Battery pack selection method and system for battery swap station

Abstract

The invention discloses a battery pack selection method and system for a battery swap station, and belongs to the technical field of electric vehicle battery swap. The battery pack selection method comprises the steps that a battery cloud hospital platform communicates with a battery swap station control system and a vehicle remote data platform, SOC data of batteries in a station are acquired, and the batteries with the SOC smaller than an SOC limit threshold value are removed; extracting a driving discharge parameter from the moment when the SOC of the battery which is not removed in the battery swap station is more than or equal to 99% last time to the moment when the SOC of the driving day is the minimum value; through the data, micro short circuit degree evaluation, SOH calculation, SOH decline rate calculation, internal resistance calculation, internal resistance increase rate calculation and battery consistency evaluation are carried out on the batteries in the battery swap station. And scoring the batteries which are not rejected, and sequentially generating battery candidate serial numbers according to scores from high scores to low scores. It can be guaranteed that a user can obtain the battery with the best performance in the battery replacing station every time battery replacing is conducted, and the vehicle using safety and the user experience of the user are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery replacement for electric vehicles, and more specifically to a battery pack selection method and system for battery replacement stations. Background Art

[0002] In recent years, the market position and technical level of China's electric vehicle industry have been significantly improved worldwide. The competition in the industry has become increasingly fierce, and the speed of survival of the fittest among enterprises has accelerated. The industry structure is facing profound changes, especially in the charging technology of electric vehicles. The charging time of existing electric vehicles is generally about 1 to 2 hours, and charging takes up the precious time of users. The battery replacement technology can complete the battery replacement in a few minutes, greatly shortening the user's waiting time. At present, most battery swap stations only select packages based on the highest SOC value of the battery and the shortest distance from the battery compartment where the battery is located to the entrance of the battery swap station. The health of the battery is not diagnosed before selecting the package, which poses certain safety risks to the use of the vehicle.

[0003] The Chinese invention patent with application publication number CN 111413628A discloses a charging and swapping station with battery evaluation function and an evaluation method. The charging and swapping station includes a battery charging and storage platform, a battery swapping platform and a charging and swapping station control center. The battery charging and storage platform mainly completes the charging and testing of battery packs, including a battery charging compartment and a battery evaluation compartment; the evaluation method includes: transporting the battery packs to be evaluated in the charging and swapping station that need to be evaluated for performance to the charging compartment with battery evaluation function, connecting the charging and discharging equipment to the battery pack through charging and discharging connectors, completing a series of electrical performance tests, and individual test items can also be connected manually; according to the battery evaluation plan and requirements, various test tasks are carried out step by step or individually.

[0004] This patent sets up a charging station with a battery evaluation function, which can test each battery pack in the station at any time and understand the performance status of the battery pack in a timely and accurate manner. However, this patent requires the establishment of a battery evaluation compartment, which increases the hardware cost investment. Moreover, only performing electrical performance testing on the battery pack that needs to be evaluated will not enable battery swap users to obtain the battery pack with the best performance status. To this end, we provide a battery pack selection method and system for battery swap stations. Summary of the invention

[0005] The present invention provides a battery pack selection method and system for a battery swap station, so as to overcome the shortcomings of existing battery swap stations that only perform electrical performance tests on battery packs that need performance evaluation, and fail to enable battery swap users to obtain battery packs with the best performance status.

[0006] The present invention adopts the following technical solution: A battery selection method for a battery swap station comprises the following steps: S1. The depleted battery entering the battery swap station uploads its traceability code to the battery cloud hospital platform; S2. The battery cloud hospital platform first performs cloud diagnosis on the low-power battery through traceability coding. If the low-power battery does not have a level 4 fault, the battery cloud hospital platform notifies the station control system of the battery swap station to issue a battery swap command. The battery swap robot unlocks the low-power battery from the vehicle and carries it to the inside of the battery swap station. S3. The battery cloud hospital platform communicates with the station control system of the battery swap station to obtain the SOC data of the batteries in the station and remove batteries with SOC values ​​less than the specified threshold. S4. The battery cloud hospital platform is interconnected with the vehicle remote data platform to extract the driving discharge parameters of all batteries that have not been removed from the battery swap station from the most recent SOC ≥ 99% to the minimum SOC value on the day of driving, and obtain the number of battery charge and discharge cycles and the number of battery BMS failures; S5. The battery cloud hospital platform evaluates the degree of micro-short circuit, calculates the battery health SOH, calculates the SOH decrease rate, calculates the battery internal resistance, calculates the battery internal resistance growth rate, and evaluates the battery consistency through the driving discharge parameters in step S4; S6. The battery cloud hospital platform scores the batteries that have not been eliminated in the battery swap station based on the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures, and generates battery candidate serial numbers from high to low scores; S7. The battery cloud hospital platform notifies the battery swap station control system to select the battery with the first candidate number and transport it to the battery swap vehicle.

