Lithium ion power battery direct cooling type thermal management system control method

Abstract

The invention provides a control method for a direct-cooling thermal management system of a lithium ion power battery. The control method comprises the following steps: establishing a battery thermal model state-space equation; performing parameter identification by adopting an adaptive forgetting factor least square method to obtain thermal model parameters; constructing a strong tracking unscented Kalman filter, inputting the thermal model parameters into the strong tracking unscented Kalman filter, introducing an adaptive particle swarm algorithm to obtain an optimized filter, and estimating the internal temperature of the battery in real time through the optimized filter; a fuzzy PID controller is constructed; and optimizing a quantization factor and a scaling factor of the fuzzy PID controller by adopting a multi-strategy improved parrot optimization algorithm to obtain an optimized fuzzy PID controller, and controlling the rotating speed of a compressor in the direct-cooling thermal management system model through the optimized fuzzy PID controller. The internal temperature of the battery can be accurately estimated, and the rotating speed of the compressor is efficiently controlled, so that the internal temperature of the battery is reduced to an ideal temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a control method for a direct-cooling thermal management system of a lithium-ion power battery. Background Art

[0002] Lithium-ion power batteries are the most commonly used batteries in electric vehicles. Their performance determines the endurance and safety of electric vehicles. During operation, the internal temperature of the battery may be much higher than the surface temperature, which will cause the internal temperature to reach the critical point in advance and cause safety accidents such as thermal runaway. In practical applications, the internal temperature of the battery is difficult to measure directly due to technical limitations. Therefore, it is necessary to establish an efficient thermal management system to accurately estimate the internal temperature of the battery and control it.

[0003] Direct cooling thermal management systems have attracted a lot of attention in recent years for their ability to achieve efficient heat transfer. As one of the core components of direct cooling thermal management systems, compressors play a role in compressing and driving the refrigerant refrigeration cycle. How to accurately estimate the internal temperature of the battery and efficiently control the compressor speed to cool the internal temperature of the battery to an ideal temperature is a technical problem that technicians in this field need to solve. Summary of the invention

[0004] The object of the present invention is to provide a control method for a direct-cooling thermal management system of a lithium-ion power battery, so as to accurately estimate the internal temperature of the battery and efficiently control the speed of the compressor, thereby cooling the internal temperature of the battery to an ideal temperature.

[0005] A method for controlling a direct-cooling thermal management system for a lithium-ion power battery comprises the following steps: Step S1, establishing a battery thermal model, and establishing a battery thermal model state space equation based on the established battery thermal model and Kirchhoff's current law; Step S2, based on the battery thermal model state space equation established in step S1, an adaptive forgetting factor least squares method is used to perform parameter identification to obtain thermal model parameters; Step S3, based on the battery thermal model state space equation established in step S1, construct a strong tracking unscented Kalman filter, input the thermal model parameters identified in step S2 into the strong tracking unscented Kalman filter, and introduce an adaptive particle swarm algorithm to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and use the optimized filter to estimate the internal temperature of the battery in real time; Step S4, constructing a fuzzy PID controller based on the battery internal temperature estimated in step S3 and taking the difference between the estimated battery internal temperature and the ideal temperature and the rate of change of the difference as input; Step S5, based on the fuzzy PID controller constructed in step S4, the quantization factor and the proportional factor of the fuzzy PID controller are optimized by using the Parrot optimization algorithm based on multi-strategy improvement to obtain an optimized fuzzy PID controller. The speed of the compressor in the direct cooling thermal management system model is controlled by the optimized fuzzy PID controller to cool the internal temperature of the battery to an ideal temperature.

