A battery thermal management control method based on a fractional order heat model and a rolling horizon chimpanzee algorithm

Abstract

The application provides a battery thermal management control method based on a fractional order thermal model and a rolling horizon chimpanzee algorithm, introduces fractional calculus theory to reconstruct a battery thermal dynamic equation, captures memory characteristics of a thermal system with high fidelity, constructs a rolling horizon control architecture, realizes advanced prediction and active intervention on future thermal states, and adopts an improved chimpanzee optimization algorithm with a fusion lens imaging reverse learning mechanism to replace a traditional iterative algorithm. The application aims to make systematic innovation from three dimensions of mechanism modeling, control architecture and optimization algorithm, so as to achieve multi-objective collaborative optimization of system energy efficiency, temperature control accuracy and temperature uniformity under the premise of guaranteeing a safe operation boundary of the battery.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, specifically to a battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm. Background Technology

[0002] As the energy hub of a new energy vehicle's power system, the health of the power battery directly determines the vehicle's driving range, acceleration performance, and passive safety. Although lithium-ion batteries have become the industry's preferred choice due to their excellent energy density and cycle life, their thermal sensitivity remains a bottleneck restricting technological breakthroughs. Existing engineering practices show that maintaining the temperature of individual battery cells within the optimal electrochemical activity range of 25-40℃, and ensuring that the internal temperature difference of the module does not exceed 5℃, are core elements for delaying battery degradation and ensuring vehicle safety. Especially in high-temperature environments or under extreme driving conditions, the nonlinear superposition effect of ohmic heat and reaction heat inside the battery is significant. If the thermal management system's response is lagging or its heat dissipation capacity is insufficient, it can easily lead to irreversible capacity loss, a surge in internal resistance, or even catastrophic consequences such as thermal runaway. Therefore, exploring an advanced thermal management strategy that can balance high control precision, rapid dynamic response, and low energy consumption has become a critical technological challenge that urgently needs to be overcome in this field.

[0003] Currently, control strategies for battery thermal management systems mainly include PID control, model predictive control (MPC), and heuristic optimization methods based on genetic algorithms (GA). However, a deeper analysis of existing technologies reveals the following significant limitations:

[0004] First, the physical modeling mechanism lacks sufficient depth of description. Most existing mainstream control strategies are based on traditional integer-order differential equations, such as the first-order RC equivalent thermal circuit model. However, from a microscopic perspective, lithium-ion batteries are complex electrochemical systems composed of porous electrodes, separators, and electrolytes. Their heat generation, conduction, and diffusion processes exhibit significant "anomalous diffusion" behavior and "long-term memory" effects. Traditional models based on integer-order calculus are limited by the locality of their mathematical definition, making it difficult to accurately characterize this nonlocal, nonlinear thermal dynamic evolution process. This results in significant prediction errors when facing severe operating conditions such as high-rate pulse charging and discharging, especially in reproducing the thermal relaxation tail phenomenon after current cutoff, thus weakening the baseline accuracy of the control system.

[0005] Second, the control strategy lacks time-domain foresight. The widely used PID control is essentially a hysteresis feedback mechanism based on the current error. When dealing with battery pack thermal systems characterized by large heat capacity and inertia, it is highly susceptible to overshoot and oscillations. Conventional online optimization strategies, such as genetic algorithms, are typically limited to optimizing the current state at a single moment, lacking the ability to predict future trends in heat load. This short-sighted control logic often leads to lag in the cooling actuator's action, and in order to compensate for accumulated errors, it easily forces the actuator to drastically change its output within a very short time, causing violent fluctuations in speed and accelerating the mechanical wear of components.