[0007] Furthermore, if the low-power battery in the above step S2 has a level 4 fault, the cloud hospital platform will send an alarm signal to the battery swap station control system. The battery swap station control system will remind the vehicle user through the screen and speaker in the battery swap channel that there is a serious battery fault, and please drive to the vehicle manufacturer's maintenance point as soon as possible for after-sales service.

[0008] Specifically, the driving discharge parameters of the above step S4 include the discharge current I, the maximum cell voltage Vmax, the minimum cell voltage Vmin and the time t.

[0009] In a preferred embodiment, the specific method for evaluating the degree of micro-short circuit in the above step S5 is as follows: carry out identification of the open circuit voltage of the two monomers, integrate the difference between the open circuit voltages of the two monomers with a fixed position as the cutoff point after fitting or smoothing, and obtain the S value that can stably reflect the micro-short circuit characteristics by controlling the relevant parameters, and obtain the self-discharge rate value of the battery by calculating the rate of change of S over time, thereby quantifying the degree of short circuit.

[0010] In a preferred embodiment, the specific method for calculating SOH in the above step S5 is as follows: through the ampere-hour integral transfer function, the battery capacity Q(k) corresponding to each moment is obtained, and a battery aging model that describes the change of battery capacity over time is established. The optimal model parameters are found through parameter identification and optimization solution methods so that the error between the capacity predicted by the model and the actual capacity is minimized, and the battery health SOH is calculated using the finally determined model parameters; the specific method for calculating the SOH decrease rate is as follows: select two time points t1 and t2 (t2>t1), and calculate SOH(t2) and SOH(t1) according to the method for calculating SOH. The SOH decrease rate can be calculated by dividing the SOH difference by the time t difference.

[0011] In a preferred embodiment, the specific method for calculating the battery internal resistance in the above step S5 is as follows: the BMS collects the charging voltage U and charging current data I of the battery, processes the collected data in real time, calculates the real-time voltage drop of the battery during the constant current charging stage, and finally calculates the battery internal resistance; the battery internal resistance growth rate is calculated as follows: two time points t1 and t2 (t2>t1) are selected, and the battery internal resistance R(t2) and the battery internal resistance R(t1) are calculated according to the method for calculating the battery internal resistance. The battery internal resistance R difference is divided by the time t difference to calculate the growth rate of the battery internal resistance R.

[0012] In a preferred embodiment, the specific method for evaluating battery consistency in the above step S5 is as follows: during the charging process, the BMS collects the voltage U and current I of each battery cell, calculates the average value of all battery cell voltages, calculates the difference between each battery cell voltage and the average voltage, and obtains the standard deviation of the voltage difference. The smaller the standard deviation, the better the cell voltage consistency. The above method is used to evaluate the capacity consistency, internal resistance consistency, and temperature consistency of the battery cell.

[0013] In a preferred embodiment, the specific method for scoring the batteries that have not been rejected in the battery swap station in the above step S6 is as follows: (1) setting weight coefficients for the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures; (2) converting the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures calculated or evaluated in step S5 into scores; (3) multiplying the score of each item in step (2) by the corresponding weight coefficient of step (1) to obtain the battery score.

[0014] The present invention also provides a battery package selection system for a battery swap station, based on the battery package selection method described in claim 1, comprising: a battery cloud hospital platform, wherein the battery cloud hospital platform is respectively communicated with the station control system of the battery swap station and the vehicle remote data platform, and the battery cloud hospital platform is provided with a battery diagnosis module. The battery cloud hospital platform is provided with a battery parameter extraction module and a battery diagnosis module, and the battery parameter extraction module is used to extract the driving discharge parameters of all batteries that have not been eliminated in the battery swap station, and the battery diagnosis module is used to perform cloud diagnosis and preliminary screening of the batteries in the battery swap station, as well as to evaluate the degree of micro-short circuit of all batteries that have not been eliminated in the battery swap station, calculate the battery health SOH, calculate the SOH decrease rate, calculate the battery internal resistance, calculate the battery internal resistance growth rate, and evaluate the battery consistency.

