5g base station cascade battery energy storage system battery recycling quantity prediction method and device

Abstract

The application discloses a 5G base station cascade battery energy storage system battery recycling quantity prediction method and device, comprising: establishing the input and output model of the system under different digital signal processing schemes using low-precision ADC; constructing an ADC-quantized received signal model under different multi-user detection schemes; calculating the upper limit of the transmission rate; calculating the minimum energy allocation scheme when the rate of each user is given based on the upper limit of the transmission rate; calculating the total power of the base station; and calculating the target recycling battery capacity. By using the technical scheme of the application, the accurate cascade utilization battery demand quantity is provided for different cascade battery capacities under different precision ADC and digital signal processing technologies of the base station, so that the required quantity can be accurately controlled by a waste old power battery recycling enterprise, and the economy of cascade utilization is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of 5G network, and particularly relates to a 5G base station cascade battery energy storage system battery recycling quantity prediction method and device. BACKGROUND

[0002] With the wide deployment of 5G networks, the number and energy consumption of base stations have increased significantly. To reduce operating costs, improve power supply reliability, and achieve green and low-carbon goals, 5G base stations powered by energy storage systems based on cascade batteries have become a research hotspot. Accurate market demand prediction of cascade batteries is the core basis for resource management, environmental protection, economic planning, and policy making. Through scientific prediction, resource supply and demand can be balanced, environmental risks can be reduced, industrial investment can be guided, and technology innovation and policy improvement can be promoted, ultimately achieving sustainable development of the entire life cycle of power batteries.

[0003] Large-scale MIMO is a core technology of 5G base stations. Compared with traditional MIMO systems, large-scale MIMO systems have more antennas, which can provide higher data transmission rates and better signal quality. In actual situations, each antenna in a large-scale MIMO system usually needs a set of 2 ADCs (Analog-to-Digital Converters) to convert analog signals into digital signals for processing. Due to the increase in the number of antennas, the number of required ADCs also increases, resulting in increased system power consumption. To reduce the power consumption of 5G base stations, low-precision ADCs with low power consumption need to be used to build high-energy-efficiency 5G base stations.

[0004] In recent years, academia and industry have carried out some technical research on market demand prediction of cascade batteries for specific industry applications. However, traditional prediction methods mostly rely on enterprise data analysis and predict the number of retired power batteries in the upstream, and have not established a precise market demand prediction method for cascade batteries for individual high-energy-efficiency 5G base stations using low-precision ADCs in downstream scenarios, which makes it difficult for waste power battery recycling companies to accurately control the required quantity and greatly affects the economic efficiency of cascade utilization. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a 5G base station cascade battery energy storage system battery recycling quantity prediction method and device.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0007] A 5G base station cascade battery energy storage system battery recycling quantity prediction method, comprising:

[0008] Step 1: Construct an input-output model of a MIMO system;

[0009] Step 2: Construct the received signal model based on ADC quantization according to the input-output model;

[0010] Step 3: Calculate the upper bound of transmission rate according to the received signal model;

[0011] Step 4: Calculate the minimum energy allocation scheme for each user given the upper bound of transmission rate;

[0012] Step 5: Calculate the total power of the base station according to the minimum energy allocation scheme.

[0013] Step 6: Calculate the target recycling battery capacity.

[0014] As a preferred, in step 2, for channel G, the quantized received vector is processed by W H = G H before detection. The signal vector after multi-user detection processing is r = W H y, and set as the kth column of W H . From the above formula, the element of the kth column of r can be expressed as:

[0015]

[0016] As a preferred, in step 3, the upper bound of transmission rate is:

[0017]

[0018] where, is the total uplink transmission rate of the kth user; γ k is the signal-to-noise ratio of the kth user under the condition of using a multi-user detection receiver;

[0019]

[0020] where, 1≤k≤K, including channel noise ADC quantization noise inter-user interference

[0021] As a preferred, in step 4, the optimal power allocation is obtained as:

[0022]

[0023] As a preferred, in step 5, the 5G base station load model is:

[0024] P total = M(P BS + 2P ADC ) + NP UE + PSYN +P other +P CD +P CE +P SD +P TX

[0025] Wherein, P total is the total energy consumption of the base station;

[0026] P TX is the power consumption of pilot and data transmission, and the calculation formula is:

[0027]

[0028] Wherein, η is the power amplifier efficiency; Pt is the pilot transmission power of a single UE; Pu is the data transmission power of a single UE.