[0006] Third, balancing real-time performance and global applicability is challenging in optimization algorithms. While some solutions attempt to incorporate swarm intelligence algorithms for parameter optimization, traditional genetic algorithms and particle swarm optimization suffer from inherent drawbacks such as numerous control parameters, oscillating convergence curves, and susceptibility to local extremum traps. Given the limited computing power of embedded chips in automotive BMS (Battery Management System), quickly locking the global optimum within millisecond-level control cycles and avoiding premature convergence is a key challenge in achieving real-time and precise control. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide a battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm. By constructing a high-fidelity battery thermal model based on fractional-order calculus theory, introducing a prediction mechanism using a rolling time-domain control framework, and combining an improved chimpanzee optimization algorithm based on fusion lens imaging back learning for online rapid optimization, the method achieves accurate, forward-looking, and low-energy-consumption closed-loop control of the battery thermal management system.

[0008] To achieve the above-mentioned technical effects, the present invention adopts the following technical solution:

[0009] A battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm, provided by the present invention, includes the following steps:

[0010] Step S1: Construct a thermal dynamic model of the battery cell and module based on fractional calculus theory; establish a fractional energy conservation equation describing the heat generation and heat diffusion process inside the battery using the definition of Caputo fractional derivative and Grünwald-Letnikov discretization method, and dynamically correct the battery internal resistance by combining the state of health factor (SOH).

[0011] Step S2: Design a multi-objective optimization function under the rolling time-domain control framework; set the prediction time domain and control time domain, select the coolant flow rate as the control variable, construct a comprehensive objective function that includes temperature tracking error, inter-module temperature difference and cooling system energy consumption, and set the physical constraints of the control variables;

[0012] Step S3: An improved chimpanzee optimization algorithm with a fusion lens imaging inverse learning mechanism is introduced as an online solver for the rolling time domain controller; based on the standard chimpanzee optimization algorithm, an inverse population is generated using the lens imaging principle, and a chaotic mapping strategy is introduced to update the algorithm parameters.

[0013] Step S4: Execute the rolling time-domain optimization control process; In each control cycle, collect the current system state and update the historical temperature memory sequence, use the fractional-order model of step S1 to predict the system temperature response in the future prediction time domain, call the improved chimpanzee optimization algorithm of step S3 to solve the objective function of step S2, and obtain the optimal coolant flow rate control sequence.

[0014] Step S5: Implement rolling optimization and feedback correction; convert the first control quantity of the optimal control sequence into an actuator command to act on the battery thermal management system, and repeat steps S4 and S5 at the next sampling time to achieve closed-loop control through the rolling time window.

[0015] Further, in step S1, the discrete form of the fractional-order energy conservation equation is expressed as:

[0016]

[0017] in, For the first Each battery module Temperature at any moment; Sampling rate; It is a fractional order; Generalized thermal capacity of the battery pack; , , These are heat generation rate, convective heat transfer rate, and thermal conduction heat transfer rate, respectively. The fractional weighting coefficients are calculated using the following formula:

[0018] .

[0019] Further, in step S2, the multi-objective optimization objective function Defined as:

[0020]

[0021] in, To predict the time domain, To control the time domain; The predicted highest monomer temperature, The target reference temperature; This represents the predicted maximum temperature difference between modules; Let be the power consumption function of the water pump, satisfying , The pump power coefficient, This refers to the coolant flow rate; , , These are the weighting coefficients.

[0022] Furthermore, in step S3, the lens imaging reverse learning mechanism is used for population initialization, for the current individual position. Its reverse position The calculation formula is:

[0023]

[0024] in, , These are the lower and upper limits of the coolant flow rate, respectively. This is the lens scaling factor.

[0025] Further, in step S3, the position update strategy of the improved chimpanzee optimization algorithm includes: sorting the individuals in the population according to their fitness, marking the top four as attackers, drivers, obstructors, and pursuers, respectively, and updating the positions of the remaining individuals based on the positions of these four types of leaders; and introducing a Logistic chaotic mapping to adjust the chaotic vectors in the algorithm. Perform iterative updates:

[0026]

[0027] in, For control parameters, The current chaotic value; the individual position update formula involves convergence coefficients. The calculation formula is as follows:

[0028]

[0029] in, This represents the current iteration number. The maximum number of iterations, It is a random number.