[0015] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following advantages: The present invention interconnects the battery cloud hospital platform with the vehicle remote data platform to extract data such as battery discharge current I, maximum single cell voltage Vmax, minimum single cell voltage Vmin, time t, etc., and uses these data to evaluate the degree of micro-short circuit of the batteries in the battery swap station, calculate the battery health SOH, calculate the SOH decrease rate, calculate the battery internal resistance, calculate the battery internal resistance growth rate, and evaluate the battery consistency. The batteries that are not eliminated are scored, and battery candidate serial numbers are generated in descending order according to the scores, to ensure that the user can obtain the best-performing battery in the battery swap station each time the battery is swapped, thereby greatly improving the user's vehicle safety and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural block diagram of the system of the present invention.

[0017] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] Refer to the following Figure 1 The specific implementation of the present invention is described. In order to fully understand the present invention, many details are described below, but for those skilled in the art, the present invention can be implemented without these details. For well-known components, methods and processes, they are not described in detail below.

[0019] A battery selection method for a battery swap station is based on a battery selection system. Figure 1The battery selection system includes: a battery cloud hospital platform, which is connected to the station control system of the battery swap station and the vehicle remote data platform respectively. The battery cloud hospital platform is equipped with a battery parameter extraction module and a battery diagnosis module. The battery parameter extraction module is used to extract the driving discharge parameters of all batteries that have not been eliminated in the battery swap station. The battery diagnosis module is used to perform cloud diagnosis and preliminary screening of batteries in the battery swap station, and to evaluate the micro-short circuit degree of all batteries that have not been eliminated in the battery swap station, calculate the battery health SOH, calculate the SOH decline rate, calculate the battery internal resistance, calculate the battery internal resistance growth rate, and evaluate the battery consistency.

[0020] Reference Figure 2 The battery pack selection method includes the following steps: S1. The depleted battery entering the battery swap station will have its own traceability code uploaded to the battery cloud hospital platform through the battery swap controller.

[0021] S2. The battery cloud hospital platform first performs cloud diagnosis on the deflated battery through traceability coding. If the deflated battery does not have a level 4 fault, the battery cloud hospital platform notifies the station control system of the battery swap station to issue a battery swap command, and the battery swap robot unlocks the deflated battery from the vehicle and moves it to the inside of the battery swap station. If a level 4 fault exists, the cloud hospital platform sends an alarm signal to the station control system of the battery swap station, and the station control system of the battery swap station reminds the vehicle user through the screen and speaker in the battery swap channel that the battery has a serious fault, and asks the user to drive to the vehicle manufacturer's maintenance point as soon as possible for after-sales service.

[0022] S3. The battery cloud hospital platform communicates with the station control system of the battery swap station to obtain the SOC data of the batteries in the station and eliminate batteries with SOC values ​​less than the limit threshold.

[0023] S4. The battery cloud hospital platform is interconnected with the vehicle remote data platform to extract the driving discharge parameters of all batteries that have not been eliminated in the battery swap station from the most recent SOC ≥ 99% to the minimum SOC value on the day of driving, including discharge current I, maximum single cell voltage Vmax, minimum single cell voltage Vmin and time t.

[0024] At the same time, the Battery Cloud Hospital Platform also obtains the number of charge and discharge cycles and BMS failures of all batteries that have not been eliminated in the battery swap station. The smaller the value of the battery charge and discharge cycle number, the better the battery performance. Frequent error reports may indicate problems with the battery or battery BMS. The smaller the number of battery BMS failures, the better the battery performance.

[0025] S5. The battery cloud hospital platform evaluates the degree of micro-short circuit, calculates the battery health SOH, calculates the SOH decrease rate, calculates the battery internal resistance, calculates the battery internal resistance growth rate, and evaluates the battery consistency through the driving discharge parameters in step S4; Evaluate the degree of micro-short circuit: Carry out the identification of the open circuit voltage of two cells, integrate the difference of the open circuit voltage of the two cells with a fixed position as the cutoff point after fitting or smoothing, and obtain the S value that can stably reflect the micro-short circuit characteristics by controlling the relevant parameters. By calculating the rate of change of S over time, the self-discharge rate value of the battery is obtained, thereby quantifying the degree of short circuit. The lower the short circuit degree, the better the battery performance.