[0006] The direct cooling thermal management system control method for lithium-ion power batteries provided by the present invention has the following beneficial effects: 1. The present invention fully considers the thermal characteristics of the battery to establish a battery thermal model, obtains thermal model parameters by performing parameter identification on the battery thermal model through the adaptive forgetting factor least squares method, and estimates the internal temperature of the battery in real time through the strong tracking unscented Kalman filter optimized by the adaptive particle swarm algorithm. Finally, the internal temperature of the battery is controlled by optimizing the fuzzy PID controller based on the improved Parrot optimization algorithm based on multiple strategies. 2. Different from the general Kalman filter, the present invention adopts a strong tracking unscented Kalman filter improved by an adaptive particle swarm algorithm. The traditional Kalman filter has low estimation accuracy. The present invention introduces a strong tracking filter on the basis of the traditional unscented Kalman filter, which enhances the stability of the filtering process, and adopts an adaptive particle swarm algorithm to optimize the process noise covariance and the observation noise covariance, thereby improving the estimation accuracy of the internal temperature of the battery; 3. Different from the general PID controller, the present invention adopts a fuzzy PID controller optimized by the Parrot optimization algorithm based on multi-strategy improvement. The traditional PID controller relies on empirical formulas or trial and error methods, and it is difficult to achieve optimal performance. The present invention introduces fuzzy control on the basis of the traditional PID controller, and automatically adjusts the PID parameters through fuzzy rules, which can adapt to different working conditions. The present invention adopts the Parrot optimization algorithm based on multi-strategy improvement to optimize the fuzzy PID controller, and obtains better control effect. 4. The method proposed in the present invention does not require a large number of sensors for estimating the internal temperature of the battery, which reduces costs. The control process reduces the energy consumption of the compressor while controlling the internal temperature of the battery, and has wide applicability and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flow chart of a control method of a direct-cooling thermal management system for a lithium-ion power battery of the present invention; Figure 2 A comparison diagram of the effect of estimating the internal temperature of a battery using the method proposed in the present invention and the traditional extended Kalman filter; Figure 3 This is a comparison chart of the effects of controlling battery temperature by the method proposed in the present invention and the traditional PID controller. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0009] See also Figure 1 The lithium-ion power battery direct cooling thermal management system control method provided by the embodiment of the present invention includes steps S1 to S5.

[0010] Step S1, establishing a battery thermal model, and establishing a battery thermal model state space equation based on the established battery thermal model and Kirchhoff's current law.

[0011] The direct cooling thermal management system for lithium-ion power batteries includes a compressor, a condenser, and an electronic expansion valve. The present invention mainly controls the compressor.

[0012] Among them, the established battery thermal model state space equation is: ; ; After discretization, we get: ; ; in, C in and C o are the internal heat capacity and surface heat capacity of lithium-ion power batteries, T ine The value is the internal temperature of the battery minus the ambient temperature. T oe The value is the battery surface temperature minus the ambient temperature. T ine = T in - T e , T oe = T o - T e , T in is the internal temperature of the battery, T o is the battery surface temperature, Te is the ambient temperature, dT ine express T ine The differential of dt Indicates time t The differential of dT oe express T oe The differential of T ine ( k +1) and T ine ( k ) are respectively k +1 moment with k The value of the battery internal temperature minus the ambient temperature at that moment. T oe ( k +1) and T oe ( k ) are respectively k +1 moment with k The value of the battery surface temperature minus the ambient temperature at the moment, is the sampling interval, R in and R o are the internal thermal resistance and external thermal resistance of the battery, Q o The heat generated during battery operation. Q o ( k ) is the battery k The heat generated at any time, Q o The calculation is performed using the following formula: ; ; in, I a is the charge and discharge current of the battery, U a and U are the open circuit voltage and terminal voltage of the battery respectively, T a is the average temperature of the battery, is the entropy thermal coefficient, Ud a for U a The differential of dT a for T a The differential of .

[0013] Step S2, based on the battery thermal model state space equation established in step S1, an adaptive forgetting factor least square method is used to perform parameter identification to obtain thermal model parameters.

