CN117799502B - Energy management method of UUV hybrid power system - Google Patents
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- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 38
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 6
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Abstract
The embodiment of the disclosure relates to an energy management method of a UUV hybrid power system. The embodiment of the disclosure can describe the mapping relation between the power supply output power and the complex working condition, thereby realizing the real-time aging quantification of the fuel cell and the lithium ion battery, and the constructed SOH prediction model can more accurately predict the attenuation of the fuel cell; and the attenuation interaction between the solid oxide fuel cell and the lithium ion battery can be better established by utilizing the discrete optimization method so as to decouple the service life competition between the two batteries, and the cruising ability of the UUV is improved based on the hybrid system.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of battery energy management, in particular to an energy management method of a UUV hybrid power system.
Background
With the increasing demand for mitigating the environmental impact of fossil fuels, the technological development of solid oxide fuel cells (Solid Oxide Fuel Cell, SOFC) has also progressed. Due to the dynamic nature of electrochemical power resources, such as Fuel Cells (FCs) and auxiliary energy storage systems (Energy Storage System, ESS), various energy management strategies (ENERGY MANAGEMENT STRATEGY, EMS) have been extensively developed and analyzed to achieve optimal power distribution. The fuel cell is expected to improve the endurance of unmanned submarine vehicles (Unmanned undersea vehicle, UUV). Hydrogen-oxygen fuel cells are commonly used on the seafloor, and present high cost and technical problems. However, almost all commercial fuel cells are designed for open space and are not suitable for subsea applications.
Currently, most commercial UUVs are battery powered. Batteries are a simple solution that can provide sufficient power for current applications. However, the energy density of batteries is limited, which limits the cruising ability of UUVs and limits the tasks they can be used for. Based on the endurance requirements of future UUVs, fuel cell power systems are becoming a promising technology for subsea energy. The hydrogen has a high heating value of up to 143 MJ/kg. The energy supplied by hydrogen is almost three times that of gasoline per kilogram. The fuel cell power system has advantages of low vibration noise, low carbon emission, and high energy conversion efficiency. The cell has a higher power density and faster response than a fuel cell, and can compensate for the disadvantages of the fuel cell. Therefore, a hybrid system (FCBS) composed of a solid oxide Fuel cell and a battery is considered suitable. Such hybrid systems typically reduce the size of the energy system, thereby reducing manufacturing costs, improving system efficiency, and extending durability. However, the degradation of the performance of fuel cells is a well-established problem. Therefore, in addition to efficiency and hydrogen consumption, the problem of life deterioration in the use of fuel cells must be solved, which is extremely susceptible to the operating environment.
Disclosure of Invention
In order to avoid the defects of the prior art, the application provides an energy management method of a hybrid power system of a UUV, which is used for solving the problems of fast performance degradation and fast service life degradation of the hybrid power system in the prior art.
According to an embodiment of the present disclosure, there is provided an energy management method of a hybrid power system of a UUV, the method including:
According to the mixing degree of a solid oxide fuel cell and a lithium ion battery in a hybrid power system, constructing a dimensionless parameter model;
Respectively constructing an electrochemical model, a degradation model, an internal resistance model and an SOH (state of health) prediction model based on the dimensionless parameter model; wherein, according to the electrochemical reaction formula of the solid oxide fuel cell, an electrochemical model is constructed; constructing a degradation model according to an aging formula of the solid oxide fuel cell; constructing an internal resistance model according to an equivalent circuit formula of the lithium ion battery; according to the battery cycle life test data, constructing an SOH prediction model;
constructing a viscous resistance model according to external structural parameters, navigation resistance and navigation speed of the UUV;
Obtaining hydrogen consumption cost and fuel cell degradation cost according to an electrochemical model and a degradation model, obtaining lithium ion battery aging cost according to an internal resistance model and an SOH prediction model, obtaining SOC (State of Charge) deviation cost according to a viscous resistance model, and constructing a cost function according to the hydrogen consumption cost, the fuel cell degradation cost, the lithium ion battery aging cost and the SOC deviation cost;
And optimizing the cost function by using a discrete optimization algorithm of the minimum-maximum game to obtain an optimal energy distribution scheme.
Further, the step of constructing the dimensionless parameter model according to the mixing degree of the solid oxide fuel cell and the lithium ion battery in the hybrid power system comprises the following steps:
based on the mixing degree of the solid oxide fuel cell and the lithium ion battery of the load power, constructing a dimensionless parameter model; wherein the equations for the dimensionless parametric model include:
（1）
（2）
（3）
（4）
（5）
（6）
（7）
（8）
（9）
（10）
In the method, in the process of the invention, Average power provided for SOFC,/>Is the low power of the power curve,/>For the first discharge time,/>For the first charging time,/>For the second discharge time,/>For the second charging time,/>As base power,/>For cruising power,/>For power hybridization,/>For maximum load power,/>Power provided for lithium batteries,/>Is specific power,/>Is of a specific time,/>For lithium battery energy consumption,/>For the purposes of energy consumption of the SOFC,Is the degree of energy hybridization.
