CN115473306A - A hybrid energy storage system reuse control method based on intelligent algorithm - Google Patents

Abstract

The invention relates to the technical field of battery energy storage optimization configuration, discloses a method for controlling the reuse of a hybrid energy storage system based on an intelligent algorithm based on waste batteries, and proposes a short-term strategy and A dual-strategy method that combines long-term strategies. The short-term strategy uses the flamingo algorithm to optimize the configuration of the system capacity, and the long-term strategy uses the sparrow search algorithm to optimize the configuration; The parameter synthesis model can flexibly control and configure the hybrid battery pack through the control module; the long-term strategy is used to adjust the operation strategy of the system from time to time to optimize the economic cost of the comprehensive operation. The invention can be well applied to small and medium-sized industrial and commercial energy systems, solves the problem of difficult disposal of waste batteries, provides good economic and social benefits, and has long-term application prospects.

Description

A hybrid energy storage system reuse control method based on intelligent algorithm

technical field

The invention relates to an intelligent algorithm-based recycling control method for a hybrid energy storage system based on waste batteries, which belongs to the technical field of optimal configuration of battery energy storage.

Background technique

With the increasingly serious energy crisis and environmental pollution, hybrid electric vehicles and electric vehicles provide a new direction for the development of the automotive industry. Due to the advantages of long cycle life, high power, and high energy density, lithium-ion batteries can achieve longer battery life and mileage, and are widely used as a power source for electric vehicles. However, for the use of lithium batteries for electric vehicles, when the real capacity of the battery decays to 80% of the rated capacity, the default lithium battery has reached the state of waste. However, less than 80% of lithium batteries still have huge capacity and utilization value inside, so how to apply waste batteries to the production field is of great significance.

The traditional use of waste batteries is to disassemble and decompose the waste batteries and carry out new process production, which will leave behind heavy metal materials such as mercury to pollute the environment; although some enterprises use waste batteries as energy storage power sources to generate electricity for equipment, but their power generation The efficiency is not high, and it is only used in very small power supply systems.

Contents of the invention

Purpose of the invention: Aiming at the problems pointed out in the background technology, the present invention provides a hybrid energy storage system reuse control method based on intelligent algorithms, which fully utilizes waste batteries and greatly saves economic and environmental costs.

Technical solution: The present invention discloses a method for regulating and controlling the reuse of a hybrid energy storage system based on an intelligent algorithm, which includes the following steps:

Step 1: Establishing a battery multi-parameter comprehensive model that affects battery aging, the battery multi-parameter comprehensive model includes a battery electric power model, a battery thermal model, a battery aging model, and a battery economic model;

Step 2: According to the multi-parameter comprehensive model of the battery obtained in step 1, optimize the system capacity configuration by using the short-term strategy, and the short-term strategy uses the flamingo algorithm to optimize the configuration of the system capacity;

Step 3: According to the multi-parameter comprehensive model of the battery obtained in step 1, the economic indicators of the system are optimized using a long-term strategy, and the long-term strategy uses a sparrow search algorithm to optimize configuration.

Further, the modeling of the battery multi-parameter comprehensive model in the step 1 is divided into the following steps:

11) Establish battery electric power model, thermal model, aging model and economic model;

12) The electric power model of the battery is as follows:

Among them, R ₁ , C ₁ , R ₂ , and C ₂ represent the resistance and capacitance of the battery, C _bat represents the total capacity of the battery, I(t) represents the current, and V ₁ and V ₂ represent the voltage;

13) The thermal model of the battery is as follows:

Among them, R _c , _Ru , C _c and C _s represent thermal conduction resistance, convection resistance, core heat capacity and surface heat capacity respectively, and the two state variables are core temperature T _c and surface temperature T _s , ambient temperature T _f is considered an uncontrollable input;

14) The aging model of the battery is as follows:

Q _remain ＝3600·C _bat ·f ₁ (N)·T

k₁是个定值；Among them, SOH represents the state of battery life, that is, the degree of aging, N represents the number of cycles, Q _remain represents the remaining capacity of the battery, T=T _c +T _s ,

k ₁ is a fixed value;

15) The economic model of the battery is as follows:

Among them, E is the total cost of economic operation, Battery _ex is the cost of battery replacement, Battery _load is the cost of battery operation and maintenance, SOH _loss is a parameter related to the cost of battery life loss, and w _a is the weight coefficient of battery cost.