[0014] Wherein, step S2 specifically includes: The battery thermal model state space equation established in step S1 is written in differential form: ; ; ; ; ; ; in, T o ( k +2) k +2 The surface temperature of the battery at time T o ( k +1) k The surface temperature of the battery at time +1, T o ( k )for k The surface temperature of the battery at the moment, is the parameter matrix to be identified, X, , Z is the parameter to be identified, T represents transposition, for k A collection of data at a certain moment; Then the adaptive forgetting factor least squares method is used for calculation: ; ; ; in, and They are k +1 moment and k The parameter estimate matrix at time , G ( k +1) k The gain matrix at time +1, N a ( k +1) and N a ( k ) are respectively k +1 moment and kThe error covariance matrix at time , for k +1 moment data set, is the identity matrix, For the forgetting factor, , the adaptive adjustment formula of the forgetting factor is as follows: ; ; in, , and Forgetting Factor k The value at the moment, the maximum forgetting factor and the minimum forgetting factor, is the sensitivity coefficient, for k The forgetting factor update coefficient at the moment, round is the rounding function, R ( k )for k The error in time, R y For allowable error.

[0015] Step S3, based on the battery thermal model state space equation established in step S1, construct a strong tracking unscented Kalman filter, input the thermal model parameters identified in step S2 into the strong tracking unscented Kalman filter, and introduce an adaptive particle swarm algorithm to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and use the optimized filter to estimate the internal temperature of the battery in real time.

[0016] Wherein, step S3 specifically includes: Define a nonlinear discrete dynamic system: ; ; ; ; ; ; in, and They are k +1 moment with k The state variables at time, g and h are the state function and the measurement function, respectively. S ( k )for k The input variables at time, for k +1 time observation variables, and They are k The state noise at the moment and k The measurement noise at each moment; Then the strong tracking unscented Kalman filter is obtained by the following steps: According to UT transformation, we can get 2 n +1 Sigma point set, generate the expression of Sigma point set and corresponding weight:

[0017]

[0018] in, n is the state dimension, 1 to 2 n Integers between is the initial point set, for k The state estimate at time , For the Sgima point set, is the process variable, , and To control the parameters of Sigma point distribution, P a ( k )for k The covariance matrix at time , and Respectively k The covariance matrix of the moment Column and List, and are the initial state mean weight and covariance weight respectively, and Respectively The initial state weights and covariance weights of the Sigma points, is the weighting coefficient; The expression for propagating Sigma points is as follows: ; in, For the Sigma point set according to k Predicted at all times k +1 point set, z is the state transfer function; The prior estimates and covariance matrix are calculated using the following formula: ; ; in, for k +1 time state estimation, for k The covariance matrix at time +1, for k The process noise covariance matrix at time ; A new set of Sigma points is generated by UT transformation, and the expression is:

[0019] in, is the updated initial Sigma point set, and Respectively k The covariance matrix of the Column and List; Substitute the new Sigma point set into the observation equation to obtain the observation value expression of the sampling point: ; in, Indicates A set of observation points; Calculate the new predicted observation mean using the weights: ; in, for k +1 The observed mean of the forecast; In order to solve the problem that the system state suddenly changes and the unscented Kalman filter cannot accurately track, the fading factor is introduced into the prediction covariance matrix to obtain a strong tracking unscented Kalman filter. The calculation formula of the fading factor is as follows:

