CN114329760A - Vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twinning - Google Patents
Vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twinning Download PDFInfo
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Abstract
The invention relates to a vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twins, which is characterized in that the vehicle-mounted lithium ion battery is modeled based on data transmitted by a running vehicle in real time, wireless communication, a neural network algorithm and a gradient descent optimization algorithm, a twinning model capable of accurately analyzing the running state of the battery, predicting the full life cycle health state and future performance of the battery is finally obtained, whether the battery is abnormal or not is judged through the twinning model, information is fed back to the running vehicle in real time, and further action or intervention is taken on a battery body. The method provided by the application establishes dynamic relation between the vehicle-mounted battery body and the twin battery model in the full life cycle of the battery; the running state of the vehicle-mounted lithium ion battery can be accurately monitored and analyzed in real time.
Description
Technical Field
The invention relates to the field of lithium ion battery modeling, in particular to a vehicle-mounted lithium ion battery modeling method based on digital twinning. In particular to a battery model established under the combined action of data real-time transmission of a vehicle-mounted lithium ion battery and a neural network algorithm of a digital platform during operation.
Background
Due to sustainable development of energy, environmental protection consideration and support of national policies, new energy automobiles have gradually become the first choice when people buy vehicles, and power batteries are the most important part of the new energy automobiles, and among the power batteries, lithium ion batteries have the most development potential with high specific power and specific energy, but the models established for the lithium ion batteries have many defects at present, and some challenges still exist in the aspect of lithium ion battery modeling. The parameters of the vehicle-mounted lithium ion battery are influenced by various factors in the charging and discharging processes, so that the running state, the health state and the like of the battery can be changed, the endurance mileage of the electric automobile is reduced, and even the safety problem is caused. If the state information of the battery can be uploaded to an accurate digital battery model on a cloud platform in real time through the vehicle-mounted sensor and wireless communication, and connection mapping between uploaded data and the digital model is established, so that the digital model can truly reflect the behavior of the battery in the charging and discharging processes in real time, accurate analysis of the current state of the battery, scientific prediction of the future state and direct intervention of the running state can be realized through a twin battery model. Therefore, it is necessary to model the vehicle-mounted lithium ion battery and accurately establish the relationship between the battery body and the digital model thereof in real time.
Disclosure of Invention
Aiming at the defects in the existing lithium ion battery modeling, the invention aims to provide a vehicle-mounted lithium ion battery modeling method based on digital twinning, and solves the problems that the running state of a vehicle-mounted lithium ion battery cannot be accurately monitored and analyzed in real time, the full life cycle health state of the vehicle-mounted lithium ion battery cannot be predicted, the fault vehicle-mounted lithium ion battery cannot be intervened, and the like in the prior art.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twinning comprises the following steps:
s1, storing data representing the running state of the vehicle-mounted lithium ion battery on the digital platform, inputting the data into a neural network as historical data, and calculating a value U of terminal voltagebat_refAnd terminal voltage measurement value UbatTraining to obtain an initial lithium ion battery model with the error as a target within an allowable range;
s2, transmitting the collected lithium ion battery data of the running vehicle to a digital platform in real time through a vehicle-mounted battery management system and a wireless communication technology, taking the data as a real-time data set, updating a neural network input layer by adopting the real-time data set in consideration of factors of temperature, charge state and aging, and performing strengthening training on the initial lithium ion battery model parameters to obtain updated lithium ion battery model parameters;
s3, calculating battery terminal voltage by using the updated lithium ion battery model parameters obtained in the step S2 to obtain a battery terminal voltage calculated value, and updating a weight factor by a gradient descent optimization algorithm by taking the minimum accumulated error of the battery terminal voltage measured value and the battery terminal voltage calculated value as a target function;
s4, continuously and circularly updating the twin vehicle-mounted lithium ion battery model according to the real-time vehicle lithium ion battery data acquired in the step S2 and the weight factors obtained in the step S3, and finally obtaining the twin model capable of accurately analyzing the running state of the battery, predicting the full life cycle health state of the battery and predicting the future performance of the battery, so that the maximum closed-loop optimization is achieved;
and S5, comparing the normal value of the end voltage output by the twin model according to the real-time vehicle lithium ion battery data acquired in the step S2 with the measured value of the end voltage of the vehicle lithium ion battery, judging whether the vehicle lithium ion battery is abnormal or not, feeding back information to the running vehicle in real time, and taking further action or intervention on the vehicle lithium ion battery body.
