CN111537887A - Hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristic - Google Patents
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Abstract
The invention discloses a hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristics, which comprises the following steps: carrying out a lithium iron phosphate power battery capacity test under each working condition; performing an open-circuit voltage hysteresis characteristic test of the lithium iron phosphate battery, and establishing a simplified open-circuit voltage hysteresis model; performing parameter identification on the selected equivalent circuit model; and optimizing an open-circuit voltage hysteresis model considering hysteresis characteristics by using a particle swarm optimization algorithm. The method improves the accuracy of the equivalent circuit model terminal voltage.
Description
Technical Field
The invention belongs to the field of battery state of charge estimation methods, and particularly relates to a hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristics.
Background
With the gradual popularization of new energy automobiles, the demand of the market for improving the performance of the new energy automobiles is continuously increased, the new energy automobiles are particularly concerned about the whole vehicle dynamic performance and the battery pack performance, and an accurate battery State of Charge (SOC) estimation method is taken as a key technology of the new energy automobiles, so that the new energy automobiles, especially hybrid automobiles, can select the most appropriate driving mode to ensure the vehicle dynamic performance and reduce the energy loss under different driving conditions. Meanwhile, the battery SOC is also a factor influencing the control strategy of the battery management system, and the phenomenon of overcharge and overdischarge of the battery can be prevented during charging and discharging, so that the cycle life of the battery is prolonged, and the probability of accidents is reduced. The method widely used and researched at present is a Kalman filtering method based on an equivalent circuit model, can estimate the SOC of a battery under the condition of noise and has certain robustness. However, at present, some new energy automobile power batteries use lithium iron phosphate power batteries, which have higher safety and lower cost, but for the equivalent circuit model, the open-circuit voltage has a hysteresis characteristic, which shows that the open-circuit voltages in different charging and discharging states under the same SOC are different, and the open-circuit voltage is smaller than the charging open-circuit voltage and larger than the discharging open-circuit voltage in a period of time when the charging and discharging states are switched, which seriously affects the accuracy of the end voltage of the equivalent circuit model, thereby reducing the accuracy of the kalman filter SOC estimation. Therefore, based on the problems of the lithium iron phosphate battery in the aspect of an equivalent circuit model, it is important to establish an open-circuit voltage modeling method considering hysteresis characteristics and optimize the established model so as to optimize the voltage accuracy.
Disclosure of Invention
In view of the defects of the prior art, the present invention aims to provide a hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristics, so as to solve the problem that in the prior art, the open-circuit voltage hysteresis characteristics of a ferric phosphate lithium battery cause the terminal voltage of an equivalent circuit model to be inaccurate, thereby affecting the SOC estimation accuracy; the method can reduce the terminal voltage error caused by the hysteresis problem by using a simpler model, and provides a model basis for the accurate estimation of the SOC.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristics, which comprises the following steps of:
1) carrying out a lithium iron phosphate power battery capacity test under each working condition;
2) performing an open-circuit voltage hysteresis characteristic test of the lithium iron phosphate battery, and establishing a simplified open-circuit voltage hysteresis model;
3) performing parameter identification on the selected equivalent circuit model;
4) and optimizing an open-circuit voltage hysteresis model considering hysteresis characteristics by using a particle swarm optimization algorithm.
Further, the step 1) specifically includes: and (3) carrying out capacity test on the lithium iron phosphate battery by using the battery test stand under different temperatures and charge-discharge multiplying powers to obtain the charge-discharge capacity of the battery under different environments.
Further, the step 2) specifically includes: performing an open-circuit voltage test of a charging state and an open-circuit voltage test of a discharging state of the lithium iron phosphate battery at different environmental temperatures and SOC (state of charge), and obtaining a hysteresis characteristic curve of the open-circuit voltage of the battery, wherein the SOC is obtained by calculating according to the capacity measured in the step 1) at different temperatures by using an ampere-hour integration method; the battery charging and discharging states are modeled separately, and the established simplified open-circuit voltage hysteresis characteristic model considering the influence of the temperature and the charging and discharging states is as follows:
and (3) discharging state: u shaped_op(T)＝λ1(T)*(Uc(T)-Ud(T))+Ud(T)
The charging state is as follows: u shapec_op(T)＝λ2(T)*(Uc(T)-Ud(T))+Ud(T)
In the formula of Ud_op(T) an open circuit voltage of a model established at the discharge state at a temperature T; u shapec_op(T) represents the open circuit voltage of the model established at the state of charge at temperature T; u shaped(T) is the open circuit voltage measured in the discharge state at temperature T; u shapec(T) is the open circuit voltage measured in the charged state at temperature T; lambda [ alpha ]1(T) and lambda2(T) is a ratio coefficient according to different temperaturesAnd the variation is also the coefficient to be optimized in the model.