Further, the step of constructing an electrochemical model according to an electrochemical reaction formula of the solid oxide fuel cell includes:
determining the oxidation reaction of H 2 based on the mole fraction of the substance and the SOFC temperature according to the Gibbs free energy change to obtain a Nernst voltage;
according to a Butler-Volmer equation of the SOFC working temperature, obtaining an activation overvoltage;
According to the SOFC working temperature and film thickness, ohmic overvoltage is obtained;
Depending on the depletion of the reactant concentration at the reaction site, to obtain a concentration overvoltage;
removing the deactivation overvoltage, ohmic overvoltage and concentration overvoltage from the nernst voltage to obtain a net voltage of the SOFC stack;
according to the electrochemical reaction between the hydrogen and the oxygen, the instantaneous hydrogen consumption is obtained;
based on the dimensionless parameter model, combining the net voltage of the SOFC stack and the instantaneous hydrogen consumption to obtain the net power of the SOFC stack, and constructing an electrochemical model according to the net power of the SOFC stack.
Further, the degradation model is:
（11）
In the method, in the process of the invention, For the first model parameter,/>For the second model parameter,/>For the third model parameter,/>For the fourth model parameter,/>For the time of SOFC operation under high power conditions,/>For the time of operation of the SOFC at idle,For SOFC start-stop sequence number,/>For variation of SOFC output power,/>Is a correction factor.
Further, the internal resistance model is:
（12）
In the method, in the process of the invention, Is open circuit voltage,/>Is equivalent internal resistance,/>Is the battery capacity; /(I)Is the total electric quantity of the battery.
Further, the step of constructing the SOH prediction model according to the battery cycle life test data includes:
constructing a life data pool according to the battery cycle life test data, and counting the quantity and severity of capacity recovery effects in the life data pool by using a rain flow counting method;
Correcting the counted life data pool, and constructing an SOH prediction model according to the corrected life data pool; the SOH prediction model is as follows:
（13）
In the method, in the process of the invention, For the life of the battery in the current life cycle,/>Life of battery in next life cycle,/>Is a decrease in battery life,/>To quantify the correction factor for the decrease in battery life.
Further, the equation for the viscous resistance model includes:
（14）
（15）
（16）
（17）
（18）
（19）
（20）
In the method, in the process of the invention, For cruising resistance,/>Is the sailing resistance coefficient,/>Is the mass density of sea water,/>Is the area of the underwater vehicle,/>For sailing speed,/>Is an effective drag coefficient,/>Is the friction coefficient,/>Is Reynolds number/>For the length of UUV,/>Is the viscosity coefficient,/>Is a shape factor,/>Is the diameter of UUV,/>For Nernst potential,/>For efficiency of proposal, R is the range of UUV.
Further, the hydrogen consumption cost is:
（21）
In the method, in the process of the invention, For instantaneous hydrogen consumption,/>Change in stored energy for battery,/>Is the low heating value of hydrogen/(As an efficiency factor,/>Is the price of hydrogen;
the degradation cost of the fuel cell is as follows:
（22）
In the method, in the process of the invention, Investment for fuel cell,/>Maximum power for SOFC;
The aging cost of the lithium ion battery is as follows:
（23）
In the method, in the process of the invention, For lithium ion battery health status change,/>For the price of the battery,/>Is the battery capacity;
The SOC deviation cost is:
（24）
In the method, in the process of the invention, Is SOC weight coefficient,/>For reference SOC,/>Is the actual SOC;
the cost function constructed is:
（25）
In the method, in the process of the invention, For hydrogen consumption cost,/>For fuel cell degradation cost,/>For the aging cost of lithium ion battery,/>Is the SOC offset cost.
