In recent years, the global automotive industry has accelerated its electrification transformation, leading to a significant increase in the market penetration of new energy vehicles. As a key component, the performance of power batteries is crucial for the range, safety, and lifespan of these vehicles. The battery management system (BMS) serves as the “energy brain” of new energy vehicles, responsible for real-time monitoring of critical parameters such as battery state, temperature, and voltage. It optimizes battery operation through intelligent regulation, ensuring that batteries operate under optimal conditions. However, lithium-ion batteries, which are mainstream due to their high energy density and long cycle life, have a narrow operating temperature range (ideally 25–40°C) and require high temperature uniformity. When the temperature difference within a battery pack exceeds 5°C, it accelerates uneven degradation, leading to overall capacity reduction. Temperatures above 60°C can trigger thermal runaway risks due to intensified chemical reactions, while temperatures below -20°C can cause capacity decay of over 30% and reduced charging-discharging efficiency. Thus, efficient thermal management is essential to address these temperature sensitivity issues. In this research, I propose a multi-objective optimized battery thermal management system (BTMS) that integrates phase change materials (PCM) and liquid cooling technology, employs intelligent predictive control, and optimizes the heat flow field to enhance heat dissipation efficiency, energy balance, and adaptability. This solution aims to control the battery pack temperature difference to within ±2°C, reduce temperature by 12% under high-temperature conditions, and increase preheating efficiency by 35% under low-temperature conditions, thereby significantly improving the working environment and providing theoretical and practical guidance for engineering applications.
The battery management system (BMS) plays a pivotal role in ensuring the safety and performance of batteries. It continuously monitors parameters such as state of charge (SOC), state of health (SOH), and temperature, and implements control strategies to prevent overcharging, over-discharging, and thermal issues. However, traditional thermal management systems, including air cooling, liquid cooling, PCM cooling, and heat pipe/plate cooling, have limitations. Air cooling is simple and low-cost but suffers from low heat dissipation efficiency; liquid cooling offers strong heat exchange but involves complex piping and high energy consumption; PCM provides passive cooling with stability but has low thermal conductivity, requiring integration with other methods. Moreover, traditional PID control strategies in BMS often struggle to adapt to dynamic conditions, affecting system energy efficiency and cooling performance. Therefore, my research focuses on developing a composite thermal management system that combines active liquid cooling with passive PCM, optimized through advanced design and control strategies.
To address these challenges, I first designed a composite cooling structure. The coupling of PCM and liquid cooling plates utilizes paraffin-based PCM with a melting point of 35°C and latent heat of 210 kJ/kg as a heat dissipation medium between battery cells. This PCM stabilizes temperature through phase change processes. A serpentine liquid cooling plate is integrated at the bottom of the battery pack, using a 50% ethylene glycol-water solution as coolant, driven by a pump for active heat removal. The PCM is arranged in a porous prismatic structure perpendicular to the liquid cooling channels, forming a three-dimensional thermal conductive network to enhance heat exchange efficiency by increasing contact area and promoting convective synergy. For flow channel optimization, I employed CFD parametric modeling to optimize channel dimensions for temperature uniformity and pressure drop, achieving multi-objective goals. Orthogonal experiments revealed that increasing channel width to 6 mm reduces temperature difference (ΔT) by 18% but increases pressure drop (ΔP) by 25%, while reducing channel spacing to 10 mm reduces ΔT by 12% and increases ΔP by 15%. A design with 5 mm width, 12 mm spacing, and 135° bending angle balances flow resistance and temperature uniformity, optimizing heat dissipation efficiency and energy consumption. This integrated approach ensures that the battery management system (BMS) can effectively regulate thermal conditions.
In terms of intelligent control, I implemented a model predictive control (MPC) strategy to overcome the limitations of traditional PID control in dynamic scenarios like rapid acceleration and frequent charging-discharging. The MPC algorithm integrates battery thermal models and工况预测 for real-time optimization of coolant flow rate and pump power. The steps include: (1) Building a battery thermal model using equivalent circuit and heat transfer theories to describe dynamic performance, with key parameters identified through pulse charge-discharge experiments. (2) Inputting工况数据 from onboard sensors (e.g., vehicle speed, battery SOC, ambient temperature) and cloud-based map data to predict driving conditions over a 10-minute horizon, accurately calculating battery power demand and heat generation rate. (3) Implementing rolling optimization control with objectives such as a target temperature of 30°C, temperature difference within 2°C, and minimal energy consumption; solving optimization problems with state and input constraints every 50 ms to output optimal coolant flow and pump speed schemes. This enhances the adaptability of the battery management system (BMS) to varying conditions.