Further, the step 3) specifically includes: selecting a Thevenin model as an equivalent circuit model, wherein the parameters of the equivalent circuit model comprise: internal resistance, polarization internal resistance describing polarization phenomenon, and capacitance; deriving a corresponding mathematical model from the selected model; and respectively carrying out charging pulse identification and discharging pulse identification tests at different temperatures and SOC (state of charge), and identifying equivalent circuit model parameters by using a genetic algorithm.
Further, the step 4) specifically includes: performing continuous DST charging and discharging working condition tests on the lithium iron phosphate battery at different temperature nodes in the step 3), deriving a model terminal voltage difference equation according to the equivalent circuit model, and combining the established open-circuit voltage hysteresis characteristic model to obtain a model terminal voltage value; respectively using the obtained model terminal voltage value and the accumulated error of the actual measured terminal voltage value of the DST working condition as the minimum optimization target under different temperature nodes, and using the particle swarm optimization algorithm to carry out lambda optimization on different temperatures1(T) and lambda2And (T) optimizing to obtain an optimal simplified open-circuit voltage hysteresis characteristic model.
The invention has the beneficial effects that:
the invention provides a simpler open-circuit voltage modeling method for simulating the hysteresis characteristic of the lithium iron phosphate battery, and optimizes a hysteresis model by using a particle swarm optimization algorithm, so that the accuracy of the voltage at the end of an equivalent circuit model is improved, and a model basis is provided for accurately estimating the SOC.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the structural connection of an exemplary battery test rack.
FIG. 3 is a first-order equivalent circuit model circuit diagram.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1, a hybrid system battery open-circuit voltage model optimization method considering hysteresis characteristics according to the present invention includes the following steps:
step 1: carrying out a lithium iron phosphate power battery capacity test under each working condition;
the method comprises the following steps of carrying out capacity test on the lithium iron phosphate battery by using a battery test stand under different temperatures and charge-discharge multiplying powers to obtain the charge-discharge capacity of the battery under different environments:
the battery test bench used in the test is composed of an upper computer, a charge and discharge test platform and a thermostat as shown in fig. 2, wherein the upper computer is a control unit of the bench, controls the current of the charge and discharge test platform and the temperature of the thermostat, measures and records the voltage of a test battery terminal, and the charge and discharge test platform is connected with the test battery through a positive and negative power line and a voltage measurement line. At present, the lowest normal working temperature of the lithium iron phosphate power battery of the new energy automobile is about-10 ℃, and the highest temperature is about 40 ℃, so the test temperature node is selected to be 40 ℃, 30 ℃, 20 ℃, 10 ℃, 0 ℃ and-10 ℃. At present, the maximum sustained discharge rate of the lithium iron phosphate battery for the vehicle is 10C, but the capacity and the cycle life of the battery are seriously reduced by high-rate discharge, so that the high-rate discharge condition is less, and the nodes of the discharge rates are divided into 0.1C, 0.3C, 0.5C, 1C, 2C, 5C and 10C. And the maximum charging rate of the lithium iron phosphate battery is about 2C, so the charging rate nodes are divided into 0.1C, 0.3C, 0.5C, 1C and 2C.
The discharge capacity test procedure was as follows: selecting a corresponding temperature node and a discharge rate node, carrying out 0.3C constant current charging on the battery to a cut-off voltage at the environmental temperature, then carrying out constant voltage charging to 0.05C, standing for 1 hour, and carrying out constant current discharging according to the selected discharge rate until the discharge cut-off voltage is reached to obtain a discharge capacity at the temperature and the discharge rate; and circulating the steps until all temperature and discharge rate node combinations are completed.