[0030] Further, in step S4, the execution of the rolling time-domain optimization control process specifically includes:

[0031] Step S41: Collect the current time through the battery management system. The battery module temperature, coolant inlet temperature, and load current are used to estimate the state using a Kalman filter algorithm.

[0032] Step S42, Update the memory sequence: Store the measured temperature at the current moment into the historical temperature queue, and remove the oldest data outside the time window. For use by the fractional derivative discrete operator;

[0033] Step S43: Starting from the current state, invoke the improved chimpanzee optimization algorithm to iteratively solve for the objective function while satisfying the flow velocity and temperature safety constraints. Minimum control sequence:

[0034] .

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The battery thermal management control method based on fractional thermal model and rolling time-domain chimpanzee algorithm provided by this invention breaks through the limitations of traditional integer-order models and uses fractional calculus theory to construct a thermal model. The fractional derivative has the characteristic of "full-process memory" and can accurately describe the anomalous thermal diffusion phenomenon and thermal relaxation process in the porous medium of lithium-ion battery. It significantly reduces the prediction error of the model under the condition of drastic current change and provides a high-precision benchmark for the control algorithm.

[0037] 2. The battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm provided in this invention adopts a rolling time-domain control architecture. Unlike the "post-event adjustment" of PID, rolling time-domain control can predict the temperature trend over a future period based on the model and adjust the coolant flow rate in advance. This forward-looking mechanism effectively solves the lag problem of large heat capacity systems, avoids frequent and drastic adjustments of the actuator, and extends the life of components while ensuring temperature control performance.

[0038] 3. The battery thermal management control method based on fractional-order thermal model and rolling time-domain chimpanzee algorithm provided by this invention adopts an improved chimpanzee optimization algorithm to replace the traditional genetic algorithm. By introducing a lens imaging reverse learning mechanism, the diversity and quality of the population are greatly improved, effectively preventing the algorithm from getting trapped in local extrema. At the same time, the application of nonlinear convergence factor accelerates the convergence speed of the algorithm, enabling it to meet the millisecond-level real-time calculation requirements of the vehicle controller.

[0039] 4. The battery thermal management control method based on fractional-order thermal model and rolling time-domain chimpanzee algorithm provided by this invention simultaneously considers temperature safety, temperature uniformity and system energy consumption in the objective function. The algorithm automatically finds the optimal balance point among the three, and minimizes the power consumption of the electronic water pump while ensuring that the temperature difference of the battery module is less than the set threshold, thus realizing the efficient operation of the thermal management system. Attached Figure Description

[0040] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0041] Figure 1 A flowchart of a battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm provided for an embodiment;

[0042] Figure 2 This is a comparison chart of the total energy consumption of the cooling system in the example and the existing control strategy;

[0043] Figure 3 A comparison chart of battery pack temperature control accuracy under different control strategies;

[0044] Figure 4 This is a comparison chart of the maximum temperature difference changes inside the battery module under different control strategies. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0047] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, all directional indications (such as up, down, left, right, front, back, bottom, etc.) in this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indication will also change accordingly. Furthermore, descriptions involving "first," "second," etc., in this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0048] like Figure 1 As shown, this embodiment provides a battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm, including steps S1 to S5:

[0049] Step S1: Construct thermal dynamic models of battery cells and modules based on fractional calculus theory. Using the Caputo fractional derivative definition to replace the traditional first derivative, establish fractional energy conservation equations describing the heat generation and diffusion processes inside the battery. Simultaneously, establish a coolant convection heat transfer model, characterizing the thermal memory and damping properties of the battery materials through fractional-order parameters, forming a high-fidelity fractional-order state-space equation that reflects the influence of the battery's historical temperature state on current temperature changes.

[0050] This embodiment uses fractional calculus theory to reconstruct the battery's thermal dynamics equations in order to accurately describe the complex electrochemical thermal memory characteristics inside the battery.