[0029] As preferred, in step 6, the battery pack requirement is:

[0030] n=(P total ÷C)×2

[0031] Wherein, n is the battery pack requirement, and C is the available capacity of the battery monomer.

[0032] The application also provides a 5G base station gradient battery energy storage system battery recycling amount prediction device, comprising:

[0033] A first processing module is configured to construct an input-output model of a MIMO system.

[0034] A second processing module is configured to construct a received signal model based on ADC quantization according to the input-output model.

[0035] A third processing module is configured to calculate an upper limit of transmission rate according to the received signal model.

[0036] A fourth processing module is configured to calculate a minimum energy allocation scheme when a given rate of each user is based on the upper limit of transmission rate.

[0037] A fifth processing module is configured to calculate the total power of the base station according to the minimum energy allocation scheme.

[0038] A sixth processing module is configured to calculate the target recycling battery capacity.

[0039] The application provides accurate gradient utilization battery requirement quantity under the use of different precision ADC and digital signal processing technology of the base station, aiming at different gradient battery capacity, for waste power battery recycling enterprises to accurately control the required quantity, improve the economy of gradient utilization, and has the following technical effects:

[0040] 1. Variable ADC accuracy: The required number of batteries for secondary use can be predicted based on the ADC accuracy selected for the base station;

[0041] 2. Variable multi-user detection scheme: The required number of batteries for tiered utilization can be predicted based on the multi-user detection scheme selected for the base station.

[0042] 3. Variable transmission rate: Based on the service quality required by the cell, the required number of batteries for tiered utilization can be predicted by combining the ADC accuracy and multi-user detection scheme with the base station. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a flowchart of the battery recycling prediction method for a 5G base station cascade battery energy storage system according to an embodiment of the present invention.

[0045] Figure 2 A schematic diagram showing the total power of the base station under different conditions;

[0046] Figure 3 This diagram illustrates the number of batteries under different conditions. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Example 1:

[0050] like Figure 1 As shown, this embodiment of the invention provides a method for predicting the amount of battery recycling in a 5G base station cascade battery energy storage system, including:

[0051] Step 1: Establish the system's input-output model under different digital signal processing schemes using a low-precision ADC;

[0052] Step 2: Construct the received signal model based on ADC quantization under different multi-user detection schemes;

[0053] Step 3: Calculate the upper limit of the transmission rate;

[0054] Step 4: Based on the upper limit of the rate, calculate the minimum energy allocation scheme when the rate of each user is given;

[0055] Step 5: Calculate the total power of the base station;

[0056] Step 6: Calculate the target recycling battery capacity.

[0057] As an embodiment of the embodiment of the present application, in step 1, consider the uplink of a single-cell multi-user massive MIMO system, where a base station (BS) with M antennas receives information from K single-antenna users. Each base station antenna is equipped with a radio frequency chain and a pair of ADCs for quantizing the in-phase and quadrature components of the received signal. Let x be the vector of information symbols at the users, with the k-th element being the symbol of user k. Assume that the symbols are independent of zero mean and unit power. u is the average transmit power of each user. The received signal vector y at the BS antennas can be represented as

[0058]

[0059] where, is the channel matrix from users to the BS, g K is the channel vector of user k. is a normalized additive white Gaussian noise (AWGN) vector. The channel is modeled as: G = HD 1 / 2 where is a matrix of fast fading coefficients following a Rayleigh distribution, whose elements are independent and identically distributed (i.i.d.) and follow a circularly symmetric complex Gaussian distribution with zero mean, i.e., and unit variance, i.e. is a diagonal matrix, β K is the large-scale fading coefficient of user k. To facilitate analysis, AQNM is used, and it is assumed that the additive quantization noise n b follows a Gaussian distribution. Using this model, the quantized received vector at the BS can be approximated as

[0060] y q ≈ αy + n b (2)

[0061] where α is the attenuation coefficient, α = 1 - ρ, where ρ is the quantization distortion factor, defined as the inverse of the quantized signal-to-noise ratio:

[0062]

[0063] b is defined as the number of quantization bits, when b≤5, the quantization precision corresponding distortion factor p is shown in Table 1, when b≥5, the distortion factor p is approximately:

