CN113536455A - State of charge estimation method based on adaptive particle filtering and battery management system - Google Patents

State of charge estimation method based on adaptive particle filtering and battery management system Download PDF

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CN113536455A
CN113536455A CN202110656590.3A CN202110656590A CN113536455A CN 113536455 A CN113536455 A CN 113536455A CN 202110656590 A CN202110656590 A CN 202110656590A CN 113536455 A CN113536455 A CN 113536455A
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吴宏涛
孟颖
周丽军
薛春明
周晓旭
孙贝
刘博�
段英杰
牛秉青
孙川
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Abstract

The invention discloses a state of charge estimation method and a battery management system based on adaptive particle filtering, which combine the problem of battery state of charge estimation, fully utilize identified model information, and adaptively adjust the state noise and observation noise of a model according to the difference between a model estimation value and an observation value, thereby achieving the purpose of improving the accuracy, speed and robustness of the battery state of charge estimation. Meanwhile, a hardware platform of the power battery management system of the electric automobile is built. The complete hardware platform comprises main functions of battery parameter detection, information display, safety alarm, battery state of charge estimation and the like. And verifying the lithium battery pack algorithm through the established hardware platform.

Description

State of charge estimation method based on adaptive particle filtering and battery management system
Technical Field
The invention relates to the technical field of batteries, in particular to a state of charge estimation method based on adaptive particle filtering and a battery management system.
Background
With the rapid development of global economy and technology in recent years, automobiles are also continuously improved as indispensable vehicles for human society. With the continuous increase of global automobile reserves, the consumption of fossil fuel energy is also continuously increasing. Automobile exhaust is one of the main sources of air pollution at present, and the deterioration of the global atmospheric environment is further aggravated due to the annual increase of the emission amount of the automobile exhaust. The unmanned electric automobile is taken as a newly-developed research object in the automobile industry at present, integrates the advantages of environmental protection, no noise, safe use and the like, and has unlimited future development space.
The cost, specific energy, cycle life and the like of the power battery are key factors for restricting the development of the electric automobile. To solve this problem, two points of view can be considered: the battery has the advantages of low development cost, high specific energy and long cycle life; and secondly, an effective battery management system is developed, and the performance advantages of the battery management system are fully exerted through scientific management. The SOC (State Of Charge) estimation is important content Of battery management, researches the SOC estimation problem Of the electric vehicle, and has practical significance for scientific management Of the battery and reasonable arrangement Of charging time. The battery management system is used as a core part of an electric vehicle monitoring battery, can monitor external characteristics of the battery and estimate the SOC of the battery in real time, and aims to ensure that the energy of the battery can be efficiently utilized under a complex running condition of the electric vehicle, so that the performance of the battery is exerted to the maximum extent, the use safety of the battery can be ensured, and the service life of the battery can be prolonged. If the battery management system is compared with a brain control system of the electric automobile, the residual battery capacity is equivalent to the heart of the automobile, and the estimation of the residual battery capacity without errors has important research value and significance for future development of the electric automobile, supervision of a vehicle-mounted power battery and meeting the requirement of human beings on the continuous improvement of the automobile: (1) economic benefits are improved, and cost is saved; (2) the safety performance of the battery is improved, and the performance of the whole vehicle is improved; (3) the battery is protected, and the service life of the battery is prolonged.
Battery SOC estimation methods can be classified into 3 types: traditional methods include open circuit voltage method, internal resistance method, ampere-hour integration method, etc.; the estimation method based on the black box model comprises a neural network model, a fuzzy logic model, a support vector regression model and the like; common estimation methods based on the state space model include kalman filtering, particle filtering, H ∞ algorithm, and the like. The traditional method is not suitable for estimation when the battery is used, the black box model-based estimation method has strong dependence on the number and accuracy of test data, and the state space model-based estimation method is the key point of the current research. Various adaptive improvements and applications of particle filtering are also widely researched, for example, a particle filtering method with the particle number changing adaptively along with time is provided, and the method determines the particle number at the next moment according to the relative entropy, so that the purpose of reducing the calculated amount is achieved. The particle filtering method for adaptively updating the important density function is provided by research, the problem of low sampling efficiency is solved, and the algorithm estimation precision is improved. There are also studies to adjust the number of particles according to the estimation error, control the estimation error within a certain range, and at the same time, focus on reducing the amount of calculation.
The popularization and rapid development of electric vehicles at home and abroad promote the continuous updating and progress of the internal core technology. Through the continuous perfection of the battery model and the estimation algorithm and the combination of the hardware processor with the faster and faster operation speed, the reliability of the battery management system is gradually improved, the whole vehicle performance of the electric vehicle is greatly improved, and the requirements of consumers are further met.