[0051] The Caputo derivative is approximated using the Grünwald-Letnikov (GL) discretization definition. The Caputo fractional derivative operator is defined. ,in Fractional order ( The value represents the system's "memory strength" or "anomalous diffusion level," obtained through pulse charge-discharge testing of the battery; in this embodiment, it is 0.92. Using the GL definition, the discrete form of the fractional derivative is expressed as:

[0052]

[0053] in, The sampling rate for the battery pack management system is 0.1 seconds. For historical temperatures, store temperature measurements from past moments. The coefficients for the fractional weights are calculated using the following formula:

[0054]

[0055] Based on the above, the fractional-order energy conservation equation for a single battery cell is established:

[0056]

[0057] in, The generalized heat capacity of the battery pack is 1128.45 J / (kg⋅℃) in this embodiment; The temperature of the battery; The heat generation rate of the battery; This refers to the convective heat transfer rate of the battery. The heat generation rate of the battery module. It consists of Joule heat and reaction entropy heat; This refers to the heat transfer rate between battery modules.

[0058] To simulate the real characteristics of a battery throughout its entire life cycle, this embodiment introduces a State of Health (SOH) factor to dynamically correct the internal resistance. The heat generation equation for each battery module is:

[0059] ;

[0060] in, This represents the load current at time t; Indicates the first The temperature of each battery module The entropy heat coefficient is shown in Table 1; The dynamic internal resistance of the battery module is calculated using the following formula:

[0061]

[0062] in, The reference internal resistance of the new battery at the current temperature and SOC is 0.8mΩ in this embodiment; The aging sensitivity coefficient is set to 0.6 in this embodiment. This formula shows that as SOH decreases, the internal resistance increases non-linearly, thus accurately reflecting the increase in heat generation after aging.

[0063] Table 1 Entropy-Heat Coefficient Lookup Table

[0064] stage SOC range Entropy heat coefficient 1 0.9-0.8 0.5 2 0.8-0.7 0.95 3 0.7-0.6 1.55 4 0.6-0.5 1.3 5 0.5-0.4 1.05 6 0.4-0.3 0.85 7 0.3-0.2 0.35 8 0.2-0.1 0.2

[0065] In this embodiment, the coolant is a 50% (by volume) ethylene glycol aqueous solution. In the liquid cooling system, heat transfer within the battery pack mainly includes convective heat transfer and thermal conduction. For convective heat transfer between the battery module and the coolant, the following equation holds:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] in, For the first The convective heat transfer between the battery module and the coolant The convective heat transfer coefficient is calculated using the Dittus-Boelter correlation in this embodiment; In this embodiment, the effective heat exchange area is 0.012283 m². 2 ; For the first The temperature of the coolant at each battery module The Reynolds coefficient of the coolant within the system. For Prandtl coefficient, The dynamic viscosity of the coolant at the average temperature inside the pipe. In this embodiment, the dynamic viscosity of the coolant is at the pipe wall temperature. Take 1.02; The thermal conductivity of the coolant is taken as 0.4 W / (m⋅K) in this embodiment; The diameter of the coolant pipe is 22 mm in this embodiment; The specific heat capacity of the coolant is 3320 J / (kg⋅℃); The dynamic viscosity of the coolant is 3.5 mPa⋅s at 25°C. The density of the coolant is 1082 kg / m³. 3 ; This refers to the coolant flow rate.

[0071] For heat transfer through conduction between battery modules, the following equation applies:

[0072] ;

[0073] in, For the first Thermal conductivity of each battery module The thermal conductivity coefficient between adjacent battery cells is taken as 1000 W / (m⋅℃) in this embodiment; For the first The temperature of each battery module For the first The temperature of each battery module For the first The battery module and the first Contact area between battery modules For the first The battery module and the first The thermal conductivity characteristic length between battery modules For the first The battery module and the first Contact area between battery modules For the first The battery module and the first The thermal conductivity characteristic length between individual battery modules. In this embodiment, the thermal conductivity coefficient, contact area, and thermal conductivity characteristic length are all the same between adjacent battery cells. and The value is 0.035811 mm. 2 ; and The value is 0.01m.

