New energy automobile battery recycling management system and method

Abstract

The invention discloses a new energy automobile battery recycling management system and a new energy automobile battery recycling management method, and relates to the technical field of battery recycling. A multi-dimensional evaluation standard is introduced in a screening stage, and batteries capable of being used in an echelon mode are screened through a weighted scoring method in combination with the service life, the driving mileage and the operation data abnormal condition of a vehicle; the situation that the batteries with too large health state differences enter the echelon utilization process is reduced; in the health assessment stage, SOC is calculated by adopting a coulometric method, SOH is measured in combination with a state-of-health ratio method, and an exponential attenuation model is introduced to predict the future life of the battery, so that the state-of-health of the battery entering the echelon utilization link is similar, and the problem of echelon utilization imbalance caused by individual difference is reduced; in the matching link, the batteries are grouped based on capacity, internal resistance and attenuation trend, deviation threshold values are set, optimal matching is screened, it is guaranteed that performance parameters of the assembled battery pack are balanced as much as possible, energy loss and local heating are reduced, and system stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery recycling, in particular to a management system and method for recycling new energy vehicle batteries. Background Art

[0002] With the popularization of new energy vehicles, power batteries have been widely used. However, as the usage time increases, the batteries gradually degrade and can no longer meet the power requirements of the vehicles.

[0003] For batteries that cannot meet the power requirements, two methods are usually adopted: cascade utilization and disassembly and recycling. In the former method, through screening and matching, batteries with a relatively long remaining life are reassembled and applied to energy storage or low-speed electric vehicles. In the latter method, key metals are extracted through hydrometallurgy or pyrometallurgy. Compared with disassembly and recycling, cascade utilization can extend the service life of batteries, improve resource utilization rate, and reduce recycling costs, so it has been widely promoted.

[0004] During the cascade utilization process, due to the different aging rates of different battery cells, even after screening and reassembly, the problem of uneven attenuation may still occur. Some cells decay too fast, resulting in a decline in the overall performance of the battery pack and even premature scrapping, that is, the so-called barrel effect. This not only reduces the economic benefits of cascade utilization but also increases the maintenance and replacement costs. Therefore, a management solution for recycling new energy vehicle batteries is urgently needed to solve such problems. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a management system and method for recycling new energy vehicle batteries to solve the problems of uneven attenuation of cascade-utilized battery packs, large differences in monomer aging, resulting in a decline in overall performance, premature scrapping of battery packs, and increased maintenance costs.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a management system for recycling new energy vehicle batteries, which includes

[0009] a recycling module, configured to recycle retired power batteries, and based on the vehicle service life, driving mileage, and historical operation data, preliminarily screen the retired power batteries, and input the screened power batteries into a health assessment module;

[0010] a health assessment module, configured to receive the retired power batteries input by the recycling module, and screen out power batteries with similar health states;

[0011] A matching module, which is used to receive power batteries with similar health states input by the health assessment module, group the power batteries based on capacity, internal resistance and attenuation trend, and assemble the matched power batteries into a cascade utilization battery pack;

[0012] An equalization management module, based on the cascade utilization battery pack input by the matching module, adopts dynamic equalization management OBMS to monitor the SOC, SOH and attenuation rate of each cell in real time, and performs active circuit equalization to adjust charge and discharge.

[0013] As a preferred solution of the new energy vehicle battery recycling and utilization management system described in the present invention, wherein: the health assessment module, based on the state of charge SOC, state of health SOH, internal resistance and usage condition data, uses an attenuation prediction model to evaluate the future life of the battery, and screens out power batteries with similar health states.

[0014] In a second aspect, the present invention provides a new energy vehicle battery recycling and utilization management method, including,

[0015] Step S1, recycling retired power batteries, based on the vehicle service life, driving mileage and historical operation data, preliminarily screening the retired power batteries, and inputting the screened power batteries into Step S2 for health assessment;

[0016] Step S2, performing health assessment on the retired power batteries input in Step S1, based on the state of charge SOC, state of health SOH, internal resistance and usage condition data, using an attenuation prediction model to evaluate the future life of the battery, and screening out power batteries with similar health states;

[0017] Step S3, matching the power batteries with similar health states input in Step S2, based on capacity, internal resistance and attenuation trend, assembling the power batteries with similar health states into a cascade utilization battery pack, and inputting it into Step S4 for equalization management;

[0018] Step S4, performing equalization management on the cascade utilization battery pack input in Step S3, adopting dynamic equalization management OBMS to monitor the SOC, SOH and attenuation rate of each cell in real time, and performing active circuit equalization to adjust charge and discharge.