Disclosure of Invention
In order to solve the limitations and defects in the prior art, the invention provides a self-adaptive particle filter-based unmanned electric vehicle charge state estimation method, which comprises the following steps:
the lithium battery is modeled by using an equivalent circuit model, the equivalent circuit comprises an ohmic internal resistance module, an RC network module and a hysteresis module, and the relationship between the voltage of the ith RC network terminal and the resistance and the capacitance is as follows:
Upi,k+1＝exp(-Δt/τi)Upi,k+Rp(1-exp(-Δt/τi))Ik
where Δ t is the sampling period, τi＝RiCiIs a time constant, Upi,kTerminal voltage of i-th RC network at time k, RpIs a polarization resistance, IkRC network current at the k moment;
the calculation formula of the hysteresis module is as follows:
hk+1＝exp(-|κik|Δt)hk+(1-exp(-|κik|Δt))H
wherein h iskLag voltage at time k, k attenuation factor, ikCurrent at time k, H is the maximum hysteresis voltage;
fitting open circuit voltage U using nth order polynomialocvThe relationship with the state of charge value z is as follows:
Figure BDA0003113137570000031
wherein alpha isjIs a fitting coefficient;
using experimental and polynomial fitting pairs
Figure BDA0003113137570000032
Identifying to obtain test data under different charge state values;
fitting open-circuit voltage U by using polynomial according to test data under different charge state valuesocvThe relationship with the state of charge value z is as follows:
Figure BDA0003113137570000033
testing the capability characteristics of the hybrid power pulse, and searching the optimal parameters of each model by using a particle swarm algorithm;
selecting a state vector kx＝(Up1，k，hk，zk)TDiscretizing the lithium battery equivalent circuit model and the open-circuit voltage equation to obtain a state transfer equation and an observation equation, wherein the calculation formula is as follows:
Figure BDA0003113137570000034
Ut,k＝Uocv(zk)-IkR0-Up1,k+hk+vk
wherein, CaIs the available capacity of the battery, eta is the coulomb coefficient, okIs process noise, vkTo observe noise, Ut,kFor the value of the battery output voltage at time k, zkIs the state of charge, R, of the battery at time k0Is a resistance, IkThe current value of the battery at time k, UocvIs an open circuit voltage;
calculating a particle weight, and performing normalization processing on the particle weight;
and estimating the state of the battery to obtain a state of charge estimated value.
Optionally, the step of calculating the particle weight and performing normalization processing on the particle weight includes:
setting a nonlinear space equation as follows:
Figure BDA0003113137570000035
wherein x iskIs the state vector at time k, ykF () and h () are known functions which are observed quantities at the time k;
setting the posterior distribution of the system state to p (x)k|yk) And a probability distribution q (x)k|yk) Probability distribution q (x)k|yk) Collecting N samples according to the importance function to obtain a sample set
Figure BDA0003113137570000041
Introducing the importance function into the posterior distribution to obtain the following expression:
Figure BDA0003113137570000042
where ξ is the dirac function,
Figure BDA0003113137570000043
is the weight of the ith sample at the normalized k-th time, and
Figure BDA0003113137570000044
Figure BDA0003113137570000045
according to the importance function and Bayesian estimation theory, the following expression is obtained:
Figure BDA0003113137570000046
optionally, the method further includes:
setting the measured value of the ith particle to
Figure BDA0003113137570000047
The model estimate is
Figure BDA0003113137570000048
The error between the measured value and the estimated value is
Figure BDA0003113137570000049
The mean error is μ and the standard deviation is δ, giving the following expression:
Figure BDA00031131375700000410
wherein, w is the data number of the identification interval;
according to the law of large numbers, when w is sufficiently large, the mean value of error μ and the variance σ2Approximation is carried out according to the interval estimation principle of the parameters, the confidence coefficient is 95 percent, and the observation error boundary is obtained
Figure BDA00031131375700000411
Wherein U isaIs the upper quantile of a standard normal distribution;
according to the observation error boundary, adaptively adjusting observation noise as follows:
Figure BDA00031131375700000412
Figure BDA0003113137570000051
wherein e isy,kFor observation error estimation, σy,kIs the standard deviation of observation error, sigma, after adaptive adjustmenty,maxAnd setting the maximum noise standard deviation of the observed quantity.
The invention also provides a battery management system of the unmanned electric vehicle based on the adaptive particle filter, wherein the battery management system uses the state of charge estimation method, the battery management system comprises a system control part, a protection execution part, a series battery pack part and an upper computer information acquisition part, and the system control part comprises a battery information detection module, a main communication module and a state of charge estimation module;
the battery information detection module comprises a voltage detection module, a current detection module and a temperature detection module, the voltage detection module comprises a single voltage acquisition chip, the single voltage acquisition chip comprises 12 AD converters, a precise voltage reference, a high-voltage input multiplexer and a serial peripheral interface, and each chip can detect the single voltage of 12 batteries connected in series at most simultaneously, the voltage of the battery is 60V, the current detection module is used for converting the collected current signals into voltage signals, converting analog signals into digital signals through an AD converter, the temperature detection module is used for monitoring the temperature of the single battery in real time, supporting single bus communication and multipoint temperature detection, each temperature sensor is distinguished according to the serial number of a product solidified in the factory, and is used for reading temperature data in any sensor;
the main communication module comprises a serial communication interface, the serial communication interface is used for realizing asynchronous serial communication with other external equipment, the baud rate is programmable, a transmitter and a receiver of the serial communication interface have work enabling and interrupt control, and are double-buffered, and the work modes are diversified.