To validate the design, I established a simulation model using ANSYS Fluent for a battery pack with 24 18650-type 3.3 Ah cells, equipped with PCM and liquid cooling channels. Boundary conditions were set based on NEDC工况 with adjustments for 45°C high-temperature and -10°C low-temperature scenarios, coolant inlet temperature fixed at 25°C, and flow rate at 0.5 L/min. Material parameters included thermal conductivity of battery and PCM as 1.2 W/(m·K) and 0.35 W/(m·K) respectively, with PCM enhanced to 1.8 W/(m·K) by adding 10% graphene; the liquid cooling plate was made of aluminum alloy with thermal conductivity of 205 W/(m·K). The simulation results showed significant improvements: under high-temperature conditions (45°C), the optimized composite system reduced the maximum battery temperature from 58°C to 51°C, with temperature difference controlled within 1.5°C; under low-temperature conditions (-10°C), the system achieved preheating to 5°C in 29 minutes compared to 45 minutes for traditional systems, with temperature difference reduced to within 1°C. These outcomes demonstrate the efficacy of the battery management system (BMS) in thermal regulation.
For experimental verification, I built a test platform including a battery pack test chamber, temperature-controlled environmental chamber, liquid cooling circulation device, and data acquisition system with accuracy of ±0.1°C. The battery pack consisted of ternary lithium cells in a 6S4P configuration with rated capacity of 60 Ah, with sensors placed on each cell’s surface and within PCM layers. Tests included temperature uniformity and energy consumption comparisons. Under 3C discharge conditions, the optimized system reduced battery pack temperature difference to 1.8°C, significantly better than traditional liquid cooling (5.2°C). Energy consumption experiments under NEDC工况 showed that MPC optimization reduced average power consumption from 120 W to 85 W, saving 29.2%. These results highlight the role of the battery management system (BMS) in improving energy efficiency and thermal stability.
The impact of the optimized thermal management system on battery performance was analyzed through accelerated life testing, charging-discharging efficiency evaluation, and safety assessments. For cycle life, tests at 45°C and 1C cycling showed that capacity衰减 to 80% increased from 320 cycles with traditional systems to 450 cycles with the optimized system, extending lifespan by 40.6%. This is attributed to improved temperature uniformity, which reduces differential degradation among cells and suppresses SEI膜 growth at high temperatures. For charging efficiency at -20°C, the optimized system allowed direct charging at 0.5C without preheating, reducing time by 25% and increasing energy efficiency from 82% to 88%, due to synergistic heating from PCM and liquid cooling. Safety was enhanced by extending thermal扩散 time from 120 s to 360 s, with emergency cooling strategies increasing flow rate to 2 L/min to遏制 thermal runaway, giving the battery management system (BMS) more time to implement protection measures.
To provide a comprehensive summary, I present key formulas and tables that encapsulate the design and performance. The thermal model for the battery can be expressed using heat generation and dissipation equations. The heat generation rate $Q_{\text{gen}}$ during charging or discharging is given by:
$$Q_{\text{gen}} = I^2 R_{\text{int}} + I \left( T \frac{\partial E}{\partial T} \right)$$
where $I$ is the current, $R_{\text{int}}$ is the internal resistance, $T$ is temperature, and $E$ is the open-circuit voltage. The heat dissipation through the composite system involves conduction and convection terms. For PCM, the energy balance during phase change is:
$$\rho_{\text{PCM}} C_{\text{PCM}} \frac{\partial T}{\partial t} = \nabla \cdot (k_{\text{PCM}} \nabla T) + \rho_{\text{PCM}} L \frac{\partial f}{\partial t}$$
where $\rho_{\text{PCM}}$ is density, $C_{\text{PCM}}$ is specific heat, $k_{\text{PCM}}$ is thermal conductivity, $L$ is latent heat, and $f$ is the liquid fraction. For liquid cooling, the heat transfer rate is:
$$Q_{\text{cool}} = \dot{m} C_p (T_{\text{out}} – T_{\text{in}})$$
with $\dot{m}$ as mass flow rate, $C_p$ as specific heat of coolant, and $T_{\text{in}}$ and $T_{\text{out}}$ as inlet and outlet temperatures. The MPC optimization problem minimizes a cost function:
$$J = \sum_{k=0}^{N-1} \left( \| T(k) – T_{\text{ref}} \|^2 + \lambda \| u(k) \|^2 \right)$$
subject to constraints on temperature, flow rate, and pump power, where $T(k)$ is predicted temperature, $T_{\text{ref}}$ is reference temperature, $u(k)$ is control input (e.g., pump speed), and $\lambda$ is a weighting factor.