[0026] Calculate the battery health SOH: Obtain the battery capacity Q(k) corresponding to each moment through the ampere-hour integral transfer function, establish a battery aging model that describes the change of battery capacity over time, find the best model parameters through parameter identification and optimization solution methods, so that the error between the model predicted capacity and the actual capacity is minimized, and use the final model parameters to calculate the battery health SOH; SOH is an indicator to measure the maximum capacity retention ability of a battery relative to a new battery. The higher the SOH value, the better the battery performance.

[0027] Calculate the SOH decline rate: Select two time points t1 and t2 (t2>t1), calculate SOH(t2) and SOH(t1) according to the method for calculating SOH, and divide the SOH difference by the time t difference to calculate the SOH decline rate. Evaluate the rate at which the battery health declines over time. A rapid decline indicates battery aging or damage.

[0028] Calculate battery internal resistance: BMS collects the battery's charging voltage U and charging current data I, processes the collected data in real time, calculates the real-time voltage drop of the battery during the constant current charging stage, and finally calculates the battery's internal resistance. The smaller the battery's internal resistance value, the less internal loss the battery has.

[0029] Calculate the battery internal resistance growth rate: Select two time points t1 and t2 (t2>t1), and calculate the battery internal resistance R(t2) and battery internal resistance R(t1) according to the method for calculating the battery internal resistance. The difference of the battery internal resistance R divided by the time difference t can be used to calculate the growth rate of the battery internal resistance R. The battery internal resistance growth rate is an important indicator for measuring battery aging and performance degradation. The larger the battery internal resistance growth rate, the faster the battery ages.

[0030] Evaluate battery consistency: During the charging process, the BMS collects the voltage U and current I of each battery cell, calculates the average value of all battery cell voltages, calculates the difference between each battery cell voltage and the average voltage, and obtains the standard deviation of the voltage difference. The smaller the standard deviation, the better the cell voltage consistency; and so on, the capacity consistency, internal resistance consistency, and temperature consistency of the battery cells are evaluated through calculation.

[0031] S6. The Battery Cloud Hospital Platform scores the batteries that have not been eliminated in the battery swap station based on the battery SOC, degree of micro-short circuit, SOH, SOH decrease rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures, and generates battery candidate serial numbers from high to low scores.

[0032] The specific scoring method is as follows: (1) setting weight coefficients for the battery SOC, micro-short circuit degree, SOH, SOH decrease rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures; (2) converting the battery SOC, micro-short circuit degree, SOH, SOH decrease rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures calculated or evaluated in step S5 into scores; (3) multiplying the score of each item in step (2) by the corresponding weight coefficient in step (1) to obtain the battery score.

[0033] S7. The battery cloud hospital platform notifies the battery swap station control system to select the battery with the first candidate number and transport it to the battery swap vehicle.

[0034] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A battery selection method for a battery swap station, characterized in that: The following steps are involved: S1. The depleted battery entering the battery swap station uploads its traceability code to the battery cloud hospital platform; S2. The battery cloud hospital platform first performs cloud diagnosis on the low-power battery through traceability coding. If the low-power battery does not have a level 4 fault, the battery cloud hospital platform notifies the station control system of the battery swap station to issue a battery swap command. The battery swap robot unlocks the low-power battery from the vehicle and carries it to the inside of the battery swap station. S3. The battery cloud hospital platform communicates with the station control system of the battery swap station to obtain the SOC data of the batteries in the station and remove batteries with SOC values ​​less than the specified threshold. S4. The battery cloud hospital platform is interconnected with the vehicle remote data platform to extract the driving discharge parameters of all batteries that have not been removed from the battery swap station from the most recent SOC ≥ 99% to the minimum SOC value on the day of driving, and obtain the number of battery charge and discharge cycles and BMS failure times; S5. The battery cloud hospital platform evaluates the degree of micro-short circuit, calculates the battery health SOH, calculates the SOH decrease rate, calculates the battery internal resistance, calculates the battery internal resistance growth rate, and evaluates the battery consistency through the driving discharge parameters in step S4; S6. The battery cloud hospital platform scores the batteries that have not been eliminated in the battery swap station based on the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures, and generates battery candidate serial numbers from high to low scores; S7. The battery cloud hospital platform notifies the battery swap station control system to select the battery with the first candidate number and transport it to the battery swap vehicle.

