CN116500444A - Multi-state joint estimation method for battery of electric flying car facing operation safety - Google Patents
Multi-state joint estimation method for battery of electric flying car facing operation safety Download PDFInfo
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 37
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- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 claims description 2
- 238000007599 discharging Methods 0.000 claims 4
- 238000012544 monitoring process Methods 0.000 abstract 1
- 239000004973 liquid crystal related substance Substances 0.000 description 14
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Abstract
The invention relates to a battery multi-state joint estimation method for an electric flying car oriented to operation safety, and belongs to the field of battery management. The method comprises three parts: (1) Based on a battery equivalent circuit model, combining an adaptive extended Kalman filtering algorithm and considering the state of charge (SOC) estimation updated in real time by the model parameters; (2) Based on a two-dimensional distributed thermal model with high calculation efficiency and combined with key temperature SOT estimation of an adaptive Kalman filtering algorithm; (3) Instantaneous/continuous charge-discharge peak power SOP estimation taking into account battery current, voltage, SOC and temperature constraints. Compared with the traditional SOP estimation, the combined estimation method creatively considers the average temperature constraint of the battery updated in real time, can realize more accurate and reliable peak power estimation, and prevents the battery from being overcharged and overdischarged under the high-power working condition. Meanwhile, the accurate monitoring of the key electric heating information of the battery can avoid the over-temperature and over-pressure of the battery, and ensure the safe, efficient and reliable operation of the battery system of the electric flying automobile.
Description
Technical Field
The invention belongs to the field of battery management, and relates to a battery multi-state joint estimation method for an electric flying car for running safety.
Background
Accurate estimation of the critical state of a battery system remains one of the key challenges in the battery management field. There have been many studies on the SOC estimation, SOT estimation, and SOP estimation of individual lithium batteries. Common SOC estimation methods are mainly ampere-hour integration, open circuit voltage, data-driven based, model-based, and some hybrid methods. For ease of implementation, the integration at ampere-hours, model-based methods and some simple hybrid methods, such as integration at ampere-hours in combination with open circuit voltage, are now widely used. Common SOT estimation methods include EIS measurement-based methods, simplified thermal model-based methods, data-driven methods, and combined EIS measurement and simplified thermal model methods. The current method that can be applied to real vehicle applications is a SOT estimation method based on a simplified thermal model. Common SOP estimation methods include MAP interpolation-based methods and model-based methods. Many reports are also presented on multi-state joint estimation, the double-state joint estimation mainly comprises SOC-SOT joint estimation, SOC-SOP joint estimation, SOC-SOH joint estimation and SOP-SOE joint estimation, the three-state joint estimation comprises SOC-SOH-SOP and the like, the current research method is seldom related to SOC-SOT-SOP joint estimation, and the current research method is mainly oriented to ground electric vehicles and static energy storage systems, and is seldom related to UAM scenes.
Therefore, the invention builds a SOC-SOT-SOP multi-state joint estimation framework for operation safety aiming at the battery energy storage system of the electric flying vehicle in the UAM application scene, and realizes more accurate and robust battery key state estimation. The estimation method is based on an electrothermal coupling model of the lithium ion battery, and combines an adaptive filter to monitor key electrothermal information of the battery, and then considers the constraints of battery current, voltage, SOC and temperature to realize real-time estimation of instantaneous/continuous charge-discharge peak power. Compared with the traditional SOP estimation method, the average temperature constraint is increased, and the charge and discharge peak current of the battery can be calculated in real time through the centralized mass thermal model, so that the peak power of the battery is calculated.