[0020] ; ; ; in, F ( k +1) k +1 time innovation covariance matrix, F ( k )for k The innovation covariance matrix at time , is the forgetting factor of the strong tracking filter, e (k +1) k The residual at time +1, for k The mean of the observations at time +1, is the undetermined factor, for k +1 time fading factor, Q m ( k +1) and R m ( k +1) respectively k The process noise covariance matrix and measurement noise covariance matrix at time +1, I is the observation matrix, tr is the trace operator, and max is the maximum value function; Then the prediction covariance matrix of the strong tracking unscented Kalman filter is expressed as: ; ; in, and They are k The measurement covariance matrix and cross covariance matrix at time +1, R m ( k )for k The measurement noise covariance matrix at time t; The calculation formula of Kalman gain is: ; in, K ( k +1) k Kalman gain at time +1; The system state update formula and covariance update formula are: ; ; in, For the updated k +1 time state estimation, For the updated k +1 moment covariance matrix; In the traditional particle swarm algorithm, dynamic inertia weight and learning factor are introduced for optimization: ; ; ; in, iter and iter max are the current number of iterations and the maximum number of iterations respectively, w iter is the inertia weight of the current iteration number, w max and w min is the maximum and minimum value of the inertia weight, z 1 and z 2 are local learning factor and global learning factor respectively, z max and z min are the maximum and minimum values ​​of the learning factor respectively; Optimization Dimension of Adaptive Particle Swarm Optimization D best for: ; in, diag represents a diagonal matrix, q m1 , q m2 is the value of process noise, r m1 is the value of the observation noise; Fitness function of adaptive particle swarm optimization algorithm Function for: ; in, t 1 Optimizing the runtime of a strong tracking unscented Kalman filter for an adaptive particle swarm optimization algorithm, for k The observed variables at time, for k Strongly track the estimated value of the unscented Kalman filter output at all times; The process noise and observation noise optimized by the adaptive particle swarm algorithm are then input into the strong tracking unscented Kalman filter to obtain the optimized filter, through which the internal temperature of the battery is estimated in real time.

[0021] Step S4, based on the battery internal temperature estimated in step S3, and taking the difference between the estimated battery internal temperature and the ideal temperature and the rate of change of the difference as input, construct a fuzzy PID controller.

[0022] In this embodiment, the ideal temperature is, for example, 25°C.

[0023] Step S4 specifically includes: The difference between the estimated internal battery temperature and the ideal temperature e w Sum difference change rate ec w is the input variable of the fuzzy PID controller, and the proportional coefficient of the fuzzy PID controller K p Adjustment amount , integral coefficient K i Adjustment amount , differential coefficient K d Adjustment amount For the output variable, a fuzzy PID controller is constructed, and the fuzzy domain of the input variable and the output variable is divided into five fuzzy subsets, which are: NB (Negative big), NS (negative small), ZO (zero), PS (Small), PB (Zhengda); The triangular function is used as the membership function of the fuzzy subset, and the analytical expression is:

[0024] in, f t ( x ) is a triangular function, x is the input variable of the fuzzy PID controller; , and are the left endpoint, vertex, and right endpoint of the triangle, corresponding to the left boundary, peak, and right boundary of the fuzzy set; The quantitative factor calculation formula for converting the clarity into the blur is: ; ; in, and They are e w and ec w The quantization factor, n w1 and n w2 They are e w and ec w The scope of the domain, and They are e wand ec w The maximum value of The proportional factor calculation formula is: ; ; ; in, is the proportionality factor, is the integral coefficient proportional factor, is the differential coefficient proportional factor, n w3 , n w4 , n w5 They are respectively the domain range of proportional coefficient, the domain range of integral coefficient, and the domain range of differential coefficient.

[0025] The design of fuzzy rules for compressor speed control is the core of fuzzy PID controller. , and The size is positively correlated with the speed of the compressor. , and It can efficiently control the internal temperature of the battery while minimizing the energy consumption of the compressor.

[0026] In this embodiment, fuzzy logic reasoning uses "if-then" statements to infer the corresponding compressor speed according to the changes in battery temperature difference and temperature difference change rate. When the battery temperature difference and temperature difference change rate are large, the compressor speed should be increased. and , and The appropriate value should be selected to make the internal temperature of the battery quickly reach the ideal temperature value. When the battery temperature difference and the temperature difference change rate are small, the and , and Appropriate values ​​are taken to reduce the energy consumption of the compressor and make the internal temperature of the battery reach the ideal temperature value. Based on the above principles, a fuzzy rule base is formulated, and the weighted average method is used to defuzzify the compressor speed control. The calculation formula of the weighted average method is: ; in, u v is the actual value after defuzzification, x i is the first i values, u t ( x i )forx i The membership value of o is the total number of fuzzy sets.

[0027] Step S5, based on the fuzzy PID controller constructed in step S4, the quantization factor and the proportional factor of the fuzzy PID controller are optimized by using the Parrot optimization algorithm based on multi-strategy improvement to obtain an optimized fuzzy PID controller. The speed of the compressor in the direct cooling thermal management system model is controlled by the optimized fuzzy PID controller to cool the internal temperature of the battery to an ideal temperature.

