In the rapidly evolving field of electric vehicles (EVs), the performance and longevity of power batteries are paramount. As a researcher focused on thermal management, I have undertaken an in-depth study to address the critical issue of battery temperature control during operation. The battery management system (BMS) is integral to this endeavor, as it oversees the thermal, electrical, and safety aspects of the battery pack. In this article, I will present our comprehensive approach to optimizing a liquid cooling-based thermal management system, which is a core component of the overall BMS. Our goal is to enhance temperature uniformity, improve散热 efficiency, and extend battery life, thereby contributing to the advancement of EV technology.
The importance of an effective thermal management system within the BMS cannot be overstated. Batteries generate significant heat during charging and discharging cycles, primarily due to Joule heating, polarization, and side reactions. Excessive temperatures can lead to performance degradation, reduced lifespan, and even safety hazards such as thermal runaway. Therefore, maintaining the battery within an optimal temperature range (typically 25–35 °C) is crucial. Through our research, we have developed and refined a liquid cooling system that integrates seamlessly with the BMS to achieve precise temperature control. This system not only mitigates thermal issues but also enhances the overall reliability of the battery pack.
Various thermal management methods exist for EV batteries, including air cooling, liquid cooling, phase change material (PCM) cooling, direct contact cooling, and heat pipe systems. Each method has its advantages and limitations. For instance, air cooling is simple and cost-effective but offers limited散热 efficiency, making it suitable only for low-power-density applications. In contrast, liquid cooling provides superior heat transfer capabilities, with换热 coefficients ranging from 300 to 500 W/m²·K, and enables precise temperature control within ±1 °C. This makes it the preferred choice for high-power-density battery packs. Our study focuses on optimizing liquid cooling due to its scalability and effectiveness, which align with the demands of modern BMS designs. The integration of cooling strategies into the BMS allows for real-time monitoring and adjustment, ensuring optimal battery performance under varying conditions.
To understand the thermal behavior of batteries, we first modeled the battery’s thermal characteristics. The total heat generation in a battery can be expressed using the following formula, which accounts for Joule heat and reversible and irreversible polarization heats:
$$Q = I^2 R + I \left( T \frac{\partial E}{\partial T} – (U – E) \right)$$
where \( Q \) is the total heat generation rate in watts, \( I \) is the current in amperes, \( R \) is the internal resistance in ohms, \( T \) is the temperature in kelvins, \( U \) is the terminal voltage in volts, and \( E \) is the open-circuit voltage in volts. This equation forms the basis for our thermal analysis. Through experimental testing, we observed that under a 1C charge-discharge rate, the battery surface temperature exhibits significant spatial variation, with the center region reaching up to 45 °C and edges around 35 °C. Hotspots near electrode tabs were 3–5 °C higher than surrounding areas. These insights guided our design of the cooling system to target uneven temperature distribution.
The散热 system parameters were calculated based on energy conservation and heat transfer theory. For forced convection in cooling channels, the Nusselt number (\( Nu \)) is a key dimensionless parameter that characterizes convective heat transfer. We employed the Dittus-Boelter correlation, modified for channel bends and entrance effects:
$$Nu = 0.023 \, Re^{0.8} \, Pr^{0.4}$$
where \( Re \) is the Reynolds number and \( Pr \) is the Prandtl number. The heat transfer coefficient (\( h \)) is derived from \( Nu \) as \( h = \frac{Nu \cdot k}{D_h} \), with \( k \) being the thermal conductivity and \( D_h \) the hydraulic diameter. Our analysis showed that \( h \) increases nonlinearly with coolant flow rate, plateauing beyond 6 L/min. After optimization, we determined an optimal flow rate of 4.8 L/min, corresponding to a Reynolds number of 4200 and a heat transfer coefficient of 2800 W/m²·K. The pressure drop (\( \Delta P \)) was calculated considering both frictional and minor losses, following the relation:
$$\Delta P = f \frac{L}{D_h} \frac{\rho v^2}{2} + \sum K \frac{\rho v^2}{2}$$
where \( f \) is the friction factor, \( L \) is the channel length, \( \rho \) is the density, \( v \) is the velocity, and \( K \) represents local loss coefficients. The total pressure drop was reduced to 32 kPa after optimization. These calculations are essential for the BMS to manage coolant flow and pump energy efficiently.
We constructed a numerical simulation model using ANSYS Fluent to predict temperature and flow fields. The computational domain included the battery cells, cooling plates, and fluid channels. A structured mesh with over 2 million elements was used, validated through grid independence studies. The RNG k-ε turbulence model was selected for its accuracy in predicting transitional flows. The governing equations solved were the continuity, momentum, and energy equations:
$$\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) = 0$$
$$\frac{\partial (\rho \mathbf{v})}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v}) = -\nabla p + \nabla \cdot \boldsymbol{\tau} + \rho \mathbf{g}$$
$$\frac{\partial (\rho c_p T)}{\partial t} + \nabla \cdot (\rho c_p \mathbf{v} T) = \nabla \cdot (k \nabla T) + Q$$
where \( \rho \) is density, \( \mathbf{v} \) is velocity vector, \( p \) is pressure, \( \boldsymbol{\tau} \) is stress tensor, \( \mathbf{g} \) is gravity, \( c_p \) is specific heat, \( T \) is temperature, \( k \) is thermal conductivity, and \( Q \) is heat source. The model was validated against experimental data, showing temperature errors within ±0.5 °C. This simulation framework aids the BMS in predicting thermal responses and optimizing control strategies.
