In the context of electric vehicles (EVs), the thermal management of power battery packs is critical due to complex operational conditions that can lead to overheating or undercooling, adversely affecting performance and lifespan. Traditional fixed-parameter control methods often fail to adapt swiftly to dynamic changes, resulting in inefficient temperature regulation. To address this, we propose an adaptive cooling control method based on fuzzy PID for China EV battery systems. This approach leverages real-time thermal power calculations, battery module temperature analysis, and a fuzzy logic-based adaptive strategy to maintain optimal temperature levels. By integrating continuous temperature monitoring and multi-stage control mechanisms, the method ensures stable and efficient cooling, enhancing the reliability and safety of EV power battery packs. The following sections detail the theoretical foundations, control design, and experimental validation, with an emphasis on formulas and tables to summarize key aspects.
The core of this method involves calculating the heat generation power of individual batteries, analyzing temperature distribution within battery modules, and designing an adaptive control model using fuzzy PID. The fuzzy PID controller dynamically adjusts cooling parameters based on real-time data and operational states, such as discharge rates and power demands. This adaptability is crucial for China EV battery applications, where varying driving patterns and environmental conditions can cause significant thermal fluctuations. Additionally, a continuous temperature control mechanism is implemented to monitor and share data, enabling prompt adjustments to maintain the battery temperature within a preset target range. The integration of these components provides a robust solution for managing the thermal behavior of EV power battery packs under diverse scenarios.
Heat Generation Power Calculation for Single Battery
The heat generation in a China EV battery primarily arises from internal electrochemical reactions, ohmic losses, and polarization effects. Accurately estimating the heat generation power is essential for effective thermal management. We begin by calculating the ohmic heat loss ratio, which serves as a foundational parameter. The formula for the ohmic heat loss ratio is given by:
$$ h = \frac{m + n}{w} $$
where \( h \) represents the ohmic heat loss ratio, \( m \) and \( n \) denote the preset heat loss value and sudden heat loss value, respectively, and \( w \) is the identifiable heat loss point. This ratio helps in quantifying the heat dissipation characteristics under different operating conditions. Based on this, the heat generation power of a single battery can be computed using the following equation:
$$ P_{\text{gen}} = I^2 \times R_{\text{int}} + P_{\text{rev}} + P_{\text{pol}} $$
Here, \( P_{\text{gen}} \) is the heat generation power of the single battery, \( I \) is the operating current, \( R_{\text{int}} \) is the internal resistance, \( P_{\text{rev}} \) is the reversible heat power, and \( P_{\text{pol}} \) is the polarization heat power. This model captures real-time thermal dynamics, providing critical input for the adaptive cooling control strategy. For instance, in high-discharge scenarios common in EV power battery usage, the heat generation power increases significantly, necessitating precise control actions to prevent overheating.
To illustrate the relationship between operational parameters and heat generation, Table 1 summarizes key variables and their typical ranges in China EV battery systems. This table aids in understanding how different factors influence thermal behavior and guides the adjustment of control parameters.
| Parameter | Symbol | Typical Range | Description |
|---|---|---|---|
| Operating Current | \( I \) | 50-200 A | Current drawn during discharge/charge |
| Internal Resistance | \( R_{\text{int}} \) | 0.01-0.05 Ω | Resistance causing ohmic losses |
| Reversible Heat Power | \( P_{\text{rev}} \) | 5-20 W | Heat from reversible reactions |
| Polarization Heat Power | \( P_{\text{pol}} \) | 10-30 W | Heat due to polarization effects |
| Ohmic Heat Loss Ratio | \( h \) | 0.1-0.5 | Ratio of ohmic losses to total heat |
This calculation forms the basis for real-time thermal monitoring, enabling the fuzzy PID controller to respond proactively to changes in heat generation. By continuously updating \( P_{\text{gen}} \) based on sensor data, the system can predict thermal trends and initiate cooling actions before critical temperatures are reached, thus safeguarding the China EV battery from potential damage.
