The rapid evolution of the new energy vehicle industry has placed stringent demands on its core component: the traction battery. The performance, safety, and service life of the battery pack are decisive factors for vehicle competitiveness. A critical challenge arises from the substantial heat generated during electrochemical reactions in charge/discharge cycles. Ineffective thermal management can lead to elevated temperatures, resulting in performance degradation, accelerated capacity fade, and severe safety risks like thermal runaway. Conversely, low-temperature environments increase internal resistance, reducing charge/discharge efficiency. Therefore, developing a high-performance Battery Thermal Management System (BTMS) is paramount.
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 heat dissipation, struggling with high-power-density cells. Liquid cooling provides strong cooling capacity and precise temperature control but adds complexity, cost, and potential leakage risks. PCM cooling utilizes latent heat for excellent temperature uniformity but suffers from low thermal conductivity, limiting its cooling rate. This article proposes an innovative modular battery pack architecture with an optimized thermal management system. The goal is to enhance heat dissipation, improve temperature uniformity, reduce system cost and complexity, and ultimately boost the reliability and stability of the battery pack under diverse operating conditions.
Design Philosophy of the Novel Modular Architecture
The core design philosophy is to decompose the large battery pack into smaller, self-contained functional modules. Each module incorporates integrated thermal management components. This modular approach simplifies overall system architecture, enhances production and maintenance flexibility, and allows for scalable customization based on application-specific voltage and capacity requirements. The intelligent orchestration of these modules by a central battery management system (BMS) is crucial for achieving global performance optimization.
Module Composition and Structural Characteristics
A single battery module is a compact, integrated unit comprising several key components:
- Cells: High-energy-density lithium-ion cells arranged in a specific configuration to achieve the module’s nominal voltage and capacity.
- Liquid Cooling Plates: Employing a dual-side cooling strategy, aluminum alloy plates with optimized microchannel structures are positioned at the top and bottom of the cell stack. This design doubles the effective heat transfer area compared to traditional single-side cooling, significantly improving heat dissipation efficiency. The heat transfer rate can be described by Newton’s law of cooling:
$$q = h \cdot A \cdot (T_{cell} – T_{coolant})$$
where \(q\) is the heat flux, \(h\) is the convective heat transfer coefficient, \(A\) is the contact area, \(T_{cell}\) is the cell surface temperature, and \(T_{coolant}\) is the coolant temperature. - Thermal Insulation: Aerogel-based insulation material with exceptionally low thermal conductivity encapsulates the module’s sides. This minimizes parasitic heat transfer between adjacent modules and with the external environment, which is vital for maintaining temperature uniformity across the entire pack under uneven operational or ambient conditions.
- Structural Enclosure: High-strength composite side panels provide mechanical integrity and protection, while also contributing to the module’s thermal isolation.
The heat generation within a lithium-ion cell during operation is a complex function of its state and operating point. A common model for heat generation rate (\(Q_{gen}\)) is:
$$Q_{gen} = I \cdot (V_{ocv} – V_t) + I \cdot T \cdot \frac{dV_{ocv}}{dT}$$
where \(I\) is the current (positive for discharge), \(V_{ocv}\) is the open-circuit voltage, \(V_t\) is the terminal voltage, and \(T\) is the absolute temperature. The first term represents irreversible Joule heating, and the second term represents reversible entropic heat. The battery management system (BMS) must accurately estimate these parameters for effective thermal control.
Optimized Thermal Management System Architecture
The proposed BTMS is an active liquid-based system integrated with intelligent control, designed for precise thermal regulation across all operating scenarios (cooling and heating).
1. System Overview
The system comprises a coolant circulation loop, a refrigeration/heating unit, a network of temperature sensors, a master controller (the BMS), and associated pumps and valves. The coolant absorbs heat from the modules via the cold plates. The refrigeration/heating unit conditions the coolant temperature. Sensors provide real-time data on cell temperatures and coolant states to the BMS, which executes control algorithms to actuate pumps, valves, and the chiller/heater.