Disclosure of Invention
Therefore, the invention aims to provide a battery multi-state joint estimation method for an electric flying car, which is safe to operate.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the battery multi-state joint estimation method for the electric flying car facing to running safety comprises the following steps:
s1: according to the operation condition characteristics of the electric flying automobile in the urban air traffic UAM scene, analyzing the change rule of characteristic parameters of the lithium ion battery and a coupling mechanism between electric heating processes, and establishing a control-oriented electric heating model;
s2: based on an electric flying automobile dynamics model, acquiring a current loading working condition of a lithium ion battery system, carrying out a characteristic working condition test and a typical UAM operation working condition test on a high-power and large-size soft package battery, and establishing a battery characteristic test and dynamic test database;
s3: parameterizing the electric model based on a characteristic test data set, and establishing a quantitative function relation between electric parameters and temperature, state of charge (SOC), current multiplying power and current direction; parameterizing a physical model in the lithium ion battery hybrid thermal model based on a dynamic working condition data set, training a data driving model in the lithium ion battery hybrid thermal model, and finally verifying the accuracy of the hybrid electric heating model;
s4: based on the established lithium ion battery electric model, updating electric model parameters in real time according to battery electric heating information, and combining with a self-adaptive extended Kalman filtering AEKF algorithm to realize SOC estimation of the lithium ion battery;
s5: based on the established thermal model of the lithium ion battery, the self-adaptive Kalman filtering AKF algorithm is combined to realize the SOT estimation of the lithium ion battery;
s6: based on the real-time updated battery key electric heating information, current, voltage, SOC and temperature constraint are considered, and real-time calculation of instantaneous/continuous charge and discharge peak power of the lithium ion battery is realized.
Optionally, in the step S1, the control-oriented battery electrothermal model is a hybrid electrothermal model with high computation efficiency, wherein the electrothermal model is a first-order RC equivalent circuit model, and the thermal model is a two-dimensional distributed thermal model driven by physical-data fusion.
Optionally, in S2, the characteristic working conditions include a static capacity test SCT and a multi-rate mixed pulse test HPPC at each temperature, and the typical UAM operation working conditions include a flying discharge working condition and a fast charge working condition, where the flying discharge working condition considers a standard flying discharge working condition and a flying discharge working condition including an emergency evacuation condition.
Optionally, in S4, the battery electrothermal information includes a battery SOC, a current, and an average temperature, which are used for calibrating the parameters of the electric model online, and the SOC estimation algorithm includes an adaptive extended kalman filter AEKF, an adaptive bulk kalman filter CKF, an adaptive unscented kalman filter AUKF, and a particle filter PF.
Optionally, in the step S5, the temperature SOT of the battery is estimated to be able to monitor the critical thermal information of the volume average temperature, the maximum temperature and the maximum temperature difference of the battery on line.
Optionally, in the step S6, the continuous time is 1S,10S,30S and 60S, the battery charge-discharge peak power is calculated by calculating the battery charge-discharge peak current by using the average battery temperature based on the centralized mass thermal model and considering the battery current, voltage, SOC and temperature constraint.
Optionally, the step S1 specifically includes the following steps:
s11: determining a proper electric model according to the positive electrode material of the lithium ion battery, selecting a first-order RC equivalent circuit model aiming at the NCM battery, and selecting the first-order RC equivalent circuit model with first-order hysteresis aiming at the lithium iron phosphate LFP battery;
s12: establishing a distributed physical thermal model of a large-size square battery based on a spectrum-Galerkin method;
s13: taking the high-power characteristic of UAM operation conditions into consideration, utilizing a data driving model based on a gate control cyclic neural network GRU to improve the prediction precision of a physical model, and establishing a battery physical-data fusion driving hybrid thermal model;
s14: and establishing a control-oriented electrothermal model according to a coupling mechanism between electrothermal characteristics of the lithium ion battery.
Optionally, the step S3 specifically includes the following steps:
s31: based on the characteristic test data set, parameterizing a battery electric model by using a recursive least square algorithm, and establishing a quantitative function relation between electric parameters and temperature, SOC, current multiplying power and current direction;
s32: parameterizing the distributed physical thermal model using a constrained nonlinear minimization function based on the dynamic operating mode dataset;
s33: training a GRU-based data driving model based on the dynamic working condition data set for calibrating the physical thermal model;
s34: and verifying the hybrid electric heating model of the lithium ion battery by using the dynamic working condition data set.