Optionally, the state of charge estimation module includes a main control flow module and a state of charge estimation program module;
the main control flow module is used for initializing all modules, detecting whether each module works normally or not, detecting whether the current of a main circuit of the system is normal or not, giving an alarm prompt if an overcurrent phenomenon occurs, and automatically disconnecting the main circuit by the battery management system when the overcurrent phenomenon is serious so as to stop discharging the battery pack; detecting the temperature of each single battery, judging whether the temperature of each single battery is too high, if so, giving an alarm signal, and when the temperature exceeds the highest allowable value, automatically disconnecting the main circuit by the battery management system; detecting the voltage of each single battery; judging whether the single battery has an overvoltage or low voltage phenomenon according to the voltage of the detected single battery, and if the overvoltage or low voltage phenomenon occurs, giving an alarm signal or directly disconnecting the main circuit according to the degree of the overvoltage or low voltage; estimating the state of charge of the battery according to the detected battery information; the detected battery information, alarm information and state of charge estimation information are transmitted to the upper computer information acquisition part and are displayed through a display;
the SOC estimation program module is used for detecting voltage, current and temperature information of the battery and judging the current running state of the electric automobile, if the automobile stops running for more than two hours, the chemical reaction in the battery tends to be stable, the battery management system adopts the collected battery information and calculates the initial SOC value of the battery according to an open-circuit voltage method; if the automobile stops running for less than two hours, the internal state of the battery does not tend to be stable due to the influence of polarization effect, and the state of charge value before the automobile stops running needs to be selected as the current initial value; the battery management system judges whether the state of charge of the battery is in a normal working interval or not according to the obtained initial value of the state of charge, and if the state of charge is less than 10%, the battery management system gives corresponding alarm information according to the current state of charge value; if the state of charge is more than 10%, the battery management system identifies basic parameters of a used battery model according to the current state of charge value, estimates the state of charge value at the current moment by adopting a Kalman filtering algorithm, and gives corresponding alarm information according to the estimated state of charge value at the current moment; judging whether the currently estimated state of charge value is less than 10% or the terminal voltage is lower than 2.1V, and if the two conditions are not met, estimating the next moment by the battery management system; if either condition is true, the battery management system stops discharging.
The invention has the following beneficial effects:
the technical scheme provided by the invention combines the problem of battery state of charge estimation, fully utilizes the identified model information, and adaptively adjusts the state noise and observation noise of the model according to the difference between the model estimation value and the observation value, thereby achieving the purpose of improving the accuracy, speed and robustness of the battery state of charge estimation. Meanwhile, a hardware platform of the power battery management system of the electric automobile is built. The complete hardware platform comprises main functions of battery parameter detection, information display, safety alarm, battery state of charge estimation and the like. And verifying the lithium battery pack algorithm through the established hardware platform.
Drawings
Fig. 1 is a flowchart of an SOC estimation algorithm according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of HPPC test data according to a first embodiment of the present invention.
Fig. 3a-3b are schematic diagrams of the parameter optimization results of the 1 st-order RC model according to the first embodiment of the present invention.
Fig. 4a-4b are schematic diagrams of the parameter optimization results of the 1 st order RC hysteresis model according to the first embodiment of the present invention.
Fig. 5a-5b are schematic diagrams of the parameter optimization results of the 2 nd-order RC model according to the first embodiment of the present invention.
Fig. 6a-6b are schematic diagrams of the parameter optimization results of the 2 nd order RC hysteresis model according to the first embodiment of the present invention.
Fig. 7a-7b are schematic diagrams of the optimization results of the 3 rd order RC model parameters according to the first embodiment of the present invention.
Fig. 8a-8b are schematic diagrams of the parameter optimization results of the 3 rd order RC hysteresis model according to the first embodiment of the present invention.
Fig. 9a is a schematic diagram of SOC estimation values according to a first embodiment of the present invention.
Fig. 9b is a schematic diagram of an SOC estimation error according to a first embodiment of the present invention.
Fig. 10a is a schematic diagram of an SOC estimation value of a conventional particle filter according to an embodiment of the present invention.
Fig. 10b is a schematic diagram of an SOC estimation value of adaptive filtering according to an embodiment of the present invention.
Fig. 11 is a block diagram of a battery management system according to a second embodiment of the present invention.
Fig. 12 is a schematic diagram of a main control flow provided by the second embodiment of the present invention.
Fig. 13 is a schematic diagram of a SOC estimation process according to a second embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following describes in detail a state of charge estimation method based on adaptive particle filtering and a battery management system provided by the present invention with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart of an SOC estimation algorithm according to an embodiment of the present invention. The embodiment provides an electric vehicle SOC estimation method based on adaptive particle filtering, which comprises the steps of a lithium battery model, parameter identification, adaptive particle filtering analysis, algorithm verification and the like, wherein the lithium battery model comprises an ohmic internal resistance module, an RC network module and a hysteresis module; the self-adaptive particle filter analysis comprises the steps of determining an observation error boundary and self-adaptively adjusting an observation error; and the algorithm verification comprises algorithm convergence speed and algorithm precision verification.