Further, the step of optimizing the cost function by using the discrete optimization algorithm of the min-max game to obtain the optimal energy allocation scheme includes:
The energy management system selects one strategy from the strategy set to minimize a cost function, thereby obtaining robustness of response to different driving conditions; the energy management system is a nonlinear discrete time system and is described as follows:
（26）
In the method, in the process of the invention, Is the system state/>Is the control input of the power system,/>Is the predicted speed;
The constraint should be set as:
（27）
In the method, in the process of the invention, Is the minimum SOC permitted,/>Is the minimum SOC permitted,/>For minimum current variation of SOFC,/>For maximum current change of SOFC,/>Is the minimum allowable output power of the fuel cell system,/>Is the maximum allowable output power of the fuel cell system;
Taking an energy management system and a prediction speed as two parties, and making a decision by using a method based on a minimum-maximum game; wherein, the decision process is:
（28）
In the method, in the process of the invention, As a cost function,/>Decisions made for energy management systems,/>As a set of policies for the energy management system,G is a constraint of a system state;
Introducing a discrete optimization problem, and optimizing a cost function by using a discrete optimization algorithm; wherein, the discrete optimization problem is described as follows:
（29）
In the method, in the process of the invention, For optimal control sequence,/>For a pure strategy of predicting speed,/>Is a pure policy for the environment to set up at each step,/>A strategy set for the energy management system at each step, which is a discrete value of SOFC output power; wherein, the energy management system sets strategy/>, at each stepIs described as follows:
（30）
In the method, in the process of the invention, The output power of the SOFC of the ith time.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
In the embodiment of the disclosure, by the energy management method of the UUV hybrid power system, the mapping relation between the power output power and the complex working condition can be described, so that the real-time aging quantization of the fuel cell and the lithium ion battery is realized, the SOH prediction model can more accurately predict the attenuation of the fuel cell, and the discrete optimization method can better establish the attenuation interaction between the solid oxide fuel cell and the lithium ion battery so as to decouple the life competition between the two batteries, and the cruising ability of the UUV is improved based on the hybrid system.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
Fig. 1 illustrates a step diagram of a method of energy management for a hybrid power system of a UUV in an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a raw data diagram in SOH data processing in an exemplary embodiment of the present disclosure;
FIG. 3 illustrates the results after data cleansing in SOH data processing in an exemplary embodiment of the present disclosure;
FIG. 4 illustrates results after data filling in SOH data processing in an exemplary embodiment of the present disclosure;
FIG. 5 illustrates first derivatives of all data in SOH data processing in an exemplary embodiment of the present disclosure;
FIG. 6 illustrates the results of a first set of data cleaning among the predicted results of a training data set in an exemplary embodiment of the present disclosure;
FIG. 7 illustrates the results of a second set of data cleaning among the predicted results of a training data set in an exemplary embodiment of the present disclosure;
FIG. 8 illustrates the results of a third set of data cleansing among the predicted results of a training data set in an exemplary embodiment of the present disclosure;
FIG. 9 illustrates the results of a fourth set of data cleansing among the predicted results of a training data set in an exemplary embodiment of the present disclosure;
fig. 10 illustrates a flow chart of a method of energy management for a hybrid system of a UUV in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of embodiments of the disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
The embodiment provides an energy management method of a UUV hybrid power system. Referring to fig. 1, the method for energy management of the hybrid power system of the UUV may include: step S101 to step S105.
Step S101: according to the mixing degree of a solid oxide fuel cell and a lithium ion battery in a hybrid power system, constructing a dimensionless parameter model;
Step S102: respectively constructing an electrochemical model, a degradation model, an internal resistance model and an SOH prediction model based on the dimensionless parameter model; wherein, according to the electrochemical reaction formula of the solid oxide fuel cell, an electrochemical model is constructed; constructing a degradation model according to an aging formula of the solid oxide fuel cell; constructing an internal resistance model according to an equivalent circuit formula of the lithium ion battery; according to the battery cycle life test data, constructing an SOH prediction model;
Step S103: constructing a viscous resistance model according to external structural parameters, navigation resistance and navigation speed of the UUV;
Step S104: obtaining hydrogen consumption cost and fuel cell degradation cost according to an electrochemical model and a degradation model, obtaining lithium ion battery aging cost according to an internal resistance model and an SOH prediction model, obtaining SOC deviation cost according to a viscous resistance model, and constructing a cost function according to the hydrogen consumption cost, the fuel cell degradation cost, the lithium ion battery aging cost and the SOC deviation cost;
Step S105: and optimizing the cost function by using a discrete optimization algorithm of the minimum-maximum game to obtain an optimal energy distribution scheme.
By the energy management method of the UUV hybrid power system, the mapping relation between the power output power and the complex working condition can be described, so that real-time aging quantification of the fuel cell and the lithium ion battery is realized, the SOH prediction model can more accurately predict the attenuation of the fuel cell, the discrete optimization method can better establish the attenuation interaction between the solid oxide fuel cell and the lithium ion battery so as to decouple the service life competition between the two batteries, and the cruising ability of the UUV is improved based on the hybrid system.
