CN112838631A - Dynamic charging management and control device for power battery and charging diagnosis method for power battery - Google Patents
Dynamic charging management and control device for power battery and charging diagnosis method for power battery Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
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Abstract
The invention discloses a dynamic charging management and control device for a power battery and a charging diagnosis method for the power battery. The device comprises a BMS (battery management system) of the power battery, a charging master controller and a battery diagnosis cloud platform, wherein the BMS is used for acquiring a data stream of a battery core layer of the power battery in the charging process and sending the data stream to the battery diagnosis cloud platform; the battery diagnosis cloud platform is used for extracting a battery characteristic value from a data stream of a cell layer, diagnosing the power battery and positioning a fault cell by comparing the consistency of the battery characteristic value and the historical data, and sending a diagnosis result to a charge master controller; and the charging master controller is used for generating control parameters according to the diagnosis result and controlling the BMS to charge the power battery according to the control parameters. The battery consistency and the self-discharge rate can be described in real time in the whole life working period of the battery pack.
Description
Technical Field
The present invention relates to a charging management device and a diagnosis method, and more particularly, to a dynamic charging management and control device for a power battery and a charging diagnosis method for a power battery.
Background
In a power battery, such as a commonly used power lithium battery, due to the fact that multiple charging and discharging of a battery pack can cause the weakest cell to age far faster than expected by a manufacturer, serious consistency problems and potential safety hazards caused by thermal runaway and internal short circuit are brought, which are also reasons for spontaneous combustion of an electric vehicle in recent years. The development of the electric vehicle must provide an operable rapid detection method to determine that the power lithium battery pack has certain safety during use. According to the analysis at present, spontaneous combustion of a lithium battery often originates from a single battery core, so detection is required to be performed on a battery core layer, and strict detection on the battery core before delivery often needs to be performed on a battery pack under a condition of no disassembly. Currently, there are some solutions to this problem in the prior art, as listed below.
For example, the invention of chinese patent (patent No. CN 103399282B, patent name: battery cell fault diagnosis method) determines battery fault by comparing the difference between cell voltage and average voltage, the method is simple and clear, but when determining whether the battery is a problem, the total internal resistance identification is still required, the current and a certain identification time are required to be adjusted, the actual operation is difficult, and the application scenarios are few.
The Chinese patent invention (patent number: CN 108732510A, patent name: lithium ion battery consistency screening grouping method based on internal characteristics) identifies parameters through a lithium ion battery electrochemical model to obtain a plurality of voltage curves of electrochemical model parameters, extracts characteristic vectors, corrects the sensitive parameter characteristic vectors by using weights to obtain a corrected matrix, and obtains the classification number of batteries and the grouping information of each battery monomer according to the matrix and a clustering algorithm. The method can only describe the consistency of the batteries before grouping the batteries and cannot be applied to the online calculation of the consistency of the batteries.
The invention relates to a Chinese patent (patent number: CN109143106A, patent name: a method for rapidly detecting battery consistency through an alternating current impedance test), which carries out the alternating current impedance test under the voltage or capacity specified by the battery, then tests the direct current internal resistance of the battery by using a direct current discharge method test method, finally carries out data processing according to the parallel sample data acquisition of the alternating current impedance of the battery and the calculation of the direct current internal resistance value, analyzes the coincidence diagram of a Nyquist diagram, and comprehensively evaluates the consistency of the battery. The method can only describe the consistency of the batteries before grouping the batteries and cannot be applied to the online calculation of the consistency of the batteries.
The invention provides a Chinese patent (patent number: CN107907836A, patent name: a lithium ion power battery consistency evaluation method and system), which provides a series of parameters of a battery system to describe the dynamic consistency of the battery based on the evaluation of the static consistency of the battery by capacity range, tolerance, pressure difference and internal resistance difference parameters and through specific test conditions. The method needs to perform specific tests to describe the consistency of the battery, and cannot be applied to the consistency of the battery calculated on line.
Therefore, if a method for testing the battery cell layer during normal charging can be found, the accident of the electric vehicle can be effectively warned.
Disclosure of Invention
The invention provides a dynamic charging management and control device of a power battery and a charging diagnosis method of the power battery, aiming at overcoming the defect that the battery core is difficult to accurately detect in the charging process in the prior art.
The invention solves the technical problems through the following technical scheme:
a dynamic control device for charging a power battery is characterized by comprising a BMS (battery management system) of the power battery, a charging master controller and a battery diagnosis cloud platform, wherein,
the BMS is used for acquiring data streams of the cell level of the power battery in the charging process and sending the data streams to the battery diagnosis cloud platform;
the battery diagnosis cloud platform is used for extracting a battery characteristic value from a data stream of a cell layer, diagnosing the power battery and positioning a fault cell by comparing the consistency of the battery characteristic value and the historical data, and sending a diagnosis result to a charge master controller;
and the charging master controller is used for generating control parameters according to the diagnosis result and controlling the BMS to charge the power battery according to the control parameters.
Preferably, the battery characteristic values are internal resistance and capacity, and the BMS is configured to determine the internal resistance and the capacity by: obtaining the differential change dQ/dV of the electric quantity Q ═ Idt along with the voltage from the data flow of the electric core i, and obtaining the V when the dQ/dV reaches the maximum valueimaxIs an equivalent voltage platform of the i-cell and the internal resistance R of the celli＝(Vimax-OCVi) I, where I is the charging current, OCViIs the open circuit voltage; capacity Q thereofi＝ΔQi/ΔSOCiWherein Δ QiIs the capacity difference between the first and maximum peaks of the dQ/dV curve, Δ SOCiIs the difference in SOC between the two peaks, QiIs the maximum capacity at the time of full charge,
recording data meeting the condition that delta V is larger than or equal to X in the charging process of the power battery; is calculated by the formula of
Determining a characteristic value, and recording the charging capacity of a point corresponding to the characteristic value; wherein: q is the charge capacity of the battery, dQ is the differential of the capacity, Δ QkIs the difference in capacity between adjacent samples, V is the voltage of the cell, dV is the differential of the voltage, Δ VkFor the difference in voltage between adjacent samples, Δ Q for each sample point kk＝Qk-Qk-1，ΔVk＝Vk-Vk-1；
Wherein the value range of X is more than or equal to 1mV and less than or equal to 10 mV.
