...,M
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