The charge capacity test procedure was as follows: selecting a corresponding temperature node and charging rate node combination, performing constant current discharge on the battery to a discharge cut-off voltage at 0.3 ℃ under the selected environmental temperature, standing for 1 hour, performing constant current charge to the charge cut-off voltage according to the selected charging rate, and then performing constant voltage charge to 0.05 ℃, wherein the current change during constant voltage charge is not considered, so that the capacity of a constant current charging section is only calculated according to the discharge capacity under the temperature and the charging rate; and circulating until all temperature and charging rate node combinations are completed.
Step 2: performing an open-circuit voltage hysteresis characteristic test of the lithium iron phosphate battery, and establishing a simplified open-circuit voltage hysteresis model;
when the lithium iron phosphate battery is at different environmental temperatures, the open-circuit voltage value of the lithium iron phosphate battery has a certain difference when the SOC is larger and smaller, so that the open-circuit voltage test of the charging state and the open-circuit voltage test of the discharging state of the lithium iron phosphate battery need to be carried out at different environmental temperatures and SOCs to obtain a hysteresis characteristic curve of the open-circuit voltage of the battery, and the test method is as follows:
selecting the temperature node in the step 1, after the temperature of the battery is the same as the ambient temperature, performing constant current and constant voltage on 0.3C of the lithium iron phosphate battery to a charging cut-off voltage, then performing constant voltage charging to 0.05C, wherein the battery is in a full charge state (SOC is 100%), the terminal voltage of the battery after standing for 1h is the open-circuit voltage in the state, then performing constant current discharging of 0.3C until the SOC is reduced by 5%, and after standing for 1h, the terminal voltage is the open-circuit voltage in a discharging state when the SOC is 95%, and by the circulation, performing constant current discharging to delta SOC is 5% every time, and after the open-circuit voltage when the SOC is 0% is recorded, finishing the open-circuit voltage discharging test under the temperature node. And then, fully charging the battery with a constant current and a constant voltage of 0.3C, discharging the battery with a constant current of 0.3C to a cut-off voltage after standing for 1h to ensure that the battery is in an electroless state, recording a terminal voltage after standing for 1h, then, carrying out constant current charging of 0.3C to ensure that the delta SOC is 5%, recording terminal voltage data as an open-circuit voltage after standing for 1h, circulating until the SOC is 100%, standing for 1h to obtain an open-circuit voltage value in a full-charge state, and finishing the test.
According to the method, an open-circuit voltage test is carried out under each temperature node, and battery open-circuit voltage hysteresis characteristic curves with different temperatures are obtained.
Considering that the hysteresis voltage change of the open-circuit voltage is different in the charging and discharging states, in order to obtain more accurate open-circuit voltage and larger degree of freedom of optimized parameters, the charging and discharging states of the battery are used as a model for distinguishing and separately modeling, and the established simplified open-circuit voltage hysteresis characteristic model considering the influences of the temperature and the charging and discharging states is as follows:
and (3) discharging state: u shaped_op(T)＝λ1(T)*(Uc(T)-Ud(T))+Ud(T)
The charging state is as follows: u shapec_op(T)＝λ2(T)*(Uc(T)-Ud(T))+Ud(T)
In the formula of Ud_op(T) represents the open circuit voltage of the model established at the discharge state at temperature node T; u shapec_op(T) represents the open circuit voltage of the model established at the state of charge at temperature T; u shaped(T) is the open circuit voltage measured in the discharge state at temperature T; u shapec(T) is the open circuit voltage measured in the charged state at temperature T; lambda [ alpha ]1(T) and lambda2And (T) is a ratio coefficient which changes with different temperatures and is also a coefficient to be optimized in the model.