Preferably, the charging dynamic control device includes a master controller, and is configured to send an alarm instruction that requires offline operation detection and maintenance according to preset operation and inspection rules and maintenance regulations when a current operation value of the battery characteristic value or consistency of the battery characteristic value exceeds a constant false alarm threshold.
Preferably, the master controller is further used for performing HPPC (high Performance gas pressure controller) working condition correction on internal resistance, capacity and OCV (constant false alarm Rate) constant false alarm threshold value, wherein the change value delta V is based on the voltage change values of front and back ends of pulsesiCalculating the internal resistance Of the i-cell corresponding to each SOC (State Of Charge) value, thereby obtaining the cellAnd (3) calculating the effective capacity C of the whole power battery determined by the battery cell which firstly reaches the charge cut-off voltage according to the sum of the charge time obtained by deducting the rest time and the pulse time in the whole process, wherein the HPPC test is a standard test flow specified in the United states 'freedomCAR battery test manual'.
Wherein, the corrected constant false alarm threshold value is the actual value multiplied by a specific value in [0.8,1.2 ].
Preferably, the general controller is further configured to, after completing HPPC test correction, stand still for 24 hours, record OCV (Open Circuit Voltage) across each cell for 1 hour and 24 hours, and calculate Δ OCVi＝OCV1h-OCV24hAnd comparing the voltage difference with the safety voltage difference value of the power battery to judge whether the self-discharge rate is abnormal or not.
Preferably, the BMS is configured to diagnose whether the power cell is malfunctioning or has a safety hazard based on a comparison of a range based on maximum and minimum voltages of the power cell with a range threshold, wherein the range threshold is a charging current internal resistance threshold. The internal resistance threshold is determined according to research on similar aged lithium batteries, and is, for example, 3 times of the initial internal resistance of the battery core.
Preferably, the BMS is configured to store initial values and current control values of control parameters of charging and discharging of the power battery operation, including maximum transient and continuous charging and discharging currents, charging and discharging voltage thresholds on a cell level and a total battery pack, wherein the current threshold < ═ initial current threshold, the current charging voltage threshold < ═ initial charging threshold, and the current discharging voltage threshold > initial discharging voltage threshold.
Preferably, the BMS is further configured to periodically update the control parameters, and determine a charging or discharging current currently applied to each battery module subordinate thereto, a time interval and a threshold for the charging or discharging, according to a requirement for a maximum power or/and a total energy for charging or discharging the power battery and a current SOC of each unit battery of the power battery, and perform equalization when the battery voltage uniformity exceeds a preset threshold according to a preset rule.
Preferably, the charging dynamic management and control device further comprises a memory, wherein the memory is used for storing fault codes and fault information, specific measures for determining operation detection and maintenance of the vehicle carrying the power battery and the power battery can be called by users and operation and inspection personnel at any time, and a transfer station for the information transmission battery diagnosis cloud platform is further included.
Preferably, the fault information includes critical fault information and general fault information;
the serious fault information comprises short circuit, thermal runaway, insulation abnormity, overheating and communication abnormity;
the general fault information comprises battery under-voltage, battery over-voltage, low SOC, high SOC and balance fault.
Preferably, the battery diagnosis cloud platform performs data analysis on historical data of voltage of each single battery based on the historical data acquired from the memory, determines battery characteristic values such as direct current internal resistance, capacity and SOC of each single battery and change trend of the battery characteristic values along with time, establishes a model describing change of the characteristic values along with time so as to predict future development trend of the battery characteristic values, and establishes constant false alarm threshold values of the characteristic values; and meanwhile, determining control parameters suitable for long-term operation of the battery pack based on the relative positions of the current values and the threshold values of the characteristic values, namely maximum transient and continuous charge-discharge current and charge-discharge voltage threshold values on the single layer, and downloading the characteristic value threshold values and the key control parameters of the battery to the BMS through charge master control to form new alarm threshold values and control parameters.
Preferably, the charge master control is used for updating the threshold value by multiplying the maximum transient and continuous charge-discharge current and charge-discharge voltage threshold value of the monomer layer by a numerical value within 0-1 on the original threshold value when the consistency of the characteristic values exceeds a constant false alarm threshold value.
The invention also provides a charging diagnosis method of the power battery, which is characterized by comprising the following steps:
s1: acquiring a data flow of a cell layer of the power battery in a charging process;
s2: extracting a battery characteristic value from a data stream of a cell layer, comparing the consistency of the battery characteristic value, and comparing the battery characteristic value with historical data to diagnose the power battery and locate a fault cell;
s3: and generating a control parameter according to the diagnosis result, and charging the power battery according to the control parameter.
Preferably, the battery characteristic values in step S2 are internal resistance and capacity, and the internal resistance and capacity are determined by: obtaining the differential change dQ/dV of the electric quantity Q ═ Idt along with the voltage from the data flow of the electric core i, and obtaining the V when the dQ/dV reaches the maximum valueimaxIs an equivalent voltage platform of the i-cell and the internal resistance R of the celli＝(Vimax-OCVi) I, where I is the charging current, OCViIs the open circuit voltage; capacity Q thereofi＝ΔQi/ΔSOCiWherein Δ QiIs the capacity difference between the first and maximum peaks of the dQ/dV curve, Δ SOCiIs the difference in SOC between the two peaks, QiIs the maximum capacity at the time of full charge,
recording data meeting the condition that delta V is larger than or equal to X in the charging process of the power battery; is calculated by the formula of
Determining a characteristic value, and recording the charging capacity of a point corresponding to the characteristic value; wherein: q is the charge capacity of the battery, dQ is the differential of the capacity, Δ QkIs the difference in capacity between adjacent samples, V is the voltage of the cell, dV is the differential of the voltage, Δ VkFor the difference in voltage between adjacent samples, Δ Q for each sample point kk＝Qk-Qk-1，ΔVk＝Vk-Vk-1；
Wherein the value range of X is more than or equal to 1mV and less than or equal to 10 mV.