On the basis of the above technical solution, the specific steps of step S1 are:
s11, screening out a group of abnormal-free data sets according to the stored charging and discharging historical data of the vehicle-mounted lithium ion battery under various working conditions;
s12, according to Thevenin model and kirchhoff' S law Ubat＝Uocv-IbatR0-Up，dUp/dt＝Ibat/Cp-Up/CpRpDetermining the relationship between the terminal voltage measurement value and each parameter;
wherein, UbatFor terminal voltage measurements, UocvIs open circuit voltage, UpIs a polarization voltage, IbatIs a charging current or a discharging current, the discharging current is positive, the charging current is negative, R0Is ohmic internal resistance, RpFor polarizing internal resistance, CpIs a polarization capacitor;
s13, inputting the abnormal data set screened out in S11 into the neural network Uocv，R0，Rp，Cp]＝f(Ubat，IbatT, SOC, SOH), where f (U)bat，IbatT, SOC, SOH) represents a data set training network, T represents temperature;
s14, U obtained by training according to neural networkocv、R0、Rp、CpCalculating to obtain a calculated value U of the terminal voltagebat_refCalculating the value of terminal voltage Ubat_refAnd terminal voltage measurement value UbatTo carry outAnd comparing, observing whether the error magnitude is in an allowable range, and if so, obtaining an initial lithium ion battery model.
On the basis of the above technical solution, the specific steps of step S2 are:
s21, transmitting the running vehicle lithium ion battery data collected by the vehicle-mounted battery management system to the digital platform in real time by using the wireless communication technology in the running process of the vehicle;
s22, considering the influence of temperature, charge state and aging factors on the lithium ion battery, and utilizing real-time transmission Ubat，IbatT, SOC, SOH as a neural network [ U ]ocv(i)，R0(i)，Rp(i)，Cp(i)]＝w1(i)f(Ubat(i))+w2(i)f(Ibat(i))+w3(i)f(T(i))+w4(i)f(SOC(i))+w5(i) f (SOH (i)) inputting the data set of the layer, training and updating initial lithium ion battery model parameters on a digital platform to obtain updated lithium ion battery model parameters, wherein U (sum of the absolute values of the parameters) isocv(i) Represents the open circuit voltage, R, obtained by training the ith data point0(i) Expressing the ohmic internal resistance, R, obtained by training the ith data pointp(i) Represents the polarization internal resistance, C, obtained by the training of the ith data pointp(i) Represents the polarization capacitance, w, obtained from the training of the ith data point1(i)、w2(i)、w3(i)、w4(i) And w5(i) Represents a weight factor, f (U)bat(i) Denotes a terminal voltage training network, f (I)bat(i) A current training network, f (t (i)) a temperature training network, f (soc (i)) a state of charge training network, f (soh (i)) a health state training network, i ═ 1,2, …, n, n is the number of intercepted data sets;
on the basis of the above technical solution, the specific steps of step S3 are:
s31, calculating the battery terminal voltage according to the updated lithium ion battery model parameters: u shapebat_ref(i)＝Uocv(i)-I(i)R0(i)-f(Rp(i),Cp(i) Wherein, f (R)p(i),Cp(i) Represents a polarization voltage function;
s32, U calculated according to the step S31bat_ref(i) Measured U with actual lithium ion batterybat(i) To do so by
Selecting proper step length for optimizing the target, and updating the weight factor w of the neural network in the negative gradient direction by adopting a gradient descent method1(i+1)，w2(i+1)，w3(i+1)，w4(i+1)，w5(i+1)；
On the basis of the above scheme, the specific steps of step S4 are:
substituting the weight factor obtained by updating in the step S3 into next training and correcting the parameters of the vehicle-mounted lithium ion battery model until the terminal voltage U calculated by the vehicle-mounted lithium ion battery modelbat_refThe actual terminal voltage U of the battery can be accurately simulated under any working conditionbatObtaining a twin model which can accurately analyze the running state of the battery, predict the full life cycle health state of the battery and predict the future performance;
on the basis of the above scheme, step 5 specifically includes: inputting real-time vehicle lithium ion battery data into the twin model to obtain the normal terminal voltage value of the vehicle lithium ion battery, comparing the normal terminal voltage value with the measured terminal voltage value of the vehicle lithium ion battery, and if delta U occursbat＝Ubat-Ubat_nor，|ΔUbat|>|ΔUbat_refL, explaining that the battery fails in the operation process, and issuing a failure prompt to the electric automobile by the digital platform to prevent the safety problem, wherein UbatFor terminal voltage measurements, Ubat_norNormal value of terminal voltage, Δ U, for twin modelbat_refIs a safe threshold value of the terminal voltage difference value.