Temperature Characteristics Analysis of Battery Module
While single battery heat generation provides insights into local thermal behavior, the battery module as a whole exhibits complex temperature distributions due to factors like uneven heat conduction, cooling medium flow, and external environmental influences. Analyzing these temperature characteristics is vital for optimizing the cooling strategy. We recorded temperature data over multiple time intervals to capture the dynamic changes within the module. The temperature difference within the module is defined as a key constraint, and the relationship between the average temperature and the maximum temperature across adjacent periods is examined.
The analysis reveals that as the usage time and scheduling frequency of the China EV battery increase, the module temperature shows a clear upward trend. For example, during high-power demands, such as rapid acceleration or braking, the temperature can spike rapidly. To address this, we optimized various operational parameters based on empirical data, as detailed in Table 2. This table compares initial standards with optimized values for critical parameters, including temperature deviation, deviation change ratio, heat transfer coefficient, initial single battery temperature, and cooling time.
| Parameter | Initial Standard | Optimized Standard | Impact on Temperature Control |
|---|---|---|---|
| Temperature Deviation | ±5°C | ±2°C | Reduces overshoot and improves stability |
| Deviation Change Ratio | 0.1-0.3 | 0.05-0.15 | Enhances response to sudden changes |
| Heat Transfer Coefficient | 50 W/m²K | 75 W/m²K | Increases cooling efficiency |
| Initial Single Battery Temperature | 25°C | 20°C | Provides buffer for temperature rise |
| Cooling Time | 10-20 s | 5-15 s | Shortens response time for cooling actions |
The temperature distribution within the module is non-uniform, with certain regions being more sensitive to thermal changes. For instance, batteries located near the center of the module may experience higher temperatures due to limited cooling access. By analyzing these characteristics, we can fine-tune the cooling system to target hot spots effectively. The average temperature \( L \) of the module is calculated as:
$$ L = \frac{\kappa}{K} $$
where \( \kappa \) is the temperature peak and \( K \) is the temperature change point. This average temperature serves as a constraint for the adaptive control strategy, ensuring that the overall thermal state remains within safe limits. The insights from this analysis are integrated into the fuzzy PID controller to adjust cooling outputs dynamically, thereby maintaining the EV power battery at an optimal operating temperature.
Design of Fuzzy PID Adaptive Cooling Control Model
To tackle the issue of high temperatures in China EV battery packs under complex conditions, we designed an adaptive cooling control strategy based on fuzzy PID. This model identifies the operational state and power supply requirements, then uses a fuzzy controller to map real-time data to fuzzy sets. The fuzzy rules database generates control commands, enabling adaptive temperature regulation. When the battery temperature exceeds the expected threshold, the fuzzy controller intervenes by fuzzifying the input data and applying predefined rules to derive control actions.
The control process begins with calculating the temperature error change rate, which is essential for determining the necessary adjustments. The formula for the basic temperature control error change rate is:
$$ I_1 = \int \left( \pi – \frac{\phi^2}{\sqrt{E}} \right) – C $$
Here, \( I_1 \) represents the basic temperature control error change rate, \( \pi \) is the maximum cooling standard, \( \phi \) is the actual temperature value, \( E \) is the control cycle, and \( C \) is the directional error. This rate helps in quantifying how quickly the temperature is deviating from the setpoint, allowing the controller to respond proportionally.
As the China EV battery operates over time, the temperature may rise again due to increasing power demands. Therefore, the system continuously monitors and computes the current average temperature \( L \), as defined earlier. This value is used as a processing limit standard for adaptive cooling control. Based on this, the fuzzy PID controller performs defuzzification to convert fuzzy sets into precise control outputs. For example, if a specific region within the battery module shows heightened temperature sensitivity, special control rules are applied to adjust the cooling system output for that area. The cooling system output value \( M \) is calculated as:
$$ M = \upsilon^2 – \frac{Y = 1}{\sum \omega Y} + \tau $$
where \( M \) is the control cooling system output value, \( \upsilon \) is the maximum temperature standard, \( \omega \) is the segment single battery temperature, \( Y \) represents the preset segment, and \( \tau \) is the temperature control maximum limit standard. This equation ensures that the cooling output is optimized based on real-time thermal conditions, enhancing the adaptability of the EV power battery management system.