Next, each step of the energy management method of the hybrid system of the UUV described above in the present exemplary embodiment will be described in more detail with reference to fig. 1 to 10.
In step S101, a dimensionless parametric model is constructed based on the degree of mixing of the solid oxide fuel cell and the lithium ion battery of the load power; wherein the equations for the dimensionless parametric model include:
（1）
（2）
（3）
（4）
（5）
（6）
（7）
（8）
（9）
（10）
In the method, in the process of the invention, Average power provided for SOFC,/>Is the low power of the power curve,/>For the first discharge time,/>For the first charging time,/>For the second discharge time,/>For the second charging time,/>As base power,/>For cruising power,/>For power hybridization,/>For maximum load power,/>Power provided for lithium batteries,/>Is specific power,/>Is of a specific time,/>For lithium battery energy consumption,/>For the purposes of energy consumption of the SOFC,Is the degree of energy hybridization.
In particular, the SOFC system provides average powerThis power can be reduced to two parts, namely the base powerAnd cruising power/>As shown in formula (1) and formula (2). As shown in equation (3), it is used to describe the relative amounts of power provided by the two power sources. /(I)And/>Representing the power provided by the lithium ion battery and the power provided by the FC system, respectively. Specific power (/ >)) Sum specific time (/ >)) The dimensionless parameter definitions of (a) are shown in the formula (4) and the formula (5), respectively. When meeting/>=/>、=/>Under the condition of HDP and/>And/>The functional relationship of (2) is shown in formula (6). During the process of powering the load, the energy provided by the fuel cell system and the battery is shown in equations (7) and (8), respectively. Equation (9) represents the ratio of the cell energy consumption to the fuel cell energy consumption. The energy Hybridization (HDE) is used to describe the relative magnitudes of two power supplies over the period of the power curve, as shown in equation (10).
Considering volume and weight, a sensitivity influence factor (sen) is used to quantitatively evaluate sensitivity:
（31）
In the method, in the process of the invention, For the target parameter,/>For inputting parameters,/>Is the input parameter difference.
In step S102, an electrochemical model of the solid oxide fuel cell is constructed. Since the reaction rate of the water vapor shift (WATER GAS SHIFT, WGS) reaction is much higher than that of the electrochemical reaction, the electrochemical reaction CO oxidation is not considered by the present application. The H2 oxidation reaction is as follows:
（32）
the total heat generated in the SOFC stack is determined by subtracting the net power of the solid oxide fuel cell stack from the total enthalpy of formation of the participating fuels. The main reactions that occur in the external reformer are known as methane steam reforming, water gas shift and direct steam reforming:
（33）
（34）
（35）
The oxidation reaction of H 2 based on the mole fraction of the substance and the SOFC temperature is determined according to the Gibbs free energy change to obtain the Nernst voltage:
（36）
In the method, in the process of the invention, Is the Gibbs free energy change at standard pressure and temperature,/>Is a general gas constant,/>Is Faraday number,/>For SOFC temperature,/>For/>Partial pressure of/>For/>Partial pressure of/>For/>Is a partial pressure of (2);
according to Butler-Volmer equation of SOFC operating temperature, to obtain activation overvoltage:
（37）
In the method, in the process of the invention, Ion transfer coefficient of anode,/>Is the ion transfer coefficient of the cathode,/>For the exchange current density of the anode in SOFC,/>For the exchange current density of the cathode in SOFC,/>Is the effective area of SOFC,/>Is current,/>Is planck constant;
depending on SOFC operating temperature and film thickness, to obtain ohmic overvoltage:
（38）
In the method, in the process of the invention, For electrolyte thickness of SOFC,/>Is the effective area of SOFC,/>Is the free energy of activated Gibbs;
Depending on the depletion of the reactant concentration at the reaction site, to obtain a concentration overvoltage:
（39）
In the method, in the process of the invention, Is an empirical constant,/>Is the limiting current density;
removing the deactivation overvoltage, ohmic overvoltage and concentration overvoltage from the nernst voltage to obtain a net voltage for the SOFC stack:
（40）
based on the electrochemical reaction between hydrogen and oxygen, to obtain instantaneous hydrogen consumption:
（41）
In the method, in the process of the invention, For the output power of SOFC,/>For SOFC efficiency,/>Is the low heating value of hydrogen;
based on the dimensionless parameter model, combining the net voltage of the SOFC stack and the instantaneous hydrogen consumption to obtain the net power of the SOFC stack, and constructing an electrochemical model according to the net power of the SOFC stack.