In the embodiment, an equivalent circuit model is used for modeling the lithium battery, the equivalent circuit comprises 3 common modules of an ohmic internal resistance module, an RC network module and a hysteresis module, and the ohmic internal resistance module and the internal resistance R0 are generated by electrode materials, diaphragm resistors and the movement resistance of electrolyte to charges. The RC network module has polarization phenomenon when the battery is used, and the embodiment uses a polarization capacitor Cp and a polarization resistor RpThe resulting RC network simulates this process. When modeling a battery, RC network series is typically used to improve model accuracy. However, since the RC network of 4 th order or more significantly increases the amount of calculation and the improvement in model accuracy is small, the RC network of 3 rd order or less is generally used. Ith RC networkThe relationship between terminal voltage and resistance and capacitance is as follows:
Upi,k+1＝exp(-Δt/τi)Upi,k+Rp(1-exp(-Δt/τi))Ik
in the formula, Δ t is a sampling period; tau isi＝RiCiIs a time constant; u shapepi,kTerminal voltage of ith RC network at the moment k; rpIs a polarization resistance; i iskRC network current at time k.
The hysteresis module is used for reflecting the delay phenomenon of the voltage change of the battery relative to the current change. The formula for the hysteresis module:
hk+1＝exp(-|κik|Δt)hk+(1-exp(-|κik|Δt))H
in the formula, hkIs the lag voltage at time k; kappa is an attenuation factor; i.e. ikIs the current at time k; h is the maximum hysteresis voltage.
By using the modules, 6 models of 1-order RC, 2-order RC, 3-order RC and hysteresis models are respectively established, the 1-order RC and the hysteresis models are shown in fig. 1, and the 2-order and 2-order models are connected with RC networks with corresponding quantity in series on the basis of the 1-order model.
The 1 st order RC model equation is:
Ut,k＝Uocv(zk)-IkR0-Up1,k
in the formula of Ut,kFor the value of the battery output voltage at time k, zkThe SOC value of the battery at the moment k; r0Is a resistance; i iskThe current value of the battery at the moment k; u shapeocvIs an open circuit voltage.
To ensure fitting accuracy, an nth order polynomial is used herein to fit UocvZ relation, i.e
Figure BDA0003113137570000091
In the formula, alphajAre fitting coefficients.
The 1 st order RC lag model equation is:
Ut,k＝Uocv(zk)-IkR0-Up1,k+hk
the 2 nd order RC model and the RC lag model equation are respectively:
Ut,k＝Uocv(zk)-IkR0-Up1,k-Up2,k
Ut,k＝Uocv(zk)-IkR0-Up1,k-Up2,k+hk
the 3 rd order RC model and the RC lag model are respectively as follows:
Ut,k＝Uocv(zk)-IkR0-Up1,k-Up2,k-Up3,k
Ut,k＝Uocv(zk)-IkR0-Up1,k-Up2,k-Up3,k+hk
fitting a pair using a test method and a polynomial
Figure BDA0003113137570000092
And identifying, wherein the test is required to be carried out under the constant temperature condition of 25 ℃, standing for 2h after the battery is fully charged to enable the battery to be in a stable state, then discharging for 5min, discharging the battery to the discharge capacity of 1C, standing for 5min again for the stable state, and recording the maximum open-circuit voltage during standing as the open-circuit voltage under the SOC. Repeating the steps until the discharge is finished, obtaining test data under different SOC values, and obtaining U by polynomial fittingocv～zkThe relation is as follows:
Figure BDA0003113137570000093
FIG. 2 is a schematic diagram of HPPC test data according to a first embodiment of the present invention. For 1-order, 2-order and 3-order RC and hysteresis models, firstly, Hybrid pulse power performance (HPPC) tests are carried out, then, a particle swarm algorithm is used for searching an optimal solution, the root mean square of the voltage error of the model terminal is taken as an adaptive function, and the equivalent model of the lithium battery is selected by comprehensively considering the model error and the calculated quantity. The parameter identification is performed with SOC at intervals of every 10%, and the HPPC test data is shown in FIG. 2.
Fig. 3a-3b are schematic diagrams of the parameter optimization results of the 1 st-order RC model according to the first embodiment of the present invention. Fig. 4a-4b are schematic diagrams of the parameter optimization results of the 1 st order RC hysteresis model according to the first embodiment of the present invention. Fig. 5a-5b are schematic diagrams of the parameter optimization results of the 2 nd-order RC model according to the first embodiment of the present invention. Fig. 6a-6b are schematic diagrams of the parameter optimization results of the 2 nd order RC hysteresis model according to the first embodiment of the present invention. Fig. 7a-7b are schematic diagrams of the optimization results of the 3 rd order RC model parameters according to the first embodiment of the present invention. Fig. 8a-8b are schematic diagrams of the parameter optimization results of the 3 rd order RC hysteresis model according to the first embodiment of the present invention. In the particle swarm optimization, the updating of the particle speed is influenced by the self speed, the self historical optimal position and the population optimal position, and the updating is closed to the self historical optimal position and the population optimal position. And searching the optimal parameters of each model by using a particle swarm optimization algorithm. By calculating the average value of the root-mean-square error of the terminal voltage, the highest model accuracy is a 3-order RC lag model, the next model accuracy is a 1-order RC lag model, the difference between the average values of the two models is about 0.8mV, the model accuracy and the calculated amount are comprehensively considered, and the 1-order RC lag model is selected as the lithium battery model.
According to UocvZ model, this embodiment selects the State vector kx＝(Up1，k，hk，zk)TThe lithium battery equivalent circuit model and the open-circuit voltage equation are discretized, and the state transfer equation and the observation equation are respectively strong. The method comprises the following specific steps:
Figure BDA0003113137570000101
Ut,k＝Uocv(zk)-IkR0-Up1,k+hk+vk
in the formula, CaIs the available capacity of the battery; eta is approximately equal to 1 and is a coulomb coefficient; o ° ok、νkProcess noise and observation noise, respectively.