On the basis of the technical scheme, the lithium ion battery comprises a lithium manganate power battery, a lithium iron phosphate power battery and a ternary material power battery.
The vehicle-mounted lithium ion battery modeling method based on the digital twin has the following beneficial effects:
1. the method provided by the application establishes dynamic relation between the vehicle-mounted battery body and the twin battery model in the full life cycle of the battery;
2. the twin model obtained by the method can accurately monitor and analyze the running state of the vehicle-mounted lithium ion battery in real time;
3. under the condition that the data of the digital platform is sufficient, the health state of the full life cycle of the vehicle-mounted lithium ion battery can be predicted, and the service life of the lithium ion battery is prolonged to a great extent;
4. the trained model can reversely detect whether the uploaded data is abnormal, and if the uploaded data is abnormal, the digital platform can issue an instruction to intervene the vehicle-mounted lithium ion battery body.
Drawings
The invention has the following drawings:
FIG. 1 is a modeling flow diagram of a digital twin-based on-board lithium ion battery modeling method;
FIG. 2 is a schematic structural diagram of a modeling method of a vehicle-mounted lithium ion battery based on digital twinning;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings 1 to 2.
As shown in fig. 1, the vehicle-mounted lithium ion battery modeling and fault diagnosis method based on the digital twin, which is provided by the invention, models the vehicle-mounted lithium ion battery based on data transmitted by a running vehicle in real time, wireless communication, a neural network algorithm and a gradient descent optimization algorithm, and then carries out fault diagnosis, and specifically comprises the following steps:
s1, storing data representing the running state of the vehicle-mounted lithium ion battery on the digital platform and inputting the data into the neural network as historical data, wherein the historical data comprises: terminal voltage measurement value UbatCalculating the value U of terminal voltagebat_refAnd terminal voltage measurement value UbatTraining to obtain an initial lithium ion battery model with the error as a target within an allowable range;
s2, transmitting the collected lithium ion battery data of the running vehicle to a digital platform in real time through a vehicle-mounted battery management system and a wireless communication technology to serve as a real-time data set, wherein the real-time data set comprises a battery end voltage measured value, a neural network input layer is updated by adopting the real-time data set in consideration of temperature, a charge state and aging factors, and the initial lithium ion battery model parameters are subjected to strengthening training to obtain updated lithium ion battery model parameters;
s3, calculating battery terminal voltage by using the updated lithium ion battery model parameters obtained in the step S2 to obtain a battery terminal voltage calculated value, and updating a weight factor by a gradient descent optimization algorithm by taking the minimum accumulated error of the battery terminal voltage measured value and the battery terminal voltage calculated value as a target function;
s4, continuously and circularly updating the twin vehicle-mounted lithium ion battery model according to the real-time vehicle lithium ion battery data acquired in the step S2 and the weight factors obtained in the step S3, and finally obtaining the twin model capable of accurately analyzing the running state of the battery, predicting the full life cycle health state of the battery and predicting the future performance of the battery, so that the maximum closed-loop optimization is achieved;
and S5, comparing the normal value of the end voltage output by the twin model according to the real-time vehicle lithium ion battery data acquired in the step S2 with the measured value of the end voltage of the vehicle lithium ion battery, judging whether the vehicle lithium ion battery is abnormal or not, feeding back information to the running vehicle in real time, and taking further action or intervention on the vehicle lithium ion battery body.
