CN117996293B - Temperature control and optimization method and system for immersed energy storage battery - Google Patents
Temperature control and optimization method and system for immersed energy storage battery Download PDFInfo
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Abstract
The invention provides a temperature control and optimization method and system for an immersed energy storage battery, and relates to the technical field of temperature control, wherein the method comprises the steps of constructing a battery thermal model of the immersed energy storage battery based on an internal thermal value and a thermal propagation value of the immersed energy storage battery; combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target immersed energy storage battery, and determining an optimal cooling liquid temperature change sequence of the target immersed energy storage battery in a target time period; and respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
Description
Technical Field
The present invention relates to temperature control technology, and in particular, to a method and system for controlling and optimizing the temperature of an immersion energy storage battery.
Background
Temperature is one of the key factors affecting the performance of energy storage batteries, and is directly related to various aspects of energy density, power density, life, safety, and the like of the battery. The electrochemical reaction of the battery can be effectively performed within a normal operating temperature range (typically 20 ℃ to 60 ℃) to ensure high-efficiency output and input of the battery. However, when the temperature is too low (below 0 ℃), the viscosity of the electrolyte increases, the ion migration speed decreases, resulting in an increase in the internal resistance of the battery and a decrease in the discharge capacity; when the temperature is too high (higher than 60 ℃), the internal pressure of the battery increases, which may cause decomposition of the electrolyte, structural destruction of the active material, and even occurrence of a thermal runaway event, seriously affecting the life and safety of the battery.
The existing challenges faced by energy storage battery temperature control mainly include how to effectively achieve battery temperature stabilization over a wide range of ambient temperature conditions, how to maintain temperature uniformity between cells with increasing battery pack size, and how to ensure thermal management systems for the battery while increasing energy density are not overly complex or excessive cost.
Disclosure of Invention
The invention provides a temperature control and optimization method and system for an immersed energy storage battery, which can at least solve part of problems in the prior art.
In a first aspect of the present invention,
There is provided a temperature control and optimization method for an immersion energy storage cell, comprising:
obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target immersed energy storage battery, and determining an internal heat value of the target immersed energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
Based on the battery thermal model, acquiring the current battery temperature and the current battery current of the target submerged energy storage battery, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
and respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
Preferably, the method comprises the steps of,
Based on the internal heat value and the heat propagation value of the target submerged energy storage battery, constructing a battery thermal model of the target submerged energy storage battery comprises:
the battery thermal model was constructed according to the following formula:
；
；
Wherein, C p represents the heat capacity of the battery, T represents the temperature of the battery, T represents time, h c、hf represents a first conversion coefficient corresponding to natural convection and a second conversion coefficient corresponding to forced convection respectively, T a、Tf represents the ambient temperature and the temperature of the cooling liquid respectively, and A, A f represents the external surface area of the battery and the contact area with the cooling liquid respectively;
q g represents an internal heat value, I represents a battery current, R represents a battery internal resistance, deltaS represents an entropy change value of a battery reaction, and SOC represents a state of charge.
Preferably, the method comprises the steps of,
Based on the battery thermal model, obtaining the current battery temperature and the current battery current of the target submerged energy storage battery, and combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery, determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period comprises:
Based on the battery thermal model, obtaining a current battery temperature and a current battery current of the target submerged energy storage battery, in combination with a temperature prediction controller built with a goal of minimizing energy consumption, comprising:
Initializing controller parameters of the temperature prediction controller, wherein the controller parameters comprise at least one of a prediction step length, a control step length, a temperature reference value, a temperature constraint value and a penalty factor;
acquiring the current battery temperature and the current battery current of the target submerged energy storage battery, and determining the battery temperature change of the target submerged energy storage battery in a target time period by combining the battery thermal model;
And based on the battery temperature change in the target time period, combining the temperature constraint condition, optimizing the controller parameters of the temperature prediction controller by taking the minimum energy consumption as a target rolling, and applying the control quantity output by the temperature prediction controller to a cooling system to obtain an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in the target time period.