[0028] Wherein, step S5 specifically includes: In order to improve the global search capability of the Parrot optimization algorithm and avoid the algorithm falling into the local optimum, the Bernoulli chaotic map is introduced into the Parrot optimization algorithm. The expression of the Bernoulli chaotic map is:

[0029] in, X v+1 and X v Respectively v +1 and v The chaos of a parrot, is the control parameter, 0< <1; As the iteration proceeds, the weight gradually tends to local search to speed up convergence. In order to avoid the inefficient information propagation behavior of the Parrot Optimization Algorithm in the later iterations, the global search and local convergence capabilities of the adaptive weight switching factor optimization algorithm are introduced into the Parrot Optimization Algorithm. The expression is as follows: ; in, F is the current iteration number of the Parrot optimization algorithm, F max is the current iteration number of the Parrot optimization algorithm, For the F The weight of the iteration, and are the maximum and minimum values ​​of the weight respectively; In addition, in order to balance the needs of global exploration and local search and improve the fitness and convergence speed of the parrot algorithm in complex optimization problems, Cauchy and Gaussian mutations are introduced into the parrot optimization algorithm, and finally the parrot optimization algorithm based on multi-strategy improvement is obtained. The expressions for introducing Cauchy and Gaussian mutations are as follows: ; in, X best (F) is the parrot population inF The optimal position in the iteration, For the F The new positions generated by the Cauchy and Gaussian mutations are iterated. and are random numbers that obey Cauchy and Gaussian distributions respectively; The quantization factor and proportional factor of the fuzzy PID controller are used as the optimization dimension of the Parrot optimization algorithm based on multi-strategy improvement U best , the expression is: ; Fitness function of parrot optimization algorithm based on multi-strategy improvement F u for: ; in, t 2 To optimize the running time of fuzzy PID controller based on the improved Parrot optimization algorithm based on multiple strategies, for k The estimated value of the internal temperature of the battery at the moment, T bat is the ideal temperature for the battery; The quantization factor and proportional factor optimized by the Parrot optimization algorithm improved based on multiple strategies are input into the fuzzy PID controller to obtain the optimized fuzzy PID controller. Finally, the speed of the compressor in the direct cooling thermal management system model is controlled by the optimized fuzzy PID controller to cool the internal temperature of the battery to the ideal temperature.

[0030] Figure 2 This is a comparison diagram of the effect of estimating the internal temperature of the battery by the method proposed in the present invention and the traditional extended Kalman filter. Figure 3 This is a comparison chart of the effects of the method proposed in the present invention and the traditional PID controller in controlling the battery temperature. Figure 2 and Figure 3 It can be seen that the method proposed in the present invention significantly improves the estimation and control effect of the internal temperature of the battery.

[0031] In summary, the direct cooling thermal management system control method for lithium-ion power batteries provided by the present invention has the following beneficial effects: 1. The present invention fully considers the thermal characteristics of the battery to establish a battery thermal model, obtains thermal model parameters by performing parameter identification on the battery thermal model through the adaptive forgetting factor least squares method, and estimates the internal temperature of the battery in real time through the strong tracking unscented Kalman filter optimized by the adaptive particle swarm algorithm. Finally, the internal temperature of the battery is controlled by optimizing the fuzzy PID controller based on the improved Parrot optimization algorithm based on multiple strategies. 2. Different from the general Kalman filter, the present invention adopts a strong tracking unscented Kalman filter improved by an adaptive particle swarm algorithm. The traditional Kalman filter has low estimation accuracy. The present invention introduces a strong tracking filter on the basis of the traditional unscented Kalman filter, which enhances the stability of the filtering process, and adopts an adaptive particle swarm algorithm to optimize the process noise covariance and the observation noise covariance, thereby improving the estimation accuracy of the internal temperature of the battery; 3. Different from the general PID controller, the present invention adopts a fuzzy PID controller optimized by the Parrot optimization algorithm based on multi-strategy improvement. The traditional PID controller relies on empirical formulas or trial and error methods, and it is difficult to achieve optimal performance. The present invention introduces fuzzy control on the basis of the traditional PID controller, and automatically adjusts the PID parameters through fuzzy rules, which can adapt to different working conditions. The present invention adopts the Parrot optimization algorithm based on multi-strategy improvement to optimize the fuzzy PID controller, and obtains better control effect. 4. The method proposed in the present invention does not require a large number of sensors for estimating the internal temperature of the battery, which reduces costs. The control process reduces the energy consumption of the compressor while controlling the internal temperature of the battery, and has wide applicability and economy.