2. A battery selection method for a battery swap station according to claim 1, characterized in that: If the low-power battery in step S2 has a level 4 fault, the cloud hospital platform will send an alarm signal to the battery swap station control system. The battery swap station control system will remind the vehicle user through the screen and speaker in the battery swap channel that there is a serious battery fault, and ask the user to drive to the vehicle manufacturer's maintenance point as soon as possible for after-sales service.

3. A battery selection method for a battery swap station as claimed in claim 1, characterized in that: The driving discharge parameters of step S4 include the discharge current I, the maximum cell voltage Vmax, the minimum cell voltage Vmin and the time t.

4. A battery selection method for a battery swap station as claimed in claim 3, characterized in that: The specific method for evaluating the degree of micro-short circuit in step S5 is as follows: carry out identification of the open-circuit voltage of the two monomers, integrate the difference of the open-circuit voltage of the two monomers with a fixed position as the cutoff point after fitting or smoothing, obtain the S value that can stably reflect the micro-short circuit characteristics by controlling the relevant parameters, and obtain the self-discharge rate value of the battery by calculating the rate of change of S over time, thereby quantifying the degree of short circuit.

5. A battery selection method for a battery swap station as claimed in claim 3, characterized in that: The specific method for calculating SOH in step S5 is as follows: the battery capacity Q(k) corresponding to each moment is obtained through the ampere-hour integral transfer function, a battery aging model describing the change of battery capacity over time is established, the optimal model parameters are found through parameter identification and optimization solution methods, so that the error between the capacity predicted by the model and the actual capacity is minimized, and the battery health SOH is calculated using the finally determined model parameters; the specific method for calculating the SOH decrease rate is as follows: two time points t1 and t2 are selected, t2>t1, SOH(t2) and SOH(t1) are calculated according to the method for calculating SOH, and the SOH decrease rate can be calculated by dividing the SOH difference by the time t difference.

6. A battery selection method for a battery swap station as claimed in claim 1, characterized in that: The specific method for calculating the battery internal resistance in step S5 is as follows: the BMS collects the charging voltage U and charging current data I of the battery, processes the collected data in real time, calculates the real-time voltage drop of the battery in the constant current charging stage, and finally calculates the battery internal resistance; the battery internal resistance growth rate is calculated as follows: two time points t1 and t2 are selected, t2>t1, and the battery internal resistance R(t2) and the battery internal resistance R(t1) are calculated according to the method for calculating the battery internal resistance. The battery internal resistance R difference is divided by the time t difference to calculate the growth rate of the battery internal resistance R.

7. A battery selection method for a battery swap station as claimed in claim 1, characterized in that: The specific method for evaluating the battery consistency in step S5 is as follows: during the charging process, the BMS collects the voltage U and current I of each battery cell, calculates the average value of all battery cell voltages, calculates the difference between each battery cell voltage and the average voltage, and obtains the standard deviation of the voltage difference. The smaller the standard deviation, the better the cell voltage consistency. The above method is used to evaluate the capacity consistency, internal resistance consistency, and temperature consistency of the battery cell.

8. A battery selection method for a battery swap station as claimed in claim 1, characterized in that: The specific method for scoring the batteries that have not been eliminated in the battery swap station in step S6 is as follows: (1) setting weight coefficients for the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures; (2) converting the battery SOC, micro-short circuit degree, SOH, SOH decline rate, battery internal resistance, battery internal resistance growth rate, battery consistency, number of charge and discharge cycles, and number of BMS failures calculated or evaluated in step S5 into corresponding scores; (3) multiplying the score of each item in step (2) by the corresponding weight coefficient of step (1) to obtain the battery score.

9. A battery selection system for a battery swap station, based on the battery selection method according to claim 1, characterized in that: include: A battery cloud hospital platform, which is communicated with the station control system of the battery swap station and the vehicle remote data platform respectively. The battery cloud hospital platform is provided with a battery parameter extraction module and a battery diagnosis module. The battery parameter extraction module is used to extract the driving discharge parameters of all batteries not eliminated in the battery swap station, and the battery diagnosis module is used to perform cloud diagnosis and preliminary screening of the batteries in the battery swap station, as well as to evaluate the degree of micro-short circuit of all batteries not eliminated in the battery swap station, calculate the battery health SOH, calculate the SOH decrease rate, calculate the battery internal resistance, calculate the battery internal resistance growth rate, and evaluate the battery consistency.

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