Our liquid cooling system design features an optimized serpentine channel layout within cooling plates. The channels consist of a main artery with multiple parallel branches, designed to ensure uniform coolant distribution. Key geometric parameters include a main channel cross-sectional area of 25 mm², branch channel area of 8 mm², branch spacing of 20 mm, and a width-to-depth ratio of 1.5. Turns incorporate gradual截面变化 and guide vanes to minimize pressure losses. The cooling plates are made of 6061 aluminum alloy with an anodized surface for corrosion resistance. Finite element analysis confirmed structural integrity under 1.5 MPa pressure, with a safety factor of 2.8. The optimized channel improved flow distribution uniformity to 92%, directly enhancing temperature homogeneity—a critical factor for the BMS in maintaining cell balance.
The coolant selected is a 30% ethylene glycol aqueous solution, with properties as follows: thermal conductivity of 0.415 W/m·K, density of 1045 kg/m³, and specific heat capacity of 3.56 kJ/kg·K. The optimal flow rate of 4.8 L/min was determined through multi-objective optimization balancing temperature control and energy consumption. Under dynamic conditions, a variable flow strategy is employed: 3.5 L/min for low loads and 5.5 L/min for high loads. This adaptive control is managed by the BMS, which adjusts pump speed and valve openings based on real-time thermal data. The system operating pressure is set at 0.8 MPa with a 20% margin. Fluid dynamics analysis ensured avoidance of resonance, with pressure pulsation frequencies well below system natural frequencies.
Temperature control is implemented via a hierarchical scheme integrated with the BMS. A model predictive control (MPC) algorithm is used, with a control周期 of 1 second and a prediction horizon of 30 seconds. The coolant inlet temperature is maintained at 22 ± 0.5 °C to keep battery temperatures within the ideal 25–35 °C range. The system employs feedforward-feedback compensation, achieving a control accuracy of ±0.3 °C. Actuators use PWM modulation for precise pump and valve control. A fault diagnosis module within the BMS monitors sensor data, achieving 95% accuracy in anomaly detection. Response tests showed overshoot below 1 °C, settling time under 180 seconds, and steady-state error less than 0.2 °C, demonstrating robust performance for the BMS.
We evaluated system performance through extensive testing. Temperature uniformity was assessed using a 32-channel data acquisition system in a controlled environment. Results for various operating conditions are summarized in the table below, showing significant improvement post-optimization.
| Operating Condition | Maximum Temperature Difference (°C) | Uniformity Coefficient (%) | Response Time (s) |
|---|---|---|---|
| 1C Charging | 3.2 | 92 | 280 |
| 1C Discharging | 3.5 | 90 | 295 |
| 3C Charging | 4.5 | 85 | 180 |
| NEDC Cycle | 3.8 | 88 | 320 |
| Low-Temperature Startup | 3.8 | 87 | 450 |
The optimized system reduced the maximum temperature difference by 23% compared to the initial design, with 95% of the battery surface area within a 2.5 °C温差. This uniformity is vital for the BMS to ensure balanced cell aging and performance.
散热 efficiency was tested under different environmental temperatures and loads. Key results are presented in the following table, highlighting the enhancements achieved.
| Parameter | Before Optimization | After Optimization | Improvement (%) |
|---|---|---|---|
| 散热 Power (W) | 950 | 1200 | 26.3 |
| Heat Transfer Coefficient (W/m²·K) | 216 | 285 | 32.0 |
| System Pressure Drop (kPa) | 45 | 32 | 28.9 |
| Pump Power Consumption (W) | 120 | 90 | 25.0 |
| Coefficient of Performance (COP) | 2.8 | 3.6 | 28.6 |
The system can handle heat loads up to 1200 W, sufficient for 3C rate charging. Pump能耗 decreased by 25%, and overall COP improved by 28.6%, indicating higher energy efficiency—a key consideration for the BMS in optimizing overall vehicle energy use.
Reliability analysis involved prolonged testing under various stressors. The system operated for over 3000 hours without significant degradation. Pump flow衰减 was less than 3%, and noise increase was only 1.2 dB.密封性能 remained intact after 100 thermal cycles from -30 to 85 °C, with seal elasticity retention at 92%. Vibration tests (5–200 Hz, 2 g acceleration) showed no structural failures. Corrosion rates were below 0.01 mm/year, and fatigue analysis after 500 cycles revealed maximum deformation of 0.08 mm in cooling plates. Accelerated life testing projected a system lifespan exceeding 8 years, with component failure probability under 0.1%. These reliability metrics assure that the thermal management system, as part of the BMS, meets stringent automotive standards.
In conclusion, our optimized liquid cooling system significantly enhances the thermal management of EV batteries. By improving temperature uniformity and散热 efficiency, it directly supports the BMS in maintaining battery health and performance. The integration of advanced channel designs, precise fluid parameter control, and robust temperature regulation algorithms has resulted in a system that reduces maximum temperature differences to 3.2 °C and improves energy efficiency by over 25%. These advancements provide a solid foundation for future BMS developments, enabling safer, longer-lasting, and more efficient electric vehicles. As the automotive industry continues to evolve, the role of the BMS in thermal management will only grow in importance, and our research offers valuable insights for ongoing innovation.
Throughout this study, we have emphasized the synergy between the thermal management system and the broader battery management system (BMS). The BMS relies on accurate thermal data to execute control strategies, and our optimized cooling solution ensures that such data reflects well-regulated conditions. Future work may explore integration with other BMS functions, such as state-of-charge estimation and fault detection, to create a more holistic battery management approach. Ultimately, the success of electric vehicles hinges on reliable and efficient battery systems, and the BMS—supported by advanced thermal management—is at the heart of this endeavor.