Preferably, the method comprises the steps of,
Optimizing controller parameters of the temperature predictive controller with a goal of minimizing energy consumption includes:
Determining a regression vector of the temperature prediction controller and a covariance matrix of the temperature prediction controller at the last moment corresponding to the current moment in each control period, and determining a gain matrix of the temperature prediction controller by combining a preset forgetting factor, wherein the regression vector is used for indicating a vector formed by measured values related to controller parameters of the temperature prediction controller;
And iteratively updating the controller parameters of the temperature prediction controller according to the gain matrix, the actual output value of the temperature prediction controller and the regression vector by the following formula:
；
；
Wherein, 、/>Represents the K-th and K-1-th controller parameters, respectively, K (K) represents the gain matrix of the K-th step, y (K) represents the actual output value of the K-th step,/>Representing the regression vector of the kth step, P (k-1) representing the covariance matrix of the kth-1 step, and λ representing a preset forgetting factor.
Preferably, the method comprises the steps of,
The temperature prediction controller is shown in the following formula:
；
Wherein, T f represents a coolant temperature value, N p、Nc represents a prediction step length and a control step length, T (k) represents a battery temperature predicted value of the kth step, T ref represents a battery temperature reference value, ρ represents a penalty factor corresponding to a control amount change, and DeltaT f (k) represents a coolant temperature change amount of the kth step.
Preferably, the method comprises the steps of,
Respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm comprises the following steps:
Determining a temperature tracking error according to the optimal cooling liquid temperature change sequence and a preset target control temperature, and determining an inner layer control output of the inner layer controller by combining a PID control algorithm, wherein the inner layer control output comprises a temperature control quantity of the target submerged energy storage battery;
Setting a control weight matrix for the inner layer control output, setting a temperature weight matrix for the temperature tracking error, updating a state estimation value by the outer layer controller, optimizing the updated state estimation value to generate a target track, and synchronizing the target track to the inner layer controller so that the inner layer controller outputs a final temperature control strategy, wherein the target track is used for indicating a desired value of temperature control.
Preferably, the method comprises the steps of,
The inner layer controller is shown in the following formula:
；
The outer layer controller is shown in the following formula:
；
Wherein u f (t) represents the control output of the inner layer controller, K P、KI、KD represents the proportional, integral and differential control parameters of the inner layer controller, and e (t) represents the temperature tracking error;
u s (T) represents the control output of the outer layer controller, T f、t0 represents an initial time domain and a target time domain respectively, Q represents a temperature weight matrix, R represents a control weight matrix, and T (T) and T ref (T) represent an optimal cooling liquid temperature change sequence and a preset target control temperature respectively.
In a second aspect of the present invention,
There is provided a temperature control and optimization system for an immersion energy storage cell, comprising:
A first unit for obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target submerged energy storage battery, and determining an internal heat value of the target submerged energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
The second unit is used for acquiring the current battery temperature and the current battery current of the target submerged energy storage battery based on the battery thermal model, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
and the third unit is used for respectively constructing an inner layer controller with the temperature tracking as a target and an outer layer controller with the control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
In a third aspect of the present invention,
There is provided an electronic device including:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of the present invention,
There is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
By acquiring the internal heat value and the heat propagation value of the battery and combining the current state of the battery, a battery thermal model capable of accurately describing the temperature change rule of the battery is constructed, the model fully considers the influence of heat generation and surface heat dissipation in the battery, and the accuracy of temperature prediction is improved. In the design of the temperature prediction controller, the minimum energy consumption is taken as an optimization target, meanwhile, the battery temperature constraint condition is considered, and the optimal cooling liquid temperature change sequence capable of reducing the energy consumption of the cooling system to the maximum extent is obtained by solving the optimization problem on the premise of meeting the temperature constraint.