And constructing a degradation model of the solid oxide fuel cell. To evaluate degradation of a fuel cell in operation, the degradation model is as follows:
（11）
In the method, in the process of the invention, For degradation model, i.e. fuel cell degradation rate,/>For the first model parameter,/>For the second model parameter,/>For the third model parameter,/>For the fourth model parameter,/>For the time that the SOFC is operating in high power conditions,For the running time of SOFC under idle working condition,/>For SOFC start-stop sequence number,/>For variation of SOFC output power,/>Is a correction factor.
And constructing an internal resistance model of the lithium ion battery. Equivalent internal resistanceAnd open circuit voltage/>Is a function of the state of charge (SOC) of the battery. The internal resistance model, namely the battery SOC, is as follows:
（12）
In the method, in the process of the invention, Is open circuit voltage,/>Is equivalent internal resistance,/>Is the battery capacity; /(I)Is the total electric quantity of the battery.
And constructing an SOH prediction model of the lithium ion battery.
And constructing a life data pool by using the battery cycle life test data.
In order to further reduce abnormal data, the application adopts a rain flow counting method to count the quantity and the severity of capacity recovery effects in a life data pool so as to further carry out data scoring and cleaning. The scoring rules are as follows:
（42）
In the method, in the process of the invention, And/>State of health (SOH) recovery effects and corresponding depths, respectively, based on a rain flow algorithm; /(I)Is the total period of the test data; /(I)And/>The maximum remaining capacity and the minimum remaining capacity of the test data, respectively.
After the score of the lifetime data pool is obtained, the score of the lifetime data pool needs to be standardized in order to intuitively compare the merits of the data. The calculation formula is as follows:
（43）
In the method, in the process of the invention, Calculated value for corresponding group,/>For the smallest value in each group,/>The largest value in each group.
After normalization, data with scores higher than 0.1 were deleted directly.
In one embodiment, the data pool is constructed using the Stanford university battery cycle life test data. And providing dynamic Kalman filtering, and giving a smaller filtering coefficient when the life attenuation track of the battery suddenly fluctuates, so as to ensure the smoothness of the curve. SOH data processing results are shown in fig. 2 to 5; FIG. 2 is the original data in SOH data processing; FIG. 3 shows the results after data cleansing in SOH data processing; FIG. 4 is a graph showing the results of SOH data processing after data filling; fig. 5 is the first derivative of all data in SOH data processing.
The application randomly extracts 4 groups of data from the database as a test data set, and the rest data sets are used as training data sets. The prediction results are shown in fig. 6 to 9; FIG. 6 is a graph showing the result of the first group of data cleaning in the predicted result of the training data set; FIG. 7 is a graph showing the results of a second set of data after cleaning, among the predicted results of the training data set; FIG. 8 is a graph showing the results of a third set of data cleaning among the predicted results of the training data set; fig. 9 shows the result of the fourth group of data after cleaning, among the predicted results of the training data set.
The relationship between each degradation critical point and end of life is as follows:
（44）
Wherein, Is the relation between the history cycle life corresponding to each life key point and the total cycle life of the battery; /(I)Is a tag of the battery pack. Then, a weight vector with SOH as a state variable can be obtained:
（45）
Wherein, Is the corresponding weight vector at each key point of life degradation.
The above reflects the effect of the depth and number of charge and discharge on the degradation of the battery. Battery life is also subject to charge and discharge rates, temperature, and SOC. These auxiliary parameters are needed to correct battery life in order to better simulate battery degradation. The quantitative influence model of battery charge-discharge multiplying power and SOC on battery cycle life correction is established by combining test data and a particle swarm optimization algorithm:
（46）
（47）
（48）
Wherein, Is the correction coefficient of the battery SOC to the battery cycle life,/>Is the correction coefficient of the battery to the cycle life of the battery,/>Is a combined impact of SOC and battery cycle life modification. Visual display battery/>Correction of battery life for rate and SOC.
The power and charge-discharge rate during battery operation can be calculated by the following equation:
（49）
Wherein, Representing the output power of the lithium ion battery; /(I)Representing the battery voltage; /(I)Representing the battery capacity. The quantitative result of the battery life reduction is corrected by the Crate and the SOC, and the mathematical formula is expressed as follows: /(I)
（50）
（51）
Wherein,Is the total cycle life in the next life cycle; /(I)Is the historical cycle life of the next life cycle; /(I)Is a decrease in battery life; /(I)Is a correction factor that quantifies the decrease in battery life. The equation for quantifying the battery life degradation and updating the SOH of the battery is as follows:
（13）
In the method, in the process of the invention, For the life of the battery in the current life cycle,/>Life of battery in next life cycle,/>Is a decrease in battery life,/>To quantify the correction factor for the decrease in battery life.