[0064]

[0065] Table 1

[0066] b 1 2 3 4 5 ρ 0.3634 0.1175 0.03454 0.009497 0.002499

[0067] Meanwhile, the quantization noise of the ADC can be calculated by (12) as follows:

[0068]

[0069] As an embodiment of the present application, in step 2, for the channel G, the quantized received vector is processed by W H = G H before detection. The signal vector processed by the multi-user detection is r = W H y, and the kth column of W is set as the kth column of W H . From the above formula, the element of the kth column of r can be expressed as:

[0070]

[0071] As an embodiment of the present application, in step 3, the Shannon formula

[0072]

[0073] wherein, is the total uplink transmission rate of the kth user, and γ k is the signal-to-noise ratio of the kth user under the condition of using the multi-user detection receiver. Based on formula (7), the calculation formula of the signal-to-noise ratio of the kth user is:

[0074]

[0075] wherein, 1≤k≤K, including the channel noise ADC quantization noise inter-user interference

[0076] As an embodiment of the present application, in step 4, define the (k, n)th item of which is

[0077] ​

[0078] where g km is the mth term of g k , h mn is the mth term of h n , and * is the complex conjugate. Then (8) can be written as

[0079]

[0080] From (10), K linear equations with K unknowns p k , 1≤k≤K can be written, which can be expressed in the form of matrix vector as

[0081]

[0082] where p=[p1,p2,…,p K ] T , and and the kth term of g k is given by ||g 2 || and the kth term of h k is given by ||h 4 || is an upper triangular matrix, and the elements of the mth row are Each of the summations of this sum has the first m terms equal to zero. After matrix manipulation and simplification, the optimal power allocation for the user can be obtained as

[0083]

[0084] As an embodiment of the embodiment of the application, in step 5, the 5G base station load model is as follows:

[0085] P total =M(P BS +2P ADC )+NP UE +P SYN +P other +P CD +P CE +P SD +P TX (13)

[0086] where P is the total energy consumption of the base station.

[0087] P TX is the power consumption of pilot and data transmission, and the calculation formula is:

[0088]

[0089] Wherein: η is the power amplifier efficiency; Pt is the pilot transmission power of a single UE; Pu is the data transmission power of a single UE.

[0090] As an embodiment of the embodiment of the application, in step 6, the battery requirement can be calculated according to the following formula:

[0091] n=(P total ÷C)×2 (15)

[0092] Wherein, n is the battery requirement, C is the available capacity of the battery monomer,

[0093] (15) The formula is suitable for the case that the day and night two groups of batteries are alternately charged and discharged. The calculation result is rounded up to ensure that the power supply reliability requirement is met.

[0094] Through the above implementation, it is intended to reduce cost and power consumption while ensuring system performance. Dynamic power allocation is realized. On this basis, the battery configuration can reduce operating costs and improve power supply reliability while efficiently utilizing energy.

[0095] Embodiment 2:

[0096] Assuming that the number of users K=40 and the number of base station antennas M=200, for example. Table 2 shows the parameter settings, and channel estimation is performed according to the parameters in Table 2. In this case, K-factor=0dB.

[0097] Table 2

[0098] Parameter Value ADC 3 bit M 200 BS antennas N 40 UEs P u ]]> -10 dBm

[0099] A high-energy-efficiency 5G base station gradient battery energy storage system battery recycling amount prediction method, comprising:

[0100] Step 1: Establish the input-output model of the system under different digital signal processing schemes using low-precision ADC

[0101] Step 2: Under different multi-user detection schemes, construct a received signal model based on ADC quantization.

[0102] Step 3: On this basis, the theoretical upper limit of the transmission rate is derived through the Shannon formula.

[0103] Step 4: Based on the rate upper limit, calculate the minimum energy allocation scheme when the rate of each user is given.

[0104] Step 5: Calculate the total power of the base station through the above calculation.

[0105] Step 6: Calculate the target recycling battery capacity, and the calculation result is obtained.