[0019] As a preferred solution of the new energy vehicle battery recycling and utilization management method described in the present invention, wherein: the step of preliminarily screening the retired power batteries based on the vehicle service life, driving mileage and historical operation data includes:

[0020] 1) Obtaining relevant operation data of the vehicle to be recycled, including service life, driving mileage and historical operation data,

[0021] 2) Set the retirement threshold for power batteries. If the service life of the vehicle exceeds this threshold, the battery enters the next screening step; otherwise, it is directly eliminated.

[0022] 3) Set the retirement threshold for driving mileage. If the cumulative driving mileage of the vehicle exceeds this threshold, the battery enters the next screening step; otherwise, it is eliminated.

[0023] 4) Analyze the maximum discharge power and average operating temperature of the battery based on the historical operation data of the vehicle, and set the corresponding abnormal operation threshold. If there are any abnormalities during the operation of the battery, it is directly eliminated; otherwise, it enters the next screening step.

[0024] 5) Combine the service life, driving mileage, and abnormal operation data of the battery, and use the weighted scoring method to evaluate the retired battery. Batteries with scores lower than the set standard are directly eliminated, and those with scores higher than the standard are input into the health assessment module.

[0025] As a preferred solution of the new energy vehicle battery recycling management method described in the present invention, among them: the abnormalities existing during the operation of the battery include long-term high-temperature operation and over-discharge.

[0026] As a preferred solution of the new energy vehicle battery recycling management method described in the present invention, among them: the step of performing a health assessment on the retired power battery input in step S1 is as follows:

[0027] Perform a health assessment on the retired power battery based on the state of charge, state of health, internal resistance, and operating condition data, and establish an input data matrix, denoted as D b ,

[0028] D b = SOC b , SOH b , R b , U b ,

[0029] Among them, D b is the retired battery data matrix, SOC b is the state of charge of the battery, SOH b is the state of health of the battery, R b is the internal resistance of the battery, U b is the operating condition data of the battery.

[0030] Use the coulomb metering method to calculate the current state of charge of the battery, and the calculation formula is:

[0031]

[0032] Among them, SOC b0 is the initial state of charge, C bis the nominal capacity of the battery, I b is the discharge current, and t is the discharge time.

[0033] Define the state of health SOH b as the ratio of the current capacity to the initial capacity, and the formula is:

[0034]

[0035] where C b0 is the nominal capacity of the battery when it leaves the factory, and C b is the current available capacity.

[0036] As a preferred solution of the method for recycling and utilization management of new energy vehicle batteries described in the present invention, wherein: the step of performing a health assessment on the retired power batteries input in step S1 further includes:

[0037] Calculate the internal resistance R using the pulse test method b , and the calculation formula is:

[0038]

[0039] where V oc is the open-circuit voltage of the battery, V b is the battery voltage under the load state, I b is the discharge current,

[0040] Predict the future life of the battery using the exponential decay model, and the prediction formula is:

[0041] SOH b t = SOH b0 e -λt ,

[0042] where SOH b t represents the state of health at time t, SOH b0 is the initial state of health, λ is the decay coefficient, and t is the battery usage time.

[0043] Calculate the difference in the state of health, and the calculation formula is:

[0044]

[0045] Set the screening threshold ΔSOH for similar states of health c , if it satisfies: ΔSOH ≤ ΔSOH c , then the battery enters the matching stage, otherwise it is eliminated.

[0046] As a preferred solution of the method for recycling and utilization management of new energy vehicle batteries described in the present invention, wherein: the step of matching the power batteries with similar states of health input in step S2 is

[0047] According to the screening results of step S2, obtain a set of power batteries with similar health states, defined as B m :

[0048]

[0049] Among them, B m is a set of batteries with similar health states, C m is the capacity of the mth group of batteries, R m is the internal resistance of the mth group of batteries, D m is the attenuation trend of the mth group of batteries, and M is the number of matching battery packs.