The invention has the beneficial effects that:
(1) The method constructs a battery multi-state joint estimation framework of the electric flying vehicle facing to operation safety, can accurately acquire the key electric heating information of the battery in the UAM scene, and ensures the safe, efficient and reliable operation of the battery system;
(2) The method is characterized in that a battery electric heating model driven by physical-data fusion is established aiming at the operation condition of the electric flying car in the UAM scene, and the method has higher precision and higher adaptability than a battery model facing the ground electric car and a static energy storage system;
(3) The method is based on a high-efficiency hybrid electric heating model and combined with a self-adaptive online filtering algorithm, so that the real-time performance of algorithm application can be ensured;
(4) The method creatively considers the temperature constraint when estimating the peak power of the battery, and has higher estimation precision and stronger robustness compared with the traditional method which only considers the current, voltage and SOC constraint of the battery.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a combined SOC-SOT-SOP estimation framework of the present invention;
FIG. 2 is a general flow chart of the present invention;
FIG. 3 is a detailed flowchart of step S1 in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a square cell thermocouple arrangement and cooling boundary according to an embodiment of the present invention;
FIG. 5 is a modeling framework of the hybrid thermal model of the present invention;
FIG. 6 is a coupling mechanism of the battery electrothermal process of the present invention;
FIG. 7 is a schematic diagram of a UAM operation scenario in an embodiment of the present invention;
fig. 8 is a detailed flowchart of step S3 in the embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
Referring to fig. 1 and 2, the method for multi-state joint estimation of battery of electric flying car for running safety comprises the following steps:
s1: according to the operation condition characteristics of the electric flying automobile in the urban air traffic UAM scene, analyzing the change rule of characteristic parameters of the lithium ion battery and a coupling mechanism between electric heating processes, and establishing a control-oriented electric heating model;
s2: based on an electric flying automobile dynamics model, acquiring a current loading working condition of a lithium ion battery system, carrying out a characteristic working condition test and a typical UAM operation working condition test on a high-power and large-size soft package battery, and establishing a battery characteristic test and dynamic test database;
s3: parameterizing the electric model based on a characteristic test data set, and establishing a quantitative function relation between electric parameters and temperature, state of charge (SOC), current multiplying power and current direction; parameterizing a physical model in the lithium ion battery hybrid thermal model based on a dynamic working condition data set, training a data driving model in the lithium ion battery hybrid thermal model, and finally verifying the accuracy of the hybrid electric heating model;
s4: based on the established lithium ion battery electric model, updating electric model parameters in real time according to battery electric heating information, and combining with a self-adaptive extended Kalman filtering AEKF algorithm to realize SOC estimation of the lithium ion battery;
s5: based on the established thermal model of the lithium ion battery, the self-adaptive Kalman filtering AKF algorithm is combined to realize the SOT estimation of the lithium ion battery;
s6: based on the real-time updated battery key electric heating information, current, voltage, SOC and temperature constraint are considered, and real-time calculation of instantaneous/continuous charge and discharge peak power of the lithium ion battery is realized.
Referring to fig. 3, step S1 specifically includes steps S11-S13:
s11: determining a proper electric model according to the positive electrode material of the lithium ion battery, for example, selecting a first-order RC equivalent circuit model for the NCM battery, wherein the control equation is as follows:
U oc -U p -IR s -V t ＝0
wherein SOC, eta, C b The battery state of charge, coulombic efficiency, and the battery actual capacity, respectively. U (U) oc 、U p 、R s 、V t 、R p 、C p The open circuit voltage, the polarization voltage, the ohmic internal resistance, the terminal voltage, the polarization internal resistance and the polarization capacitance of the battery are respectively obtained. Defining a current direction: the charge is negative and the discharge is positive.
S12: a distributed physical thermal model of a large-size square battery is established based on a spectrum-Galerkin method, and comprises three sub-models: (1) Aiming at a two-dimensional distributed reduced-order thermal model of a battery body; (2) a concentrated mass thermal model for the positive tab; and (3) a concentrated mass thermal model for the negative electrode tab. The heat flow exchange between the battery body and the positive and negative lugs is described by an empirical heat conduction equation.