For the real-time uploaded data of the same number, model and material of the vehicle-mounted lithium ion batteries on different electric vehicles, if delta U occurs during the operation state monitoring in the twin lithium ion battery model on the digital platformbat＝Ubat_ref-Ubat_nor，|ΔUbat|>|ΔUbat_refL, explaining that the battery fails in the operation process, and issuing a failure prompt to the electric automobile by the digital platform to prevent the safety problem, wherein Ubat_refCalculated as terminal voltage, Ubat_norFor normal values of model-derived terminal voltage, Δ Ubat_refIs a safe threshold value of the terminal voltage difference value.
On the basis of the above technical solution, the specific steps of step S1 are:
s11, screening out a group of abnormal-free data sets according to the stored charging and discharging historical data of the vehicle-mounted lithium ion battery under various working conditions;
s12, according to Thevenin model and kirchhoff' S law Ubat＝Uocv-IbatR0-Up，dUp/dt＝Ibat/Cp-Up/CpRpDetermining the relationship between model terminal voltage and parameters, wherein UbatIs the terminal voltage of the battery, UocvIs open circuit voltage, UpIs a polarization voltage, IbatFor charging or discharging currents (positive for discharge, negative for charge), R0Is ohmic internal resistance, RpFor polarizing internal resistance, CpIs a polarization capacitor;
s13, inputting the screened data set into the neural network U on the digital platformocv，R0，Rp，Cp]＝f(Ubat，IbatT, SOC, SOH), where f (U)bat，IbatT, SOC, SOH) represents a data set training network, T represents temperature;
s14, U obtained by training according to neural networkocv、R0、Rp、CpCalculating to obtain a calculated value U of the terminal voltagebat_refCalculating the value of terminal voltage Ubat_refAnd terminal voltage measurement value UbatAnd comparing, and observing whether the error magnitude is in an allowable range, wherein if the error magnitude is in the allowable range, the model is an initial lithium ion battery model.
On the basis of the above technical solution, the specific steps of step S2 are:
s21, transmitting the running vehicle lithium ion battery data collected by the vehicle-mounted battery management system to the digital platform in real time by using the wireless communication technology in the running process of the vehicle;
s22, considering the influence of temperature, charge state and aging factors on the lithium ion battery, and utilizing real-time transmission Ubat，IbatT, SOC, SOH as a neural network [ U ]ocv(i)，R0(i)，Rp(i)，Cp(i)]＝w1(i)f(Ubat(i))+w2(i)f(Ibat(i))+w3(i)f(T(i))+w4(i)f(SOC(i))+w5(i) f (SOH (i)) inputting the data set of the layer, training and updating each parameter of the model on a digital platform to obtain updated lithium ion battery model parameters, wherein Uocv(i) Represents the open circuit voltage, R, obtained by training the ith data point0(i) Expressing the ohmic internal resistance, R, obtained by training the ith data pointp(i) Represents the polarization internal resistance, C, obtained by the training of the ith data pointp(i) Represents the polarization capacitance, w, obtained from the training of the ith data point1(i)、w2(i)、w3(i)、w4(i) And w5(i) Represents a weight factor, f (U)bat(i) Denotes a terminal voltage training network, f (I)bat(i) A current training network, f (t (i)) a temperature training network, f (soc (i)) a state of charge training network, f (soh (i)) a health state training network, i ═ 1,2, …, n, n is the number of intercepted data sets;
on the basis of the above technical solution, the specific steps of step S3 are:
s31, calculating the battery terminal voltage according to the battery model parameters updated by training: u shapebat_ref(i)＝Uocv(i)-I(i)R0(i)-f(Rp(i),Cp(i) Wherein, f (R)p(i),Cp(i) Represents a polarization voltage function;
s32, U calculated according to the step S31bat_ref(i) U corresponding to actual battery outputbat(i) To do so by
Selecting proper step length for optimizing the target, and updating the weight factor w of the neural network in the negative gradient direction by adopting a gradient descent method1(i+1)，w2(i+1)，w3(i+1)，w4(i+1)，w5(i+1)；
On the basis of the above scheme, the specific steps of step S4 are:
substituting the updated weight factor of the step S3 into the next training and correcting the vehicleCarrying lithium ion battery model parameters until the terminal voltage U calculated by the vehicle-mounted lithium ion battery modelbat_refThe actual terminal voltage U of the battery can be accurately simulated under any working conditionbatObtaining a twin model which can accurately analyze the running state of the battery, predict the full life cycle health state of the battery and predict the future performance;
according to the digital twin-based vehicle-mounted lithium ion battery modeling method, the lithium ion battery is used in an electric vehicle and can be a lithium manganate power battery, a lithium iron phosphate power battery or a ternary material power battery and the like.