[0050] Calculate the average capacity of each battery in the battery pack The formula is:

[0051]

[0052] Set the capacity deviation threshold δ C , and screen the batteries that meet the following conditions:

[0053]

[0054] Calculate the average internal resistance of the battery pack The calculation formula is:

[0055]

[0056] Set the internal resistance deviation threshold δ R , and screen the batteries that meet the following conditions:

[0057]

[0058] As a preferred solution of the method for recycling and utilization management of new energy vehicle batteries described in the present invention, wherein: the step of matching the power batteries with similar health states input in step S2 further includes:

[0059] Calculate the attenuation trend deviation ΔD of the battery m , expressed as:

[0060]

[0061] Among them,

[0062] represents the average attenuation trend of the battery pack,

[0063] Set the attenuation trend deviation threshold δ D , and screen the batteries that meet the following conditions:

[0064] ΔD m ≤δ D ;

[0065] Group the power batteries that meet the matching conditions of capacity, internal resistance and attenuation trend, and set the number of batteries in each group to N g :

[0066]

[0067] Among them, G n is the nth echelon utilization battery pack, N g is the number of batteries in each group, and N is the total number of echelon utilization battery packs matched.

[0068] As a preferred solution of the method for recycling and management of new energy vehicle batteries described in the present invention, wherein: the step of performing equalization management on the echelon utilization battery pack input in step S3 is

[0069] Based on the echelon utilization battery pack matched in step S3, establish a data set G n , expressed as:

[0070]

[0071] Among them, SOC n is the state of charge of the echelon utilization battery pack, SOH n is the state of health of the echelon utilization battery pack, R n is the internal resistance of the echelon utilization battery pack,

[0072] Adopt dynamic equalization management OBMS to real-time monitor the state of charge, state of health and attenuation rate of each battery pack, expressed as:

[0073] Among them,

[0074] ΔSOC n represents the state of charge deviation of the nth group of batteries, represents the average state of charge of the echelon utilization battery pack;

[0075] Set the state of charge equalization threshold δ SOC , if ΔSOC n >δ SOC , then trigger active equalization adjustment, execute the circuit equalization control strategy, and adjust to P eq :

[0076]

[0077] Among them, P eq is the equalization control power, and k is the equalization coefficient;

[0078] Adopt a dynamic equilibrium management strategy to actively adjust the charging and discharging process.

[0079] The beneficial effects of the present invention are as follows: In the screening stage, a multi-dimensional evaluation criterion is introduced. By combining the vehicle service life, driving mileage, and abnormal operation data, the batteries that can be used in a ladder-like manner are screened through a weighted scoring method, reducing the entry of batteries with too large differences in health status into the ladder-like utilization process;

[0080] In the health assessment stage, the Coulomb metering method is used to calculate the SOC, the health status ratio method is combined to measure the SOH, and an exponential decay model is introduced to predict the future life of the battery, making the health status of the batteries entering the ladder-like utilization link similar and reducing the unbalanced problem of ladder-like utilization caused by individual differences;

[0081] In the matching link, the batteries are grouped based on capacity, internal resistance, and decay trend, and a deviation threshold is set to screen the optimal match, ensuring that the assembled battery pack is as balanced as possible in performance parameters, reducing energy loss and local heating, and improving system stability;

[0082] In the equilibrium management stage, a dynamic equilibrium management OBMS is adopted. By real-time monitoring the SOC, SOH, and decay rate of each single battery, calculating the SOC deviation, and setting an equilibrium threshold, an active equilibrium adjustment charging and discharging strategy is triggered to maintain the dynamic equilibrium of the charge state inside the battery pack at all times, preventing individual batteries from overcharging or over-discharging, and improving the overall life and operation safety of the battery pack.

[0083] In summary, the present invention improves the economic value of ladder-like utilization, reduces the operation and maintenance cost, and significantly improves the utilization efficiency of retired batteries of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0085] Figure 1 It is a schematic framework diagram of the management system for recycling and utilization of new energy vehicle batteries of the present invention.

[0086] Figure 2 It is a schematic flow diagram of the management method for recycling and utilization of new energy vehicle batteries of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0088] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0089] Secondly, the so-called "one embodiment" or "embodiment" refers to specific features, structures, or characteristics that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments.