According to the optimal cooling liquid temperature change sequence, a layered control structure is constructed, the inner layer controller aims at temperature tracking, accurate adjustment of the battery temperature is achieved, the outer layer controller optimizes control parameters of the inner layer controller, and the adaptability of the control system to battery state change is improved. The operation of the two-layer controllers is coordinated through a multi-time-scale optimization control algorithm, and the robustness and the instantaneity of a control strategy are improved.
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FIG. 1 is a schematic flow chart of a temperature control and optimization method for an immersion energy storage battery according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a temperature control and optimization system for an immersion energy storage cell according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a schematic flow chart of a temperature control and optimization method for an immersion energy storage battery according to an embodiment of the present invention, as shown in fig. 1, the method includes:
S101, obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target immersed energy storage battery, and determining an internal heat value of the target immersed energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
by way of example, a high-precision current sensor, such as a hall effect sensor or a shunt, may be used to measure the charge and discharge current of the battery in real time, and appropriate signal conditioning circuitry, such as amplification, filtering, etc., may be selected depending on the type of current sensor to obtain a stable, reliable current measurement. An online parameter identification method, such as a recursive least square method or a Kalman filtering method, can be adopted to estimate the internal resistance of the battery in real time according to the input and output data of the battery. According to the chemical characteristics of the battery, the corresponding relation between the entropy change value of the battery reaction and the state of charge is obtained by looking up a table or fitting a function, and the entropy change value under the current working condition is calculated by combining the state of charge measured in real time. And using a coulomb counting method, an open-circuit voltage method or a model observer and the like to estimate the charge state of the battery in real time by combining current integration and voltage measurement.
Alternatively, the heat exchange coefficient depends on the flowing state of the cooling liquid, physical parameters, shape characteristics of the battery surface, and the like, and can be predetermined by theoretical calculation, numerical simulation, experimental test, or the like, and can be generally regarded as a constant for a specific battery structure and cooling mode.
For example, in order to achieve accurate temperature control of an immersion energy storage cell, it is first necessary to establish a thermal model that accurately describes the dynamic changes in cell temperature. The application adopts a lumped parameter thermal model based on the law of conservation of energy. The model regards the battery as a uniform heat capacity, and takes factors such as heat generation inside the battery, convection heat exchange with the environment, forced convection heat exchange with cooling liquid and the like into consideration.
By adopting the distribution parameter thermal model, the temperature distribution in the battery can be predicted more accurately, and finer temperature control can be realized. At the same time, factors such as battery aging and capacity fade can also be considered to be included in the thermal model to accommodate long-term changes in battery performance.
In an alternative embodiment of the present invention,
Based on the internal heat value and the heat propagation value of the target submerged energy storage battery, constructing a battery thermal model of the target submerged energy storage battery comprises:
the battery thermal model was constructed according to the following formula:
；
；
Wherein, C p represents the heat capacity of the battery, T represents the temperature of the battery, T represents time, h c、hf represents a first conversion coefficient corresponding to natural convection and a second conversion coefficient corresponding to forced convection respectively, T a、Tf represents the ambient temperature and the temperature of the cooling liquid respectively, and A, A f represents the external surface area of the battery and the contact area with the cooling liquid respectively;
q g represents an internal heat value, I represents a battery current, R represents a battery internal resistance, deltaS represents an entropy change value of a battery reaction, and SOC represents a state of charge.
It can be understood that the battery thermal model can calculate the internal heat generation and the surface heat dissipation of the battery in real time according to parameters such as the measured battery current, the measured internal resistance, the measured entropy change value and the like, so that the real-time temperature of the battery can be estimated. The battery management system provides important temperature information for the battery management system, and is beneficial to realizing functions of temperature monitoring, over-temperature protection and the like. Based on the battery thermal model, the temperature change trend of the battery in a future period of time can be predicted. The battery thermal model can estimate the dynamic response of the battery temperature through the prediction of future working conditions, such as charge and discharge current, ambient temperature and the like, and provides a reference for temperature optimization control.
