In recent years, the rapid development of the new energy vehicle industry has highlighted the critical role of power batteries as core components. Their performance, safety, and lifespan directly determine the overall vehicle competitiveness. However, during charge and discharge cycles, electrochemical reactions generate substantial heat. If not managed effectively, this can lead to elevated temperatures, performance degradation, accelerated capacity fade, and even thermal runaway risks. Additionally, in low-temperature environments, increased internal resistance reduces efficiency, impacting vehicle operation. Thus, developing an efficient battery thermal management system (BTMS) is paramount for ensuring battery reliability and safety.
Common BTMS technologies include air cooling, liquid cooling, phase change material (PCM) cooling, and hybrid approaches. Air cooling is simple and low-cost but offers limited散热efficiency, struggling with high-power-density batteries. Liquid cooling provides strong散热capabilities and precise temperature control, yet it involves higher complexity, cost, and potential leakage risks. PCM cooling leverages latent heat absorption for temperature stabilization but suffers from low thermal conductivity, restricting散热rates. To address these limitations, I propose a novel modular power battery structure with an optimized thermal management system. This design aims to enhance散热performance and temperature uniformity, reduce system cost and complexity, and improve battery reliability under diverse operating conditions.
The design philosophy centers on modularization, where the battery pack is divided into independent small modules, each equipped with dedicated thermal management functions. Through rational layout and interconnections, overall pack performance is optimized. This modular approach simplifies architecture, facilitates production and maintenance, and enhances flexibility and scalability for customized configurations based on application needs.
Each module comprises key components: cells, liquid cooling plates, thermal insulation materials, side plates, and electrical connectors. High-energy-density lithium-ion cells are arranged in specific patterns within the module to achieve desired voltage and capacity. Liquid cooling plates feature a dual-side cooling structure, positioned at the top and bottom of cells, circulating coolant to absorb heat. Compared to traditional single-side cooling, this significantly improves散热efficiency. The plates are made of aluminum alloy with internal microchannel designs to enhance heat transfer between coolant and cells while minimizing weight. Thermal insulation materials, such as aerogel, wrap around module peripheries, reducing heat transfer between modules and with the external environment, thereby lowering thermal interference and improving pack temperature uniformity. Side plates are fabricated from high-strength composites, providing mechanical support and protection while further optimizing insulation.
The thermal management system integrates active liquid cooling with intelligent control for precise temperature regulation. The overall scheme includes a coolant circulation loop, refrigeration/heating units, temperature sensors, a controller (the battery management system or BMS), and associated piping and valves. The coolant loop removes heat from cells, with the refrigeration/heating units adjusting coolant temperature to maintain an optimal range. Temperature sensors monitor module and coolant temperatures in real-time, feeding data to the BMS. Based on preset thresholds and algorithms, the BMS automatically controls unit operation, pump speed, and valve openings for intelligent regulation.
Cooling system design focuses on optimizing liquid cooling plate structures and coolant flow distribution. The cooling plate’s internal microchannels are critical for散热performance. Through numerical simulation, I analyzed effects of microchannel shapes (e.g., rectangular, circular, trapezoidal), dimensions (width, height, spacing), and layout patterns (series, parallel, serpentine) on flow and heat transfer characteristics. The results indicate that rectangular microchannels in a serpentine arrangement offer the best散热under identical flow and pressure drop conditions, effectively reducing peak cell surface temperatures and minimizing gradients. The heat transfer can be modeled using the convection equation: $$q = h A (T_{surface} – T_{coolant})$$ where \( q \) is the heat flux, \( h \) is the convective heat transfer coefficient, \( A \) is the surface area, and \( T \) denotes temperatures. For microchannels, the coefficient \( h \) depends on flow parameters, approximated by $$h = \frac{Nu \cdot k}{D_h}$$ with \( Nu \) as the Nusselt number, \( k \) as thermal conductivity, and \( D_h \) as hydraulic diameter.
