Distributed thermal management system

Abstract

The invention discloses a distributed thermal management system, relates to the technical field of battery thermal management, and realizes efficient temperature equalization of a lithium battery pack through a sub-controller autonomous decision and an inter-node consistency protocol. The system dynamically adjusts PID parameters to adapt to battery aging and environment change, and cooperates with global control quantity through lightweight communication, and compared with a traditional method, the response speed is increased by 53%, the temperature difference is reduced by 75%, and the energy consumption is reduced by 18%.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal management, and more particularly to a distributed thermal management system. Background Art

[0002] Existing lithium battery thermal management systems mostly use centralized control (e.g., a single controller manages all battery modules), which has the following problems:

[0003] Single point failure risk: failure of the main controller will lead to global loss of control;

[0004] High communication latency: Data from multiple nodes needs to be uploaded to a central node for processing, which limits the response speed.

[0005] Poor adaptability: Fixed threshold strategies are difficult to cope with dynamic conditions such as battery aging and environmental changes.

[0006] Therefore, it is necessary to propose a distributed thermal management system to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to solve the problems existing in the background technology.

[0008] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0009] A distributed thermal management system includes multiple battery modules, each module containing several lithium battery cells;

[0010] Each battery module is equipped with an independent sub-controller, and each sub-controller is connected to the following units:

[0011] The data acquisition unit includes a temperature sensor, a voltage / current sensor, and a battery health status calculation module that are in communication with the sub-controller;

[0012] a control unit including a microcontroller in communication with the sub-controller and running an adaptive cooperative control algorithm;

[0013] Communication unit, which is a CANFD communication module;

[0014] Communication bus, including CANFD bus, connects all sub-controllers;

[0015] Actuator, each battery module corresponds to a heat dissipation actuator.

[0016] Furthermore, the adaptive cooperative control algorithm of the sub-controller includes the following steps:

[0017] Step 1: Distributed data collection and preprocessing;

[0018] Step 2: Generate local adaptive control strategy;

[0019] Step 3: Collaborative optimization of consistency between nodes;

[0020] Step 4: Actuator output and feedback correction.

[0021] Furthermore, the distributed data collection and preprocessing in step 1 includes the following steps:

[0022] Step 1.1, data input:

[0023] Set the i-th sub-controller to collect the following data: battery temperature T i , battery voltage V i Current I i , Battery Health Status SOH i 、Ambient temperature T env ;

[0024] Step 1.2, data preprocessing:

[0025] Battery temperature T i Perform sliding average filtering with a window size of 5 sampling points:

[0026]

[0027] Where: T i (k) is the original temperature value of the i-th module at time k, is the estimated temperature after filtering;

[0028] Normalized battery voltage V i and current I i :

[0029]

[0030] Where: i mIn and I max The minimum / maximum current value allowed by the battery module, V min and V max The minimum / maximum voltage allowed by the battery module.

[0031] Furthermore, the step 2 of generating the local adaptive control strategy includes the following steps:

[0032] Step 2.1, temperature deviation calculation:

[0033] Define the target temperature T target =35℃, calculate the deviation:

[0034]

[0035] Step 2.2: Dynamically adjust the actuator's PID parameters:

[0036]

[0037] Where: K p，base =2.0 is the base ratio coefficient, SOH threshol d=0.8,T env is the ambient temperature; 0.2 and 0.1 are empirical coefficients;

[0038] Step 2.3: Generate local control volume:

[0039] Using incremental PID algorithm:

[0040] △u i (t) = K p，i ·(e i (t)-e i (t-1))+K i ·e i (t)+K d ·(e i (t)-2e i (t-1)+e i (t-2))

[0041] Where: K p，i is the proportional coefficient of the i-th node, SOH i The battery health status is 0.8-1.0, T env is the ambient temperature, K i =0.05, K d =0.1.

[0042] Furthermore, the step 3 of inter-node consistency collaborative optimization includes the following steps:

[0043] Step 3.1, Neighbor node data exchange:

[0044] The i-th sub-controller sends the information to the adjacent nodes send and the output value u of the control quantity at time t i (t), receiving u j (t);

[0045] Step 3.2: The consistency protocol updates the control amount:

[0046] Calculate the global temperature gradient weight:

[0047]

[0048] Update control volume:

[0049]

[0050] Among them, γ = 0.2 is the convergence coefficient, which controls the rate of collaborative adjustment. The neighbor set of node i;

[0051] Step 3.3, constraint processing:

[0052] Limit the control range:

[0053]

[0054] Where: is the intermediate control quantity calculated by the i-th sub-controller at time t through the consensus protocol collaborative optimization, The final control value of the i-th sub-controller at time t.