S102, acquiring the current battery temperature and the current battery current of the target submerged energy storage battery based on the battery thermal model, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
Illustratively, the basic idea of selecting an appropriate controller architecture, such as model predictive control, based on the characteristics of the battery thermal model and control objectives is to solve an optimal control problem in a finite domain based on current state and future predictions at each sampling instant, and to apply a first control quantity to the object. The objective function of the temperature prediction controller is constructed with the aim of optimizing the minimum energy consumption. The energy consumption can be measured by the power consumption of the cooling system in the control process, and is related to the flow rate, temperature change and other factors of the cooling liquid.
And setting the constraint conditions of the controller according to the temperature constraint conditions of the target submerged energy storage battery, such as the maximum allowable temperature, the maximum temperature rise rate and the like. The temperature constraints are converted into constraints of the coolant temperature, and the physical constraints of the actuator, such as maximum flow, minimum temperature, etc., are combined to form a complete set of constraints.
Optionally, a rolling optimization strategy is employed, and at each sampling instant, based on the current measured battery temperature and current, the state of the battery thermal model is updated and the optimal coolant temperature sequence over a period of time in the future (prediction horizon) is recalculated. The length of the predicted time domain depends on the dynamics of the battery thermal model and the response speed of the control system. The main time constant that can cover the battery temperature response is generally chosen and the computational complexity of the optimization problem is considered. Too short a prediction horizon may lead to poor control performance, and too long a prediction horizon may increase computational burden.
In the optimization process, the change rate of the temperature of the cooling liquid is required to be restrained so as to meet the physical limitation of an actuator and the stability requirement of a system; the rate of change constraint can be translated into a constraint on the difference between the temperatures of the adjacent two steps of coolant and incorporated into a constraint set of optimization problems.
In an alternative embodiment of the present invention,
Based on the battery thermal model, obtaining the current battery temperature and the current battery current of the target submerged energy storage battery, and combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery, determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period comprises:
Based on the battery thermal model, obtaining a current battery temperature and a current battery current of the target submerged energy storage battery, in combination with a temperature prediction controller built with a goal of minimizing energy consumption, comprising:
Initializing controller parameters of the temperature prediction controller, wherein the controller parameters comprise at least one of a prediction step length, a control step length, a temperature reference value, a temperature constraint value and a penalty factor;
acquiring the current battery temperature and the current battery current of the target submerged energy storage battery, and determining the battery temperature change of the target submerged energy storage battery in a target time period by combining the battery thermal model;
And based on the battery temperature change in the target time period, combining the temperature constraint condition, optimizing the controller parameters of the temperature prediction controller by taking the minimum energy consumption as a target rolling, and applying the control quantity output by the temperature prediction controller to a cooling system to obtain an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in the target time period.
Illustratively, the prediction step size determines the time domain length that the controller expects to be future at each sampling instant, generally selects the dominant time constant that can cover the battery temperature response, and considers the computational complexity of the optimization problem. The control step size determines the number of control actions of the controller in the prediction time domain, and typically a value less than or equal to the prediction step size is selected to reduce the number of decision variables for the optimization problem.
The temperature reference value is a target temperature tracked by the controller and is generally set according to the optimal working temperature range of the battery and the requirements of an application scene. The temperature constraint values include upper and lower limits of the battery temperature and upper and lower limits of the temperature change rate, and are determined according to the safe operating range of the battery and the applicable conditions of the thermal model to ensure the feasibility and stability of the control process. The penalty factor is used to balance the tradeoff between temperature tracking performance and energy consumption. A larger penalty factor would make the controller more focused on temperature tracking, while a smaller penalty factor would make the controller more focused on energy optimization.
At each sampling instant, state variables of the battery thermal model, such as battery internal temperature, surface temperature, etc., are updated based on the currently measured battery temperature and current. The state update generally adopts state estimation algorithms such as Kalman filtering, particle filtering and the like to integrate model prediction and measurement information. Substituting the predicted current sequence into a battery thermal model, and calculating a battery temperature change sequence in a target time period.