According to the above formula, R0、Up1,k、hkKnown through the process of parameter identification, Ik、Ut,kCan be measured in real time, and v is adjusted by a particle filtering methodkMake Uocv(zk) And is more accurate.
In this embodiment, a certain nonlinear space equation is written as:
Figure BDA0003113137570000102
in the formula, xk、ykRespectively are a state vector and an observed quantity at the moment k; f (), h () are both known functions.
The posterior distribution of the system state is p (x) in this embodimentk|yk) But it does not have a standard form and is difficult to sample. Using the importance sampling method, an easily sampled probability distribution q (x) is introducedk|yk) Requires q (x)k|yk) Also defined in the state space, over a range of values greater than p (x)k|yk)。q(xk|yk) Called importance function, and collecting N samples under the importance function to obtain a sample set
Figure BDA0003113137570000111
The embodiment introduces an importance function into the posterior distribution
Figure BDA0003113137570000112
In the formula, xi is a dirac function;
Figure BDA0003113137570000113
is the weight of the ith sample at the k moment after normalization, an
Figure BDA0003113137570000114
In order to realize the recursive calculation of the filtering process, the importance function is deformed and combined with the Bayesian estimation theory, so that the importance function can be obtained
Figure BDA0003113137570000115
After the algorithm is iterated for a plurality of times, a particle degradation phenomenon occurs, namely, particles with large weights keep advantages, even become larger, the weights of other particles are smaller and smaller, useless calculation is generated, and estimation accuracy is reduced. To solve this problem, a particle resampling is required.
The method is improved on the basis of the adaptive particle filtering, and the observation noise is also adaptively adjusted. The improved idea is as follows: and determining the error range of the model when the equivalent model is established, substituting the state estimation value into the observation equation to obtain the observation estimation value, wherein the estimation error of the observation value is also within a certain range, and if the error of the observation value is larger than the error of the model, the algorithm is quickly converged by needing larger observation noise variance.
1) Determining observation error boundaries
The observed error can be considered as following normal distribution, and the measured value of the ith particle is recorded as
Figure BDA0003113137570000116
The model estimate is
Figure BDA0003113137570000117
Error between measured value and estimated value
Figure BDA0003113137570000118
The mean error value is recorded as mu, the standard deviation is recorded as delta, then
Figure BDA0003113137570000121
In the formula, w is the number of data in the identification interval.
According to bigLaw of numbers, when w is large enough, the mean and equation of the observation error can use μ and σ2Approximate substitution is carried out, according to the interval estimation principle of the parameters, the confidence coefficient is 95 percent, and the observation error boundary is obtained
Figure BDA0003113137570000122
Wherein U isaIs the upper quantile of a standard normal distribution.
2) Adaptive adjustment of observation error
And substituting the state estimation value into an observation equation to obtain possible sources of errors of the observation estimation value, wherein the possible sources of the errors comprise state noise, observation noise, initial value errors and the like. Generally speaking, the state noise and the observation noise are in a certain range, and other interference such as an initial value error may cause a large error, and the observation noise is adaptively adjusted to be within the observation error boundary set in the above
Figure BDA0003113137570000123
Figure BDA0003113137570000124
In the formula, ey,kIs an observation error estimation value; sigmay,kIs the standard deviation of observation error after adaptive adjustment; sigmay,maxAnd setting the maximum noise standard deviation of the observed quantity.
The two equations use the average error value as a criterion to prevent the jitter interference.
Fig. 9a is a schematic diagram of SOC estimation values according to a first embodiment of the present invention. Fig. 9b is a schematic diagram of an SOC estimation error according to a first embodiment of the present invention. The initial true value of SOC is set to 0.80 and the estimated SOC value is set to 0.50, i.e. the initial error reaches 0.30, and the SOC value is estimated using the conventional particle filter method and the adaptive particle filter method proposed herein, respectively, as shown in fig. 4. Under the condition that the initial error of the SOC of the battery is large, the proposed adaptive particle filter converges to be near the true value in about 30s, and the traditional particle filter converges to be near the true value after 24min, so that the adaptive particle filter provided by the invention has obvious advantages in the aspects of convergence speed and robustness. The adaptive filtering provided by the invention realizes the adaptive adjustment of observation noise according to the observation error by comparing the observation error value with the set error boundary on the basis of the existing adaptive algorithm.
Fig. 10a is a schematic diagram of an SOC estimation value of a conventional particle filter according to an embodiment of the present invention. Fig. 10b is a schematic diagram of an SOC estimation value of adaptive filtering according to an embodiment of the present invention. In order to verify the SOC estimation precision of the algorithm provided by the invention, the SOC estimation interval is set to be 0.80-0.20, the SOC initial value is set to be 0.70, and namely, an initial error of 0.10 exists. The SOC is estimated using conventional particle filtering and the adaptive particle filtering proposed herein, respectively, and the resulting estimation value and estimation error are shown in fig. 5.
In order to compare the estimation accuracy of the two algorithms more accurately, the statistical data of the SOC estimation of the two algorithms are shown in table 1.