[0106] Further, in step 1, consider the uplink of a single-cell multi-user massive MIMO system, where a base station (BS) with M antennas receives information from K single-antenna users. Each BS antenna is equipped with one radio frequency chain and a pair of ADCs for quantizing the in-phase and quadrature components of the received signal. Let x be the vector of information symbols at the users, with the k-th element being the symbol of user k. Assume that the symbols are independent and identically distributed with zero mean and unit power. u is the average transmit power of each user. The received signal vector y at the BS antennas can be expressed as

[0107]

[0108] where, is the channel matrix from the users to the BS, g K is the channel vector of user k. is a normalized additive white Gaussian noise (AWGN) vector. The channel is modeled as G = HD 1 / 2 where is a matrix of fast fading coefficients, whose entries are independent and identically distributed (i.i.d.) complex circularly symmetric Gaussian with zero mean and unit variance, i.e., is a diagonal matrix, β K is the large-scale fading coefficient of user k. To facilitate the analysis, the AQNM is adopted, and it is assumed that the additive quantization noise n b is Gaussian distributed. Using this model, the quantized received vector at the BS can be approximated as

[0109] y q ≈ αy + n b ( 2)

[0110] where α is the attenuation coefficient, α = 1 - ρ, where ρ is the quantization distortion factor, defined as the inverse of the quantized signal-to-noise ratio:

[0111]

[0112] Define b as the number of quantization bits, when b ≤ 5, the distortion factor ρ corresponding to the quantization accuracy is shown in Table 1, when b ≥ 5, the distortion factor ρ is approximately

[0113]

[0114] Table 1

[0115] b 1 2 3 4 5 ρ 0.3634 0.1175 0.03454 0.009497 0.002499

[0116] It can also be obtained that the quantization noise of the ADC can be calculated by equation (11):

[0117]

[0118] Among them, α=1-ρ=0.96546.

[0119] Furthermore, in step 2, the feasibility of a large-scale MIMO system using an MRC-SIC receiver is discussed. For channel G, the quantized receive vector is determined by W before detection. H =G H Processing. The signal vector after passing through the MRC-SIC receiver is r = W H y, setting As W H The k-th column of r. From the above formula, the elements of the k-th column of r can be expressed as:

[0120]

[0121] Furthermore, in step 3, the formula from Shannon is used.

[0122]

[0123] in, (bits / second) represents the total uplink transmission rate of the k-th user, SINR k Given an MRC-SIC receiver, the signal-to-noise ratio of the k-th user is given.

[0124] Based on formula (6), the signal-to-noise ratio (SINR) of the kth user can be obtained as follows: The calculation formula is:

[0125]

[0126] in, 1≤k≤K, including channel noise, ADC quantization noise, inter-user interference, and channel estimation error.

[0127] Furthermore, in step 4, define Its (k,n)th term is:

[0128]

[0129] Among them, g km It is g k The m-th item, h mn It is h n The m-th term, * is the complex conjugate. Then (7) can be written as

[0130]

[0131] From (9), we can write down the K unknowns as P. kThe linear equations of 1≤k≤K can be expressed in the form of matrix vector

[0132]

[0133] where p=[p1,p2,…,p K ] T , and and the kth term of h is given by ||g k || 2 is a diagonal matrix and the (k,k)th term of h is given by ||h k || 4 is an upper triangular matrix whose elements in the mth row are Each of the summations of this sum is zero for the first m terms. After some manipulation, the optimal power allocation for the users is given by

[0134]

[0135] Further, in step 5, the 5G base station load model can be summarized as follows:

[0136] P total =M(P BS +2P ADC )+NP UE +P SYN +P other +P CD +P CE +P SD +P TX (13)

[0137] where P is the total energy consumption of the base station; in specific cases, the parameters are calculated as shown in Table 3:

[0138] Table 3

[0139]

[0140]

[0141] where R is the transmission rate.

[0142] P TX The power consumption of pilot and data transmission is calculated by the formula:

[0143]

[0144] where η is the power amplifier efficiency; Pt is the pilot transmission power of a single UE; and Pu is the data transmission power of a single UE.

[0145] As​​Figure 2 As shown, this represents the total base station power relationship under different group numbers and ADC resolutions when K-factor = 0dB.

[0146] Furthermore, in step 6, based on the above typical energy consumption model for 5G base stations, the battery configuration is calculated under extreme operating conditions (completely dependent on the independent power supply of the energy storage system).