The objective function of the temperature prediction controller is constructed with the aim of minimizing the energy consumption, which can be measured by the square sum or absolute value of the temperature change of the cooling liquid in the control process, and penalty factors are introduced to balance the temperature tracking performance. And adding the optimized cooling liquid temperature change sequence to the cooling liquid temperature value at the last moment to obtain the cooling liquid temperature sequence in the future control step length.
In an alternative embodiment of the present invention,
Optimizing controller parameters of the temperature predictive controller with a goal of minimizing energy consumption includes:
Determining a regression vector of the temperature prediction controller and a covariance matrix of the temperature prediction controller at the last moment corresponding to the current moment in each control period, and determining a gain matrix of the temperature prediction controller by combining a preset forgetting factor, wherein the regression vector is used for indicating a vector formed by measured values related to controller parameters of the temperature prediction controller;
And iteratively updating the controller parameters of the temperature prediction controller according to the gain matrix, the actual output value of the temperature prediction controller and the regression vector by the following formula:
；
；
Wherein, 、/>Represents the K-th and K-1-th controller parameters, respectively, K (K) represents the gain matrix of the K-th step, y (K) represents the actual output value of the K-th step,/>Representing the regression vector of the kth step, P (k-1) representing the covariance matrix of the kth-1 step, and λ representing a preset forgetting factor.
In an alternative embodiment of the present invention,
The temperature prediction controller is shown in the following formula:
；
Wherein, T f represents a coolant temperature value, N p、Nc represents a prediction step length and a control step length, T (k) represents a battery temperature predicted value of the kth step, T ref represents a battery temperature reference value, ρ represents a penalty factor corresponding to a control amount change, and DeltaT f (k) represents a coolant temperature change amount of the kth step.
According to the application, the battery temperature and the current are obtained in real time, the state estimation and the temperature prediction are carried out by combining the battery thermal model, and the controller can accurately grasp the thermal behavior characteristic of the battery. Conventional battery temperature control methods often employ a fixed cooling strategy, such as a constant flow or constant temperature, which can easily result in excessive or insufficient cooling, resulting in wasted energy. The scheme based on temperature prediction control can dynamically adjust the flow and the temperature of the cooling liquid according to the actual heat load and the predicted temperature change of the battery, and the energy consumption of a cooling system is minimized while the temperature control requirement is met, so that energy conservation and efficiency improvement are realized. In large-scale battery applications, the temperature distribution between individual cells tends to be non-uniform, easily resulting in localized hot spots and charge imbalance. The temperature prediction control can be used for carrying out personalized adjustment on the temperature characteristics of each single battery, coordinating the temperature difference among the single batteries and realizing the equalization and the consistency of the temperatures in the group. The method can not only prevent potential safety hazards caused by local over-temperature, but also improve the energy utilization efficiency and the cycle life of the battery pack.
S103, respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
Illustratively, the appropriate type of inner layer controller is selected, such as a PID controller, a state feedback controller, a slip-mode controller, etc., based on the heat transfer characteristics and control requirements of the submerged energy storage battery. Taking a PID controller as an example, the temperature control device has the advantages of simple structure, easily-adjusted parameters, good robustness and the like, and is suitable for most temperature control scenes; and (3) preliminarily determining the parameter value of the inner-layer controller by using a classical PID parameter setting method according to the optimal cooling liquid temperature change sequence. By conducting open loop or closed loop tests on the system, the methods give an empirical formula or iterative algorithm of PID parameters according to a step response curve or critical oscillation characteristics.
And designing and realizing the parameter optimization process of the outer layer controller according to the selected optimization algorithm. This process typically requires multiple iterations, each of which includes the steps of generating candidate parameters, evaluating control performance, updating the direction of optimization, and the like. In order to improve the optimization efficiency, measures such as parallel calculation, dimension reduction technology and the like can be adopted, and iteration stop conditions are reasonably set, such as that the change of objective function values is smaller than a threshold value, the number of iterations reaches an upper limit and the like.