First, assume that the temperature distribution of a square lithium battery obeys the following two-dimensional unsteady state heat conduction equation of cartesian coordinates:
ρC h D t T-k x D xx T-k y D yy T＝q
as in fig. 4, the following boundary conditions are satisfied:
where T (x, y, T) is a temperature function that is a function of position and time, and q (x, y, T) is the heat generation rate per unit volume of the battery. ρ, C h And k is the density, specific heat capacity and thermal conductivity of the cell.Andx∈[0,w]，y∈[0,l]w and l are the width and length of the cell. Subscripts r, l, t, and b represent the right, left, upper, and lower boundaries of the cell. h is a x And h y Is the convective heat transfer coefficient in the x and y directions of the cell. In the oven, it is assumed that convection conditions at the respective boundaries of the battery are the same. T (T) x,∞ And T y,∞ The ambient temperature in the x and y directions of the battery is indicated, assuming the same cooling environment on each side of the battery.
The partial differential equation can be converted into a normal differential equation to be solved by using a Chebyshev-Galerkin approximation method and a space-time separation technology, and written into a state space expression as follows:
where E, A, B and C represent system matrices, x and u refer to system states and system inputs, respectively. T (T) e As a boundary lifting function. The system output y can be flexibly set according to the actual control system requirement, and the average temperature, the highest temperature and the maximum temperature difference on the battery plane are considered in the important point, so that y, C and T e Expressed as:
secondly, the thermodynamic behavior of the positive and negative tabs of the battery is described by a focused mass thermal model:
wherein T is t And T ∞ Respectively represent the temperature of the tab and the ambient temperature, m t 、C pt 、h t And A t Respectively refers to the mass, specific heat capacity, convection coefficient and convection area of the tab. q ct The heat flow between the battery body and the tab is defined as negative in the inflow tab and positive in the outflow tab. q t And the electrode lug ohm generates heat.
Finally, the heat flow exchange between the battery body and the two pole ears is simulated by an empirical heat conduction equation as follows:
q ct ＝h ct A ct (T t -T m )
wherein T is m Represents the temperature of the nearest discrete volume unit to the tab, h ct Is a thermal coefficient of resistance, A ct Is the contact area between the body and the tab. Therefore, the detailed temperature distribution condition of the two-dimensional plane of the battery can be obtained through the joint solution of the battery body and the polar ear thermal model.
S13: and taking the high power characteristic of UAM operation conditions into consideration, improving the prediction precision of a physical model by using a data driving model based on a gate control cyclic neural network GRU, and establishing a battery physical-data fusion driving hybrid thermal model, as shown in figure 5. Wherein the mathematical representation of the GRU data-driven model can be expressed as:
where σ and tanh represent gating activation functions, σ represents an S-type function, σ=1/(1+exp (-x)), tanh represents hyperbolic tangent functions, tanh= (exp (x) -exp (-x))/(exp (x) +exp (-x)).As a linear activation function, ++and [ x, x ]]Representing the element multiplication and the matrix concatenation, respectively. W (W) r 、W z 、W h And W is 0 The weight matrix of the reset gate, the update gate, the transition output and the output layer are respectively represented. Similarly, b r 、b z 、b h And b 0 Is the corresponding bias matrix. r is (r) t And z t The outputs of the reset gate and the update gate, respectively.And h t Representing candidate hidden states and hidden states, respectively. y is t Representing the output of the GRU model. The measurement index of the training error is the mean square error of the output temperature vector, expressed as:
wherein M and N respectively represent the length of the output vector and the length of the whole temperature sequence at a single moment of GRU. y is i,j Andindicating the temperature T at the moment j i And a reference value. In the invention, the Adam optimization algorithm is used to minimize the loss function and achieve an adaptive calculation learning rate with less memory requirements. On-line calibration based on GRU neural network model can greatly promote the prediction accuracy of battery thermal model under the high-power operation condition.
S14: as shown in fig. 6, a control-oriented battery hybrid thermoelectric model is built according to a coupling mechanism between thermoelectric properties of a lithium ion battery. Specifically, the battery thermal model outputs a volume average temperature for updating the electrical model parameters including OCV, R o 、R p And C p And then the electric model parameters are used for calculating the heat generation of the battery at the next moment, and the heat generation is fed back to update the battery temperature, so that the cycle iterates.
The characteristic conditions in step S2 include a static capacity test SCT and a multi-rate mixed pulse test HPPC, and typical UAM operation conditions include a flying discharge condition and a fast charge condition, wherein the flying discharge condition considers a standard flying discharge condition and a flying discharge condition including an emergency evacuation condition, and a UAM operation scene is shown in fig. 7. SCT and HPPC experiments need to be performed at a number of temperatures (0 ℃, 5 ℃,10 ℃, 15 ℃, 25 ℃, 40 ℃) within a given temperature range.