And step 3: performing parameter identification on the selected equivalent circuit model;
selecting Thevenin model as equivalent circuit model, as shown in fig. 3, the parameters include: internal resistance, polarization internal resistance describing polarization phenomenon, and capacitance; a corresponding mathematical model is derived from the selected model. Respectively carrying out charging pulse identification and discharging pulse identification tests at different temperatures and SOC (system on chip), and identifying equivalent circuit model parameters by using a genetic algorithm; the specific method comprises the following steps:
establishing a first-order integer order equivalent circuit model transfer function as follows:
wherein Y(s) ═ UOCV(s)-Ut(s)，UOCV(s) is open circuit voltage, Ut(s) is terminal voltage;
through bilinear transformation, order
Obtaining:
wherein h is a sampling step length, and t is kh;
the differential equation of the internal resistance and the total voltage of the polarization ring is as follows:
the discharge state parameter identification test scheme is as follows:
selecting one temperature node in the step 1, carrying out constant-current and constant-voltage charging on the battery to a full-charge state, standing for 1h, then carrying out pulse discharging (discharging with 2C for 10s of constant current and standing for 40s), then discharging with C/3 constant current until SOC is reduced by 10%, standing for 1h, then carrying out pulse discharging, then discharging with C/3 constant current until SOC is reduced by 10%, circulating until SOC is reduced to 0%, discharging electric pulse parameter identification test at the temperature is finished, and then carrying out discharge state parameter identification test under all temperature nodes according to the method.
The charge state parameter identification test scheme is as follows:
selecting one temperature node in the step 1, performing constant current discharge on the battery until the discharge cut-off voltage is reached, standing for 1h, performing pulse charging (2C is used for continuous 10s constant current charging and standing for 40s), then performing constant current charging with C/3 until SOC rises by 10%, standing for 1h, then performing pulse charging, then performing constant current charging with C/3 until SOC rises by 10%, circulating until SOC is 100%, and ending the charging pulse parameter identification test at the temperature, and then performing the charging state parameter identification test at all temperature nodes according to the method.
Extracting pulse test battery terminal voltage data under different temperatures, charge-discharge states and SOC (system on chip) as identification data, respectively identifying parameters by using a genetic algorithm, wherein the total identification is carried out for 132 times, the time length of each section of data is 50s, and the fitness function of the genetic algorithm is as follows:
in the formula of Ut(k)＝UOCV(k) -y (k), which is the model terminal voltage; ginseng radix (Panax ginseng C.A. Meyer)The charging and discharging states of the battery are separately carried out in the number identification test without hysteresis characteristic, then UOCV(k) The open-circuit voltage measured by the corresponding temperature, charge-discharge state and open-circuit voltage test under SOC in the step 2; u shapet_r(k) The measured terminal voltage in the parameter identification test.
And 4, step 4: optimizing an open-circuit voltage model considering hysteresis characteristics by using a particle swarm optimization algorithm;
and (3) respectively carrying out continuous DST charging and discharging working condition tests on the lithium iron phosphate battery at different temperature nodes in the step (3), and combining the established open-circuit voltage hysteresis characteristic model according to a voltage difference equation Y (k) in the step (3) to obtain a model terminal voltage value. Respectively using the obtained model terminal voltage value and the accumulated error of the actual measured terminal voltage value of the DST working condition as the minimum optimization target under different temperature nodes, and using the particle swarm optimization algorithm to carry out lambda optimization on different temperatures1(T) and lambda2(T) optimizing to obtain an optimal simplified open-circuit voltage hysteresis characteristic model; the fitness function of the particle swarm optimization algorithm is as follows:
in the formula of UDST(k) For actually measuring the terminal voltage of the battery under the DST working condition, Uh(k) In order to use the terminal voltage value of the open-circuit voltage hysteresis model in the step 2, the calculation method comprises the following steps:
in a discharge state: u shapeh(k)＝Ud_op(T)-Y(k)＝λ1(T)*(Uc(T)-Ud(T))+Ud(T)-Y(k)
In the charging state: u shapeh(k)＝Uc_op(T)-Y(k)＝λ2(T)*(Uc(T)-Ud(T))+Ud(T)-Y(k)；
Wherein the open circuit voltage Ud(T)、Uc(T) linear interpolation is carried out by taking the SOC as a variable according to the measured value in the step 2. When the battery is in a standing state, the open-circuit voltage is calculated according to the previous state, and if the battery is in a discharging state before standing, the open-circuit voltage during standing is the open-circuit voltage in the discharging state.