Further, the step 2 specifically includes the following steps:

21) Initialize parameters, input the quantity that affects the battery load: voltage V ₁ , V ₂ , total battery capacity C, battery current I(t);

22) Initialize the population: set the population size as P, the maximum number of iterations as Iter _Max , and the proportion of flamingos migrated in the first part as MP _b ;

23) Find the fitness of each flamingo and sort the flamingo population according to the fitness value of individual flamingos; low fitness pre-flamingo MP _b and high fitness pre-flamingo MP _t is regarded as migrating flamingos, while other flamingos are regarded as foraging flamingos, the iteration formula is as follows:

MP _r =rand[0,1]×P×(1−MP _b )

Among them, MP _r is the number of iterations of the rth time;

24) Update the location of migratory flamingos and foraging flamingos, the update formula is as follows:

表示第t、(t+1)次迭代中第i只火烈鸟在种群第j维中的位置，

在t迭代中种群中具有最佳适应度的火烈鸟的第j维位置；G₂和G₁遵循标准正态分布的随机数，范围是[-1，1]；ε₁、ε₂是个[-1，1]的随机数；K是一个随机数，遵循卡方分布，它被用来增加火烈鸟觅食范围的大小，模拟自然界中个体选择的机会，提高其全局择优能力；in,

Indicates the position of the i-th flamingo in the j-th dimension of the population in the t-th and (t+1) iterations,

The j-th dimension position of the flamingo with the best fitness in the population in the t iteration; G ₂ and G ₁ are random numbers following the standard normal distribution, and the range is [-1, 1]; ε ₁ and ε ₂ are A random number of [-1, 1]; K is a random number that follows the chi-square distribution, which is used to increase the size of the flamingo's foraging range, simulate the chance of individual selection in nature, and improve its global selection ability;

Wherein, ω=N(0,n) is a Gaussian random number with n degrees of freedom;

25) Check if there are any flamingos out of bounds, the maximum range formula is defined as:

L _max ＝|G ₁ ×xb _j +ε×x _ij |

Among them, L _max represents the maximum range, ε represents the random number of [-1, 1];

26) If the maximum number of iterations is reached, then go to 27); otherwise, go to 22);

27) Output the optimal solution and optimal value of the capacity configuration.

Further, the step 3 specifically includes the following steps:

31) Initialize the sparrow population, input parameters that affect battery replacement costs, operation and maintenance costs, and battery life loss costs;

32) Initialize fitness sorting of sparrow population and classify discoverers and followers;

33) update the finder's position, the formula is as follows:

Among them, i, j represent the position information of the i-th sparrow in the j-th dimension, iter _max represents the maximum number of iterations, Q represents a normal distribution random number, L represents a 1Xd matrix and its elements are all 1;

34) Update the follower's position according to the discoverer's position, and the formula is as follows;

And x _worst represents the current global worst position, x _p represents the best position currently occupied by the discoverer, and represents the lxd matrix, each element of which is randomly assigned a value of 1 or -1;

35) Randomly select the reconnaissance warning with a 20% ratio and update its position, the formula is as follows;

为当前全局最优位置，β作为步长控制参数，服从均值为0，方差为1的正态分布随机数；K是一个随机数，表示麻雀移动方向同时是步长控制参数，f_i表示当前麻雀个体的适应度值，f_g表示当前全局最佳适应度值，f_w表示当前全局最差适应度值，ε为常数，作用于避免分母出现零值；in,

is the current global optimal position, β is used as the step size control parameter, and _obeys the normal distribution random number with the mean value of 0 and the variance of 1; The fitness value of the sparrow individual, f _g represents the current global best fitness value, f _w represents the current global worst fitness value, and ε is a constant, which is used to avoid zero values in the denominator;

36) Judging whether the condition is satisfied, if not, return to step 32), otherwise output the optimal position.