[0074] Simultaneously, the energy conservation equation for the coolant node is established. For the first... The energy balance equation for a fluid node is:

[0075]

[0076] in, This indicates the mass of coolant within a single battery module. For the coolant mass flow rate, in order to calculate and The value of is defined and calculated in the embodiment based on the geometric parameters of the cooling pipe as follows, for the cross-sectional area of ​​the cooling pipe. :

[0077]

[0078] In the embodiment, the calculated value is as follows: m 2 The length of the cooling pipe corresponding to a single battery module; The calculation formula is as follows:

[0079]

[0080] In the embodiment, the calculated value is as follows: m; coolant mass within a single battery module range The calculation formula is as follows:

[0081]

[0082] Coolant mass flow rate The calculation formula is as follows:

[0083]

[0084] Finally, by simultaneously solving the above equations and discretizing them, we obtain the system state update equations for rolling time-domain control:

[0085]

[0086]

[0087] This set of equations forms the basis of the prediction model for the chimpanzee algorithm optimization in subsequent steps.

[0088] Step S2: Design a multi-objective optimization function under the rolling time-domain control framework. Define the prediction time domain and control time domain, select coolant flow rate as the control variable, and select the battery's maximum temperature, the maximum temperature difference between modules, and the cooling system's energy consumption as optimization indicators. Construct a comprehensive objective function that includes a temperature tracking error term, a temperature uniformity penalty term, and a control variable change rate penalty term, and set physical constraints on the control variables to guide the system to achieve optimal energy consumption while meeting safety constraints.

[0089] This embodiment employs a rolling time-domain control strategy, utilizing the prediction model constructed in step S1 to solve for the optimal coolant flow rate sequence online within each control cycle. The prediction time domain is set as... (This embodiment takes) ), control time domain is (This embodiment takes) ).definition The control input sequence to be optimized at any given time is:

[0090]

[0091] exist to Assuming the control variables remain constant over the time period, i.e. .

[0092] Constructing a multi-objective optimization objective function The aim is to balance temperature tracking accuracy, temperature difference consistency between modules, and system energy consumption.

[0093]

[0094] in, Based on The first time information prediction The highest temperature of a single cell within the battery pack at any given time; The target reference temperature is set to 35°C in this embodiment; The formula for calculating the predicted maximum temperature difference between modules is as follows:

[0095]

[0096] in, The predicted maximum temperature of the module. This is the predicted minimum temperature for the module. Let be the power consumption function of the water pump. According to the similarity law of fluid machinery, the power of the pump is proportional to the cube of the flow velocity, that is:

[0097]

[0098] in, The pump power coefficient comprehensively reflects the coolant density, pipeline flow resistance characteristics, and the electromechanical conversion efficiency of the pump motor. In this embodiment, it is obtained by fitting pump bench test data. .

[0099] , , The weighting coefficients for each objective are set to 0.5, 0.3, and 0.2 in this embodiment. Simultaneously, system constraints are set:

[0100]

[0101] in, , These are the lower limits of coolant flow rate ( ) and upper limit ( ); Limitation on the rate of change of flow velocity ( This is used to prevent drastic fluctuations in the speed of the water pump; This represents the upper limit of the battery's safe operating temperature (55°C).

[0102] Step S3 introduces an improved chimpanzee optimization algorithm with a fused lens imaging inverse learning mechanism as the online solver for the rolling time-domain controller. To quickly solve the nonlinear constraint optimization problem in step S2 within a finite control cycle, this embodiment employs an improved chimpanzee optimization algorithm. Based on the standard chimpanzee optimization algorithm, an inverse population is generated using the lens imaging principle to increase the diversity of the initial population. Furthermore, a nonlinear convergence factor and an adaptive weighting strategy are introduced during the iteration process to balance the algorithm's global exploration and local exploitation capabilities.