And separating the time scales of the inner layer controller and the outer layer controller according to the dynamic characteristics of the inner layer controller and the outer layer controller. The inner layer controller directly interacts with the controlled object (the immersed energy storage battery) in real time, and the sampling period and the control period are short, usually in the order of milliseconds to seconds. The outer layer controller optimizes its parameters based on the performance evaluation results of the inner layer controller, and its sampling period and control period are long, usually on the order of minutes to hours.
And respectively executing optimization algorithms of the inner layer controller and the outer layer controller on different time scales. And the inner layer controller calculates the control quantity in real time according to the current state and the reference value and acts on the controlled object to realize quick temperature tracking control. And the outer layer controller evaluates the performance index of the inner layer controller on a longer time scale, and adjusts the parameters thereof through an optimization algorithm to realize the self-optimization of the slow controller.
In order to ensure the coordination of the inner layer control and the outer layer optimization, a reasonable control strategy is required to be designed. One common strategy is hierarchical control, i.e., the outer layer controller issues parameter adjustment instructions to the inner layer controller based on the optimization results, and the inner layer controller gradually updates its parameters while performing the temperature tracking task. Another strategy is to switch control, i.e. to switch different sets of controller parameters at different control phases or conditions to accommodate changes in system state.
In an alternative embodiment of the present invention,
Respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm comprises the following steps:
Determining a temperature tracking error according to the optimal cooling liquid temperature change sequence and a preset target control temperature, and determining an inner layer control output of the inner layer controller by combining a PID control algorithm, wherein the inner layer control output comprises a temperature control quantity of the target submerged energy storage battery;
Setting a control weight matrix for the inner layer control output, setting a temperature weight matrix for the temperature tracking error, updating a state estimation value by the outer layer controller, optimizing the updated state estimation value to generate a target track, and synchronizing the target track to the inner layer controller so that the inner layer controller outputs a final temperature control strategy, wherein the target track is used for indicating a desired value of temperature control.
And reasonably configuring the working periods of the inner layer controller and the outer layer controller according to the dynamic characteristics of the inner layer controller and the outer layer controller. The inner layer controller needs to respond to temperature change quickly, the sampling period is generally set to be in the second level, the outer layer controller mainly performs parameter optimization, and the sampling period can be set to be in the minute level or longer.
And synchronizing the optimized target track to the inner layer controller at the beginning of each outer layer control period to serve as a temperature tracking target of a new period. And simultaneously, feeding back the actual temperature information acquired by the inner layer controller to the outer layer controller for state estimation and parameter optimization. And a coordination mechanism is established between the inner layer controller and the outer layer controller, so that the coordination work of the inner layer controller and the outer layer controller is ensured. One strategy is that the outer layer controller only updates the target track and the optimized parameters at the sampling time, the inner layer controller controls based on the latest target track and parameters at the rest time, and the other strategy is that the outer layer controller continuously monitors the working state of the inner layer controller and adjusts the target track and the optimized parameters in real time according to the requirement.
Over time, battery characteristics and environmental conditions may change, requiring continuous optimization of the control system in a closed loop iterative manner. Specifically, the outer layer controller periodically re-evaluates the cost function according to the long-term temperature tracking effect and the energy consumption condition, triggers a new round of state estimation and parameter optimization, and generates an updated target track so as to adapt to the change of the system characteristics.
In an alternative embodiment of the present invention,
The inner layer controller is shown in the following formula:
；
The outer layer controller is shown in the following formula:
；
Wherein u f (t) represents the control output of the inner layer controller, K P、KI、KD represents the proportional, integral and differential control parameters of the inner layer controller, and e (t) represents the temperature tracking error;
u s (T) represents the control output of the outer layer controller, T f、t0 represents an initial time domain and a target time domain respectively, Q represents a temperature weight matrix, R represents a control weight matrix, and T (T) and T ref (T) represent an optimal cooling liquid temperature change sequence and a preset target control temperature respectively.