The vehicle-mounted lithium ion batteries with different quantities, types and materials have different cut-off voltages, different capacities and different heat production rates, but aiming at the batteries with the same quantities, types and materials, the modeling method can quickly and accurately establish the dynamic relation between the vehicle-mounted lithium ion batteries and the twin model, and further carry out real-time monitoring of the running state and the health state prediction of the full life cycle.
The above-described embodiments of the present invention are merely examples for clearly illustrating the invention and are not to be construed as limiting the embodiments of the present invention, and it is obvious to those skilled in the art that various changes and modifications can be made on the basis of the above description.
Those not described in detail in this specification are within the skill of the art.
Claims (7)
1. A vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twinning is characterized by comprising the following steps:
s1, storing data representing the running state of the vehicle-mounted lithium ion battery on the digital platform, inputting the data into a neural network as historical data, and calculating a value U of terminal voltagebat_refAnd terminal voltage measurement value UbatThe error is within the allowable rangeTraining to obtain an initial lithium ion battery model;
s2, transmitting the collected lithium ion battery data of the running vehicle to a digital platform in real time through a vehicle-mounted battery management system and a wireless communication technology, taking the data as a real-time data set, updating a neural network input layer by adopting the real-time data set in consideration of factors of temperature, charge state and aging, and performing strengthening training on the initial lithium ion battery model parameters to obtain updated lithium ion battery model parameters;
s3, calculating battery terminal voltage by using the updated lithium ion battery model parameters obtained in the step S2 to obtain a battery terminal voltage calculated value, and updating a weight factor by a gradient descent optimization algorithm by taking the minimum accumulated error of the battery terminal voltage measured value and the battery terminal voltage calculated value as a target function;
s4, continuously and circularly updating the twin vehicle-mounted lithium ion battery model according to the real-time vehicle lithium ion battery data acquired in the step S2 and the weight factors obtained in the step S3, and finally obtaining the twin model capable of accurately analyzing the running state of the battery, predicting the full life cycle health state of the battery and predicting the future performance of the battery, so that the maximum closed-loop optimization is achieved;
and S5, comparing the normal value of the end voltage output by the twin model according to the real-time vehicle lithium ion battery data acquired in the step S2 with the measured value of the end voltage of the vehicle lithium ion battery, judging whether the vehicle lithium ion battery is abnormal or not, feeding back information to the running vehicle in real time, and taking further action or intervention on the vehicle lithium ion battery body.
2. The digital twin-based vehicle-mounted lithium ion battery modeling and fault diagnosis method according to claim 1, wherein the specific steps of step S1 are as follows:
s11, screening out a group of abnormal-free data sets according to the stored charging and discharging historical data of the vehicle-mounted lithium ion battery under various working conditions;
s12, according to Thevenin model and kirchhoff' S law Ubat＝Uocv-IbatR0-Up，dUp/dt＝Ibat/Cp-Up/CpRpDetermining the relationship between the terminal voltage measurement value and each parameter;
wherein, UbatFor terminal voltage measurements, UocvIs open circuit voltage, UpIs a polarization voltage, IbatIs a charging current or a discharging current, the discharging current is positive, the charging current is negative, R0Is ohmic internal resistance, RpFor polarizing internal resistance, CpIs a polarization capacitor;
s13, inputting the abnormal data set screened out in S11 into the neural network Uocv，R0，Rp，Cp]＝f(Ubat，IbatT, SOC, SOH), where f (U)bat，IbatT, SOC, SOH) represents a data set training network;
s14, U obtained by training according to neural networkocv、R0、Rp、CpCalculating to obtain a calculated value U of the terminal voltagebat_refCalculating the value of terminal voltage Ubat_refAnd terminal voltage measurement value UbatAnd comparing, observing whether the error magnitude is in an allowable range, and if so, obtaining an initial lithium ion battery model.