Further, the hybrid energy storage system includes a battery pack, a battery multi-parameter comprehensive model, a supercapacitor, a control module, and a DC/AC converter; the battery pack is a hybrid battery pack composed of different health levels. It is coupled, combined with the multi-parameter comprehensive model of the battery, the energy system is regulated by the dual strategy of short-term strategy and long-term strategy combination through the regulation module.

Beneficial effect:

1. By establishing a comprehensive aging model, the present invention can utilize the waste batteries eliminated from electric vehicles, and solve the problem of difficult disposal of waste batteries; combining short-term strategies and long-term strategies, it is more stable than the method that only uses a single strategy Reliable, making the system more flexible and compatible; introducing a new energy supply method of battery-supercapacitor hybrid energy storage, compared with a single energy storage energy supply method, the continuous energy supply time is longer, the cost is lower, Strong reliability.

2. The key of the present invention is that the short-term strategy can be used to continuously and uninterruptedly supply energy to the system, and at the same time, the hybrid battery pack can be flexibly regulated and configured through the regulation module by using the short-term strategy combined with the multi-parameter comprehensive model of the battery; Adjust the operation strategy to optimize the economic cost of comprehensive operation.

Description of drawings

Fig. 1 is the flowchart of the short-term strategy method of the present invention;

Fig. 2 is the flowchart of the long-term strategy method of the present invention;

Fig. 3 is the structural representation of the system of the present invention;

Fig. 4 is a comparison diagram of economic cost between the system of the present invention and the battery-based hybrid energy storage system and the single-battery-based energy storage system;

Fig. 5 is a comparison chart of life attenuation between the system of the present invention and the energy storage system of a single waste battery;

Fig. 6 is a comparison chart of energy utilization between the system of the present invention and the other two systems.

detailed description

The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

As shown in Figures 1 and 2, the intelligent algorithm-based control method for the reuse of a hybrid energy storage system based on waste batteries disclosed by the present invention includes the following steps:

Step 1: Establish a battery multi-parameter comprehensive model that affects battery aging.

The modeling of the battery aging model is divided into the following steps:

11) Establish battery electric power model, thermal model, aging model and economic model;

12) According to step 11), the electric power model of battery is established, and described formula is as follows:

Among them, R ₁ , C ₁ , R ₂ , and C ₂ represent the resistance and capacitance of the battery, C _bat represents the total capacity of the battery, I(t) represents the current, and V ₁ and V ₂ represent the voltage;

13) According to step 11, establish the thermal model of battery, described formula is as follows:

Among them, R _c , _Ru , C _c and C _s represent thermal conduction resistance, convection resistance, core heat capacity and surface heat capacity respectively, and the two state variables are core temperature T _c and surface temperature T _s , ambient temperature T _f considered as an uncontrollable input.

14) According to step 11, set up the aging model of battery, described formula is as follows:

Q _remain ＝3600·C _bat ·f ₁ (N)·T

k₁是个定值；Among them, SOH represents the state of battery life, that is, the degree of aging, N represents the number of cycles, Q _remain represents the remaining capacity of the battery, T=T _c +T _s ,

k ₁ is a fixed value;

15) According to step 11, set up the economic model of battery, described formula is as follows:

Among them, E is the total cost of economic operation, Battery _ex is the cost of battery replacement, Battery _load is the cost of battery operation and maintenance, SOH _loss is a parameter related to the cost of battery life loss, and w _a is the weight coefficient of battery cost.

Step 2: According to the battery multi-parameter comprehensive model obtained in step 1, use a short-term strategy to optimize the system capacity configuration. The short-term strategy mainly uses the flamingo algorithm to optimize the configuration, including the following steps:

21) Initialize parameters, input the quantities that affect the battery load: voltage V ₁ , V ₂ , total battery capacity C, battery current I(t).

22) Initialize the population: set the population size as P, the maximum number of iterations as Iter _Max , and the proportion of flamingos migrated in the first part as MP _b .