And then, fitting the two curves obtained at different temperatures by using a polynomial. In the subsequent estimation of the SOC by Kalman filtering, a charging and discharging curve fitting formula corresponding to a temperature node closest to the ambient temperature is used, for example, when the ambient temperature is 22 ℃, a polynomial fitting formula based on the hysteresis characteristic curves of the lithium iron phosphate battery in a charging state and a discharging state corresponding to a temperature node of 20 ℃ is used for estimating the SOC.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (5)
1. A hybrid power system battery open-circuit voltage model optimization method considering hysteresis characteristics is characterized by comprising the following steps:
1) carrying out a lithium iron phosphate power battery capacity test under each working condition;
2) performing an open-circuit voltage hysteresis characteristic test of the lithium iron phosphate battery, and establishing a simplified open-circuit voltage hysteresis model;
3) performing parameter identification on the selected equivalent circuit model;
4) and optimizing an open-circuit voltage hysteresis model considering hysteresis characteristics by using a particle swarm optimization algorithm.
2. The hysteresis characteristic-considered hybrid power system battery open-circuit voltage model optimization method according to claim 1, wherein the step 1) specifically comprises: and (3) carrying out capacity test on the lithium iron phosphate battery by using the battery test stand under different temperatures and charge-discharge multiplying powers to obtain the charge-discharge capacity of the battery under different environments.
3. The hysteresis characteristic-considered hybrid power system battery open-circuit voltage model optimization method according to claim 1, wherein the step 2) specifically comprises: performing an open-circuit voltage test of a charging state and an open-circuit voltage test of a discharging state of the lithium iron phosphate battery at different environmental temperatures and SOC (state of charge), so as to obtain a hysteresis characteristic curve of the open-circuit voltage of the battery, wherein the SOC is obtained by calculating according to the capacity measured in the step 1) at different temperatures by using an ampere-hour integration method; the battery charging and discharging states are modeled separately, and the established simplified open-circuit voltage hysteresis characteristic model considering the influence of the temperature and the charging and discharging states is as follows:
and (3) discharging state: u shaped_op(T)＝λ1(T)*(Uc(T)-Ud(T))+Ud(T)
The charging state is as follows: u shapec_op(T)＝λ2(T)*(Uc(T)-Ud(T))+Ud(T)
In the formula of Ud_op(T) an open circuit voltage of a model established at the discharge state at a temperature T; u shapec_op(T) represents the open circuit voltage of the model established at the state of charge at temperature T; u shaped(T) is the open circuit voltage measured in the discharge state at temperature T; u shapec(T) is the open circuit voltage measured in the charged state at temperature T; lambda [ alpha ]1(T) and lambda2And (T) is a ratio coefficient which changes with different temperatures and is also a coefficient to be optimized in the model.
4. The hysteresis characteristic-considered hybrid power system battery open-circuit voltage model optimization method according to claim 3, wherein the step 3) specifically comprises: selecting a Thevenin model as an equivalent circuit model, wherein the parameters of the equivalent circuit model comprise: internal resistance, polarization internal resistance describing polarization phenomenon, and capacitance; deriving a corresponding mathematical model from the selected model; and respectively carrying out charging pulse identification and discharging pulse identification tests at different temperatures and SOC (state of charge), and identifying equivalent circuit model parameters by using a genetic algorithm.
5. The hysteresis characteristic-considered hybrid power system battery open-circuit voltage model optimization method according to claim 4, wherein the step 4) specifically comprises: respectively carrying out continuous DST charging and discharging working condition tests on the lithium iron phosphate battery at different temperature nodes in the step 3), and carrying out the continuous DST charging and discharging working condition tests according to the conditionsThe equivalent circuit model deduces a model terminal voltage differential equation and obtains a model terminal voltage value by combining the established open-circuit voltage hysteresis characteristic model; respectively using the obtained model terminal voltage value and the accumulated error of the actual measured terminal voltage value of the DST working condition as the minimum optimization target under different temperature nodes, and using the particle swarm optimization algorithm to carry out lambda optimization on different temperatures1(T) and lambda2And (T) optimizing to obtain an optimal simplified open-circuit voltage hysteresis characteristic model.
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