[0103] Population initialization and lens imaging reverse learning: in the solution space Initial population is generated randomly within the population. To enhance population diversity and avoid premature convergence, a lens imaging inverse learning strategy is introduced to generate an inverse population. For the current individual location Its reverse position The calculation is as follows:

[0104]

[0105] in, Lens scaling factor (in this embodiment, it is taken as...) ).calculate and The fitness values ​​of all individuals (i.e., the objective function) The first 30 individuals were selected to form the initial population.

[0106] Chimpanzee hunting strategy (position update): Individuals in the population are ranked by fitness, with the top four labeled as Attackers, Drivers, Barriers, and Chasers. The remaining individuals update their own positions based on the positions of these four types of leaders.

[0107]

[0108] in, For current chimpanzees With a certain leader The distance between them; "Taking turns" refers to the positions of the attacker, the driver, the obstructor, and the pursuer. This shows the current location of the chimpanzee whose location is being updated. The natural obstacle factor has a value that is a random number taken from [0,2]. When the distance between the chimpanzee and its prey is amplified, the algorithm helps it escape local optima and explore unknown areas (increasing randomness); when At this time, the algorithm will reduce the influence of distance, which helps the algorithm converge quickly (refined search); To generate chaotic vectors based on chaotic mappings, in this embodiment, the Logistic chaotic mapping is used. Iterative updates will be performed.

[0109]

[0110] in, As a control parameter, we set it to 4; The current chaos value, The initial value is 0.7. The temporary target location is calculated based on the position of a certain leader. This is the convergence coefficient, used to balance the algorithm's global exploration capability and local exploitation capability. Its calculation formula is:

[0111]

[0112] in, This represents the current iteration number. To determine the maximum number of iterations, we set it to 200. A random number between [0, 1]. The chimpanzee in the next moment... The actual position is the average of the four temporary positions mentioned above, based on the attacker, driver, obstructor, and pursuer:

[0113]

[0114] in, , , as well as Each of these is a separate moment for the chimpanzee. The actual location is based on the four temporary locations mentioned above: attacker, driver, obstructor, and pursuer.

[0115] Step S4: Execute the rolling time-domain optimization control process based on a fractional-order model. At the beginning of each control cycle, the controller initiates online optimization calculations. The temperature of each module in the battery pack at the current moment is collected via the BMS. Coolant inlet temperature and load current Kalman filtering is used to remove sensor noise and obtain accurate initial state values. Simultaneously, the memory sequence is updated by updating the measured temperature at the current moment. Store the data in a historical temperature queue and remove the oldest data outside the time window. This is used by the fractional derivative discrete operator in step S1. A historical data sequence is constructed to meet the requirements of the Grünwald-Letnikov (GL) discrete operator in step S1, ensuring that the "memory" property of the fractional model is maintained.

[0116] Online optimization is performed, starting from the current state, by invoking the improved chimpanzee optimization algorithm described in step S3. The control variable to be optimized is set as the flow velocity sequence. In each iteration of the algorithm, the fractional-order prediction model established in step S1 is invoked to calculate the future corresponding to the current particle (i.e., a certain flow velocity sequence). The temperature trajectory of the step is calculated, and the fitness value is calculated according to the multi-objective function defined in step S2. .go through After the iteration, the optimal control sequence with the minimum fitness is output. The calculation formula is as follows:

[0117]

[0118] in, For the current moment ( The optimal flow rate; For the predicted next moment ( The optimal flow rate; To control the optimal flow rate in the final step of the time domain, this process combines model prediction with intelligent optimization to predict future system states and handle control constraints.

[0119] Step S5: Implement the rolling optimization strategy and feedback correction closed loop. Control action selection is performed, following the basic principles of rolling time-domain control; the controller does not execute the entire sequence. However, only the first element of the optimal control sequence is extracted. As the actual instruction to be executed at the current moment.