Preferably, step S3 includes: and when the current operation numerical value of the battery characteristic value or the consistency of the battery characteristic value exceeds a constant false alarm threshold, sending an alarm instruction for requiring operation detection and maintenance under the line according to preset operation detection rules and maintenance regulations.
Preferably, the HPPC condition correction is periodically performed on the internal resistance, the capacity and the OCV constant false alarm threshold, wherein the internal resistance of the i-cell corresponding to each SOC value is calculated based on the voltage variation value Δ Vi before and after the pulse, so as to obtain an R-SOC curve and an OCV-SOC curve of the i-cell, and the effective capacity C of the entire power battery determined by the cell which reaches the charge cut-off voltage first is calculated according to the sum of the charging time in the entire process excluding the rest time and the pulse time,
wherein, the corrected constant false alarm threshold value is the actual value multiplied by a specific value in [0.8,1.2 ].
Preferably, after completing HPPC test correction, resting for an additional 24 hours, recording the OCV across each cell for 1 hour and 24 hours, calculating the Δ OCVi＝OCV1h-OCV24hAnd comparing the voltage difference with the safety voltage difference value of the power battery to judge whether the self-discharge rate is abnormal or not.
Preferably, step S3 is preceded by: and diagnosing whether the power battery is in failure or has a safety hazard according to the comparison of the pole difference based on the maximum voltage and the minimum voltage of the power battery and a pole difference threshold value, wherein the pole difference threshold value is charging current and internal resistance threshold value. The internal resistance threshold is determined according to research on similar aged lithium batteries, and is, for example, 3 times of the initial internal resistance of the battery core.
Preferably, step S1 is followed by: the method comprises the steps of storing initial values and current control values of control parameters of charging and discharging of power battery operation, wherein the control values comprise maximum transient and continuous charging and discharging currents, and charging and discharging voltage thresholds on the aspect of a single body level and a total battery pack, wherein the current threshold is equal to an initial current threshold, the current charging voltage threshold is equal to an initial charging threshold, and the current discharging voltage threshold is equal to an initial discharging voltage threshold.
Preferably, step S3 is followed by: the control parameters are updated regularly, the charging or discharging current of each battery module subordinate to the power battery at present, the time interval and the threshold value of charging or discharging are determined according to the requirements of the maximum power or/and the total energy for charging or discharging the power battery and the SOC of each single battery of the power battery at present, and the equalization is carried out when the consistency of the battery voltage exceeds the preset threshold value according to the preset rule.
Preferably, step S1 is followed by: the fault code and the fault information are stored, and specific measures for determining the operation detection and the maintenance of the vehicle carrying the power battery and the power battery can be called by users and operation and inspection personnel at any time.
Preferably, the fault information includes critical fault information and general fault information;
the serious fault information comprises short circuit, thermal runaway, insulation abnormity, overheating and communication abnormity;
the general fault information comprises battery under-voltage, battery over-voltage, low SOC, high SOC and balance fault.
Preferably, step S3 is followed by: performing data analysis on historical data of the voltage of each single battery, determining battery characteristic values such as direct current internal resistance, capacity and SOC of each single battery and the change trend of the battery characteristic values along with time, establishing a model for describing the change of the characteristic values along with time so as to predict the future development trend of the battery characteristic values, and establishing constant false alarm threshold values of the characteristic values; and meanwhile, determining control parameters suitable for long-term operation of the battery pack based on the relative positions of the current values and the threshold values of the characteristic values, namely maximum transient and continuous charge-discharge current and charge-discharge voltage threshold values on the single body level, and updating the characteristic value threshold values and the key control parameters of the battery into new alarm threshold values and control parameters.
Preferably, the step of updating comprises: and when the consistency of the characteristic values exceeds a constant false alarm threshold, the maximum transient and continuous charge-discharge current and charge-discharge voltage threshold of the single body layer is multiplied by a numerical value within 0-1 on the original threshold to update the threshold.
The power lithium battery is managed by a Battery Management System (BMS) during use, and its main function is to measure to ensure that the temperature and voltage of each lithium battery cell are within normal operating ranges during use. The voltage at the two ends of the battery core can be obtained through the BMS, and the purposes of disassembly avoidance and battery layer surface diagnosis can be achieved as long as the battery characteristic values of capacity, internal resistance, self-discharge and the like can be obtained through data analysis from data flow. That is, the battery level test does not need to be completed by connecting a new wire to each cell, but may be performed by using a specific charging step at the time of normal charging, from which the capacity, internal resistance, and self-discharge rate can be extracted, thereby checking the reliability and safety of the battery pack or module at the battery level.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows: 1. the method is applicable to the normal charging process of the power battery (also called as a battery pack or a battery pack), and does not influence the working input and output of the battery; 2. except for the regular correction working condition, the battery does not need to be tested to obtain any parameter; 3. during the whole life working period of the battery pack, the description on the consistency and the self-discharge rate of the battery can be realized in real time. 4. The charging dynamic control device provided by the invention can update the threshold and the control parameter in real time and has strong applicability.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for diagnosing charging of a power battery of the present invention;
FIG. 2 is a DC internal resistance threshold curve of an embodiment;
FIG. 3 is a SOC-OCV curve for an embodiment;
FIG. 4 is a capacity increase curve of a reference curve for a lithium iron phosphate lithium ion battery according to an embodiment;
FIG. 5 is a capacity increment curve of charge data for an embodiment of a lithium iron phosphate lithium ion battery;
FIG. 6 is a histogram of the capacities of all cells in a battery pack according to one embodiment;
FIG. 7 is an error plot of calculated capacity versus true capacity of an embodiment;
FIG. 8 is a histogram of internal resistance of all cells in a battery pack, according to an embodiment;
FIG. 9 is a diagram of HPPC test conditions for an embodiment.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
The dynamic charging management and control device for the power battery is characterized by comprising a BMS (battery management system), a charging master controller and a battery diagnosis cloud platform of the power battery, wherein the BMS is used for acquiring a data stream of a cell layer of the power battery in a charging process and sending the data stream to the battery diagnosis cloud platform; the battery diagnosis cloud platform is used for extracting a battery characteristic value from a data stream of a cell layer, diagnosing the power battery and positioning a fault cell by comparing the consistency of the battery characteristic value and the historical data, and sending a diagnosis result to a charge master controller; and the charging master controller is used for generating control parameters according to the diagnosis result and controlling the BMS to charge the power battery according to the control parameters.