[0055] Furthermore, the actuator output and feedback correction in step 4 includes the following steps:

[0056] Step 4.1: Map the control quantity to the fan speed:

[0057] Fan speed RPM i Linear relationship with the control quantity:

[0058]

[0059] Step 4.2, feedback correction:

[0060] If the actual temperature T i (t+1) The deviation from the predicted value exceeds 2°C, triggering parameter adaptation:

[0061]

[0062] Where: 0.05 is the adaptive step size, balancing response speed and stability.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The present invention equips each battery module with an independent sub-controller to adjust the heat dissipation strategy in real time based on local data.

[0065] 2. The present invention achieves global temperature balance by exchanging status information between nodes through lightweight communication.

[0066] 3. The present invention dynamically optimizes control parameters by combining battery health status (SOH) and environmental parameters.

[0067] 4. Traditional systems rely on a single controller, while the present invention uses sub-controllers to make independent decisions, and the failure of any node will not affect the overall situation.

[0068] 5. The existing solution uses a fixed threshold. The present invention adjusts the parameters in real time based on the battery health status and ambient temperature to adapt to complex working conditions.

[0069] 6. The centralized system needs to upload all data to the central node. The present invention reduces the bus load by 30% through a lightweight consistency protocol (only exchanging temperature and control quantity). BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is the main program flow chart of the sub-controller of the present invention.

[0071] Figure 2 This is the hardware connection topology diagram of the system of the present invention.

[0072] Figure 3 This is a structural block diagram of the sub-controller of the present invention.

[0073] Figure 4 This is a schematic diagram of the communication protocol field of the present invention.

[0074] Figure 5 This is the control signal output timing diagram of the present invention

[0075] Figure 6 This is the software task scheduling diagram of the present invention. DETAILED DESCRIPTION

[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 are within the scope of protection of the present invention.

[0077] See also Figures 1 to 6 , a distributed thermal management system, including a plurality of battery modules, each module containing a number of lithium battery cells;

[0078] Each battery module is equipped with an independent sub-controller, and each sub-controller includes the following units:

[0079] Data acquisition unit, including temperature sensor (NTC), voltage / current sensor (Hall effect), and battery health status calculation module;

[0080] The control unit, a microcontroller (such as STM32H7), runs the adaptive cooperative control algorithm;

[0081] Communication unit, CANFD communication module;

[0082] Communication bus, CANFD bus, connects all sub-controllers, with a transmission cycle of 100ms;

[0083] Actuator, each battery module corresponds to an actuator, including:

[0084] Cooling fan, brushless DC motor, speed range 1000-3000RPM.

[0085] Specifically, the adaptive cooperative control algorithm of the sub-controller includes the following steps:

[0086] Step 1: Distributed data collection and preprocessing;

[0087] Step 1.1, data input:

[0088] Set the i-th sub-controller to collect the following data: battery temperature T i (NTC sensor, sampling frequency 10Hz), battery voltage V i Current I i , Battery Health Status SOH i (calculated by coulomb counting method), ambient temperature T env (SHT30 sensor);

[0089] Step 1.2, data preprocessing:

[0090] Battery temperature T i Perform sliding average filtering with a window size of 5 sampling points. Compared with Kalman filtering, it has lower computational complexity and is suitable for embedded real-time systems (such as STM32H7). It can reduce random noise of the temperature sensor and smooth data fluctuations.

[0091]

[0092] Where: T i (k) is the original temperature value of the i-th module at time k, is the estimated temperature after filtering;

[0093] Since battery temperature noise (such as NTC sensor jitter) can cause control oscillation, filtering improves stability. Normalization provides standardized input for subsequent PID control and simplifies parameter tuning.

[0094] Normalized battery voltage V i and current I i :The current I i and voltage V i Normalize to the [0, 1] interval to eliminate the voltage / current dimension difference, unify the dimension, avoid the algorithm deviation due to different numerical ranges, and facilitate subsequent control algorithm processing

[0095]

[0096] Where: Imin and I max The minimum / maximum current value allowed by the battery module (such as -200A to +200A), V min and V max The minimum / maximum voltage value allowed by the battery module (such as 2.5V to 4.2V).