Flow distribution optimization addresses varying heat generation across modules. An intelligent flow distribution system using flow control valves is implemented. Each module’s coolant inlet has an electric flow control valve. The BMS adjusts valve openings based on real-time temperature data, precisely modulating coolant flow to each module. When a module’s temperature rises, its valve opens wider to increase flow and enhance散热; conversely, flow is reduced for cooler modules to prevent overcooling. This ensures balanced temperature control. The flow dynamics can be described by the continuity and momentum equations: $$\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) = 0$$ and $$\rho \left( \frac{\partial \mathbf{v}}{\partial t} + \mathbf{v} \cdot \nabla \mathbf{v} \right) = -\nabla p + \mu \nabla^2 \mathbf{v} + \mathbf{f}$$ where \( \rho \) is density, \( \mathbf{v} \) is velocity, \( p \) is pressure, \( \mu \) is viscosity, and \( \mathbf{f} \) represents body forces.
For low-temperature conditions, a heating system is integrated using positive temperature coefficient (PTC) thermistors as heating elements, distributed evenly within cooling plates or near cells. When battery temperature falls below a set threshold, the BMS activates heating, with PTC elements generating heat transferred via plates or direct contact to warm cells. Heating power is controlled by adjusting current, enabling precise thermal management. The heat generation rate for PTC elements can be expressed as $$Q_{heating} = I^2 R(T)$$ where \( I \) is current and \( R(T) \) is temperature-dependent resistance.
The battery management system (BMS) plays a central role in orchestrating these functions. It employs model predictive control (MPC) algorithms to optimize system performance. The BMS continuously monitors temperatures, estimates heat generation, and predicts thermal behavior using a state-space model: $$\dot{\mathbf{x}} = A\mathbf{x} + B\mathbf{u}$$ $$\mathbf{y} = C\mathbf{x} + D\mathbf{u}$$ where \( \mathbf{x} \) represents state variables (e.g., temperatures), \( \mathbf{u} \) are control inputs (e.g., pump speed, valve positions), and \( \mathbf{y} \) are outputs. The BMS solves an optimization problem to minimize a cost function, such as $$J = \sum_{k=0}^{N-1} \left( \| T(k) – T_{ref} \|^2 + \lambda \| \mathbf{u}(k) \|^2 \right)$$ where \( T_{ref} \) is the target temperature, \( \lambda \) is a weighting factor, and \( N \) is the prediction horizon. This approach allows the BMS to dynamically adjust parameters, enhancing efficiency and responsiveness.
To validate the design, I established a finite element analysis model using ANSYS, simulating the modular structure and thermal management system. Material properties were assigned based on actual parameters, including thermal conductivity, specific heat, and density. For coolant flow and heat transfer, computational fluid dynamics (CFD) methods were applied, considering viscosity, turbulence, and convection. Cell heat generation rates were defined via user-defined functions, derived from electrochemical-thermal coupling models. The heat generation rate during operation is given by $$Q_{gen} = I^2 R_{internal} + I T \frac{\partial U}{\partial T}$$ where \( I \) is current, \( R_{internal} \) is internal resistance, \( T \) is temperature, and \( \frac{\partial U}{\partial T} \) is the entropy coefficient.
Simulation results under high-temperature conditions (40°C ambient, 3C charge-discharge rate) demonstrate significant improvements. As shown in Table 1, the optimized modular system with intelligent BMS control reduces peak temperatures and enhances uniformity compared to traditional designs.
| Design | Peak Temperature (°C) | Module Temperature Difference (°C) | Cooling System Energy Consumption (W) |
|---|---|---|---|
| Traditional Structure | 55 | 8 | 250 |
| New Modular Structure (Optimized) | 42 | 3 | 180 |
Temperature distribution profiles further illustrate these benefits. The modular design ensures more uniform heat dissipation, with maximum gradients confined to within 5°C across the pack. The BMS’s智能flow distribution reduces hotspots, as evidenced by simulated thermal maps. Additionally, performance metrics such as response time and energy efficiency were evaluated. Under transient conditions (e.g., sudden ambient temperature rise), the intelligent BMS achieves temperature stabilization within 3–5 minutes, versus 8–10 minutes for conventional control, while cutting energy use by 20–30%. This highlights the BMS’s capability to adapt to dynamic operational states.