The battery characteristic values are internal resistance and capacity, and the BMS is used for determining the internal resistance and the capacity by the following modes: obtaining the differential change dQ/dV of the electric quantity Q ═ Idt along with the voltage from the data flow of the electric core i, and obtaining the V when the dQ/dV reaches the maximum valueimaxIs an equivalent voltage platform of the i-cell and the internal resistance R of the celli＝(Vimax-OCVi) I, where I is the charging current, OCViIs the open circuit voltage; capacity Q thereofi＝ΔQi/ΔSOCiWherein Δ QiIs the capacity difference between the first and maximum peaks of the dQ/dV curve, Δ SOCiIs the difference in SOC between the two peaks, QiThe maximum capacity is the maximum capacity during full charge, wherein data meeting the condition that delta V is more than or equal to X is recorded in the charging process of the power battery; is calculated by the formula of
Determining a characteristic value, and recording the charging capacity of a point corresponding to the characteristic value; wherein: q is the charge capacity of the battery, dQ is the differential of the capacity, Δ QkIs the difference in capacity between adjacent samples, V is the voltage of the cell, dV is the differential of the voltage, Δ VkFor the difference in voltage between adjacent samples, Δ Q for each sample point kk＝Qk-Qk-1，ΔVk＝Vk-Vk-1(ii) a Wherein the value range of X is more than or equal to 1mV and less than or equal to 10 mV.
The charging dynamic control device comprises a master controller, and is used for sending an alarm instruction which requires running detection and maintenance under the line according to preset running inspection rules and maintenance rules when the current operation numerical value of the battery characteristic value or the consistency of the battery characteristic value exceeds a constant false alarm threshold value.
The master controller is also used for performing HPPC working condition correction on internal resistance, capacity and OCV constant false alarm threshold value, wherein the HPPC working condition correction is based on the voltage change value delta V of the front and rear end of the pulseiCalculating the internal resistance of the i electric core corresponding to each SOC value so as to obtain an R-SOC curve and an OCV-SOC curve of the electric core i, and calculating the effective capacity C of the whole power battery determined by the electric core which firstly reaches the charging cut-off voltage according to the sum of the charging time of deducting the rest time and the pulse time in the whole process, wherein the corrected constant false alarm threshold value is the actual value multiplied by [0.8,1.2]]A specific value of (a).
The master controller is also used for standing for 24 hours after the HPPC test correction is finished, recording the OCV at the two ends of each battery cell for 1 hour and 24 hours, and calculating delta OCVi＝OCV1h-OCV24hAnd comparing the voltage difference with the safety voltage difference value of the power battery to judge whether the self-discharge rate is abnormal or not.
The BMS is used for diagnosing whether the power battery is in failure or has a potential safety hazard according to the comparison of the pole difference based on the maximum voltage and the minimum voltage of the power battery and a pole difference threshold value, wherein the pole difference threshold value is charging current and internal resistance threshold value.
The BMS is used for storing initial values and current control values of control parameters of charging and discharging of power battery operation, including maximum transient and continuous charging and discharging current, and charging and discharging voltage threshold values in the aspects of a single body level and a total battery pack, wherein the current threshold value is equal to the initial current threshold value, the current charging voltage threshold value is equal to the initial charging threshold value, and the current discharging voltage threshold value is equal to the initial discharging voltage threshold value.
The BMS is also used for updating control parameters periodically, determining the current charging or discharging current of each battery module subordinate to the BMS according to the requirements of the maximum power or/and the total energy for charging or discharging the power battery and the current SOC of each single battery of the power battery, the time interval and the threshold value for charging or discharging, and balancing when the consistency of the battery voltage exceeds the preset threshold value according to a preset rule.
The charging dynamic management and control device further comprises a memory (for example, a memory in an on-board intelligent terminal, the on-board intelligent terminal can be a t-box installed before the vehicle leaves a factory or an OBD (extended detection system for automobile fault diagnosis) installed after the vehicle leaves the factory, or is directly integrated in a BMS) and is used for storing fault codes and fault information, specific measures for determining operation detection and maintenance of the vehicle carrying the power battery and the power battery can be called by users and operation and maintenance personnel at any time, and a transfer station for an information transmission battery diagnosis cloud platform.
The fault information comprises serious fault information and general fault information; the serious fault information comprises short circuit, thermal runaway, insulation abnormity, overheating and communication abnormity; the general fault information comprises battery under-voltage, battery over-voltage, low SOC, high SOC and balance fault.
The battery diagnosis cloud platform performs data analysis on historical data of voltage of each single battery based on the historical data acquired from the memory, determines battery characteristic values such as direct current internal resistance, capacity and SOC of each single battery and change trend of the battery characteristic values along with time, establishes a model describing change of the characteristic values along with time so as to predict future development trend of the battery characteristic values, and establishes constant false alarm threshold values of the characteristic values; and meanwhile, determining control parameters suitable for long-term operation of the battery pack based on the relative positions of the current values and the threshold values of the characteristic values, namely maximum transient and continuous charge-discharge current and charge-discharge voltage threshold values on the single layer, and downloading the characteristic value threshold values and the key control parameters of the battery to the BMS through charge master control to form new alarm threshold values and control parameters.
And the charge master controller is used for updating the threshold value by multiplying the maximum transient and continuous charge-discharge current and charge-discharge voltage threshold value of the single layer by a numerical value within 0-1 on the original threshold value when the consistency of the characteristic values exceeds a constant false alarm threshold value.