The fuzzy PID adaptive model significantly improves the system’s robustness against disturbances and its ability to handle varying temperature scenarios. By dynamically adjusting control parameters, it ensures that the China EV battery remains within the optimal temperature range, thereby extending battery life and improving overall vehicle performance. Table 3 outlines the fuzzy rule base used in the controller, illustrating how input variables like temperature error and error change rate are mapped to output control actions.
| Temperature Error | Error Change Rate | Control Action | Description |
|---|---|---|---|
| Negative Large | Negative Large | Decrease Cooling | Reduce cooling output to avoid undercooling |
| Negative Small | Negative Small | Maintain Current | Keep cooling stable for minor deviations |
| Zero | Zero | No Change | Ideal state, no adjustment needed |
| Positive Small | Positive Small | Increase Cooling Slightly | Gradual response to small temperature rises |
| Positive Large | Positive Large | Increase Cooling Significantly | Aggressive cooling for high temperature spikes |
This rule base allows the fuzzy PID controller to make intelligent decisions based on the current state of the China EV battery, ensuring efficient and responsive thermal management.
Continuous Temperature Control for Adaptive Processing
To ensure the stable operation and safety of the China EV battery, a continuous temperature control mechanism is implemented. This mechanism utilizes high-precision temperature sensors and deploys edge and core temperature perception nodes to monitor and share battery temperature data in real time. The collected data is fed back to the fuzzy PID controller, where it is compared with the preset target temperature. Based on this comparison, the controller makes adaptive adjustments to maintain optimal conditions.
A multi-stage temperature control strategy is employed, where control variables are periodically optimized to ensure temperature stability. This involves cyclic control to calibrate temperature extremes and feedback data for continuous improvement. The stages include initial monitoring, adaptive adjustment, and optimization phases, each designed to handle different thermal scenarios. Table 4 provides a detailed description of the multi-stage adaptive temperature control process, highlighting the actions taken at each stage to manage the EV power battery temperature effectively.
| Stage | Objective | Actions | Outcome |
|---|---|---|---|
| Stage 1: Initial Monitoring | Detect temperature anomalies | Deploy sensors, collect data, compare with preset values | Early identification of potential overheating |
| Stage 2: Adaptive Adjustment | Adjust cooling parameters | Apply fuzzy PID rules, modify cooling output, feedback data | Rapid response to temperature changes |
| Stage 3: Optimization | Optimize control variables | Refine fuzzy rules, calibrate sensors, update target temperatures | Enhanced long-term stability and efficiency |
In the event of abnormal temperature fluctuations, the fuzzy PID adaptive strategy quickly adjusts parameters based on the operational state and power demand, restoring the China EV battery temperature to the normal range. For instance, during high-speed driving with simultaneous use of high-power devices, the system increases cooling output proactively to counteract heat accumulation. The continuous control mechanism also involves calculating the specific heat capacity of the battery core, which is treated as a uniform heat source after prolonged operation. The specific heat capacity \( C_p \) is given by:
$$ C_p = \frac{1}{m} \sum_{i=1} C_i m_i – R_m $$
where \( C_p \) is the specific heat capacity of the single battery core, \( m \) is the core mass, \( C_i \) is the specific heat capacity of different materials, \( m_i \) is the mass of different materials, and \( R_m \) is the density of different materials. This calculation aids in determining the thermal inertia of the battery, which influences how quickly the cooling system must respond.