23) Find the fitness of each flamingo: and sort the flamingo population according to the fitness value of the individual flamingos. The former flamingos MP _b with low fitness and the former flamingos MP _t with high fitness are regarded as migratory flamingos, while other flamingos are regarded as foraging flamingos. The iteration formula is as follows:

MP _r =rand[0,1]×P×(1−MP _b )

Among them, MP _r is the number of the rth iteration.

24) Update the location of migratory flamingos and foraging flamingos, the update formula is as follows:

表示第t、(t+1)次迭代中第i只火烈鸟在种群第j维中的位置，

在t迭代中种群中具有最佳适应度的火烈鸟的第j维位置；G₂和G₁遵循标准正态分布的随机数，范围是[-1，1]；ε₁、ε₂是个[-1，1]的随机数，主要是增加火烈鸟觅食的搜索范围，量化个体选择的差异；K是一个随机数，遵循卡方分布，它被用来增加火烈鸟觅食范围的大小，模拟自然界中个体选择的机会，提高其全局择优能力；in,

Indicates the position of the i-th flamingo in the j-th dimension of the population in the t-th and (t+1) iterations,

The j-th dimension position of the flamingo with the best fitness in the population in the t iteration; G ₂ and G ₁ are random numbers following the standard normal distribution, and the range is [-1, 1]; ε ₁ and ε ₂ are The random number of [-1, 1] is mainly to increase the search range of flamingos foraging and quantify the differences in individual choices; K is a random number that follows the chi-square distribution, which is used to increase the foraging range of flamingos The size of , simulating the chance of individual selection in nature, and improving its global selection ability;

Among them, ω=N(0,n) is a Gaussian random number with n degrees of freedom, which is used to increase the search space in the process of flamingo migration and simulate the randomness of individual behavior of flamingos in a specific migration process .

25) Check if there are any flamingos out of bounds, the maximum range formula is defined as:

L _max ＝|G ₁ ×xb _j +ε×x _ij |

Among them, L _max represents the maximum range, ε represents a random number of [-1, 1], and G ₁ is a random number following the standard normal distribution.

26) If the maximum number of iterations is reached, go to step 27); otherwise, go to step 22).

27) Output the optimal solution and optimal value of the capacity configuration.

Step 3: According to the battery multi-parameter comprehensive model obtained in step 1, use the long-term strategy to optimize the economic indicators of the system. The long-term strategy mainly uses the sparrow search algorithm to optimize the configuration, including the following steps:

31) Initialize the sparrow population, and input parameters that affect battery replacement costs, operation and maintenance costs, and battery life loss related costs.

32) Initialize the sparrow population to sort the fitness and classify the discoverers and followers.

33) update the finder's position, the formula is as follows:

Among them, i and j represent the position information of the i-th sparrow in the j-th dimension, iter _max represents the maximum number of iterations, Q represents a normal distribution random number, and L represents a 1Xd matrix with all elements being 1.

34) Update the follower's position according to the discoverer's position, and the formula is as follows;

And x _worst represents the current global worst position, x _p represents the best position currently occupied by the discoverer, and represents the lxd matrix, each element of which is randomly assigned 1 or -1.

35) Randomly select the reconnaissance warning with a 20% ratio and update its position, the formula is as follows;

为当前全局最优位置，β作为步长控制参数，服从均值为0，方差为1的正态分布随机数。K是一个随机数，表示麻雀移动方向同时是步长控制参数，f_i表示当前麻雀个体的适应度值，f_g表示当前全局最佳适应度值，f_w表示当前全局最差适应度值，ε为常数，作用于避免分母出现零值。in,

is the current global optimal position, β is used as a step size control parameter, and obeys a normal distribution random number with a mean value of 0 and a variance of 1. K is a random number, indicating the direction of the sparrow's movement and the step size control parameter, f _i represents the fitness value of the current individual sparrow, f _g represents the current global best fitness value, f _w represents the current global worst fitness value, ε is a constant, which is used to prevent the denominator from appearing zero.

36) Judging whether the condition is satisfied, if not, return to step 32), otherwise output the optimal position.