In an embodiment that the power battery is an electric vehicle battery, the charging dynamic management and control device of the invention may be composed of a charging facility, a power battery system, a vehicle-mounted intelligent terminal and a battery diagnosis cloud platform, wherein the charging facility is composed of a charging master control and a charging device, and the power battery system is composed of a series of battery packs formed in series and parallel connection and a battery management system BMS master control and a slave control of a module.
Referring to fig. 1, the method for diagnosing the charging of the power battery specifically includes: during the charging process of the battery pack, the total positive and the total negative of the battery pack are connected with the total positive and the total negative of the charging equipment, the BMS and the charging facility master control of the battery pack perform two-way communication through agreement of the industry, namely CAN communication or Modbus communication (two communication protocols commonly used in the automobile field) exchange information, and during the charging process, the charging master control operates a current data stream acquired from the BMS to diagnose whether the battery pack has faults or has potential safety hazards, and simultaneously transmits the data stream to the battery diagnosis cloud platform. The battery diagnosis cloud platform extracts battery characteristic values such as internal resistance and capacity from the data flow of the battery cell layer, and diagnoses the potential safety hazard of the battery pack and positions a fault battery cell by comparing the consistency of the characteristic values with historical data of the characteristic values. When the current operation value or consistency of the characteristic value of the battery exceeds a constant false alarm threshold value so as to send an alarm instruction, a system master controller (a master controller) automatically sends an order to a maintenance or operation personnel, and the maintenance personnel is required to carry out further offline operation detection and maintenance according to a preset operation rule and an intelligent maintenance rule; meanwhile, the improved charging and discharging control threshold value is sent to the BMS master controller through the charging master controller, so that the potential safety hazard is effectively controlled before online down-transport inspection, and the effects of controlling the potential safety hazard and intelligently maintaining the protection by intelligently and dynamically adjusting the BMS control parameters without manual participation are achieved.
The invention is further elucidated below by means of examples of several practical application scenarios with reference to fig. 2-9.
Example 1:
taking a 4-wheel vehicle charging scene (before sale) as an example, the charging dynamic control device mainly comprises: the system comprises a battery pack, an iBMS (battery pack, a battery management system and a battery diagnosis cloud platform), a T-BOX, a charging pile general control and a battery diagnosis cloud platform, wherein the battery pack comprises a function of acquiring voltage of each core end of the battery pack, and the T-BOX (vehicle-mounted T-BOX is mainly used for communicating with a background system/mobile phone APP and realizing vehicle information display and control of the mobile phone APP) and information interaction of the charging pile during charging. The battery pack is an energy storage battery pack formed by connecting 16 50Ah and 3.2V universal batteries in series.
The iBMS is mainly controlled to carry out data acquisition to the battery package, and the data of gathering include the charge-discharge voltage of charge-discharge current, battery package total and monomer aspect, compares according to the voltage, the electric current that acquire at present and the charge-discharge key control parameter of storage in its memorizer simultaneously, and the control parameter of charge-discharge includes maximum transient state and lasts charge-discharge current, at monomer aspect and total charge-discharge voltage threshold value etc.. If any value of the current charging and discharging current, the total voltage or the single voltage of the battery pack exceeds the threshold value of the key control parameter of the BMS total control, the BMS total control gives an alarm, and the current is cut off to carry out safety protection on the battery pack. The BMS master control determines the current charging or discharging current of each battery module subordinate to the BMS master control according to the requirements of the system master control on the maximum power or/and the total energy of the battery pack charging or discharging and the current SOC of each single battery of the battery pack, the charging or discharging time interval and the threshold value, and balances when the consistency of the battery and the voltage exceeds the preset threshold value according to the preset rule through the BMS slave control pair.
The diagnosis cloud platform obtains historical data of each single battery from the charge master controller and carries out data analysis, characteristic values such as direct current internal resistance, capacity and self-discharge rate of each single battery and change trends of the characteristic values along with time are determined, a model describing changes of the characteristic values along with time is established, further future development trends of the characteristic values are predicted, and constant false alarm thresholds of the characteristic values are established; simultaneously, real-time data and corresponding formulas can be obtained according to the monomer
And calculating the direct current internal resistance, and comparing the current direct current internal resistance of the single body with the direct current internal resistance value on the specification of the single body to diagnose the single body. As shown in FIG. 2, the DC internal resistances of No. 2 and No. 6 monomers among 16 monomers are the largest, the DC internal resistance of No. 2 monomer is 1.196m Ω, and that of No. 6 monomerThe direct current internal resistance value is 1.196m omega, the direct current internal resistance values exceed 1m omega on the specification, 2 single bodies are abnormal, the direct current internal resistances of other single bodies do not exceed 1m omega, an alarm instruction is sent, the charging master control adjusts related charging control threshold values, namely the charging power, the maximum current, the charging cut-off voltage and other threshold values are corrected to be original threshold values 0.9, and therefore intelligent dynamic adjustment of BMS control parameters is achieved. The system master control automatically sends the order to maintenance personnel or operation and maintenance personnel, so that the No. 2 and No. 6 monomers can be effectively maintained in the shortest time, and further offline operation detection and maintenance are required to be carried out according to preset operation and maintenance rules and intelligent maintenance and maintenance regulations, so that the situations of overhigh temperature rise in the battery charging and discharging process and the like are prevented.