As shown in fig. 8, step S3 specifically includes steps S31 to S34:
s31: based on the characteristic test data set, a Recursive Least Squares (RLS) algorithm is used for parameterizing a battery electric model, and a quantitative functional relation between electric parameters and temperature, SOC, current multiplying power and current direction is established:
U oc ＝f(SOC,T,I)
the principle of the least squares algorithm is described as follows.
According to the least squares theory, it is first necessary to organize the battery model into parameterized representations:
wherein y, θ andrespectively observed quantity, parameter vector and recursion quantity. By using LaplaraAnd (3) performing a forward and inverse transformation to transform the discrete mathematical expression of the first-order RC battery model into:
U d (k+1)＝b 1 U d (k)-R s I(k+1)+(b 1 R s -b 2 )I(k)
wherein, the liquid crystal display device comprises a liquid crystal display device,
U d (k)＝V t (k)-U oc (k,SOC,T)
θ k ＝[b 1 R s b 1 R s -b 2 ] T
wherein, the liquid crystal display device comprises a liquid crystal display device,
then, based on the above parameterized equation, a recursive least squares algorithm can be implemented. Considering that the conventional recursive least square algorithm has infinite memory characteristics, as the recursive data increases, new data is easy to cause difficulty in playing a correction role, thereby influencing the algorithm effect. To avoid this problem, the concept of forgetting factors was introduced, forming an improved recursive least squares algorithm-a recursive least squares algorithm with forgetting factors. The specific implementation process is as follows:
(1) Initializing a parameter matrix: the observation vector, the recursion vector, and the optimization parameter vector in the parameterized equation are determined, and the optimization parameter vector θ (k) and the systematic error covariance matrix P (k) are initialized.
(2) The gain matrix K (k+1) at the next time is calculated:
(3) Calculating an optimized parameter vector theta (k+1) at the next moment:
(4) Calculating a system error covariance matrix P (k+1) at the next moment:
based on the RLS algorithm and the battery HPPC working condition data, the quantitative function relation between the key electric parameters of the battery and the SOC, the temperature, the current multiplying power and the current direction is obtained.
S32: based on a dynamic working condition data set, a nonlinear minimization function fmincon parameterized distributed physical thermal model with constraint in Matlab is used, parameters of the model to be optimized need to be initialized, and reasonable change intervals of the parameters are set;
s33: based on the dynamic working condition data set, a GRU-based data driving model is trained for calibrating the physical thermal model. Specifically, during model training, a set of optimal parameters needs to be determined for subsequent model verification, such as optimizing a model structure, a sliding window length, a number of GRU layers, the number of hidden neurons, a learning rate, and the number of iterations.
S34: and verifying the hybrid electric heating model of the lithium ion battery by using the dynamic working condition data set.
The battery electrothermal information in step S4 includes battery SOC, current and average temperature, which are used for calibrating the parameters of the electric model online, where the SOC estimation algorithm is not limited to AEKF, but may be adaptive volume kalman filter CKF, adaptive unscented kalman filter AUKF and particle filter PF. The battery SOC estimation principle based on AEKF is as follows:
since AEKF is a model-based state estimation algorithm, a nonlinear discrete expression of the battery system is given here. Generally, the state space equations of a nonlinear discrete system are shaped as:
x k+1 ＝f(x k ,u k )+w k
y k ＝h(x k ,u k )+v k
wherein x is k And y k The state vector and the observation vector at the time of the system k are respectively represented. f () and h () represent the state equation function and the observation equation function of the system, respectively. u (u) k Is the input vector of the system. w (w) k And v k The process noise and the measurement noise of the system are respectively represented as uncorrelated zero-mean Gaussian white noise.
From the state space expression of the battery model, the state vector and input of the system can be expressed as:
x k ＝[SOC k U p,k ] T
u k ＝I k
the state equation and the measurement equation of the system can be expressed as:
h(x k ,u k )＝U oc (x k (1))-R s u k -x k (2)
the system matrix of battery system state space equations may be expressed as:
D k ＝-R s
so far, the state space model of the nonlinear discrete battery system is clear, and the AEKF algorithm starts to be implemented to design the SOC estimator. It should be noted that the parameters in this model come from online updates of the RLS.