In step S103, the equation of the constructed viscous resistance model includes:
（14）
（15）
（16）
（17）
（18）
（19）
（20）
In the method, in the process of the invention, For cruising resistance,/>Is the sailing resistance coefficient,/>Is the mass density of sea water,/>Is the area of the underwater vehicle,/>For sailing speed,/>Is an effective drag coefficient,/>Is the friction coefficient,/>Is Reynolds number/>For the length of UUV,/>Is the viscosity coefficient,/>Is a shape factor,/>Is the diameter of UUV,/>For Nernst potential,/>For efficiency of proposal, R is the range of UUV.
In step S104, the hydrogen consumption cost and the fuel cell degradation cost are obtained according to the electrochemical model and the degradation model, the lithium ion battery aging cost is obtained according to the internal resistance model and the SOH prediction model, the SOC deviation cost is obtained according to the viscous resistance model, and the cost function is constructed according to the hydrogen consumption cost, the fuel cell degradation cost, the lithium ion battery aging cost and the SOC deviation cost. Wherein, the hydrogen consumption cost is:
（21）
In the method, in the process of the invention, For instantaneous hydrogen consumption,/>Change in stored energy for battery,/>Is the low heating value of hydrogen/(As an efficiency factor,/>Is the price of hydrogen;
the degradation cost of the fuel cell is as follows:
（22）
In the method, in the process of the invention, Investment for fuel cell,/>Maximum power for SOFC;
The aging cost of the lithium ion battery is as follows:
（23）
In the method, in the process of the invention, For lithium ion battery health status change,/>For the price of the battery,/>Is the battery capacity;
The SOC deviation cost is:
（24）
In the method, in the process of the invention, Is SOC weight coefficient,/>For reference SOC,/>Is the actual SOC;
the cost function constructed is:
（25）
In the method, in the process of the invention, For hydrogen consumption cost,/>For fuel cell degradation cost,/>For the aging cost of lithium ion battery,/>Is the SOC offset cost.
In step S105, the participant EMS minimizes the objective function by selecting a strategy from the set of strategies, where the speed is a prediction horizon, so that the EMS can handle the worst case and thus achieve robustness to the response to different driving conditions. Consider a nonlinear discrete-time system, described below:
（26）
In the method, in the process of the invention, Is the system state/>Is the control input of the power system,/>Is the predicted speed;
To ensure safe operation of the system, constraints should be set to:
（27）
In the method, in the process of the invention, Is the minimum SOC permitted,/>Is the minimum SOC permitted,/>For minimum current variation of SOFC,/>For maximum current change of SOFC,/>Is the minimum allowable output power of the fuel cell system,/>Is the maximum allowable output power of the fuel cell system;
An Energy Management System (EMS) and a predicted speed are used as two parties of the game. The environment is considered a "virtual" player that makes decisions that are contrary to the goals of the controller, which enables the controller to handle worst cases that may occur in the future. Then decision making is performed using a min-max game based approach; wherein, the decision process is:
（28）
In the method, in the process of the invention, As a cost function,/>Decisions made for energy management systems,/>To predict the speed,/>Is a policy set of an energy management system,/>Is a policy set of the environment,/>Is a constraint of system state;
Based on the above concept, in the discrete optimization method proposed by the present invention, the environment is a certain factor, i.e. the speed is a certain predicted value, and is regarded as the worst driving situation. In this case, the policy set of the environmental participants is a pure policy, but according to the definition of the hybrid policy, it can still be considered as within the prediction horizon given by the special hybrid policy with probability 1, the discrete optimization problem can be described as:
（29）
In the method, in the process of the invention, For optimal control sequence,/>For a pure strategy of predicting speed,/>Is a pure policy for the environment to set up at each step,/>The strategy set for the energy management system at each step is a discrete value of SOFC output power:
（30）
In the method, in the process of the invention, SOFC output power for the ith time, < ">The output power/>, of the lithium ion battery can be known by the formula (3), wherein the output power/>, is selected as an optimization variableTotal availability/>Representation of/>Is a pure strategy set by the environment at each step, and can be predicted by a Markov chain; /(I)Is a constraint on the state of the system.
In one embodiment, both the simulated theoretical voltage and the actual voltage exhibit similar decreasing trends as the current increases under the same current conditions. The maximum error is 1.4%, which shows that the fitting effect is excellent and meets the requirement of theoretical calculation.
According to the fuel cell attenuation formula, the start-up and shutdown conditions have the greatest effect on the fuel cell attenuation. Each step is set up to represent a start-stop condition. Under the discrete optimization method, the number of start-stop working conditions is minimum, which indicates that the degradation of the fuel cell is minimum.