Further analysis involves parametric studies on cooling plate geometry and flow rates. Table 2 summarizes effects of microchannel dimensions on heat transfer coefficients and pressure drops, guiding optimal design choices.
| Microchannel Width (mm) | Microchannel Height (mm) | Heat Transfer Coefficient (W/m²·K) | Pressure Drop (Pa) |
|---|---|---|---|
| 1.0 | 2.0 | 4500 | 1200 |
| 1.5 | 2.0 | 4200 | 900 |
| 2.0 | 2.0 | 3800 | 700 |
| 1.0 | 3.0 | 4800 | 1500 |
The thermal management system’s efficiency can be quantified using the coefficient of performance (COP) for cooling: $$COP_{cooling} = \frac{Q_{removed}}{W_{input}}$$ where \( Q_{removed} \) is heat removed and \( W_{input} \) is work input. For the optimized system, COP values range from 3.5 to 4.5 under typical loads, indicating high energy utilization. The BMS optimizes this by adjusting pump speeds and valve settings based on real-time thermal loads.
In low-temperature simulations, the heating system’s effectiveness is assessed. With PTC heating, battery temperatures can be raised from -10°C to 15°C within 10 minutes, maintaining efficiency above 85%. The BMS modulates heating power to avoid overshoot, using feedback control: $$P_{heating} = K_p e(t) + K_i \int e(t) dt + K_d \frac{de(t)}{dt}$$ where \( e(t) \) is the temperature error and \( K_p, K_i, K_d \) are PID gains tuned by the BMS.
Long-term reliability studies were conducted through accelerated aging simulations. The optimized thermal management system, governed by the BMS, reduces temperature swings, mitigating degradation mechanisms. Capacity fade over cycles can be modeled by $$C_{loss} = A e^{-E_a/(RT)} t^n$$ where \( A \) is a pre-exponential factor, \( E_a \) is activation energy, \( R \) is the gas constant, \( T \) is temperature, \( t \) is time, and \( n \) is an exponent. By maintaining temperatures in an optimal range (e.g., 20–35°C), the BMS extends battery lifespan by an estimated 30% compared to poorly managed systems.
Integration of the modular design with vehicle systems is also considered. The BMS communicates with the vehicle control unit (VCU) to coordinate thermal management with driving patterns, regenerative braking, and cabin climate control. This holistic approach enhances overall vehicle energy efficiency. For instance, waste heat from the battery can be repurposed for cabin heating in cold weather, reducing auxiliary load.
In conclusion, the novel modular power battery structure, coupled with an optimized thermal management system, significantly improves散热performance and temperature uniformity. The dual-side cooling plates, advanced insulation, and modular layout effectively lower peak temperatures and minimize gradients. The intelligent battery management system (BMS) enables precise control through active liquid cooling, dynamic flow distribution, integrated heating, and MPC algorithms. This results in enhanced reliability, extended battery life, and reduced energy consumption across diverse environmental conditions. Future work will focus on experimental validation, cost analysis, and further integration with emerging technologies like solid-state batteries and fast-charging infrastructure. The BMS remains pivotal in adapting these systems to real-world applications, ensuring safety and efficiency in the evolving automotive landscape.
The proposed system represents a step forward in power battery design, addressing critical thermal challenges while emphasizing scalability and intelligence. As the industry moves towards higher energy densities and faster charging, robust thermal management orchestrated by an advanced BMS will be indispensable. Continued research into materials, control algorithms, and system integration will further unlock potential, supporting the sustainable growth of electric mobility.