Furthermore, the thermal conductivity coefficient \( K \) is derived based on the specific heat capacity and discharge levels, using the formula:
$$ K = \frac{\mu}{\nu} $$
where \( \mu \) is the coverage area for heat conduction, and \( \nu \) is the thermal conductivity rate. This coefficient is used to optimize the cooling system’s performance, ensuring that heat is dissipated efficiently across the EV power battery pack. By integrating these calculations into the continuous control loop, the system maintains a stable temperature profile, even under varying loads and environmental conditions.
Experimental Validation and Results
To validate the effectiveness of the proposed adaptive cooling control method for China EV battery systems, we conducted experiments comparing it with traditional approaches, such as orthogonal experiment-based dual-flow battery cooling and fuzzy calculation-based cooling control. The experimental setup involved constructing a simulation environment using Matlab/Simulink, with batteries 1 to 4 designated as key observation points. The discharge depth was set to 0.8 to simulate realistic operating conditions, and measures were taken to avoid battery polarization during testing.
Initial test data and information were configured as outlined in Table 5, which includes parameters like battery capacity, discharge efficiency, and initial temperature. These settings ensure consistency and reliability in the experimental results.
| Parameter | Value | Description |
|---|---|---|
| Battery Capacity Range | 1962-2018 mAh | Capacity variation for tested batteries |
| Initial Discharge Efficiency | 93.5% | Efficiency at start of discharge cycle |
| Discharge Temperature | 45°C | Preset temperature to simulate high-load conditions |
| Control Cycle | 10 s | Time interval for control actions |
| Cooling Medium Flow Rate | 0.5-2.0 L/min | Range for adjustable cooling system |
During the experiment, constraint standards for the batteries were predefined, as shown in Table 6. These standards include limits on temperature deviation, current fluctuations, and cooling response times, which are critical for evaluating the control method’s performance.
| Constraint | Standard Value | Purpose |
|---|---|---|
| Maximum Temperature | 50°C | Prevent thermal runaway |
| Minimum Temperature | 10°C | Avoid undercooling effects |
| Current Fluctuation Limit | ±10% | Ensure stable power delivery |
| Cooling Response Time | <5 s | Quick reaction to temperature changes |
| Temperature Stability Range | ±2°C | Maintain optimal operating conditions |
The experimental process involved monitoring temperature changes over six randomly selected time intervals, with adjustments made from the highest temperature points. The temperature variation amplitude was calculated to be between 1.5 and 3.7, indicating the range within which the cooling system operates. By applying the fuzzy PID adaptive model, the system successfully reduced the battery temperature to a controllable zone and achieved balanced control. The results demonstrated that the proposed method provides smoother temperature control compared to traditional approaches, with minimal fluctuations and rapid adaptation to changing conditions.
For example, in high-discharge scenarios, the fuzzy PID controller adjusted the cooling output in real-time, preventing temperature spikes that are common in fixed-parameter systems. The continuous temperature control mechanism ensured that data was shared across nodes, enabling coordinated actions that enhanced overall efficiency. This experimental validation confirms the practical potential of the method for China EV battery applications, offering improved thermal management and extended battery life for EV power battery packs.
Conclusion
The proposed adaptive cooling control method based on fuzzy PID offers a robust solution for managing the thermal behavior of China EV battery systems. By integrating real-time heat generation calculations, temperature distribution analysis, and fuzzy logic control, it addresses the limitations of traditional fixed-parameter methods. The adaptive strategy dynamically adjusts cooling parameters based on operational states and power demands, ensuring stable temperature regulation under complex conditions. Experimental results validate its effectiveness, showing superior temperature stability and cooling performance compared to conventional approaches.
This method enhances the reliability and safety of EV power battery packs, contributing to the advancement of electric vehicle technology. Future work could focus on optimizing the fuzzy rule base for specific battery chemistries and integrating machine learning techniques for predictive control. Overall, the approach provides a scalable framework for thermal management in China EV battery applications, supporting the growing demand for efficient and sustainable transportation solutions.