The AEKF is implemented as follows:
system state, error covariance initialization: x is x k 、P k 、Q k And R is k 。
System state and process error covariance time updates:
where P is the state estimation error covariance and Q is the process noise error covariance.And->A priori estimates of the system state and state estimation error covariance, respectively.
Kalman gain update:
where K is the Kalman gain of AEKF and R is the measurement noise error covariance.
Update of the innovation:
system state and state error covariance measurement update:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->The posterior estimates of the system state and state estimation error covariance, respectively.
Process noise and measurement noise adaptive update:
where H is the information estimation covariance in the time window and N is the sliding window length.
The SOT estimation in step S5 can monitor key thermal information such as the volume average temperature, the maximum temperature, and the maximum temperature difference of the battery on line. The SOT estimation principle based on AKF is the same as that based on AEKF, and x, y and u are respectively the time coefficient, the key temperature output and the unit volume heat generation rate in the thermal model. In the present invention, the measured temperature of the center position of the battery body is selected as the measurement input of AKF, based on which the system matrix of the estimator may be set to:
C＝[C 2 C 3 C 4 C avg ] T
D＝T e ＝[T e,2 T e,3 T e,4 T e,avg ] T
in step S6, the battery charge-discharge peak power calculation considers the continuous time of 1S,10S,30S and 60S, and in addition to the conventional battery current, voltage and SOC constraints, the battery temperature constraints are innovatively considered, and the battery charge-discharge peak current is calculated by using the battery average temperature based on the concentrated mass thermal model, thereby calculating the charge-discharge peak power.
For the instantaneous power of the battery, the specific calculation method is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Respectively represent instantaneous charge-discharge peak power. />And->Respectively represent instantaneous charge-discharge peak currents. R is R s,k For the ohmic internal resistance of the battery at the previous moment, +.>And U p,k+1 Updated by the following formula:
calculating instantaneous charge-discharge peak current under voltage constraint:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Representing peak charge-discharge current under voltage constraint, U t,max And U t,min Upper and lower end voltage operating limits set for battery manufacturers or users.
Calculating instantaneous charge-discharge peak current under the constraint of SOC:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Representing peak charge-discharge current under SOC constraint, SOC max And SOC (System on chip) min The upper and lower limits of battery SOC operation are set in advance.
Calculating instantaneous charge-discharge peak current under temperature constraint:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Represents peak charge-discharge current under the constraint of average temperature of battery, T max And T avg Respectively, the allowable maximum temperature and the average temperature of the battery. />Beta represents the weight factor of the contribution of the polarization internal resistance to the heat generation of the battery, h, A, m and C h Respectively, the heat dissipation coefficient, surface area, mass, and specific heat capacity of the battery.
Finally, the instantaneous peak charge-discharge current needs to satisfy the following constraints:
in practice, it is important to estimate and predict the peak power of continuous charge and discharge of the battery for practical engineering applications, and the cases of duration of 10s,30s and 60s are generally considered. In the invention, the change of the electric model parameters in the duration time period is considered, and the current, voltage, SOC and temperature constraint of the battery are considered, so that the invention has obvious innovation. The specific calculation method is as follows:
/>
wherein L is the duration of charge and discharge,and->Representation holderContinuous peak charge-discharge power, < >>And->Indicating a sustained peak charge-discharge current. I max,L Representing peak current over a duration of time, MR p Is a continuous multiplication matrix.
Continuous charge-discharge peak current calculation under voltage constraint:
continuous charge-discharge peak current calculation under the constraint of SOC:
continuous charge-discharge peak current calculation under temperature constraint:
/>
wherein MRR is a continuous multiplication matrix.
