A control method for a direct-cooling thermal management system for a lithium-ion power battery

Abstract

The present invention provides a control method for a direct-cooling thermal management system for a lithium-ion power battery, comprising: establishing a battery thermal model state-space equation; employing an adaptive forgetting factor least squares method for parameter identification to obtain thermal model parameters; constructing a strong tracking unscented Kalman filter, inputting the thermal model parameters into the strong tracking unscented Kalman filter, and introducing an adaptive particle swarm optimization algorithm to obtain an optimized filter, thereby using the optimized filter to estimate the internal temperature of the battery in real time; constructing a fuzzy PID controller; employing a multi-strategy improved Parrot optimization algorithm to optimize the quantization factor and scaling factor of the fuzzy PID controller to obtain the optimized fuzzy PID controller, and controlling the speed of a compressor in the direct-cooling thermal management system model using the optimized fuzzy PID controller. The present invention can accurately estimate the internal temperature of the battery and efficiently control the compressor speed, thereby cooling the internal temperature of the battery to a desired 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 batteries are the most common battery used in electric vehicles. Their performance determines the vehicle's range and safety. During operation, the battery's internal temperature can be significantly higher than its surface temperature, causing it to reach a critical point prematurely, leading to safety incidents such as thermal runaway. In practical applications, direct measurement of the battery's internal temperature is difficult due to technical limitations. Therefore, an efficient thermal management system is needed to accurately estimate and control the battery's internal temperature.

[0003] Direct-cooling thermal management systems have garnered significant attention in recent years for their efficient heat transfer. As a core component of these systems, the compressor compresses and drives the refrigerant in the refrigeration cycle. Accurately estimating the internal battery temperature and efficiently controlling the compressor speed to cool the battery to the desired temperature are key challenges facing those skilled in the art. 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:

[0006] 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;

[0007] Step S2, based on the battery thermal model state space equation established in step S1, use the adaptive forgetting factor least squares method to perform parameter identification to obtain thermal model parameters;

[0008] Step S3: Based on the battery thermal model state space equation established in step S1, a strong tracking unscented Kalman filter is constructed, the thermal model parameters identified in step S2 are input into the strong tracking unscented Kalman filter, and an adaptive particle swarm algorithm is introduced to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and the internal temperature of the battery is estimated in real time through the optimized filter;

[0009] Step S4, constructing a fuzzy PID controller based on the battery internal temperature estimated in step S3 and using the difference between the estimated battery internal temperature and the ideal temperature and the rate of change of the difference as input;

[0010] In 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 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.

[0011] The control method of the direct-cooling thermal management system for a lithium-ion power battery provided by the present invention has the following beneficial effects:

[0012] 1. The present invention fully considers the thermal characteristics of the battery to establish a battery thermal model, performs parameter identification on the battery thermal model through the adaptive forgetting factor least squares method to obtain the thermal model parameters, and uses the strong tracking unscented Kalman filter optimized by the adaptive particle swarm algorithm to estimate the internal temperature of the battery in real time. Finally, the fuzzy PID controller is optimized by the Parrot optimization algorithm based on multiple strategies to control the internal temperature of the battery.

[0013] 2. Unlike conventional Kalman filters, the present invention adopts a strong tracking unscented Kalman filter improved by an adaptive particle swarm algorithm. Conventional Kalman filters have low estimation accuracy. This invention introduces a strong tracking filter based on the conventional unscented Kalman filter, enhancing the stability of the filtering process. Furthermore, the adaptive particle swarm algorithm is used to optimize the process noise covariance and the observation noise covariance, thereby improving the estimation accuracy of the battery internal temperature.

[0014] 3. Unlike general PID controllers, the present invention adopts a fuzzy PID controller optimized by the Parrot optimization algorithm based on multiple strategies. Traditional PID controllers rely on empirical formulas or trial and error methods, which are difficult to achieve optimal performance. The present invention introduces fuzzy control on the basis of traditional PID controllers, automatically adjusts PID parameters through fuzzy rules, and can adapt to different working conditions. In addition, the present invention adopts the Parrot optimization algorithm based on multiple strategies to optimize the fuzzy PID controller, thereby achieving better control effect.