[0120] Based on the PQ characteristic curve (pressure-flow curve) of the electronic water pump and the flow resistance characteristics of the pipeline, the flow rate is pre-calibrated. With water pump speed The functional relationship is fitted using a quadratic polynomial formula in this embodiment: ,in , , These are calibration coefficients. For the final target speed command The controller transmits data via the CAN bus. The coolant is sent to the electronic water pump drive unit, which adjusts the motor duty cycle to make the pump speed respond, thereby changing the coolant flow rate.

[0121] Finally, closed-loop control is executed. The system completes one control cycle. Then, time progressed to At this point, the system no longer uses the predictions from the previous time step. Instead, re-collect The measured state at each moment (including external disturbances not modeled in the model and deviations caused by parameter mismatch) is recorded, and steps S4 and S5 are repeated. This rolling mechanism of "prediction-optimization-implementation-correction" can effectively suppress model mismatch and environmental disturbances, forming a robust closed-loop control system and achieving precise management of battery temperature.

[0122] In this embodiment, a real vehicle was tested at its maximum speed (140 km / h) in an environmental chamber at 40°C. The energy consumption, temperature control accuracy, and maximum temperature difference of the battery module under the PID control strategy, the genetic algorithm (GA) control strategy, and the method of this invention were compared and analyzed. Figure 2 As can be seen, under the same operating conditions, the cooling system of the present invention has the lowest energy consumption. This is because the multi-objective optimization function of the present invention in step S2... The article explicitly introduces the water pump power consumption item. Furthermore, by using rolling optimization to find the minimum flow rate that meets heat dissipation requirements in real time, the excessive cooling phenomenon commonly found in PID control is avoided. Figure 3 As can be seen, both the Genetic Algorithm (GA) control strategy and the present invention have faster response speed and higher control accuracy than the PID control strategy, but the present invention further improves control accuracy compared to the Genetic Algorithm (GA). This is mainly due to the fact that the fractional-order thermal model constructed in step S1 can more accurately describe the thermal memory characteristics of the battery, reducing model prediction errors. Figure 4As can be seen, the battery module of this invention has the smallest temperature difference, only about 0.35°C. This is because the optimization objective includes a constraint on the maximum temperature difference between modules, allowing the controller to maintain temperature consistency when adjusting the flow rate.

[0123] In summary, the battery thermal management control method based on a fractional-order thermal model and the rolling time-domain chimpanzee algorithm provided in this embodiment abandons the traditional integer-order thermal model and innovatively uses fractional-order calculus theory to reconstruct the battery thermal dynamic equation. This is achieved by introducing fractional-order calculus. (In this embodiment, the value is 0.92) and historical temperature memory sequence, accurately describing the complex electrochemical thermal memory characteristics and anomalous diffusion phenomena of lithium-ion batteries; at the same time, the internal resistance is dynamically corrected by combining the SOH factor, which significantly improves the prediction accuracy of the model throughout the battery's entire life cycle and lays the foundation for precise control.

[0124] Meanwhile, addressing the computational burden of traditional online solutions to nonlinear programming problems in rolling time-domain control, this embodiment introduces an improved chimpanzee optimization algorithm. The diversity of population initialization is enhanced through a "lens imaging back learning" strategy, and the search step size is optimized using a "logistic chaotic mapping." Compared to traditional genetic algorithms or particle swarm optimization algorithms, this method effectively avoids getting trapped in local optima and significantly shortens the iteration convergence time, enabling complex nonlinear MPC control to run in real time on an onboard embedded controller.

[0125] Finally, a rolling time-domain control framework is adopted, which effectively suppresses interference caused by model parameter mismatch (such as sudden changes in ambient temperature and sensor noise) through a closed-loop mechanism of "prediction-optimization-implementation-correction". At the same time, by constructing a multi-objective optimization function that includes temperature tracking, temperature difference consistency and water pump energy consumption, the optimal balance between heat dissipation effect and system energy consumption is achieved, solving the problem that traditional PID control cannot take into account both energy consumption and temperature difference.