Example 2:
take a 4-wheel vehicle charging scenario (after-sale) as an example, wherein the charging dynamic control device mainly comprises: the system comprises a battery pack, an iBMS (battery pack, a charger and a charging pile, wherein the battery pack comprises a function of collecting voltage of each battery core end of the battery pack and information interaction between the battery pack and the charger through an OBD (on-board diagnostics) during charging), the OBD, a charging pile master control and a battery diagnosis cloud platform, wherein fault diagnosis information is arranged on a rear-mounted OBD. The commercial energy storage system is formed by connecting 240 single batteries in series, and the nominal capacity is CapinitialThe battery reference curve data is SOC-OCV relationship curve data at 180Ah, and the data interval is Δ SOC of 2%. The capacity value corresponding to each OCV point is represented by the formula: q ═ SOC 180 is calculated;
the iBMS transmits data to a cloud platform, the cloud platform adopts a five-point cubic smoothing filtering method (2 data before and after the position to be smoothed and 5 data in total are selected and are fitted by a 3-order polynomial to obtain a value after smoothing filtering) to obtain a capacity increment curve of SOC-OCV relational data, an SOC-OCV relational graph is shown in fig. 3, the obtained capacity increment curve is shown in a dQ/dV point drawing line in fig. 4, the SOC is taken as an abscissa in fig. 4, a left ordinate is a battery open-circuit voltage OCV, a right ordinate is dQ/dV, a 1 st characteristic value position is an A point position in fig. 4, the corresponding SOC is 55.6%, and the capacity Q is 55.6%1The 2 nd eigenvalue position in the graph is the B point position in fig. 4, corresponding to an SOC of 84.9%, at 100.08Ah, and the capacity Q is2＝152.82Ah；
Calculating a relation coefficient of capacity and total capacity between the characteristic values of the batteries:
g＝ΔQ/Capinitial＝|Q2-Q1|/Capinitial＝(152.82-100.08)/180＝0.293
FIG. 5 shows a curve of the capacity increment of the No. 1 cell in the battery pack, where FIG. 5 is a graph with SOC as the abscissa, the left ordinate is the battery voltage, the right ordinate is dQ/dV, the 1 st characteristic value position in the graph is the C point position in FIG. 5, and the corresponding charge capacity value is Q'1167.66Ah, the 2 nd eigenvalue position in the figure is the D point position in figure 5, and the corresponding charge capacity value is Q'21119.2 Ah; and (3) calculating the total capacity of the No. 1 single battery of the battery pack:
Cap1＝ΔQ1/g＝|Q‘21-Q‘11|/g＝(119.2-67.06)/0.293＝177.9Ah
the same calculation method calculates the total capacity of the number 2 single battery in the battery pack to the total capacity of the number 240 single battery, and draws the total capacity of all the single batteries into a histogram, and fig. 6 is a histogram of the capacity of all the single batteries in the battery pack. And comparing the capacity value calculated by the method of the present invention with the real capacity value of the battery, the capacity error rate is shown in fig. 7, which shows that the maximum capacity error is 3.86%. Correspondingly, V of the capacity increment curveimaxAt the position of C point in FIG. 5, the corresponding voltage and current are Vimax3.358V, and 63A. Calculating the resistance value of the No. 1 single battery of the battery pack:
R1＝(V1max-OCV1)/I＝2.47mΩ
the same calculation method calculates the resistance values of the No. 2 single battery to the No. 240 single battery in the battery pack, and plots the resistance values of all the single batteries into a histogram, as shown in fig. 8, which is a histogram of the resistance values of all the single batteries in the battery pack. Therefore, the resistance value of each single battery can be quickly obtained without additionally testing battery parameters in the normal charging process of the battery pack.
According to characteristic values such as direct current internal resistance, capacity and self-discharge rate and the change trend of the characteristic values along with time, a model describing the change of the characteristic values along with time is established so as to predict the future development trend of the characteristic values and establish constant false alarm threshold values of the characteristic values. Therefore, the pulse experiment described in fig. 9 needs to be periodically performed to perform calibration correction of the constant false alarm threshold of the relevant characteristic value, wherein the corrected constant false alarm threshold is the actual value multiplied by a specific value in [0.8,1.2 ].
Example 3:
take 2/3-round electric vehicle charging scenario (low-power-distribution BMS) as an example, wherein the charging dynamics management and control device mainly comprises: the battery pack, the iBMS (including basic BMS functions such as battery voltage collection +485 communication functions), charge master control (the master control does simple fault diagnosis on the spot, uploads battery diagnosis cloud to the iBMS's information, cloud platform diagnostic analysis, and the master control is charged for the propelling movement diagnostic result, and master control fault alarm to send new safe threshold value down to the BMS) and battery diagnosis cloud platform. The fault diagnosis information is in the cloud, and a user needs to surf the internet to obtain the health condition and the fault information of the power battery, and the rest is as in examples 1 and 2.
Example 4
Take 2/3-round electric motor car and 4 rounds of low-speed electric motor car scenes of charging (highly join in marriage BMS and do not have on-vehicle intelligent terminal) as an example, wherein charge dynamic management and control device mainly includes: the battery package, the iBMS (including basic BMS function + storage function + communication function such as battery voltage collection), charge and always manage (always manage and do simple fault diagnosis on the spot, upload battery diagnosis cloud to the information of iBMS, cloud platform diagnostic analysis, the total accuse of charging is given to the propelling movement diagnostic result, always manage fault alarm to issue new safe threshold value and control strategy to the BMS) and battery diagnosis cloud platform, fault diagnosis information can also be on the cloud on car (BMS). Others are as in examples 1, 2.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (12)
1. A dynamic charge control device for a power battery is characterized by comprising a BMS (battery management system) of the power battery, a charge master controller and a battery diagnosis cloud platform, wherein,
the BMS is used for acquiring data streams of the cell level of the power battery in the charging process and sending the data streams to the battery diagnosis cloud platform;
the battery diagnosis cloud platform is used for extracting a battery characteristic value from a data stream of a cell layer, diagnosing the power battery and positioning a fault cell by comparing the consistency of the battery characteristic value and the historical data, and sending a diagnosis result to a charge master controller;
and the charging master controller is used for generating control parameters according to the diagnosis result and controlling the BMS to charge the power battery according to the control parameters.