[0015] 4. The method proposed in the present invention does not require a large number of sensors in the process of 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

[0016] Figure 1 Schematic diagram of the flow of the direct cooling thermal management system control method for lithium-ion power batteries of the present invention;

[0017] Figure 2 A comparison diagram of the effects of estimating the internal temperature of a battery using the method proposed in the present invention and the traditional extended Kalman filter;

[0018] Figure 3 This is a comparison chart of the effects of controlling battery temperature using the method proposed in the present invention and a traditional PID controller. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 making creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 The embodiment of the present invention provides a direct-cooling thermal management system control method for a lithium-ion power battery, comprising steps S1 to S5.

[0021] 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.

[0022] 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.

[0023] Among them, the state space equation of the established battery thermal model is:

[0024] ;

[0025] ;

[0026] After discretization, we get:

[0027] ;

[0028] ;

[0029] in, C in and C o are the internal heat capacity and surface heat capacity of lithium-ion power batteries, T ine The value of the battery internal temperature 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 that 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 all times, Q o Use the following formula to perform the calculation:

[0030] ;

[0031] ;

[0032] in, I a is the battery charge and discharge current, 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, dU a for U a The differential of dT a for T a The differential of .

[0033] 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.

[0034] Wherein, step S2 specifically includes:

[0035] The battery thermal model state space equation established in step S1 is written in differential form:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] in, T o ( k +2) k +2 moment battery surface temperature, 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 all times, is the parameter matrix to be identified, X, , Z is the parameter to be identified, T represents transposition, for kThe data set at a certain moment;

[0043] Then the adaptive forgetting factor least squares method is used for calculation:

[0044] ;

[0045] ;

[0046] ;

[0047] 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 time data set, is the identity matrix, For the forgetting factor, , the forgetting factor adaptive adjustment formula is as follows:

[0048] ;

[0049] ;

[0050] 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 the allowable error.

[0051] In step S3, based on the battery thermal model state-space equation established in step S1, a strong tracking unscented Kalman filter is constructed, the thermal model parameters identified in step S2 are input into the strong tracking unscented Kalman filter, and an adaptive particle swarm algorithm is introduced to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and the internal temperature of the battery is estimated in real time through the optimized filter.

[0052] Wherein, step S3 specifically includes:

[0053] Define a nonlinear discrete dynamic system:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] 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;

[0061] Then the strong tracking unscented Kalman filter is obtained by the following steps:

[0062] According to UT transformation, we can get 2 n +1 Sigma point set, generate the expression of Sigma point set and corresponding weight:

[0063]

[0064]

[0065] 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 t, 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;

[0066] The expression for propagating Sigma points is as follows:

[0067] ;

[0068] in, For the Sigma point set according to k Predicted at all times k +1 point set, z is the state transition function;

[0069] The prior estimates and covariance matrix are calculated using the following formula:

[0070] ;

[0071] ;

[0072] in, for k +1 time state estimation, for k The covariance matrix at time +1, for k The process noise covariance matrix at time t;

[0073] Generate a new set of Sigma points through UT transformation, the expression is:

[0074]

[0075] in, is the updated initial Sigma point set, and Respectively k The covariance matrix of the moment +1 Column and List;

[0076] Substitute the new Sigma point set into the observation equation to obtain the observation value expression of the sampling point:

[0077] ;

[0078] in, Indicates the A set of observation points;

[0079] Compute the new predicted observation mean using the weights:

[0080] ;

[0081] in, for k +1 time predicted observation mean;

[0082] 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:

[0083]

[0084] ;

[0085] ;

[0086] ;

[0087] in, F ( k +1) k The innovation covariance matrix at time +1, 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 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;

[0088] Then the prediction covariance matrix of the strong tracking unscented Kalman filter is expressed as:

[0089] ;

[0090] ;

[0091] 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;

[0092] The calculation formula of Kalman gain is:

[0093] ;

[0094] in, K ( k +1) k Kalman gain at time +1;

[0095] The system state update formula and covariance update formula are:

[0096] ;

[0097] ;

[0098] in, For the updated k +1 time state estimation, For the updated k The covariance matrix at time +1;

[0099] In the traditional particle swarm optimization, dynamic inertia weight and learning factor are introduced:

[0100] ;

[0101] ;

[0102] ;

[0103] 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 are the maximum and minimum values of the inertia weight, z 1 and z 2 are local learning factors and global learning factors respectively, z max and z min are the maximum and minimum values of the learning factor respectively;

[0104] Optimization Dimension of Adaptive Particle Swarm Optimization D best for:

[0105] ;

[0106] 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;

[0107] Fitness function of adaptive particle swarm optimization algorithm Function for:

[0108] ;

[0109] in, t 1 is the running time of the adaptive particle swarm optimization strong tracking unscented Kalman filter, for k The observed variables at time, for k Strong tracking of the estimated value of the unscented Kalman filter output at all times;

[0110] The process noise and observation noise after optimization by the adaptive particle swarm algorithm are then input into the strong tracking unscented Kalman filter to obtain the optimized filter, and the internal temperature of the battery is estimated in real time through the optimized filter.