| Component | Property | Value | Unit |
|---|---|---|---|
| Battery Cell | Thermal Conductivity | 1.2 | W/(m·K) |
| PCM (Base) | Thermal Conductivity | 0.35 | W/(m·K) |
| PCM (with Graphene) | Thermal Conductivity | 1.8 | W/(m·K) |
| PCM | Melting Point | 35 | °C |
| PCM | Latent Heat | 210 | kJ/kg |
| Liquid Cooling Plate | Thermal Conductivity | 205 | W/(m·K) |
| Coolant (50% EG-Water) | Specific Heat | 3.5 | kJ/(kg·K) |
| Coolant | Density | 1050 | kg/m³ |
| Condition | Metric | Traditional System | Optimized System | Improvement |
|---|---|---|---|---|
| High-Temperature (45°C) | Max Temperature | 58°C | 51°C | 12% reduction |
| High-Temperature | Temperature Difference | 5.2°C | 1.5°C | 71% reduction |
| Low-Temperature (-10°C) | Preheating Time to 5°C | 45 min | 29 min | 35% faster |
| Low-Temperature | Temperature Difference | 4°C | 1°C | 75% reduction |
| Performance Indicator | Traditional BMS with PID | Optimized BMS with MPC | Change |
|---|---|---|---|
| Average Power Consumption | 120 W | 85 W | 29.2% decrease |
| Temperature Uniformity (ΔT) | 5.2°C | 1.8°C | 65.4% improvement |
| Cycle Life to 80% Capacity | 320 cycles | 450 cycles | 40.6% increase |
| Charging Efficiency at -20°C | 82% | 88% | 7.3% increase |
| Thermal Diffusion Time | 120 s | 360 s | 200% increase |
The optimization of the battery thermal management system (BTMS) has profound implications for the overall battery management system (BMS). By integrating PCM and liquid cooling, the BMS can achieve more precise temperature control, reducing thermal stress and延长 battery life. The intelligent predictive control enhances the BMS’s ability to respond to dynamic conditions, improving energy efficiency and safety. For instance, the MPC algorithm allows the BMS to anticipate heat generation based on driving patterns, adjusting cooling parameters proactively rather than reactively. This synergy between hardware design and software control is key to advancing BMS technology. In my research, I focused on ensuring that the BMS incorporates real-time data from sensors and predictive models to optimize thermal management, thereby supporting the broader goals of new energy vehicle development.
Looking ahead, there are several avenues for future research to further enhance battery thermal management systems and their integration with BMS. First, developing battery aging models that account for thermal effects can enable adaptive temperature strategies over the battery’s lifecycle. By incorporating degradation mechanisms into the BMS, such as capacity fade and impedance growth, thermal management can be optimized to prolong lifespan. Second, advancing neural network-based工况预测 algorithms can improve the accuracy and real-time performance of MPC in BMS. Neural networks can capture complex nonlinear relationships in driving data, providing more reliable inputs for control optimization. Third, exploring novel cooling materials and microchannel structures can boost energy density and efficiency. For example, carbon nanotube-enhanced PCM or low-melting-point alloys offer higher thermal conductivity, while microchannels reduce size and weight. These innovations could be integrated into next-generation BMS designs for compact and high-performance thermal management. Additionally, standardizing BMS protocols and communication interfaces will facilitate interoperability and scalability across vehicle platforms.
In conclusion, my research presents a comprehensive approach to optimizing the battery thermal management system for new energy vehicles through composite cooling structures and intelligent control. The integration of PCM and liquid cooling, coupled with MPC strategies, significantly improves temperature uniformity, reduces energy consumption, and enhances battery performance and safety. The battery management system (BMS) plays a central role in implementing these advancements, ensuring that batteries operate within optimal thermal ranges. The findings demonstrate that such optimizations can lead to tangible benefits, including extended cycle life, improved charging efficiency, and increased thermal safety margins. As the automotive industry continues to evolve towards electrification, continued innovation in BMS and thermal management will be crucial for meeting the demands of high-energy-density batteries and sustainable transportation. Future work should focus on adaptive lifecycle management, advanced predictive algorithms, and material innovations to drive further progress in this field.