The quantification of the degradation model is different for different initial SOHs. The lower the initial SOH, the faster the lifetime degradation. The degradation rate at an initial SOH of 0.85 was 1.0% higher than at an initial SOH of 13.59.
In one embodiment, as the SOC decreases, the voltage drops rapidly first. The voltage change then slows down, approaching a level, and the SOC is approximately 0.3-0.8. At an SOC of about 0.7-0.3, the maximum accuracy of the simulated voltage is 0.8%. Since only the data portion with SOC greater than 0.3 is applied in the subsequent calculation, the model can satisfy the calculation requirement.
In one embodiment, HDP describes the primary power source at each instant, as power reflects the change in energy over time. In the region of T <9, HDP increases sharply with increasing P and decreases sharply with decreasing T. In the large region of T >9, HDP is close to zero and does not vary with T and P.
In one embodiment, the total running cost of discrete optimization is lowest, about 41.24 yuan, with the hydrogen cost contribution being greatest. Battery degradation is the second greatest expense. The degradation cost of the fuel cell is minimal compared to hydrogen and cell degradation.
As shown in fig. 4, a flow chart of a method of energy management for a hybrid power system of a UUV according to the present invention is shown. By the energy management method of the UUV hybrid power system, the mapping relation between the power output power and the complex working condition can be described, so that real-time aging quantification of the fuel cell and the lithium ion battery is realized, the SOH prediction model can more accurately predict the attenuation of the fuel cell, the discrete optimization method can better establish the attenuation interaction between the solid oxide fuel cell and the lithium ion battery so as to decouple the service life competition between the two batteries, and the cruising ability of the UUV is improved based on the hybrid system.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present disclosure, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, one skilled in the art can combine and combine the different embodiments or examples described in this specification.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (7)
1. An energy management method of a hybrid power system of a UUV, comprising:
according to the mixing degree of a solid oxide fuel cell and a lithium ion battery in a hybrid power system, constructing a dimensionless parameter model; the method specifically comprises the following steps: based on the mixing degree of the solid oxide fuel cell and the lithium ion battery of the load power, constructing a dimensionless parameter model; wherein the equations for the dimensionless parametric model include:
（1）
（2）
（3）
（4）
（5）
（6）
（7）
（8）
（9）
（10）
In the method, in the process of the invention, Average power provided for SOFC,/>High power, i.e./>, of the power curveIs the low power of the power curve,/>For the first discharge time,/>For the first charging time,/>For the second discharge time,/>For the second time of the charge-up,As base power,/>For cruising power,/>For power hybridization,/>For maximum load power,/>Power provided for lithium batteries,/>Is specific power,/>Is of a specific time,/>For lithium battery energy consumption,/>For SOFC energy consumption,/>Is the degree of energy hybridization;
Based on the dimensionless parameter model, constructing an electrochemical model according to an electrochemical reaction formula of the solid oxide fuel cell; constructing a degradation model according to an aging formula of the solid oxide fuel cell; constructing an internal resistance model according to an equivalent circuit formula of the lithium ion battery; according to the lithium ion battery cycle life test data, constructing an SOH prediction model;
constructing a viscous resistance model according to external structural parameters, navigation resistance and navigation speed of the UUV;
Obtaining hydrogen consumption cost according to an electrochemical model, obtaining fuel cell degradation cost according to a degradation model, obtaining lithium ion battery aging cost according to an internal resistance model and an SOH prediction model, obtaining SOC deviation cost according to a viscous resistance model, and constructing a cost function according to the hydrogen consumption cost, the fuel cell degradation cost, the lithium ion battery aging cost and the SOC deviation cost;
Optimizing the cost function by using a discrete optimization algorithm of the minimum-maximum game to obtain an optimal energy distribution scheme; the method specifically comprises the following steps: the energy management system selects one strategy from the strategy set to minimize a cost function, thereby obtaining robustness of response to different driving conditions; the energy management system is a nonlinear discrete time system and is described as follows:
（26）
In the method, in the process of the invention, Is the system state/>Is the control input of the power system,/>Is the predicted speed;
The constraint should be set as:
（27）
In the method, in the process of the invention, Is the minimum SOC permitted,/>Is the minimum SOC permitted,/>For minimum current variation of SOFC,/>For maximum current change of SOFC,/>Is the minimum allowable output power of the fuel cell system,Is the maximum allowable output power of the fuel cell system;
Taking an energy management system and a prediction speed as two parties, and making a decision by using a method based on a minimum-maximum game; wherein, the decision process is:
（28）
In the method, in the process of the invention, As a cost function,/>Decisions made for energy management systems,/>Is a policy set of an energy management system,/>Is a policy set of the environment,/>Is a constraint of system state;
Introducing a discrete optimization problem, and optimizing a cost function by using a discrete optimization algorithm; wherein, the discrete optimization problem is described as follows:
（29）
In the method, in the process of the invention, For optimal control sequence,/>For a pure strategy of predicting speed,/>Is a pure policy for the environment to set up at each step,/>A strategy set for the energy management system at each step, which is a discrete value of SOFC output power; wherein, the energy management system sets strategy/>, at each stepIs described as follows:
（30）
In the method, in the process of the invention, The output power of the SOFC of the ith time.