[0090] Embodiment 1, referring to Figure 1 and Figure 2 , this embodiment provides a management system for the recycling and utilization of new energy vehicle batteries, including:

[0091] A recycling module, which is used to recycle retired power batteries, and based on the vehicle service life, driving mileage, and historical operation data, preliminarily screen the retired power batteries, and input the screened power batteries into the health assessment module;

[0092] A health assessment module, which is used to receive the retired power batteries input by the recycling module and screen out power batteries with similar health states;

[0093] The health assessment module, based on the state of charge SOC, state of health SOH, internal resistance, and usage condition data, uses an attenuation prediction model to evaluate the future life of the battery and screen out power batteries with similar health states;

[0094] A matching module, which is used to receive the power batteries with similar health states input by the health assessment module, group the power batteries based on the capacity, internal resistance, and attenuation trend, and assemble the matched power batteries into a cascade utilization battery pack;

[0095] An equalization management module, based on the cascade utilization battery pack input by the matching module, adopts dynamic equalization management OBMS to real-time monitor the SOC, SOH, and attenuation rate of each cell, and perform active circuit equalization adjustment for charging and discharging.

[0096] This embodiment also provides a management method for the above-mentioned management system for the recycling and utilization of new energy vehicle batteries, including:

[0097] Step S1, recycle retired power batteries, based on the vehicle service life, driving mileage, and historical operation data, preliminarily screen the retired power batteries, and input the screened power batteries into step S2 for health assessment;

[0098] The steps for preliminary screening of retired power batteries based on the vehicle's service life, mileage, and historical operation data include:

[0099] 1) Obtain the relevant operation data of the vehicle to be recycled, including the service life, mileage, and historical operation data.

[0100] 2) Set the retirement threshold for the power battery. If the service life of the vehicle exceeds this threshold, the battery enters the next screening step; otherwise, it is directly eliminated.

[0101] 3) Set the mileage retirement threshold. If the cumulative mileage of the vehicle exceeds this threshold, the battery enters the next screening step; otherwise, it is eliminated.

[0102] 4) Analyze the maximum discharge power and average operating temperature of the battery based on the vehicle's historical operation data, and set the corresponding operation anomaly threshold. If there are any anomalies during the operation of the battery, it is directly eliminated; otherwise, it enters the next screening step.

[0103] 5) Combine the service life, mileage, and operation data anomalies, and use the weighted scoring method to evaluate the retired batteries. Batteries with scores lower than the set standard are directly eliminated, and those higher than the standard are input into the health assessment module.

[0104] Anomalies that occur during the operation of the battery include long-term high-temperature operation and over-discharge.

[0105] In step S2, conduct a health assessment of the retired power batteries input in step S1. Based on the state of charge (SOC), state of health (SOH), internal resistance, and usage condition data, use the attenuation prediction model to evaluate the future life of the battery and screen out power batteries with similar health states.

[0106] The steps for conducting a health assessment of the retired power batteries input in step S1 are as follows:

[0107] Conduct a health assessment of the retired power batteries based on the state of charge, state of health, internal resistance, and usage condition data, and establish an input data matrix, denoted as D b ,

[0108] D b = SOC b , SOH b , R b , U b ,

[0109] where D b is the retired battery data matrix, SOC b is the state of charge of the battery, SOH b is the state of health of the battery, R b is the internal resistance of the battery, and U bFor the operating condition data of the battery,

[0110] The Coulomb counting method is used to calculate the current state of charge (SOC) of the battery. The calculation formula is:

[0111]

[0112] where SOC b0 is the initial state of charge, C b is the nominal capacity of the battery, I b is the discharge current, and t is the discharge time.

[0113] Define the state of health (SOH) b as the ratio of the current capacity to the initial capacity. The formula is:

[0114]

[0115] where C b0 is the nominal capacity of the battery at the time of factory shipment, and C b is the current available capacity;

[0116] The steps for health assessment of the retired power battery input in step S1 further include:

[0117] Use the pulse test method to calculate the internal resistance R b , and the calculation formula is:

[0118]

[0119] where V oc is the open-circuit voltage of the battery, V b is the battery voltage under the load state, and I b is the discharge current.

[0120] Use the exponential decay model to predict the future life of the battery. The prediction formula is:

[0121] SOH b t = SOH b0 e -λt ,

[0122] where SOH b t represents the state of health at time t, SOH b0 is the initial state of health, λ is the decay coefficient, and t is the battery usage time.

[0123] Calculate the difference in the state of health. The calculation formula is:

[0124]

[0125] Set the screening threshold ΔSOH for similar states of health c, if the following condition is met: ΔSOH ≤ ΔSOH c , the battery enters the matching stage; otherwise, it is eliminated.