[0111] In step S4, a fuzzy PID controller is constructed based on the battery internal temperature estimated in step S3 and using the difference between the estimated battery internal temperature and the ideal temperature and the rate of change of the difference as input.

[0112] In this embodiment, the ideal temperature is, for example, 25°C.

[0113] Step S4 specifically includes:

[0114] The difference between the estimated internal battery temperature and the ideal temperature e w Sum and difference rate of change ec w is the input variable of the fuzzy PID controller, and the proportional coefficient of the fuzzy PID controller is 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 and output variables is divided into five fuzzy subsets, namely: NB (Negative big), NS (small negative), ZO (zero), PS (Small), PB (righteousness);

[0115] The triangle function is used as the membership function of the fuzzy subset, and the analytical expression is:

[0116]

[0117] 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 respectively;

[0118] The quantitative factor calculation formula for converting the clarity into the blur is:

[0119] ;

[0120] ;

[0121] 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

[0122] The proportional factor is calculated as:

[0123] ;

[0124] ;

[0125] ;

[0126] in, is the proportional coefficient, 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.

[0127] 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.

[0128] 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 selected 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 perform defuzzification operation on the compressor speed control. The calculation formula of the weighted average method is:

[0129] ;

[0130] 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.

[0131] In 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 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.

[0132] Wherein, step S5 specifically includes:

[0133] In order to improve the global search capability of the Parrot optimization algorithm and avoid the algorithm falling into the local optimum, the Bernoulli chaos map is introduced into the Parrot optimization algorithm. The expression of the Bernoulli chaos map is:

[0134]

[0135] in, X v+1 and X v Respectively v +1 and v The chaotic state of a parrot, is the control parameter, 0< <1;

[0136] 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:

[0137] ;

[0138] in, F The current iteration number of the Parrot optimization algorithm, F max 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;

[0139] In addition, in order to balance the needs of global exploration and local search and improve the adaptability 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:

[0140] ;

[0141] in, X best (F) is the parrot population in the F The optimal position in the iteration, For the F The new positions generated by the Cauchy and Gaussian mutations are generated by iterations. and are random numbers that obey Cauchy and Gaussian distributions respectively;

[0142] 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:

[0143] ;

[0144] Fitness function of the improved Parrot optimization algorithm based on multiple strategies F u for:

[0145] ;

[0146] in, t2 is the running time optimization 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;

[0147] 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.

[0148] Figure 2 This is a comparison chart of the effects of estimating the internal temperature of the 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 the method proposed in this invention and the traditional PID controller in controlling 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.

[0149] In summary, the control method for a direct-cooling thermal management system for a lithium-ion power battery provided by the present invention has the following beneficial effects:

[0150] 1. The present invention fully considers the thermal characteristics of the battery to establish a battery thermal model, performs parameter identification on the battery thermal model through the adaptive forgetting factor least squares method to obtain the thermal model parameters, and uses the strong tracking unscented Kalman filter optimized by the adaptive particle swarm algorithm to estimate the internal temperature of the battery in real time. Finally, the fuzzy PID controller is optimized by the Parrot optimization algorithm based on multiple strategies to control the internal temperature of the battery.

[0151] 2. Unlike conventional Kalman filters, the present invention adopts a strong tracking unscented Kalman filter improved by an adaptive particle swarm algorithm. Conventional Kalman filters have low estimation accuracy. This invention introduces a strong tracking filter based on the conventional unscented Kalman filter, enhancing the stability of the filtering process. Furthermore, the adaptive particle swarm algorithm is used to optimize the process noise covariance and the observation noise covariance, thereby improving the estimation accuracy of the battery internal temperature.