According to the application, the internal heat value and the heat propagation value of the battery are obtained, and the battery thermal model capable of accurately describing the temperature change rule of the battery is constructed by combining the current state of the battery, so that the influence of heat generation and surface heat dissipation in the battery is fully considered by the model, and the accuracy of temperature prediction is improved. In the design of the temperature prediction controller, the minimum energy consumption is taken as an optimization target, meanwhile, the battery temperature constraint condition is considered, and the optimal cooling liquid temperature change sequence capable of reducing the energy consumption of the cooling system to the maximum extent is obtained by solving the optimization problem on the premise of meeting the temperature constraint.
According to the optimal cooling liquid temperature change sequence, a layered control structure is constructed, the inner layer controller aims at temperature tracking, accurate adjustment of the battery temperature is achieved, the outer layer controller optimizes control parameters of the inner layer controller, and the adaptability of the control system to battery state change is improved. The operation of the two-layer controllers is coordinated through a multi-time-scale optimization control algorithm, and the robustness and the instantaneity of a control strategy are improved.
Fig. 2 is a schematic structural diagram of a temperature control and optimization system for an immersion energy storage battery according to an embodiment of the present invention, as shown in fig. 2, the system includes:
A first unit for obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target submerged energy storage battery, and determining an internal heat value of the target submerged energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
The second unit is used for acquiring the current battery temperature and the current battery current of the target submerged energy storage battery based on the battery thermal model, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
and the third unit is used for respectively constructing an inner layer controller with the temperature tracking as a target and an outer layer controller with the control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
In a third aspect of an embodiment of the present invention,
There is provided an electronic device including:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of an embodiment of the present invention,
There is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. A method for temperature control and optimization of an immersion energy storage cell, comprising:
obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target immersed energy storage battery, and determining an internal heat value of the target immersed energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
Based on the battery thermal model, acquiring the current battery temperature and the current battery current of the target submerged energy storage battery, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm;
Based on the internal heat value and the heat propagation value of the target submerged energy storage battery, constructing a battery thermal model of the target submerged energy storage battery comprises:
the battery thermal model was constructed according to the following formula:
；
；
Wherein, C p represents the heat capacity of the battery, T represents the temperature of the battery, T represents time, h c、hf represents a first conversion coefficient corresponding to natural convection and a second conversion coefficient corresponding to forced convection respectively, T a、Tf represents the ambient temperature and the temperature of the cooling liquid respectively, and A, A f represents the external surface area of the battery and the contact area with the cooling liquid respectively;
Q g represents an internal heat value, I represents a battery current, R represents a battery internal resistance, deltaS represents an entropy change value of battery reaction, and SOC represents a state of charge;
Based on the battery thermal model, obtaining the current battery temperature and the current battery current of the target submerged energy storage battery, and combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery, determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period comprises:
Based on the battery thermal model, obtaining a current battery temperature and a current battery current of the target submerged energy storage battery, in combination with a temperature prediction controller built with a goal of minimizing energy consumption, comprising:
Initializing controller parameters of the temperature prediction controller, wherein the controller parameters comprise at least one of a prediction step length, a control step length, a temperature reference value, a temperature constraint value and a penalty factor;
acquiring the current battery temperature and the current battery current of the target submerged energy storage battery, and determining the battery temperature change of the target submerged energy storage battery in a target time period by combining the battery thermal model;
Based on the battery temperature change of the target time period, combining the temperature constraint condition, optimizing the controller parameters of the temperature prediction controller in a rolling way by taking the minimized energy consumption as a target, and applying the control quantity output by the temperature prediction controller to a cooling system to obtain an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in the target time period;
Respectively constructing an inner layer controller with temperature tracking as a target and an outer layer controller with control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm comprises the following steps:
Determining a temperature tracking error according to the optimal cooling liquid temperature change sequence and a preset target control temperature, and determining an inner layer control output of the inner layer controller by combining a PID control algorithm, wherein the inner layer control output comprises a temperature control quantity of the target submerged energy storage battery;
Setting a control weight matrix for the inner layer control output, setting a temperature weight matrix for the temperature tracking error, updating a state estimation value by the outer layer controller, optimizing the updated state estimation value to generate a target track, and synchronizing the target track to the inner layer controller so that the inner layer controller outputs a final temperature control strategy, wherein the target track is used for indicating a desired value of temperature control.