[0032] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0033] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A control method for a direct cooling thermal management system of a lithium-ion power battery, characterized in that: include: Step S1, establishing a battery thermal model, and establishing a battery thermal model state space equation based on the established battery thermal model and Kirchhoff's current law; Step S2, based on the battery thermal model state space equation established in step S1, an adaptive forgetting factor least squares method is used to perform parameter identification to obtain thermal model parameters; Step S3, based on the battery thermal model state space equation established in step S1, construct a strong tracking unscented Kalman filter, input the thermal model parameters identified in step S2 into the strong tracking unscented Kalman filter, and introduce an adaptive particle swarm algorithm to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and use the optimized filter to estimate the internal temperature of the battery in real time; Step S4, constructing a fuzzy PID controller based on the battery internal temperature estimated in step S3 and taking the difference between the estimated battery internal temperature and the ideal temperature and the rate of change of the difference as input; Step S5, based on the fuzzy PID controller constructed in step S4, the quantization factor and the proportional factor of the fuzzy PID controller are optimized by using the Parrot optimization algorithm based on multi-strategy improvement to obtain an optimized fuzzy PID controller. The speed of the compressor in the direct cooling thermal management system model is controlled by the optimized fuzzy PID controller to cool the internal temperature of the battery to an ideal temperature.

2. The direct cooling thermal management system control method for a lithium-ion power battery according to claim 1, characterized in that: In step S1, the state space equation of the battery thermal model is established as: ； ； After discretization, we get: ； ； in, C in and C o are the internal heat capacity and surface heat capacity of lithium-ion power batteries, T ine The value is the internal temperature of the battery minus the ambient temperature. T oe The value is the battery surface temperature minus the ambient temperature. T ine = T in - T e , T oe = T o - T e , T in is the internal temperature of the battery, T o is the battery surface temperature, T e is the ambient temperature, dT ine express T ine The differential of dt Indicates time t The differential of dT oe express T oe The differential of T ine ( k +1) and T ine ( k ) are respectively k +1 moment with k The value of the battery internal temperature minus the ambient temperature at that moment. T oe ( k +1) and T oe ( k ) are respectively k +1 moment with k The value of the battery surface temperature minus the ambient temperature at the moment, is the sampling interval, R in and R o are the internal thermal resistance and external thermal resistance of the battery, Q o The heat generated during battery operation. Q o ( k ) is the battery k The heat generated at any time, Q o The calculation is performed using the following formula: ； ； in, I a is the charge and discharge current of the battery, U a and U are the open circuit voltage and terminal voltage of the battery respectively, T a is the average temperature of the battery, is the entropy thermal coefficient, Ud a for U a The differential of dT a for T a The differential of .

3. The direct cooling thermal management system control method for a lithium-ion power battery according to claim 2, characterized in that: Step S2 specifically includes: The battery thermal model state space equation established in step S1 is written in differential form: ； ； ； ； ； ； in, T o ( k +2) k +2 The surface temperature of the battery at time T o ( k +1) k +1 time battery surface temperature, T o ( k )for k The surface temperature of the battery at the moment, is the parameter matrix to be identified, X, , Z is the parameter to be identified, T represents transposition, for k A collection of data at a certain moment; Then the adaptive forgetting factor least squares method is used for calculation: ； ； ； in, and They are k +1 moment and k The parameter estimate matrix at time , G ( k +1) k The gain matrix at time +1, N a ( k +1) and N a ( k ) are respectively k +1 moment and k The error covariance matrix at time , for k +1 moment data set, is the identity matrix, For the forgetting factor, , the forgetting factor adaptive adjustment formula is as follows: ； ； in, , and Forgetting Factor k The value at the moment, the maximum forgetting factor and the minimum forgetting factor, is the sensitivity coefficient, for k The forgetting factor update coefficient at the moment, round is the rounding function, R ( k )for k The error in time, R y For allowable error.