[0152] 3. Unlike general PID controllers, the present invention adopts a fuzzy PID controller optimized by the Parrot optimization algorithm based on multiple strategies. Traditional PID controllers rely on empirical formulas or trial and error methods, which are difficult to achieve optimal performance. The present invention introduces fuzzy control on the basis of traditional PID controllers, automatically adjusts PID parameters through fuzzy rules, and can adapt to different working conditions. In addition, the present invention adopts the Parrot optimization algorithm based on multiple strategies to optimize the fuzzy PID controller, thereby achieving better control effect.

[0153] 4. The method proposed in the present invention does not require a large number of sensors in the process of 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.

[0154] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0155] While 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 invention, and that the scope of the 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, use the adaptive forgetting factor least squares method to perform parameter identification to obtain thermal model parameters; Step S3: Based on the battery thermal model state space equation established in step S1, a strong tracking unscented Kalman filter is constructed, the thermal model parameters identified in step S2 are input into the strong tracking unscented Kalman filter, and an adaptive particle swarm algorithm is introduced to optimize the process noise covariance and observation noise covariance of the strong tracking unscented Kalman filter to obtain an optimized filter, and the internal temperature of the battery is estimated in real time through the optimized filter; Step S4, constructing a fuzzy PID controller based on the battery internal temperature estimated in step S3 and using 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 using the Parrot optimization algorithm based on multiple strategies 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 the ideal temperature. Wherein, 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 chaotic state of a parrot, is the control parameter, 0< <1; The adaptive weight switching factor is introduced into the Parrot optimization algorithm, and the expression is as follows: ； in, F The current iteration number of the Parrot optimization algorithm, The maximum number of iterations for 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, the 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 the F The optimal position in the iteration, For the F The new positions generated by the Cauchy and Gaussian mutations are generated by iterations. 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 ; Fitness function of the improved Parrot optimization algorithm based on multiple strategies F u for: ； in, t 2 is the running time optimization 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, 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.

2. The lithium-ion power battery direct cooling thermal management system control method 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 of the battery internal temperature 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 that 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 all times, Q o Use the following formula to perform the calculation: ； ； in, is the battery charge and discharge current, and U are the open circuit voltage and terminal voltage of the battery respectively, is the average temperature of the battery, is the entropy thermal coefficient, for The differential of for The differential of .

3. The lithium-ion power battery direct cooling thermal management system control method 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 moment battery surface temperature, T o ( k +1) k +1 moment battery surface temperature, T o ( k )for k The surface temperature of the battery at all times, is the parameter matrix to be identified, X, , Z is the parameter to be identified, T represents transposition, for k The data set 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, and They are 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 the allowable error.

4. The lithium-ion power battery direct cooling thermal management system control method 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 Observed variables at time +1, 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, for k The covariance matrix at time t, 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 Time-predicted k +1 point set, z is the state transition function; The prior estimates and covariance matrix are calculated using the following formula: ； ； in, for k The state estimate at time +1, for k The covariance matrix at time +1, for k The process noise covariance matrix at time t; Generate a new set of Sigma points through UT transformation, the expression is: in, is the updated initial Sigma point set, and Respectively k The covariance matrix of the moment +1 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 the A set of observation points; Compute the new predicted observation mean using the weights: ； in, for k +1 time predicted observation mean; 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 The innovation covariance matrix at time +1, 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 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 The covariance matrix at time +1; In the traditional particle swarm optimization, dynamic inertia weight and learning factor are introduced: ； ； ； in, iter and are the current number of iterations and the maximum number of iterations respectively, w iter is the inertia weight of the current iteration number, and w min are the maximum and minimum values of the inertia weight, z 1 and z 2 are local learning factors and global learning factors respectively, and z min are the maximum and minimum values of the learning factor respectively; Optimization dimension of adaptive particle swarm optimization algorithm 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 optimization strong tracking unscented Kalman filter, for k The observed variables at time for k Strong tracking of the estimated value of the unscented Kalman filter output at all times; The process noise and observation noise after optimization by the adaptive particle swarm algorithm are then input into the strong tracking unscented Kalman filter to obtain the optimized filter, and the internal temperature of the battery is estimated in real time through the optimized filter.

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 is 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 and output variables is divided into five fuzzy subsets, namely: negative large, negative small, zero, positive small, and positive large; The triangle 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 respectively; 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 is calculated as: ； ； ； in, is the proportional coefficient, 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; 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: In step S5, the optimal dimension U best The expression is: 。

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