TABLE 1 SOC estimation statistics for two algorithms
Figure BDA0003113137570000131
The SOC estimation precision of the two algorithms is higher, and both the SOC estimation precision are not more than 3%, but the precision of the adaptive particle filter estimation algorithm is higher; the estimation error of the adaptive filtering is more stable than that of the traditional method; from the convergence time point of view, the self-adaptive algorithm greatly improves the convergence speed. The advantages of the adaptive algorithm in convergence accuracy and convergence speed are derived from the adaptive adjustment of the adaptive algorithm to the state noise and the observation noise, so that the state noise and the observation noise can adapt to error changes caused by environment and other factors.
The technical scheme provided by the embodiment combines the problem of battery state of charge estimation, fully utilizes the identified model information, and adaptively adjusts the state noise and observation noise of the model according to the difference between the model estimation value and the observation value, thereby achieving the purpose of improving the accuracy, speed and robustness of the battery state of charge estimation. Meanwhile, a hardware platform of the power battery management system of the electric automobile is built. The complete hardware platform comprises main functions of battery parameter detection, information display, safety alarm, battery state of charge estimation and the like. And verifying the lithium battery pack algorithm through the established hardware platform.
Example two
Fig. 11 is a block diagram of a battery management system according to a second embodiment of the present invention. The present embodiment provides a battery management system for an unmanned electric vehicle based on adaptive particle filtering, where the battery management system uses the state of charge estimation method provided in the first embodiment, and the battery management system mainly includes four major components: the system comprises a system I control part, a protection II execution part, a series battery pack III part and an upper computer IV information acquisition part.
The battery management system provided by this embodiment uses a DSP produced by TI corporation as a main control chip of a system control part, and further includes peripheral devices such as a battery voltage, a current, a temperature detection module, a keyboard module, a display module, an alarm module, and a CAN bus driver. The protection execution part comprises a main circuit drive module, an I/O port extension consisting of a 51 singlechip and 8255, a CAN controller and a drive and overshoot over-discharge protection circuit thereof and the like. The PC is used as an upper computer and collects monitoring data from the main control DSP so as to monitor the control system in real time. The CAN bus CAN realize the communication and information exchange between the system control circuit and the protection execution, and the SCI bus is used for realizing the information exchange between the PC (upper computer) and the system control circuit (lower computer).
The system control part comprises a battery information detection module, a main communication module and a state of charge estimation module, wherein the battery information detection module comprises a voltage detection module, a current detection module and a temperature detection module.
(1) Voltage detection module
Electric automobile is at the operation in-process, and its operational environment, road conditions are complicated changeable, and this makes on-vehicle power battery's monomer voltage amplitude change greatly, and external environment's high frequency interference is very serious, and the response is slow, the low conventional opto-coupler switching collection mode of precision can not be suitable for. The management system selects the single voltage acquisition chip of the series battery pack released by the Linear company, and is internally provided with 12 AD converters, a precise voltage reference, a high-voltage input multiplexer and an SPI serial interface, and each chip can detect the single voltage of 12 series battery packs at most simultaneously, and the voltage of the battery packs can reach 60V. Besides, the functions of battery power equalization, high-temperature protection, overshoot and over-discharge monitoring and the like can be realized.
(2) Current detection module
The module has the function of converting the collected current signals into voltage signals, and converting analog signals into digital signals which can be read by the DSP through AD conversion.
(3) Temperature detection module
When the lithium iron phosphate battery works, if the temperature is too high, dangerous accidents such as battery combustion and explosion can be caused. Therefore, a temperature detection module must be additionally arranged to monitor the temperature of the single battery in real time. The system not only supports single bus communication and realizes the function of multipoint temperature detection, but also can distinguish each temperature sensor according to the product serial number of factory solidification, and is convenient for reading the temperature data in any sensor.
The host communication module provided by the embodiment can conveniently realize asynchronous serial communication with other external devices through the SCI interface module of the host communication module, and the baud rate is programmable. The transmitter and receiver of the SCI not only have operation enable and interrupt control, but also are double buffered, with a variety of operating modes.
The SOC estimation of the battery management system provided by the present embodiment includes a main control flow design and an SOC estimation program design.
(1) Design of main control flow
Fig. 12 is a schematic diagram of a main control flow provided by the second embodiment of the present invention. The main control flow is a program which is circularly executed on the whole, detects the information of the battery in real time and realizes the estimation of the SOC state of the battery. After the system is powered on, all modules in the system are initialized, including initialization of all detection modules, a communication module, a timer and SOC estimation parameters, and whether all the modules work normally is detected. Entering a main circulation part after initialization is finished, firstly, detecting whether the current of a main circuit of the system (namely the current of a series battery pack) is normal, if so, giving an alarm to prompt, and if serious, automatically disconnecting the main circuit by the system to stop discharging the battery pack; secondly, detecting the temperature of each single battery, and judging whether the temperature of each single battery is too high, if so, giving an alarm signal, and when the temperature exceeds the maximum allowable value, automatically disconnecting the main circuit by the system; thirdly, detecting the voltage of each single battery; fourthly, judging whether the single battery has overvoltage or low voltage according to the detected voltage of the single battery, and if so, giving an alarm signal or directly disconnecting the main circuit according to the degree of the overvoltage or the low voltage; fifthly, also being a core part of the system, namely, estimating the SOC of the battery according to the detected battery information; and sixthly, after the estimation is finished, transmitting the detected battery information, alarm information and SOC estimation information to an upper computer system, and displaying the information through a liquid crystal display.