4. The direct cooling thermal management system control method for a lithium-ion power battery according to claim 3, characterized in that: Step S3 specifically includes: Define a nonlinear discrete dynamic system: ； ； ； ； ； ； in, and They are k +1 moment with k The state variables at time, g and h are the state function and the measurement function, respectively. S ( k )for k The input variables at time, for k +1 time observation variables, and They are k The state noise at the moment and k The measurement noise at each moment; Then the strong tracking unscented Kalman filter is obtained by the following steps: According to UT transformation, we can get 2 n +1 Sigma point set, generate the expression of Sigma point set and corresponding weight: in, n is the state dimension, 1 to 2 n Integers between is the initial point set, for k The state estimate at time , For the Sgima point set, is the process variable, , and To control the parameters of Sigma point distribution, P a ( k )for k The covariance matrix at time , and Respectively k The covariance matrix of the moment Column and List, and are the initial state mean weight and covariance weight respectively, and Respectively The initial state weights and covariance weights of the Sigma points, is the weighting coefficient; The expression for propagating Sigma points is as follows: ； in, For the Sigma point set according to k Predicted at all times k +1 point set, z is the state transfer function; The prior estimates and covariance matrix are calculated using the following formula: ； ； in, for k +1 time state estimation, for k The covariance matrix at time +1, for k The process noise covariance matrix at time ; A new set of Sigma points is generated by UT transformation, and the expression is: in, is the updated initial Sigma point set, and Respectively k The covariance matrix of the Column and List; Substitute the new Sigma point set into the observation equation to obtain the observation value expression of the sampling point: ； in, Indicates A set of observation points; Calculate the new predicted observation mean using the weights: ； in, for k +1 The observed mean of the forecast; The fading factor is introduced into the prediction covariance matrix to obtain the strong tracking unscented Kalman filter. The calculation formula of the fading factor is as follows: ； ； ； in, F ( k +1) k +1 time innovation covariance matrix, F ( k )for k The innovation covariance matrix at time , is the forgetting factor of the strong tracking filter, e ( k +1) k The residual at time +1, for k The mean of the observations at time +1, is the undetermined factor, for k +1 time fading factor, Q m ( k +1) and R m ( k +1) respectively k The process noise covariance matrix and measurement noise covariance matrix at time +1, I is the observation matrix, tr is the trace operator, and max is the maximum value function; Then the prediction covariance matrix of the strong tracking unscented Kalman filter is expressed as: ； ； in, and They are k The measurement covariance matrix and cross covariance matrix at time +1, R m ( k )for k The measurement noise covariance matrix at time t; The calculation formula of Kalman gain is: ； in, K ( k +1) k Kalman gain at time +1; The system state update formula and covariance update formula are: ； ； in, For the updated k The state estimate at time +1, For the updated k +1 moment covariance matrix; In the traditional particle swarm algorithm, dynamic inertia weight and learning factor are introduced for optimization: ； ； ； in, iter and iter max are the current number of iterations and the maximum number of iterations respectively, w iter is the inertia weight of the current iteration number, w max and w min is the maximum and minimum value of the inertia weight, z 1 and z 2 are local learning factor and global learning factor respectively, z max and z min are the maximum and minimum values ​​of the learning factor respectively; Optimization Dimension of Adaptive Particle Swarm Optimization D best for: ； in, diag represents a diagonal matrix, q m1 , q m2 is the value of process noise, r m1 is the value of the observation noise; Fitness function of adaptive particle swarm optimization algorithm Function for: ； in, t 1 is the running time of the adaptive particle swarm algorithm to optimize the strong tracking unscented Kalman filter. for k The observed variables at time, for k Strongly track the estimated value of the unscented Kalman filter output at all times; The process noise and observation noise optimized by the adaptive particle swarm algorithm are then input into the strong tracking unscented Kalman filter to obtain the optimized filter, through which the internal temperature of the battery is estimated in real time.