As shown in Figure 3, the energy storage system based on waste batteries in the present invention includes a battery pack, a multi-parameter comprehensive model of the battery, a supercapacitor, and a DC/AC converter; the battery pack is a hybrid battery pack composed of different health levels through a supercapacitor It can be coupled with the device, and combined with the multi-parameter comprehensive model of the battery, the energy system can be regulated by using the dual strategy of short-term strategy and long-term strategy combination.

The battery multi-parameter comprehensive model in the energy storage system based on waste batteries includes the electric power model, thermal model, aging model, and economic model of the battery. The energy system of the energy storage system based on waste batteries is suitable for industrial, commercial, etc. Small energy supply occasions.

As shown in Figure 4, the seasonal cost of the hybrid energy storage system based on waste batteries in the present invention is between US$292,000 and US$301,000, compared with the cost of the battery-based hybrid energy storage system at US$305,000 to US$327,000 per season. Compared with the annual cost of energy storage systems based on single batteries between US$317,000 and US$335,000 in each season, the cost of hybrid energy storage systems based on waste batteries is significantly reduced after optimization of the dual-strategy method, effectively solving the problem. Solved the problem of difficult disposal of used batteries.

As shown in Figure 5, the life decay rate of the hybrid energy storage system based on waste batteries in the present invention is significantly slower than that of the energy storage system with single waste batteries. At the same time, the ratio of the energy storage system with single waste batteries is The hybrid energy storage system based on waste batteries will reach the end of life faster.

As shown in Figure 6, the energy utilization rate under the short-term strategy adopted by the hybrid energy storage system based on waste batteries in the present invention is maintained between 90.94%-92.55%, compared with 73.18-73.68% of the hybrid energy storage system Compared with the 25.16%-25.32% of the single energy storage system, there is a more obvious improvement.

The above-mentioned embodiments are only for illustrating the technical concept and characteristics of the present invention, and its purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and not to limit the scope of protection of the present invention. All equivalent changes or modifications made according to the spirit of the present invention shall fall within the protection scope of the present invention.

Claims (5)

1. A hybrid energy storage system recycling regulation and control method based on an intelligent algorithm is characterized by comprising the following steps:

step 1: establishing a battery multi-parameter comprehensive model influencing battery aging, wherein the battery multi-parameter comprehensive model comprises an electric power model of a battery, a thermal model of the battery, an aging model of the battery and an economic model of the battery;

step 2: optimizing the system capacity configuration by using a short-term strategy according to the battery multi-parameter comprehensive model obtained in the step 1, wherein the short-term strategy adopts a flamingo algorithm to optimize the system capacity configuration;

and step 3: and (3) optimizing the economic indexes of the system by using a long-term strategy according to the battery multi-parameter comprehensive model obtained in the step (1), wherein the long-term strategy adopts a sparrow search algorithm for optimal configuration.

2. The hybrid energy storage system recycling regulation and control method based on the intelligent algorithm as claimed in claim 1, wherein the modeling of the battery multi-parameter comprehensive model in the step 1 is divided into the following steps:

11 Establishing an electric power model, a thermal model, an aging model and an economic model of the battery;

12 The electrical power model of the battery is as follows:

wherein ,R₁ 、C ₁ 、R ₂ 、C ₂ Representing the resistance, capacitance, C of the battery _bat Representing the total capacity of the battery, I (t) representing the current, V ₁ 、V ₂ Represents a voltage;

13 The thermal model of the cell is as follows:

wherein ,R_c 、R _u 、C _c and C_s Respectively representing heat conduction resistance, convection resistance, core heat capacity and surface heat capacity, and the two state variables are core temperature T _c And surface temperature T _s Ambient temperature T _f Considered an uncontrollable input;

14 The aging model of the battery is as follows:

Q _remain ＝3600·C _bat ·f ₁ (N)·T

wherein SOH represents the state of life, i.e., degree of aging, N represents the number of cycles, Q _remain Represents the remaining battery capacity, T = T _c +T _s ，

k ₁ Is a constant value;

15 The economic model of the battery is as follows:

wherein E is the total economic operating cost, battery _ex Battery cost for replacement of batteries _load For battery operating maintenance costs, SOH _loss Parameter for cost associated with loss of battery life, w _a A weighting factor for the cost of the battery.