(2) SOC estimation programming
Fig. 13 is a schematic diagram of a SOC estimation process according to a second embodiment of the present invention. When a battery SOC estimation program starts, a battery management system firstly detects information such as voltage, current and temperature of a battery, then the system needs to judge the running state of the electric automobile at the moment, if the stop (the automobile stops running) is more than two hours, the chemical reaction in the battery tends to be stable, the system adopts the collected battery information, and the SOC initial value of the battery is calculated according to an open-circuit voltage method; if the shutdown is less than two hours, the internal state of the battery does not tend to be stable due to the influence of the polarization effect, and the SOC value before the shutdown is required to be selected as the current initial value. Then, in order to ensure the safety of the battery and the normal running of the electric automobile, the system judges whether the SOC of the battery is in a normal working interval according to the obtained SOC initial value, if the SOC is less than 10 percent at the moment, the system gives corresponding alarm information according to the current SOC value, for example, a driver is reminded that the battery is insufficient in electric quantity, the driver is reminded to stop charging and the like immediately, and meanwhile, the discharging is finished because the SOC value of the battery is too low, so that the electric automobile stops running; if the SOC is more than 10%, the system identifies basic parameters of the used battery model according to the current SOC value, then adopts a Kalman filtering algorithm to estimate the SOC value at the current moment, and then gives corresponding alarm information according to the estimated SOC value at the current moment, such as information for reminding a driver of the residual condition of the battery power and the like. Finally, judging whether the current estimated SOC value is less than 10% or the terminal voltage is lower than 2.1V, if the two conditions are not satisfied, estimating the next moment by the system; if one of the conditions is met, the system stops discharging, so that the safety of the battery is ensured.
The technical scheme provided by the embodiment combines the problem of battery state of charge estimation, fully utilizes the identified model information, and adaptively adjusts the state noise and observation noise of the model according to the difference between the model estimation value and the observation value, thereby achieving the purpose of improving the accuracy, speed and robustness of the battery state of charge estimation. Meanwhile, a hardware platform of the power battery management system of the electric automobile is built. The complete hardware platform comprises main functions of battery parameter detection, information display, safety alarm, battery state of charge estimation and the like. And verifying the lithium battery pack algorithm through the established hardware platform.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.

Claims (5)

1. A self-adaptive particle filter-based unmanned electric vehicle state of charge estimation method is characterized by comprising the following steps:
the lithium battery is modeled by using an equivalent circuit model, the equivalent circuit comprises an ohmic internal resistance module, an RC network module and a hysteresis module, and the relationship between the voltage of the ith RC network terminal and the resistance and the capacitance is as follows:
Upi,k+1＝exp(-Δt/τi)Upi,k+Rp(1-exp(-Δt/τi))Ik
where Δ t is the sampling period, τi＝RiCiIs a time constant, Upi,kTerminal voltage of i-th RC network at time k, RpIs a polarization resistance, IkRC network current at the k moment;
the calculation formula of the hysteresis module is as follows:
hk+1＝exp(-|κik|Δt)hk+(1-exp(-|κik|Δt))H
wherein h iskLag voltage at time k, k attenuation factor, ikCurrent at time k, H is the maximum hysteresis voltage;
fitting open circuit voltage U using nth order polynomialocvThe relationship with the state of charge value z is as follows:
Figure FDA0003113137560000011
wherein alpha isjIs a fitting coefficient;
using experimental and polynomial fitting pairs
Figure FDA0003113137560000012
Identifying to obtain test data under different charge state values;
fitting open-circuit voltage U by using polynomial according to test data under different charge state valuesocvThe relationship with the state of charge value z is as follows:
Uocv＝117z7-461.5z6+743.7z5-634.2z4+308.5z3-85.73z2+12.92z+2.428
testing the capability characteristics of the hybrid power pulse, and searching the optimal parameters of each model by using a particle swarm algorithm;
selecting a state vector kx＝(Up1，k，hk，zk)TDiscretizing the equivalent circuit model and the open-circuit voltage equation of the lithium battery to obtain a state transfer equation and an observation equation,the calculation formula is as follows:
Figure FDA0003113137560000013
Ut,k＝Uocv(zk)-IkR0-Up1,k+hk+vk
wherein, CaIs the available capacity of the battery, eta is the coulomb coefficient, okIs process noise, vkTo observe noise, Ut,kFor the value of the battery output voltage at time k, zkIs the state of charge, R, of the battery at time k0Is a resistance, IkThe current value of the battery at time k, UocvIs an open circuit voltage;
calculating a particle weight, and performing normalization processing on the particle weight;
and estimating the state of the battery to obtain a state of charge estimated value.