5. The lithium-ion power battery direct cooling thermal management system control method according to claim 4, characterized in that: Step S4 specifically includes: The difference between the estimated internal battery temperature and the ideal temperature e w Sum difference change rate ec w is the input variable of the fuzzy PID controller, and the proportional coefficient of the fuzzy PID controller K p Adjustment amount , integral coefficient K i Adjustment amount , differential coefficient K d Adjustment amount For the output variable, a fuzzy PID controller is constructed, and the fuzzy domain of the input variable and the output variable is divided into five fuzzy subsets, namely: negative large, negative small, zero, positive small, and positive large; The triangular function is used as the membership function of the fuzzy subset, and the analytical expression is: in, f t ( x ) is a triangular function, x is the input variable of the fuzzy PID controller; , and are the left endpoint, vertex, and right endpoint of the triangle, corresponding to the left boundary, peak, and right boundary of the fuzzy set; The quantitative factor calculation formula for converting the clarity into the blur is: ； ； in, and They are e w and ec w The quantization factor, n w1 and n w2 They are e w and ec w The scope of the domain, and They are e w and ec w The maximum value of The proportional factor calculation formula is: ； ； ； in, is the proportionality factor, is the integral coefficient proportional factor, is the differential coefficient proportional factor, n w3 , n w4 , n w5 They are the domain range of proportional coefficient, the domain range of integral coefficient, and the domain range of differential coefficient respectively; A fuzzy rule base is formulated, and the weighted average method is used to perform defuzzification operation on the compressor speed control. The calculation formula of the weighted average method is: ； in, u v is the actual value after defuzzification, x i is the first i values, u t ( x i )for x i The membership value of o is the total number of fuzzy sets.

6. The lithium-ion power battery direct cooling thermal management system control method according to claim 5, characterized in that: Step S5 specifically includes: The Bernoulli chaotic map is introduced into the Parrot optimization algorithm. The expression of the Bernoulli chaotic map is: in, X v+1 and X v Respectively v +1 and v The chaos of a parrot, is the control parameter, 0< <1; An adaptive weight switching factor is introduced into the Parrot optimization algorithm, and the expression is as follows: ； in, F is the current iteration number of the Parrot optimization algorithm, F max is the current iteration number of the Parrot optimization algorithm, For the F The weight of the iteration, and are the maximum and minimum values ​​of the weight respectively; In addition, Cauchy and Gaussian mutations are introduced into the Parrot optimization algorithm, and finally the Parrot optimization algorithm based on multi-strategy improvement is obtained. The expressions for introducing Cauchy and Gaussian mutations are as follows: ； in, X best (F) is the parrot population in F The optimal position in the iteration, For the F The new positions generated by the Cauchy and Gaussian mutations are iterated. and are random numbers that obey Cauchy and Gaussian distributions respectively; The quantization factor and proportional factor of the fuzzy PID controller are used as the optimization dimension of the Parrot optimization algorithm based on multi-strategy improvement U best , the expression is: ； Fitness function of parrot optimization algorithm based on multi-strategy improvement F u for: ； in, t 2 is to optimize the running time of the fuzzy PID controller based on the improved Parrot optimization algorithm based on multiple strategies. for k The estimated value of the internal temperature of the battery at the moment, T bat is the ideal temperature for the battery; The quantization factor and proportional factor optimized by the Parrot optimization algorithm improved based on multiple strategies are input into the fuzzy PID controller to obtain the optimized fuzzy PID controller. Finally, the speed of the compressor in the direct cooling thermal management system model is controlled by the optimized fuzzy PID controller to cool the internal temperature of the battery to the ideal temperature.

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