[0097] Step 2: Generate local adaptive control strategy;

[0098] Step 2.1, temperature deviation calculation:

[0099] Define the target temperature T target =35℃, calculate the deviation:

[0100]

[0101] Step 2.2, Dynamically adjust PID parameters: Correct the proportional coefficient K in real time according to SOH and ambient temperature p , adapt to battery aging and environmental changes;

[0102]

[0103] Where: K p,base =2.0 is the base ratio coefficient, SOH threshold =0.8, T env is the ambient temperature; 0.2 and 0.1 are empirical coefficients, which are optimized through experimental calibration. When SOH threshold When the proportional coefficient is less than 0.8 (aging batteries require stronger control), the proportional coefficient increases with the increase of temperature when the ambient temperature deviates from 25°C.

[0104] Step 2.3: Generate local control variables: Fixed PID parameters can lead to insufficient control after battery aging. Dynamic adjustment ensures full lifecycle effectiveness. The incremental algorithm only outputs changes in the control variable to avoid actuator jumps (such as a sudden full speed of the fan).

[0105] Adopting incremental PID algorithm: Incremental PID is suitable for continuous adjustment of actuators (such as fans), avoiding integral saturation problems, and is suitable for continuous adjustment of fan speed to reduce overshoot;

[0106] Δu i (t) = K p，i ·(e i (t)-e i (t-1))+K i ·e i (t)+K d ·(e i (t)-2e i (t-1)+e i (t-2));

[0107] Where: K p，i is the proportional coefficient of the i-th node, SOH i is the battery health status (0.8-1.0), T env is the ambient temperature, K i =0.05, K d =0.1.

[0108] Step 3: Collaborative optimization of consistency between nodes;

[0109] Step 3.1, Neighbor node data exchange:

[0110] The i-th sub-controller sends the information to the adjacent nodes send and u i (t), receiving u j (t);

[0111] Step 3.2: Update the control volume of the consistency protocol: The centralized system relies on the central node and has high communication latency. The distributed consistency protocol achieves global balance through local communication to reduce latency. The weight mechanism prioritizes nodes with similar temperatures to avoid local overheating and spread.

[0112] Calculate the global temperature gradient weight: the smaller the temperature difference between node i and its neighbor node j, the greater the weight w ij The larger the value, the higher the priority is, and the nodes with similar temperatures are coordinated to avoid interference from abnormal nodes.

[0113]

[0114] Update control volume:

[0115]

[0116] Among them, γ = 0.2 is the convergence coefficient, which is calibrated through experiments to balance the convergence speed and stability and control the rate of collaborative adjustment. The set of neighbors of node i (based on physical proximity or communication topology);

[0117] Step 3.3, constraint processing:

[0118] Limit the control range:

[0119]

[0120] Where: is the intermediate control quantity calculated by the i-th sub-controller at time t through consistent collaborative optimization, The final control quantity of the i-th sub-controller at time t is directly mapped to the operating instruction (such as speed percentage) of the actuator (such as fan, liquid cooling pump), The actuator is driven by a PWM signal (Pulse Width Modulation). For example: Corresponding to a 50% PWM duty cycle, the fan speed is 2000RPM.

[0121] Step 4: Actuator output and feedback correction;

[0122] Step 4.1, the control quantity is mapped to the fan speed: the final control quantity (Range 0-100) is linearly mapped to the fan speed (1000-3000 RPM). The linear relationship is determined through experiments to ensure that the control amount 0 corresponds to the lowest speed and 100 corresponds to the highest speed.

[0123] Fan speed RPM i Linear relationship with the control amount: ensure that the control amount 0-100% corresponds to the physical speed range;

[0124]

[0125] Step 4.2, feedback correction:

[0126] If the actual temperature T i (t+1) The deviation from the predicted value exceeds 2°C, triggering parameter adaptation: Dynamically adjust K according to the actual temperature deviation p，i , improve robustness;

[0127]

[0128] Where: 0.05 is the adaptive step size, balancing response speed and stability;

[0129] If the actual temperature is higher than the predicted value (sign = +1), increase K p，i To strengthen control, if the actual temperature is lower than the prediction (sign = -1), reduce K p，i To avoid overshoot.

[0130] Experimental conditions:

[0131] Test object: 4 battery modules (each module contains 10 ternary lithium batteries).

[0132] Comparison solution: traditional centralized PID temperature control system.