[0147] Single cell usable capacity assessment:

[0148] Regarding the degradation characteristics of batteries used in tiered applications, the focus is on the capacity degradation effect caused by historical cycling. According to the standard "Residual Energy Detection for Recycled Vehicle Power Batteries" (GB / T 34015-2017), the capacity retention rate of retired power batteries typically decreases to 60%-80% of their initial capacity. For ease of calculation, a uniform 70% remaining battery capacity is set.

[0149] Based on the principle of energy conservation, a calculation model for the battery demand of an energy storage system is constructed:

[0150] n = (P total ÷C)×2 (15)

[0151] Where n is the battery pack demand and C is the available capacity of a single battery cell.

[0152] Equation (15) is adapted to the situation where the two sets of batteries are charged and discharged alternately during the day and night. The calculation results are rounded up to ensure that the power supply reliability requirements are met.

[0153] The battery used in the example is a Tianneng TN12-38E battery, whose rated capacity, as found on the official website, is 38Ah. Assuming that the capacity of the recycled batteries is 70% of the original capacity, the system's battery requirement can be calculated to be 9 batteries.

[0154] like Figure 3 The figure shows the relationship between the number of batteries under different group numbers and ADC resolutions when K-factor = 0dB.

[0155] Example 3:

[0156] The present invention also provides a battery recycling prediction device for a 5G base station cascade battery energy storage system, comprising:

[0157] The first processing module is used to construct the input-output model of the MIMO system;

[0158] The second processing module is used to construct a received signal model based on the ADC quantization according to the input-output model;

[0159] The third processing module is used to calculate the upper limit of the transmission rate based on the received signal model;

[0160] a fourth processing module, configured to calculate a minimum energy allocation scheme when calculating the rate of each user based on the upper limit of the transmission rate;

[0161] a fifth processing module, configured to calculate the total power of the base station according to the minimum energy allocation scheme.

[0162] a sixth processing module, configured to calculate the target recycled battery capacity.

[0163] The above-described embodiments are only used to describe the preferred modes of the present application, and are not used to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements of the technical solutions of the present application made by those skilled in the art shall fall within the protection scope of the present application defined by the claims.

Claims

1. A method for predicting battery recycling amount of a 5G base station cascade battery energy storage system, characterized in that, Comprising: Step 1: constructing an input-output model of the MIMO system; Step 2: constructing a received signal model based on ADC quantization according to the input-output model; Step 3: calculating the upper limit of the transmission rate according to the received signal model; Step 4: calculating the minimum energy allocation scheme when the rate of each user is given based on the upper limit of the transmission rate; Step 5: calculating the total power of the base station according to the minimum energy allocation scheme; Step 6: calculating the battery capacity for target recycling; In Step 2, for channel G, the quantized received vector is multiplied by W H = G H before detection. The signal vector after the multi-user detection processing is r = W H y, and the kth column of r is set as the kth column of W H The element of the kth column of r is expressed as: In step 3, the upper limit of the transmission rate is: wherein, is the total uplink transmission rate for the kth user; γ k is the signal-to-noise ratio for the kth user under the condition of using a multi-user detection receiver; wherein, including channel noise ADC quantization noise inter-user interference In step 4, the optimal power allocation is obtained as: In step 5, the 5G base station load model is: P total = M(P BS + 2P ADC ) + NP UE + P SYN + P other + P CD + P CE + P SD + P TX where P total is the total energy consumption of the base station; P TX The power consumption for pilot and data transmission is calculated by the following equation: Where η is the power amplifier efficiency; Pt is the pilot transmission power of a single UE; Pu is the data transmission power of a single UE; In step 6, the battery pack requirement is: n = (P total ÷ C) x 2 Where n is the battery pack requirement, and C is the available capacity of the battery monomer.

2. A 5G base station cascade battery energy storage system battery recycling amount prediction device for implementing the 5G base station cascade battery energy storage system battery recycling amount prediction method of claim 1, characterized in that, Comprising: A first processing module for constructing an input-output model of the MIMO system; A second processing module for constructing a received signal model based on ADC quantization according to the input-output model; A third processing module for calculating the upper limit of the transmission rate according to the received signal model; A fourth processing module for calculating the minimum energy allocation scheme when the rate of each user is given based on the upper limit of the transmission rate; A fifth processing module for calculating the total power of the base station according to the minimum energy allocation scheme; A sixth processing module for calculating the battery capacity for target recycling.

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