2. The method for energy management of a hybrid system of a UUV according to claim 1, wherein the step of constructing an electrochemical model from an electrochemical reaction equation of a solid oxide fuel cell comprises:
determining the oxidation reaction of H 2 based on the mole fraction of the substance and the SOFC temperature according to the Gibbs free energy change to obtain a Nernst voltage;
according to a Butler-Volmer equation of the SOFC working temperature, obtaining an activation overvoltage;
According to the SOFC working temperature and film thickness, ohmic overvoltage is obtained;
Depending on the depletion of the reactant concentration at the reaction site, to obtain a concentration overvoltage;
removing the deactivation overvoltage, ohmic overvoltage and concentration overvoltage from the nernst voltage to obtain a net voltage of the SOFC stack;
according to the electrochemical reaction between the hydrogen and the oxygen, the instantaneous hydrogen consumption is obtained;
based on the dimensionless parameter model, combining the net voltage of the SOFC stack and the instantaneous hydrogen consumption to obtain the net power of the SOFC stack, and constructing an electrochemical model according to the net power of the SOFC stack.
3. The method of energy management of a hybrid power system of a UUV according to claim 2, wherein the degradation model is:
（11）
In the method, in the process of the invention, For the first model parameter,/>For the second model parameter,/>For the third model parameter,/>For the fourth model parameter,/>For the time of SOFC operation under high power conditions,/>For the time of operation of the SOFC at idle,For SOFC start-stop sequence number,/>For variation of SOFC output power,/>Is a correction factor.
4. The method for energy management of a hybrid power system of a UUV according to claim 3, wherein the internal resistance model is:
（12）
In the method, in the process of the invention, Is open circuit voltage,/>Is equivalent internal resistance,/>Is the battery capacity; /(I)Is the total electric quantity of the battery.
5. The method of energy management of a hybrid system of a UUV according to claim 4, wherein the step of constructing an SOH prediction model based on battery cycle life test data comprises:
constructing a life data pool according to the battery cycle life test data, and counting the quantity and severity of capacity recovery effects in the life data pool by using a rain flow counting method;
Correcting the counted life data pool, and constructing an SOH prediction model according to the corrected life data pool; the SOH prediction model is as follows:
（13）
In the method, in the process of the invention, For the life of the battery in the current life cycle,/>Life of battery in next life cycle,/>Is a decrease in battery life,/>To quantify the correction factor for the decrease in battery life.
6. The method of energy management of a hybrid system of a UUV according to claim 5, wherein the equation for the viscous resistance model comprises:
（14）
（15）
（16）
（17）
（18）
（19）
（20）
In the method, in the process of the invention, For cruising resistance,/>Is the sailing resistance coefficient,/>Is the mass density of sea water,/>Is the area of the underwater vehicle,/>For sailing speed,/>Is an effective drag coefficient,/>Is the friction coefficient,/>Is Reynolds number/>For the length of UUV,/>Is the viscosity coefficient,/>Is a shape factor,/>Is the diameter of UUV,/>For Nernst potential,/>For efficiency of proposal, R is the range of UUV.
7. The method for energy management of a hybrid power system of a UUV according to claim 6, wherein the hydrogen consumption cost is:
（21）
In the method, in the process of the invention, For instantaneous hydrogen consumption,/>Change in stored energy for battery,/>Is the low heating value of the hydrogen gas,As an efficiency factor,/>Is the price of hydrogen;
the degradation cost of the fuel cell is as follows:
（22）
In the method, in the process of the invention, Investment for fuel cell,/>Maximum power for SOFC;
The aging cost of the lithium ion battery is as follows:
（23）
In the method, in the process of the invention, For lithium ion battery health status change,/>For the price of the battery,/>Is the battery capacity;
The SOC deviation cost is:
（24）
In the method, in the process of the invention, Is SOC weight coefficient,/>For reference SOC,/>Is the actual SOC;
the cost function constructed is:
（25）
In the method, in the process of the invention, For hydrogen consumption cost,/>For fuel cell degradation cost,/>For the aging cost of lithium ion battery,/>Is the SOC offset cost.
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