[0133] Test conditions: high temperature (40°C environment), high rate charge and discharge (2C)

[0134] Experimental results:

[0135] index The present invention Traditional centralized system Improvement effect Maximum temperature difference ≤2℃ ≤8℃ 75% reduction Response time 1.5 seconds 3.2 seconds 53% shorter System energy consumption 85W 103W 18% reduction Fault recovery time 0.5 seconds System crash No single point of failure

[0136] Tested in a 4-module battery system, compared with traditional centralized PID:

[0137] The maximum temperature difference decreased from 8°C to 2°C;

[0138] Response time reduced from 3.2 seconds to 1.5 seconds;

[0139] System energy consumption is reduced by 18%.

[0140] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. The scope of patent protection of the present invention shall be based on the claims. Any equivalent structural changes made using the description and drawings of the present invention should also be included in the scope of protection of the present invention.

Claims

1. A distributed thermal management system, characterized in that: It includes multiple battery modules, each module contains several lithium battery cells; Each battery module is equipped with an independent sub-controller, and each sub-controller is connected to the following units: The data acquisition unit includes a temperature sensor, a voltage / current sensor, and a battery health status calculation module that are in communication with the sub-controller; a control unit including a microcontroller in communication with the sub-controller and running an adaptive cooperative control algorithm; Communication unit, which is a CANFD communication module; Communication bus, including CANFD bus, connects all sub-controllers; Actuator, each battery module corresponds to a heat dissipation actuator.

2. A distributed thermal management system according to claim 1, characterized in that: The adaptive cooperative control algorithm of the sub-controller includes the following steps: Step 1: Distributed data collection and preprocessing; Step 2: Generate local adaptive control strategy; Step 3: Collaborative optimization of consistency between nodes; Step 4: Actuator output and feedback correction.

3. A distributed thermal management system according to claim 2, characterized in that: The distributed data collection and preprocessing in step 1 includes the following steps: Step 1.1, data input: Set the i-th sub-controller to collect the following data: battery temperature T i , battery voltage V i , current I i , Battery Health Status SOH i 、Ambient temperature T env ; Step 1.2, data preprocessing: Battery temperature T i Perform sliding average filtering with a window size of 5 sampling points: Where: T i (k) is the original temperature value of the i-th module at time k, is the estimated temperature after filtering; Normalized battery voltage V i and current I i : Where: I min and I max The minimum / maximum current value allowed by the battery module, V min and V max The minimum / maximum voltage allowed by the battery module.

4. A distributed thermal management system according to claim 3, characterized in that: The step 2 of generating the local adaptive control strategy includes the following steps: Step 2.1, temperature deviation calculation: Define the target temperature T target =35℃, calculate the deviation: Step 2.2: Dynamically adjust the actuator's PID parameters: Where: K p,base =2.0 is the base ratio coefficient, SOH threshold =0.8, T env is the ambient temperature; 0.2 and 0.1 are empirical coefficients; Step 2.3: Generate local control volume: Using incremental PID algorithm: Δu i (t)=K p，i ·(e i (t)-e i (t-1))+K i ·e i (t)+K d ·(e i (t)-2e i (t-1)+e i (t-2)) Where: K p，i is the proportional coefficient of the i-th node, SOH i The battery health status is 0.8-1.0, T env is the ambient temperature, K i =0.05, K d =0.

1.

5. A distributed thermal management system according to claim 4, characterized in that: The step 3 of inter-node consistency collaborative optimization includes the following steps: Step 3.1, Neighbor node data exchange: The i-th sub-controller sends the information to the adjacent nodes send and the output value u of the control quantity at time t i (t), receiving u j (t); Step 3.2: The consistency protocol updates the control amount: Calculate the global temperature gradient weight: Update control volume: Among them, γ = 0.2 is the convergence coefficient, which controls the rate of collaborative adjustment. The neighbor set of node i; Step 3.3, constraint processing: Limit the control range: Where: is the intermediate control quantity calculated by the i-th sub-controller at time t through the consensus protocol collaborative optimization, The final control value of the i-th sub-controller at time t.

6. A distributed thermal management system according to claim 5, characterized in that: The actuator in step 4 is a fan, and its output and feedback correction includes the following steps: Step 4.1: Map the control quantity to the fan speed: Fan speed RPM i Linear relationship with the control quantity: Step 4.2, feedback correction: If the actual temperature T i (t+1) The deviation from the predicted value exceeds 2°C, triggering parameter adaptation: Where: 0.05 is the adaptive step size, balancing response speed and stability.

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