3. The digital twin-based vehicle-mounted lithium ion battery modeling and fault diagnosis method according to claim 1, wherein the specific steps of step S2 are as follows:
s21, transmitting the running vehicle lithium ion battery data collected by the vehicle-mounted battery management system to the digital platform in real time by using the wireless communication technology in the running process of the vehicle;
s22, considering the influence of temperature, charge state and aging factors on the lithium ion battery, and utilizing real-time transmission Ubat，IbatT, SOC, SOH as a neural network [ U ]ocv(i)，R0(i)，Rp(i)，Cp(i)]＝w1(i)f(Ubat(i))+w2(i)f(Ibat(i))+w3(i)f(T(i))+w4(i)f(SOC(i))+w5(i) f (SOH (i)) data set of input layer, in digital formTraining and updating initial lithium ion battery model parameters on a chemical platform to obtain updated lithium ion battery model parameters, wherein Uocv(i) Represents the open circuit voltage, R, obtained by training the ith data point0(i) Expressing the ohmic internal resistance, R, obtained by training the ith data pointp(i) Represents the polarization internal resistance, C, obtained by the training of the ith data pointp(i) Represents the polarization capacitance, w, obtained from the training of the ith data point1(i)、w2(i)、w3(i)、w4(i) And w5(i) Represents a weight factor, f (U)bat(i) Denotes a terminal voltage training network, f (I)bat(i) A current training network, f (t (i)) a temperature training network, f (soc (i)) a state of charge training network, f (soh (i)) a health training network, and i ═ 1,2, …, n, n is the number of truncated data sets.
4. The digital twin-based vehicle-mounted lithium ion battery modeling and fault diagnosis method according to claim 1, wherein the specific steps of step S3 are as follows:
s31, calculating the battery terminal voltage according to the updated lithium ion battery model parameters: u shapebat_ref(i)＝Uocv(i)-I(i)R0(i)-f(Rp(i),Cp(i) Wherein, f (R)p(i),Cp(i) Represents a polarization voltage function;
s32, U calculated according to the step S31bat_ref(i) Measured U with actual lithium ion batterybat(i) To do so by
Selecting proper step length for optimizing the target, and updating the weight factor w of the neural network in the negative gradient direction by adopting a gradient descent method1(i+1)，w2(i+1)，w3(i+1)，w4(i+1)，w5(i+1)。
5. The digital twin-based vehicle-mounted lithium ion battery modeling and fault diagnosis method according to claim 1, wherein the specific steps of step S4 are as follows:
substituting the weight factor obtained by updating in the step S3 into next training and correcting the parameters of the vehicle-mounted lithium ion battery model until the terminal voltage U calculated by the vehicle-mounted lithium ion battery modelbat_refThe actual terminal voltage U of the battery can be accurately simulated under any working conditionbatAnd a twin model capable of accurately analyzing the running state of the battery, predicting the full-life-cycle health state of the battery and predicting the future performance is obtained.
6. The digital twin-based vehicle-mounted lithium ion battery modeling and fault diagnosis method according to claim 1, wherein the step 5 specifically comprises:
inputting real-time vehicle lithium ion battery data into the twin model to obtain the normal terminal voltage value of the vehicle lithium ion battery, comparing the normal terminal voltage value with the measured terminal voltage value of the vehicle lithium ion battery, and if delta U occursbat＝Ubat-Ubat_nor，|ΔUbat|>|ΔUbat_refL, explaining that the battery fails in the operation process, and issuing a failure prompt to the electric automobile by the digital platform to prevent the safety problem, wherein UbatFor terminal voltage measurements, Ubat_norNormal value of terminal voltage outputted for twin model, DeltaUbat_refIs a safe threshold value of the terminal voltage difference value.
7. The digital twin-based on-board lithium ion battery modeling and fault diagnosis method of claim 1, wherein the lithium ion battery comprises a lithium manganate power battery, a lithium iron phosphate power battery, and a ternary material power battery.
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| CN117706379B (en) * | 2024-02-06 | 2024-04-12 | 北京航空航天大学 | Method and device for constructing dynamic safety boundary of battery and readable storage medium |
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