Finally, the continuous charge-discharge peak current calculation needs to satisfy the following constraints:
finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (8)
1. The battery multi-state joint estimation method for the electric flying car facing to running safety is characterized by comprising the following steps of: the method comprises the following steps:
s1: according to the operation condition characteristics of the electric flying automobile in the urban air traffic UAM scene, analyzing the change rule of characteristic parameters of the lithium ion battery and a coupling mechanism between electric heating processes, and establishing a control-oriented electric heating model;
s2: based on an electric flying automobile dynamics model, acquiring a current loading working condition of a lithium ion battery system, carrying out a characteristic working condition test and a typical UAM operation working condition test on a high-power and large-size soft package battery, and establishing a battery characteristic test and dynamic test database;
s3: parameterizing the electric model based on a characteristic test data set, and establishing a quantitative function relation between electric parameters and temperature, state of charge (SOC), current multiplying power and current direction; parameterizing a physical model in the lithium ion battery hybrid thermal model based on a dynamic working condition data set, training a data driving model in the lithium ion battery hybrid thermal model, and finally verifying the accuracy of the hybrid electric heating model;
s4: based on the established lithium ion battery electric model, updating electric model parameters in real time according to battery electric heating information, and combining with a self-adaptive extended Kalman filtering AEKF algorithm to realize SOC estimation of the lithium ion battery;
s5: based on the established thermal model of the lithium ion battery, the self-adaptive Kalman filtering AKF algorithm is combined to realize the SOT estimation of the lithium ion battery;
s6: based on the real-time updated battery key electric heating information, current, voltage, SOC and temperature constraint are considered, and real-time calculation of instantaneous/continuous charge and discharge peak power of the lithium ion battery is realized.
2. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S1, the control-oriented battery electrothermal model is a high-efficiency calculation hybrid electrothermal model, wherein the electrothermal model is a first-order RC equivalent circuit model, and the thermal model is a two-dimensional distributed thermal model driven by physical-data fusion.
3. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S2, the characteristic working conditions comprise static capacity test SCT and multi-multiplying power mixed pulse test HPPC at each temperature, the typical UAM operation working conditions comprise a flight discharging working condition and a quick charging working condition, and the flight discharging working condition considers a standard flight discharging working condition and a flight discharging working condition comprising an emergency evacuation condition.
4. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S4, the battery electrothermal information includes battery SOC, current and average temperature, which are used for calibrating the parameters of the electric model on line, and the SOC estimation algorithm includes adaptive extended kalman filter AEKF, adaptive volume kalman filter CKF, adaptive unscented kalman filter AUKF and particle filter PF.
5. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: in S5, the temperature SOT of the battery is estimated to be able to monitor the critical thermal information of the volume average temperature, the maximum temperature and the maximum temperature difference of the battery on line.
6. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S6, the continuous time is 1S,10S,30S and 60S, the battery charge-discharge peak power is calculated by calculating the battery charge-discharge peak current by using the average battery temperature based on the centralized mass thermal model and considering the battery current, voltage, SOC and temperature constraint.
7. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: the step S1 specifically comprises the following steps:
s11: determining a proper electric model according to the positive electrode material of the lithium ion battery, selecting a first-order RC equivalent circuit model aiming at the NCM battery, and selecting the first-order RC equivalent circuit model with first-order hysteresis aiming at the lithium iron phosphate LFP battery;
s12: establishing a distributed physical thermal model of a large-size square battery based on a spectrum-Galerkin method;
s13: taking the high-power characteristic of UAM operation conditions into consideration, utilizing a data driving model based on a gate control cyclic neural network GRU to improve the prediction precision of a physical model, and establishing a battery physical-data fusion driving hybrid thermal model;
s14: and establishing a control-oriented electrothermal model according to a coupling mechanism between electrothermal characteristics of the lithium ion battery.
8. The operation safety-oriented battery multi-state joint estimation method for the electric flying car according to claim 1, wherein the method is characterized by comprising the following steps of: the step S3 specifically comprises the following steps:
s31: based on the characteristic test data set, parameterizing a battery electric model by using a recursive least square algorithm, and establishing a quantitative function relation between electric parameters and temperature, SOC, current multiplying power and current direction;
s32: parameterizing the distributed physical thermal model using a constrained nonlinear minimization function based on the dynamic operating mode dataset;
s33: training a GRU-based data driving model based on the dynamic working condition data set for calibrating the physical thermal model;
s34: and verifying the hybrid electric heating model of the lithium ion battery by using the dynamic working condition data set.
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