[0126] Specifically, in step S2, the state of health of the recycled retired power battery is evaluated, the state of charge is calculated by the coulomb metering method, the SOH is measured by the state of health ratio method, and the internal resistance of the battery is calculated in combination with the pulse test method; an exponential decay model is introduced to predict the future life of the battery, making the screening process more forward-looking; during the screening process, a threshold for the difference in the state of health is set to ensure that the state of health of the finally selected batteries is similar.

[0127] In step S3, the power batteries with similar states of health input in step S2 are matched. Based on the capacity, internal resistance, and decay trend, the power batteries with similar states of health are assembled into a battery pack for cascade utilization and input into step S4 for balancing management.

[0128] The steps for matching the power batteries with similar states of health input in step S2 are as follows:

[0129] According to the screening results of step S2, a set of power batteries with similar states of health is obtained and defined as B m :

[0130]

[0131] where B m is a set of batteries with similar states of health, C m is the capacity of the m-th group of batteries, R m is the internal resistance of the m-th group of batteries, D m is the decay trend of the m-th group of batteries, and M is the number of matching battery packs.

[0132] Calculate the average value of the capacities of the batteries in the battery pack The formula is:

[0133]

[0134] Set the capacity deviation threshold δ C , and screen the batteries that meet the following conditions:

[0135]

[0136] Calculate the average value of the internal resistances of the battery pack The calculation formula is:

[0137]

[0138] Set the internal resistance deviation threshold δ R , and screen the batteries that meet the following conditions:

[0139]

[0140] The steps of matching power batteries with similar health states input in step S2 further include:

[0141] Calculating the attenuation trend deviation ΔD of the battery m , which is expressed as:

[0142]

[0143] where

[0144] represents the average attenuation trend of the battery pack,

[0145] Setting the attenuation trend deviation threshold δ D , and screening the batteries that meet the following conditions:

[0146] ΔD m ≤δ D ;

[0147] Grouping the power batteries that meet the capacity, internal resistance, and attenuation trend matching conditions, and setting the number of batteries in each group to N g :

[0148]

[0149] where G n is the nth echelon utilization battery pack, N g is the number of batteries in each group, and N is the total number of echelon utilization battery packs matched;

[0150] Specifically, step S3 performs matching on the batteries screened in step S2 to ensure the balance of the assembled echelon utilization battery packs in terms of capacity, internal resistance, and attenuation trend; through average value calculation and threshold setting, the key characteristics of the batteries are matched, and the batteries with too large deviations are removed to form echelon utilization battery packs with similar health states;

[0151] The key to this step is to minimize the performance differences of the internal batteries, ensure the energy consistency of the battery pack, reduce energy loss and heating problems, and effectively avoid excessive aging caused by battery parameter differences;

[0152] Step S4 performs balancing management on the echelon utilization battery packs input in step S3, adopts dynamic balancing management OBMS, monitors the SOC, SOH, and attenuation rate of each monomer in real time, and performs active circuit balancing adjustment for charging and discharging;

[0153] The steps of performing balancing management on the echelon utilization battery packs input in step S3 are

[0154] Based on the second-life battery pack matched in step S3, a data set G is established n , expressed as:

[0155]

[0156] where SOC n is the state of charge of the second-life battery pack, SOH n is the state of health of the second-life battery pack, and R n is the internal resistance of the second-life battery pack.

[0157] Adopt dynamic equalization management OBMS to monitor the state of charge, state of health, and decay rate of each battery pack in real time, expressed as:

[0158] where

[0159] ΔSOC n represents the state of charge deviation of the nth battery pack, represents the average state of charge of the second-life battery pack;

[0160] Set the state of charge equalization threshold δ SOC . If ΔSOC n > δ SOC , then trigger active equalization adjustment, execute the circuit equalization control strategy, and adjust to P eq :

[0161]

[0162] where P eq is the equalization control power and k is the equalization coefficient;

[0163] Adopt the dynamic equalization management strategy to actively adjust the charge and discharge process;

[0164] Specifically, for the matched second-life battery pack here, adopt dynamic equalization management OBMS for real-time monitoring and control. By calculating the SOC deviation and setting the equalization threshold, the state of charge between batteries is kept balanced, thus preventing individual batteries from overcharging or over-discharging. The equalization management strategy includes active equalization adjustment and charge and discharge management to maintain the long-term stability of the battery pack;

[0165] Here, through dynamic equalization management, the state of charge of the battery pack is adjusted in real time, reducing the problem of battery pack performance degradation caused by differences in single battery parameters. By calculating the equalization control power, the charge and discharge process becomes more intelligent, improving the overall life and utilization rate of the battery pack.