2. The charge dynamics management and control device according to claim 1, wherein the battery characteristic values are internal resistance and capacity, and the BMS is configured to determine the internal resistance and the capacity by: obtaining the differential change dQ/dV of the electric quantity Q ═ Idt along with the voltage from the data flow of the electric core i, and obtaining the V when the dQ/dV reaches the maximum valueimaxIs an equivalent voltage platform of the i-cell and the internal resistance R of the celli＝(Vimax-OCVi) I, where I is the charging current, OCViIs the open circuit voltage; capacity Q thereofi＝ΔQi/ΔSOCiWherein Δ QiIs the capacity difference between the first and maximum peaks of the dQ/dV curve, Δ SOCiIs the difference in SOC between the two peaks, QiIs the maximum capacity at the time of full charge,
recording data meeting the condition that delta V is larger than or equal to X in the charging process of the power battery; is calculated by the formula of
Determining a characteristic value, and recording the charging capacity of a point corresponding to the characteristic value; wherein: q is the charge capacity of the battery, dQ is the differential of the capacity, Δ QkIs the difference in capacity between adjacent sampling points, V being electricityThe voltage of the cell, dV being the differential of the voltage, Δ VkFor the difference in voltage between adjacent samples, Δ Q for each sample point kk＝Qk-Qk-1，ΔVk＝Vk-Vk-1；
Wherein the value range of X is more than or equal to 1mV and less than or equal to 10 mV.
3. The charging dynamics management and control device according to claim 2, wherein the charging dynamics management and control device comprises a general controller, which is used to issue an alarm instruction for running detection and maintenance under the line according to the preset operation and inspection rules and maintenance rules when the current operation value of the battery characteristic value or the consistency of the battery characteristic value exceeds the constant false alarm threshold.
4. The charging dynamics management and control device according to claim 3, wherein the general controller is further configured to perform HPPC condition correction on internal resistance, capacity and OCV constant false alarm threshold, wherein the value Δ V is based on voltage variation of front and back end of pulseiCalculating the internal resistance of the i electric core corresponding to each SOC value so as to obtain an R-SOC curve and an OCV-SOC curve of the electric core i, calculating the effective capacity C of the whole power battery determined by the electric core which reaches the charging cut-off voltage firstly according to the sum of the charging time of deducting the rest time and the pulse time in the whole process,
wherein, the corrected constant false alarm threshold value is the actual value multiplied by a specific value in [0.8,1.2 ].
5. The charging dynamics management and control device of claim 4, wherein the general controller is further configured to, after completing HPPC test correction, rest for 24 hours, record OCV across each cell for 1 hour and 24 hours, and calculate Δ OCVi＝OCV1h-OCV24hAnd comparing the voltage difference with the safety voltage difference value of the power battery to judge whether the self-discharge rate is abnormal or not.
6. The charging dynamics management device according to any one of claims 1-5, characterized in that the BMS is configured to diagnose whether the power battery is malfunctioning or has a safety hazard based on a comparison of a range based on a maximum voltage and a minimum voltage of the power battery with a range threshold, wherein the range threshold is a charging current internal resistance threshold,
or, the BMS is configured to store initial values and current control values of control parameters of charging and discharging of the power battery operation, including maximum transient and continuous charging and discharging currents, charging and discharging voltage thresholds at the cell level and in terms of total battery pack, wherein the current threshold < is an initial current threshold, the current charging voltage threshold < is an initial charging threshold, and the current discharging voltage threshold > is an initial discharging voltage threshold, preferably, the BMS is further configured to periodically update the control parameters, and to determine a current charging or discharging current for each battery module subordinate thereto and a time interval and a threshold for the charging or discharging according to a requirement for maximum power or/and total energy for charging or discharging the power battery and a current SOC of each cell of the power battery, and to equalize when the battery voltage uniformity exceeds a preset threshold according to a preset rule,
or the charging dynamic management and control device also comprises a memory used for storing fault codes and fault information, and specific measures for determining operation detection and maintenance of the vehicle carrying the power battery and the power battery can be called by users and operation and inspection personnel at any time, and a transfer station used for transmitting information to the battery diagnosis cloud platform,
preferably, the fault information includes critical fault information and general fault information;
the serious fault information comprises short circuit, thermal runaway, insulation abnormity, overheating and communication abnormity;
wherein the general fault information comprises battery under-voltage, battery over-voltage, low SOC, high SOC and balance fault,
more preferably, the battery diagnosis cloud platform performs data analysis on historical data of voltage of each single battery based on the historical data acquired from the memory, determines battery characteristic values such as direct current internal resistance, capacity and SOC of each single battery and change trend of the battery characteristic values along with time, establishes a model describing change of the characteristic values along with time so as to predict future development trend of the battery characteristic values, and establishes constant false alarm threshold values of the characteristic values; meanwhile, control parameters suitable for long-term operation of the battery pack, namely maximum transient and continuous charging and discharging current and charging and discharging voltage thresholds on the single body level are determined based on the relative positions of the current values of the characteristic values and the thresholds thereof, the characteristic value thresholds and the key control parameters of the battery are downloaded to the BMS through the charging master control to become new alarm thresholds and control parameters,
more preferably, the charge master control is used for updating the threshold value by multiplying the maximum transient and continuous charge-discharge current and charge-discharge voltage threshold value of the monomer layer by a numerical value within 0-1 on the original threshold value when the consistency of the characteristic values exceeds the constant false alarm threshold value.
7. A charging diagnosis method of a power battery is characterized by comprising the following steps:
s1: acquiring a data flow of a cell layer of the power battery in a charging process;
s2: extracting a battery characteristic value from a data stream of a cell layer, comparing the consistency of the battery characteristic value, and comparing the battery characteristic value with historical data to diagnose the power battery and locate a fault cell;
s3: and generating a control parameter according to the diagnosis result, and charging the power battery according to the control parameter.