3. The hybrid energy storage system recycling regulation and control method based on the intelligent algorithm as claimed in claim 2, wherein the step 2 specifically comprises the following steps:

21 Initialization parameters) input quantities that affect the battery load: voltage V ₁ 、V ₂ Total battery capacity C, battery current I (t);

22 Initialization population): setting the population number as P and the maximum iteration number as Iter _Max The proportion of flamingo migrated in the first part is MP _b ；

23 Finding out the fitness of each flamingo, and sorting the flamingo populations according to the fitness value of the individual flamingo; front flamingo MP with low adaptability _b High-adaptability front flamingo MP _t Considered as migrating flamingos and other flamingos considered as foraging flamingos, the iterative formula is given by:

MP _r ＝rand[0，1]×P×(1-MP _b )

wherein ,MP_r The number of the r-th iteration;

24 Update the locations of migrating and foraging flamingos, the update formula is as follows:

wherein ,

represents the position of the ith flamingo in the jth dimension of the population in the t (t + 1) iterations,

the j-th dimension position of the flamingo with the best fitness in the population in the t iteration; g ₂ and G₁ Random numbers following a standard normal distribution, ranging from [ -1,1]；ε ₁ 、ε ₂ Is [ -1,1 [ ]]The random number of (2); k is a random number, follows chi-square distribution, is used for increasing the size of the foraging range of the flamingo, simulates the chance of individual selection in nature and improves the global preference capability of the flamingo;

where ω = N (0, N) is a gaussian random number with N degrees of freedom;

25 To check for an out-of-bounds flamingo, the maximum range formula is defined as:

L _max ＝|G ₁ ×xb _j +ε×x _ij |

wherein ,L_max Denotes the maximum range, ε denotes [ -1,1]The random number of (2);

26 Go to 27) if the maximum number of iterations is reached); otherwise, go to 22);

27 Output the optimal solution and the optimal value for the resulting capacity allocation.

4. The hybrid energy storage system recycling regulation and control method based on the intelligent algorithm as claimed in claim 2, wherein the step 3 specifically comprises the following steps:

31 Initializing sparrow population, and inputting parameters influencing the purchase cost, the operation and maintenance cost and the relevant cost of the battery life loss of the battery;

32 Initializing fitness ranking of sparrow population and classifying discoverers and followers;

33 Update finder location, the formula is as follows:

wherein, i, j represents the position information of the ith sparrow in the jth dimension, iter _max Expressing as the maximum iteration number, Q as a normally distributed random number, L as a 1Xd matrix and elements of the matrix are all 1;

34 Update the follower location based on the finder location, the formula is as follows;

and x _worst Representing the current global worst position, x _p Represents the optimal position occupied by the current finder, represents a 1xd matrix, each element of which is randomly assigned a value of 1 or-1;

35 Randomly selecting a reconnaissance early warning at a ratio of 20% and updating the position of the reconnaissance early warning, wherein the formula is as follows;

wherein ,

for the current global optimal position, beta is taken as a step length control parameter and obeys a normal distribution random number with the mean value of 0 and the variance of 1; k is a random number representing the direction of movement of the sparrows and is a step size control parameter, f _i Representing the fitness value of the current sparrow individual, f _g Representing the current global best fitness value, f _w Representing the current global worst fitness value, wherein epsilon is a constant and is used for avoiding the denominator from generating a zero value;

36 ) whether the condition is met is judged, if not, the step 32) is returned, otherwise, the optimal position is output.

5. The hybrid energy storage system recycling control method based on the intelligent algorithm as claimed in claim 1, wherein the hybrid energy storage system comprises a battery pack, a battery multi-parameter comprehensive model, a super capacitor, a DC/AC converter; the battery pack is a hybrid battery pack composed of different health degrees, the hybrid battery pack is coupled through a super capacitor, and a battery multi-parameter comprehensive model is combined to regulate and control an energy system by utilizing a short-term strategy and a long-term strategy.

Images