2. The unmanned electric vehicle state of charge estimation method based on adaptive particle filtering of claim 1, wherein the step of calculating the particle weight and normalizing the particle weight comprises:
setting a nonlinear space equation as follows:
Figure FDA0003113137560000021
wherein x iskIs the state vector at time k, ykF () and h () are known functions which are observed quantities at the time k;
setting the posterior distribution of the system state to p (x)k|yk) And a probability distribution q (x)k|yk) Probability distribution q (x)k|yk) Collecting N samples according to the importance function to obtain a sample set
Figure FDA0003113137560000022
Introducing the importance function into the posterior distribution to obtain the following expression:
Figure FDA0003113137560000023
where ξ is the dirac function,
Figure FDA0003113137560000024
is the weight of the ith sample at the normalized k-th time, and
Figure FDA0003113137560000025
Figure FDA0003113137560000026
according to the importance function and Bayesian estimation theory, the following expression is obtained:
Figure FDA0003113137560000027
3. the unmanned electric vehicle state of charge estimation method based on adaptive particle filtering of claim 2, further comprising:
setting the measured value of the ith particle to
Figure FDA0003113137560000031
The model estimate is
Figure FDA0003113137560000032
The error between the measured value and the estimated value is
Figure FDA0003113137560000033
The mean error is μ and the standard deviation is δ, giving the following expression:
Figure FDA0003113137560000034
wherein, w is the data number of the identification interval;
according to the law of large numbers, when w is sufficiently large, the mean value of error μ and the variance σ2Approximation is carried out according to the interval estimation principle of the parameters, the confidence coefficient is 95 percent, and the observation error boundary is obtained
Figure FDA0003113137560000035
Wherein U isaIs the upper quantile of a standard normal distribution;
according to the observation error boundary, adaptively adjusting observation noise as follows:
Figure FDA0003113137560000036
Figure FDA0003113137560000037
wherein e isy,kFor observation error estimation, σy,kIs the standard deviation of observation error, sigma, after adaptive adjustmenty,maxAnd setting the maximum noise standard deviation of the observed quantity.
4. An unmanned electric vehicle battery management system based on adaptive particle filtering is characterized in that the battery management system uses the state of charge estimation method according to any one of claims 1 to 3, the battery management system comprises a system control part, a protection execution part, a series battery pack part and an upper computer information acquisition part, and the system control part comprises a battery information detection module, a main communication module and a state of charge estimation module;
the battery information detection module comprises a voltage detection module, a current detection module and a temperature detection module, the voltage detection module comprises a single voltage acquisition chip, the single voltage acquisition chip comprises 12 AD converters, a precise voltage reference, a high-voltage input multiplexer and a serial peripheral interface, and each chip can detect the single voltage of 12 batteries connected in series at most simultaneously, the voltage of the battery is 60V, the current detection module is used for converting the collected current signals into voltage signals, converting analog signals into digital signals through an AD converter, the temperature detection module is used for monitoring the temperature of the single battery in real time, supporting single bus communication and multipoint temperature detection, each temperature sensor is distinguished according to the serial number of a product solidified in the factory, and is used for reading temperature data in any sensor;
the main communication module comprises a serial communication interface, the serial communication interface is used for realizing asynchronous serial communication with other external equipment, the baud rate is programmable, a transmitter and a receiver of the serial communication interface have work enabling and interrupt control, and are double-buffered, and the work modes are diversified.
5. The unmanned electric vehicle battery management system based on adaptive particle filtering of claim 4, wherein the state of charge estimation module comprises a main control flow module and a state of charge estimation program module;
the main control flow module is used for initializing all modules, detecting whether each module works normally or not, detecting whether the current of a main circuit of the system is normal or not, giving an alarm prompt if an overcurrent phenomenon occurs, and automatically disconnecting the main circuit by the battery management system when the overcurrent phenomenon is serious so as to stop discharging the battery pack; detecting the temperature of each single battery, judging whether the temperature of each single battery is too high, if so, giving an alarm signal, and when the temperature exceeds the highest allowable value, automatically disconnecting the main circuit by the battery management system; detecting the voltage of each single battery; judging whether the single battery has an overvoltage or low voltage phenomenon according to the voltage of the detected single battery, and if the overvoltage or low voltage phenomenon occurs, giving an alarm signal or directly disconnecting the main circuit according to the degree of the overvoltage or low voltage; estimating the state of charge of the battery according to the detected battery information; the detected battery information, alarm information and state of charge estimation information are transmitted to the upper computer information acquisition part and are displayed through a display;
the SOC estimation program module is used for detecting voltage, current and temperature information of the battery and judging the current running state of the electric automobile, if the automobile stops running for more than two hours, the chemical reaction in the battery tends to be stable, the battery management system adopts the collected battery information and calculates the initial SOC value of the battery according to an open-circuit voltage method; if the automobile stops running for less than two hours, the internal state of the battery does not tend to be stable due to the influence of polarization effect, and the state of charge value before the automobile stops running needs to be selected as the current initial value; the battery management system judges whether the state of charge of the battery is in a normal working interval or not according to the obtained initial value of the state of charge, and if the state of charge is less than 10%, the battery management system gives corresponding alarm information according to the current state of charge value; if the state of charge is more than 10%, the battery management system identifies basic parameters of a used battery model according to the current state of charge value, estimates the state of charge value at the current moment by adopting a Kalman filtering algorithm, and gives corresponding alarm information according to the estimated state of charge value at the current moment; judging whether the currently estimated state of charge value is less than 10% or the terminal voltage is lower than 2.1V, and if the two conditions are not met, estimating the next moment by the battery management system; if either condition is true, the battery management system stops discharging.
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