[0166] In summary, in the screening stage of the present invention, a multi-dimensional evaluation criterion is introduced, combined with the vehicle service life, driving mileage, and abnormal operation data conditions. The batteries that can be used in a stepped manner are screened by the weighted scoring method, reducing the entry of batteries with too large differences in health status into the stepped utilization process;

[0167] In the health assessment stage, the Coulomb metering method is used to calculate the SOC, combined with the health status ratio method to measure the SOH, and an exponential decay model is introduced to predict the future life of the battery, making the health status of the batteries entering the stepped utilization link similar, and reducing the problem of uneven stepped utilization caused by individual differences;

[0168] In the matching link, the batteries are grouped based on capacity, internal resistance, and decay trend, and a deviation threshold is set to screen the optimal match, ensuring that the performance parameters of the assembled battery pack are as balanced as possible, reducing energy loss and local heating, and improving system stability;

[0169] In the equalization management stage, the dynamic equalization management OBMS is adopted. By real-time monitoring the SOC, SOH, and decay rate of each single battery, calculating the SOC deviation, and setting an equalization threshold, the charging and discharging strategy is triggered for active equalization adjustment, so that the dynamic equalization of the charge state is always maintained inside the battery pack, preventing individual batteries from being overcharged or over-discharged, and improving the overall life and operation safety of the battery pack.

[0170] In summary, the present invention improves the economic value of stepped utilization, reduces the operation and maintenance costs, and significantly improves the utilization efficiency of retired batteries of new energy vehicles.

[0171] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A new energy vehicle battery recycling and utilization management system, characterized by: include, The recycling module is used to recycle retired power batteries and conduct preliminary screening of retired power batteries based on vehicle age, mileage and historical operating data, and input the screened power batteries into the health assessment module; The health assessment module is used to receive the retired power batteries input by the recycling module and screen out the power batteries with similar health status; A matching module is used to receive power batteries with similar health status input by the health assessment module, group the power batteries based on capacity, internal resistance and attenuation trend, and assemble the matched power batteries into a second-life battery pack; The balancing management module uses dynamic balancing management OBMS based on the cascade utilization battery pack input by the matching module to monitor the SOC, SOH and decay rate of each cell in real time, and actively balances the circuit to adjust the charging and discharging.

2. A new energy vehicle battery recycling and utilization management system as claimed in claim 1, characterized in that: The health assessment module uses an attenuation prediction model to assess the future life of the battery based on the state of charge SOC, state of health SOH, internal resistance and usage condition data, and screens out power batteries with similar health status.

3. A new energy vehicle battery recycling management method, based on a new energy vehicle battery recycling management system as claimed in any one of claims 1 to 2, characterized in that: include: Step S1, recycle retired power batteries, conduct preliminary screening of retired power batteries based on vehicle age, mileage and historical operation data, and input the screened power batteries into step S2 for health assessment; Step S2, performing a health assessment on the retired power battery input in step S1, using a decay prediction model to assess the future life of the battery based on the state of charge SOC, state of health SOH, internal resistance and operating condition data, and screening out power batteries with similar health status; Step S3, matching the power batteries with similar health status input in step S2, assembling the power batteries with similar health status into a battery pack for cascade utilization based on capacity, internal resistance and attenuation trend, and inputting the battery pack into step S4 for balancing management; Step S4, balance management is performed on the cascade utilization battery pack input in step S3, and dynamic balance management OBMS is used to monitor the SOC, SOH and decay rate of each cell in real time, and the circuit is actively balanced to adjust the charge and discharge.

4. A new energy vehicle battery recycling management method as claimed in claim 3, characterized in that: The steps of preliminary screening of retired power batteries based on vehicle age, mileage and historical operation data include: 1) Obtain relevant operating data of the vehicle to be recycled, including age, mileage and historical operating data, 2) Set a threshold for power battery retirement. If the vehicle's service life exceeds this threshold, the battery will enter the next step of screening, otherwise it will be directly eliminated. 3) Set a mileage retirement threshold. If the vehicle's cumulative mileage exceeds this threshold, the battery will enter the next step of screening, otherwise it will be eliminated. 4) According to the historical operation data of the vehicle, analyze the maximum discharge power and average operating temperature of the battery, and set the corresponding abnormal operation threshold. If the battery has an abnormality during operation, it will be directly eliminated, otherwise it will enter the next step of screening. 5) Based on the years of use, mileage and abnormal operating data, a weighted scoring method is used to evaluate retired batteries. Batteries with scores below the set standard are directly eliminated, and batteries with scores above the standard are entered into the health assessment module.