2. The method of claim 1, wherein optimizing controller parameters of the temperature predictive controller for target rolling with minimized energy consumption comprises:
Determining a regression vector of the temperature prediction controller and a covariance matrix of the temperature prediction controller at the last moment corresponding to the current moment in each control period, and determining a gain matrix of the temperature prediction controller by combining a preset forgetting factor, wherein the regression vector is used for indicating a vector formed by measured values related to controller parameters of the temperature prediction controller;
And iteratively updating the controller parameters of the temperature prediction controller according to the gain matrix, the actual output value of the temperature prediction controller and the regression vector by the following formula:
；
；
Wherein, 、/>Represents the K-th and K-1-th controller parameters, respectively, K (K) represents the gain matrix of the K-th step, y (K) represents the actual output value of the K-th step,/>Representing the regression vector of the kth step, P (k-1) representing the covariance matrix of the kth-1 step, and λ representing a preset forgetting factor.
3. The method of claim 2, wherein the temperature predictive controller is represented by the formula:
；
Wherein, T f represents a coolant temperature value, N p、Nc represents a prediction step length and a control step length, T (k) represents a battery temperature predicted value of the kth step, T ref represents a battery temperature reference value, ρ represents a penalty factor corresponding to a control amount change, and DeltaT f (k) represents a coolant temperature change amount of the kth step.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
The inner layer controller is shown in the following formula:
；
The outer layer controller is shown in the following formula:
；
Wherein u f (t) represents the control output of the inner layer controller, K P、KI、KD represents the proportional, integral and differential control parameters of the inner layer controller, and e (t) represents the temperature tracking error;
u s (T) represents the control output of the outer layer controller, T f、t0 represents an initial time domain and a target time domain respectively, Q represents a temperature weight matrix, R represents a control weight matrix, and T (T) and T ref (T) represent an optimal cooling liquid temperature change sequence and a preset target control temperature respectively.
5. A temperature control and optimization system for an immersion energy storage cell for implementing the method of any of the preceding claims 1-4, comprising:
A first unit for obtaining a battery current, a battery internal resistance, an entropy change value of a battery reaction and a state of charge of a target submerged energy storage battery, and determining an internal heat value of the target submerged energy storage battery; acquiring the outer surface area of the target immersed energy storage battery, the cooling liquid temperature of the cooling liquid and a predetermined conversion coefficient, and determining the heat propagation value of the target immersed energy storage battery; constructing a battery thermal model of the target submerged energy storage battery based on the internal thermal value and the thermal propagation value of the target submerged energy storage battery;
The second unit is used for acquiring the current battery temperature and the current battery current of the target submerged energy storage battery based on the battery thermal model, and determining an optimal cooling liquid temperature change sequence of the target submerged energy storage battery in a target time period by combining a temperature prediction controller constructed by taking the minimum energy consumption as a target and a temperature constraint condition of the target submerged energy storage battery;
and the third unit is used for respectively constructing an inner layer controller with the temperature tracking as a target and an outer layer controller with the control parameters of the inner layer controller as a target according to the optimal cooling liquid temperature change sequence, and determining a temperature control strategy for the target submerged energy storage battery through a multi-time scale optimization control algorithm.
6. An electronic device, comprising:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 4.
7. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 4.
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