[0126] The specific embodiments of the present invention have been described above. Based on the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention.

Claims

1. A battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm, characterized in that, Includes the following steps: Step S1: Construct a thermal dynamic model of the battery cell and module based on fractional calculus theory; establish a fractional energy conservation equation describing the heat generation and heat diffusion process inside the battery using the definition of Caputo fractional derivative and Grünwald-Letnikov discretization method, and dynamically correct the battery internal resistance by combining the state of health factor (SOH). Step S2: Design a multi-objective optimization function under the rolling time-domain control framework; set the prediction time domain and control time domain, select the coolant flow rate as the control variable, construct a comprehensive objective function that includes temperature tracking error, inter-module temperature difference and cooling system energy consumption, and set the physical constraints of the control variables; Step S3: An improved chimpanzee optimization algorithm with a fusion lens imaging inverse learning mechanism is introduced as an online solver for the rolling time domain controller; based on the standard chimpanzee optimization algorithm, an inverse population is generated using the lens imaging principle, and a chaotic mapping strategy is introduced to update the algorithm parameters. Step S4: Execute the rolling time-domain optimization control process; In each control cycle, the current system state is collected and the historical temperature memory sequence is updated. The fractional-order model in step S1 is used to predict the system temperature response in the future prediction time domain. The improved chimpanzee optimization algorithm in step S3 is called to solve the objective function in step S2 and obtain the optimal coolant flow rate control sequence. Step S5: Implement rolling optimization and feedback correction; convert the first control quantity of the optimal control sequence into an actuator command to act on the battery thermal management system, and repeat steps S4 and S5 at the next sampling time to achieve closed-loop control through the rolling time window; In step S3, the lens imaging reverse learning mechanism is used for population initialization, for the current individual position. Its reverse position The calculation formula is: ； in, , These are the lower and upper limits of the coolant flow rate, respectively. This is the lens scaling factor.

2. The battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm as described in claim 1, characterized in that, In step S1, the discrete form of the fractional-order energy conservation equation is expressed as: ； in, For the first Each battery module Temperature at any moment; Sampling rate; It is a fractional order; Generalized thermal capacity of the battery pack; , , These represent the heat generation rate, convective heat transfer rate, and thermal conduction heat transfer rate, respectively; L is the fractional memory window length, a positive integer representing the number of historical temperature sampling points involved in the fractional derivative discrete calculation; The fractional weighting coefficients are calculated using the following formula: 。 3. The battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm according to claim 1, characterized in that, In step S3, the position update strategy of the improved chimpanzee optimization algorithm includes: sorting the individuals in the population by fitness, marking the top four as attackers, drivers, obstructors, and pursuers, respectively, and updating the position of the remaining individuals based on the positions of these four types of leaders; and introducing a Logistic chaotic mapping to adjust the chaotic vectors in the algorithm. Perform iterative updates: ； in, For control parameters, The current chaotic value; the individual position update formula involves convergence coefficients. The calculation formula is as follows: ； in, This represents the current iteration number. The maximum number of iterations, It is a random number.

4. The battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm as described in claim 1, characterized in that, In step S4, the execution of the rolling time-domain optimization control process specifically includes: Step S41: Collect the current time through the battery management system. The battery module temperature, coolant inlet temperature, and load current are used to estimate the state using a Kalman filter algorithm. Step S42, Update the memory sequence: Store the measured temperature at the current moment into the historical temperature queue, and remove the oldest data outside the time window. For use by the fractional derivative discrete operator; Step S43: Starting from the current state, invoke the improved chimpanzee optimization algorithm to iteratively solve for the objective function while satisfying the flow velocity and temperature safety constraints. Minimum control sequence: 。

Images