5. A new energy vehicle battery recycling management method as claimed in claim 4, characterized in that: Abnormalities in battery operation include long-term high-temperature operation and over-discharge.

6. A new energy vehicle battery recycling management method as claimed in claim 5, characterized in that: The step of performing health assessment on the retired power battery input in step S1 is: Based on the state of charge, health state, internal resistance and operating condition data, the health assessment of retired power batteries is carried out, and the input data matrix is ​​established, which is expressed as D b , D b =(SOC b ,SOH b ,R b ,U b ), Among them, D b Data matrix for retired batteries, SOC b is the state of charge of the battery, SOH b is the battery health status, R b is the internal resistance of the battery, U b The battery usage data. The coulomb counting method is used to calculate the current state of charge of the battery. The calculation formula is: Among them, SOC b0 is the initial charge state, C b is the nominal capacity of the battery, I b is the discharge current, t is the discharge time, Defining State of Health (SOH) b is the ratio of the current capacity to the initial capacity, and the formula is: Among them, C b0 is the nominal capacity of the battery when it leaves the factory, C b The current available capacity.

7. A new energy vehicle battery recycling management method as claimed in claim 6, characterized in that: The step of performing health assessment on the retired power battery input in step S1 further includes: Calculate the internal resistance R using the pulse test method b , the calculation formula is: Among them, V oc is the battery open circuit voltage, V b is the battery voltage under load, I b is the discharge current, The exponential decay model is used to predict the future life of the battery. The prediction formula is: SOH b (t)=SOH b0 e -λt , Among them, SOH b (t) represents the health status at time t, SOH b0 is the initial health state, λ is the attenuation coefficient, t is the battery usage time, Calculate the health status difference, the calculation formula is: Set the screening threshold ΔSOH for similar health status c , if: ΔSOH≤ΔSOH c , the battery enters the matching stage, otherwise it is eliminated.

8. A new energy vehicle battery recycling management method as claimed in claim 7, characterized in that: The step of matching the power batteries with similar health status input in step S2 is: According to the screening result of step S2, a set of power batteries with similar health status is obtained, which is defined as B m : Among them, B m is a collection of batteries with similar health status, C m is the capacity of the mth battery group, R m is the internal resistance of the mth battery group, D m is the decay trend of the mth battery group, M is the number of matching battery groups, Calculate the average capacity of each battery in the battery pack The formula is: Set capacity deviation threshold δ C , filter the batteries that meet the following conditions: Calculate the average internal resistance of the battery pack The calculation formula is: Set the internal resistance deviation threshold δ R , filter the batteries that meet the following conditions:

9. A new energy vehicle battery recycling management method as claimed in claim 8, characterized in that: The step of matching the power batteries with similar health status input in step S2 further includes: Calculate the battery's decay trend deviation ΔD m , expressed as: in, Represents the mean value of battery pack attenuation trend, Set the attenuation trend deviation threshold δ D , filter the batteries that meet the following conditions: ΔD m ≤δ D ; The power batteries that meet the matching conditions of capacity, internal resistance and attenuation trend are grouped, and the number of batteries in each group is set to N. g : Among them, G n For the nth group of second-life battery packs, N g is the number of batteries in each group, and N is the total number of matched cascade utilization battery groups.

10. A new energy vehicle battery recycling management method as claimed in claim 9, characterized in that: The step of performing balancing management on the cascade utilization battery pack input in step S3 is: According to the matched cascade utilization battery pack in step S3, a data set G is established. n , expressed as: Among them, SOC n The state of charge of the battery pack is SOH. n is the health status of the battery pack, R n To utilize the internal resistance of the battery pack in a step-by-step manner, Dynamic balancing management OBMS is used to monitor the state of charge, health status and decay rate of each battery pack in real time, which can be expressed as: in, ΔSOC n represents the charge state deviation of the nth group of batteries, Indicates the average state of charge of the battery pack used in cascade utilization; Set the state of charge equalization threshold δ SOC , if ΔSOC n >δ SOC , then the active balancing adjustment is triggered, and the circuit balancing control strategy is executed to adjust to P eq : Among them, P eq is the balanced control power, k is the balanced coefficient; A dynamic balancing management strategy is adopted to actively adjust the charging and discharging process.

Images