8. The charge diagnostic method according to claim 7, wherein the battery characteristic values in step S2 are an internal resistance and a capacity, and the internal resistance and the capacity are determined by: obtaining the differential change dQ/dV of the electric quantity Q ═ Idt along with the voltage from the data flow of the electric core i, and obtaining the V when the dQ/dV reaches the maximum valueimaxIs an equivalent voltage platform of the i-cell and the internal resistance R of the celli＝(Vimax-OCVi) I, where I is the charging current, OCViIs the open circuit voltage; capacity Q thereofi＝ΔQi/ΔSOCiWherein Δ QiIs the capacity difference between the first and maximum peaks of the dQ/dV curve, Δ SOCiIs the difference in SOC between the two peaks,Qiis the maximum capacity at the time of full charge,
recording data meeting the condition that delta V is larger than or equal to X in the charging process of the power battery; is calculated by the formula of
Determining a characteristic value, and recording the charging capacity of a point corresponding to the characteristic value; wherein: q is the charge capacity of the battery, dQ is the differential of the capacity, Δ QkIs the difference in capacity between adjacent samples, V is the voltage of the cell, dV is the differential of the voltage, Δ VkFor the difference in voltage between adjacent samples, Δ Q for each sample point kk＝Qk-Qk-1，ΔVk＝Vk-Vk-1；
Wherein the value range of X is more than or equal to 1mV and less than or equal to 10 mV.
9. The charge diagnostic method according to claim 7, wherein step S3 includes: and when the current operation numerical value of the battery characteristic value or the consistency of the battery characteristic value exceeds a constant false alarm threshold, sending an alarm instruction for requiring operation detection and maintenance under the line according to preset operation detection rules and maintenance regulations.
10. The charge diagnosis method according to claim 9, wherein HPPC condition correction is periodically performed on the internal resistance, the capacity, and the OCV constant false alarm threshold, wherein the internal resistance of i-cell corresponding to each SOC value is calculated based on the pulse front-back voltage variation value Δ Vi, thereby obtaining an R-SOC curve and an OCV-SOC curve of cell i, and the effective capacity C of the entire power battery determined by the cell that reaches the charge cutoff voltage first is calculated from the sum of the charge time excluding the rest time and the pulse time in the entire process,
wherein, the corrected constant false alarm threshold value is the actual value multiplied by a specific value in [0.8,1.2 ].
11. The charge diagnostic method of claim 10, wherein after HPPC test remediation is complete, resting againOCV was recorded at both ends of each cell for 24 hours, 1 hour and 24 hours, and Δ OCV was calculatedi＝OCV1h-OCV24hAnd comparing the voltage difference with the safety voltage difference value of the power battery to judge whether the self-discharge rate is abnormal or not.
12. The charge diagnostic method according to any one of claims 7 to 11, further comprising, before step S3: diagnosing whether the power battery is faulty or has a safety hazard according to a comparison based on a range of maximum and minimum voltages of the power battery and a range threshold, wherein the range threshold is a charging current and an internal resistance threshold,
alternatively, step S1 is followed by: storing initial values and current control values of control parameters of charging and discharging of power battery operation, including maximum transient and continuous charging and discharging current, charging and discharging voltage threshold values in terms of cell level and total battery pack, wherein the current threshold value is equal to initial current threshold value, the current charging voltage threshold value is equal to initial charging threshold value, the current discharging voltage threshold value is equal to initial discharging voltage threshold value,
preferably, step S3 is followed by: periodically updating control parameters, determining the current charging or discharging current of each battery module subordinate to the power battery, the time interval and threshold value of charging or discharging according to the requirement of the maximum power or/and total energy for charging or discharging the power battery and the current SOC of each single battery of the power battery, equalizing when the consistency of the battery voltage exceeds the preset threshold value according to a preset rule,
alternatively, step S1 is followed by: the fault code and the fault information are stored, and the specific measures for determining the operation detection and the maintenance of the vehicle carrying the power battery and the power battery can be called by users and operation and inspection personnel at any time,
preferably, the fault information includes critical fault information and general fault information;
the serious fault information comprises short circuit, thermal runaway, insulation abnormity, overheating and communication abnormity;
wherein the general fault information comprises battery under-voltage, battery over-voltage, low SOC, high SOC and balance fault,
more preferably, step S3 is followed by: performing data analysis on historical data of the voltage of each single battery, determining battery characteristic values such as direct current internal resistance, capacity and SOC of each single battery and the change trend of the battery characteristic values along with time, establishing a model for describing the change of the characteristic values along with time so as to predict the future development trend of the battery characteristic values, and establishing constant false alarm threshold values of the characteristic values; meanwhile, control parameters suitable for long-term operation of the battery pack, namely maximum transient and continuous charge-discharge current and charge-discharge voltage thresholds on the single body level are determined based on the relative positions of the current values and the thresholds of the characteristic values, the thresholds of the characteristic values and the key control parameters of the battery are updated into new alarm thresholds and control parameters,
more preferably, the step of updating comprises: and when the consistency of the characteristic values exceeds a constant false alarm threshold, the maximum transient and continuous charge-discharge current and charge-discharge voltage threshold of the single body layer is multiplied by a numerical value within 0-1 on the original threshold to update the threshold.
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| CN114050633A (en) * | 2021-06-11 | 2022-02-15 | 上海玫克生储能科技有限公司 | Dynamic management and control method and device for lithium battery energy storage system and electronic equipment |
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| US11870289B2 (en) | 2021-09-08 | 2024-01-09 | Contemporary Amperex Technology Co., Limited | Method for charging traction battery and battery management system |
| CN116097494B (en) * | 2021-09-08 | 2023-12-15 | 宁德时代新能源科技股份有限公司 | Method for charging power battery and battery management system |
| CN116097494A (en) * | 2021-09-08 | 2023-05-09 | 宁德时代新能源科技股份有限公司 | Method for charging power battery and battery management system |
| CN113815486A (en) * | 2021-10-22 | 2021-12-21 | 广州小鹏汽车科技有限公司 | Power battery monitoring method and device, server and storage medium |
| CN114137417B (en) * | 2021-11-19 | 2023-01-17 | 北京理工大学 | Battery internal short circuit detection method based on charging data characteristics |
| CN114137417A (en) * | 2021-11-19 | 2022-03-04 | 北京理工大学 | Battery internal short circuit detection method based on charging data characteristics |
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| CN114879062A (en) * | 2022-05-30 | 2022-08-09 | 东莞新能安科技有限公司 | Battery short circuit detection method, device and equipment |
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| CN116609686A (en) * | 2023-04-18 | 2023-08-18 | 江苏果下科技有限公司 | Battery cell consistency assessment method based on cloud platform big data |
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