2. Cooling System Optimization
a) Cold Plate Microchannel Optimization:
Numerical simulations (CFD) were conducted to analyze the impact of microchannel geometry, dimensions, and layout on thermal-hydraulic performance. Key parameters included pressure drop (\(\Delta P\)) and heat transfer coefficient (\(h\)). An optimized rectangular serpentine microchannel design was selected. The pressure drop in a channel can be estimated using the Darcy-Weisbach equation:
$$\Delta P = f \cdot \frac{L}{D_h} \cdot \frac{\rho v^2}{2}$$
where \(f\) is the friction factor, \(L\) is the channel length, \(D_h\) is the hydraulic diameter, \(\rho\) is the coolant density, and \(v\) is the flow velocity. The optimized design achieved the best trade-off between high \(h\) and manageable \(\Delta P\).
b) Intelligent Coolant Flow Distribution:
To address uneven heat generation among modules, a dynamic flow distribution system was implemented. An electronically controlled proportional valve is installed at the inlet of each module’s cooling circuit. The battery management system (BMS) continuously monitors individual module temperatures. Using a control algorithm (e.g., a PID controller tuned for thermal balancing), the BMS adjusts each valve’s opening to modulate coolant flow. The control logic for the i-th module’s valve can be expressed as:
$$u_i(t) = K_p \cdot e_i(t) + K_i \cdot \int_0^t e_i(\tau) d\tau + K_d \cdot \frac{de_i(t)}{dt}$$
where \(u_i(t)\) is the control signal (valve opening), \(e_i(t) = T_{target} – T_{module,i}(t)\) is the temperature error, and \(K_p, K_i, K_d\) are the controller gains. This ensures more flow to hotter modules and less to cooler ones, promoting excellent temperature uniformity.
3. Integrated Heating System and Control
For low-temperature operation, Positive Temperature Coefficient (PTC) heating elements are embedded within or adjacent to the cold plates. When the BMS detects cell temperatures below a defined threshold (e.g., 5°C), it energizes the PTC heaters. The heating power (\(P_{heat}\)) is regulated by the BMS via pulse-width modulation (PWM) of the current to prevent localized overheating:
$$P_{heat} = I_{rms}^2 \cdot R(T)$$
where \(R(T)\) is the temperature-dependent resistance of the PTC element. The heat is transferred to the cells via the coolant or direct conduction, raising the pack to its optimal operating temperature range efficiently and safely.
Simulation Analysis and Performance Evaluation
A comprehensive 3D finite element model was built using ANSYS Fluent and Thermal modules to simulate coupled electrochemical-thermal-fluid dynamics.
1. Model Setup and Parameters
The model incorporated realistic material properties and boundary conditions. Cell heat generation was implemented as a volumetric heat source based on the Bernardi model. The following table summarizes key simulation parameters:
| Parameter | Value / Specification |
|---|---|
| Cell Type | NMC 811 Li-ion, 100 Ah |
| Module Configuration | 12 cells in series (1P12S) |
| Coolant | 50% Ethylene Glycol-Water Mix |
| Coolant Inlet Temp (Cooling) | 25 °C |
| Ambient Temperature (High-Temp Case) | 40 °C |
| Discharge Rate | 3C (300 A) |
| Cold Plate Material | Aluminum 6061 |
| Insulation Thermal Conductivity | 0.02 W/(m·K) |
2. Simulation Results and Discussion
a) Temperature Distribution:
Simulations compared the novel modular design with a traditional pack design under a high-stress scenario (40°C ambient, 3C discharge).
| Thermal Metric | Traditional Pack Design | Novular Modular Pack with Optimized BTMS | Improvement |
|---|---|---|---|
| Maximum Cell Temperature | 55.2 °C | 42.1 °C | ~13.1 °C reduction |
| Maximum Inter-Module Temperature Difference | 8.5 °C | 2.7 °C | ~5.8 °C reduction |
| Maximum Intra-Module Temperature Difference | 6.8 °C | 3.1 °C | ~3.7 °C reduction |
The results demonstrate the superior capability of the new architecture and BTMS in mitigating temperature rise and ensuring remarkable uniformity. This directly translates to reduced degradation rates and enhanced safety margins.
b) BTMS Energy Consumption and Responsiveness:
The energy efficiency of the intelligent, model-predictive control (MPC) strategy in the BMS was compared against a conventional rule-based (RB) controller with fixed pump speeds and valve settings. The MPC strategy in the battery management system uses a simplified thermal model of the pack to predict future temperature states and optimizes control inputs (pump power, valve positions) over a receding horizon to minimize total energy consumption while respecting temperature constraints.
The objective function \(J\) for the MPC at time step \(k\) can be formulated as:
$$J(k) = \sum_{j=0}^{N_p-1} \left( \| T(k+j|k) – T_{ref} \|^2_Q + \| P_{sys}(k+j|k) \|^2_R \right)$$
subject to:
$$T_{min} \le T(k+j|k) \le T_{max}$$
$$P_{sys, min} \le P_{sys}(k+j|k) \le P_{sys, max}$$
where \(N_p\) is the prediction horizon, \(T(k+j|k)\) is the predicted temperature, \(T_{ref}\) is the target temperature, \(P_{sys}\) is the BTMS power consumption (pump + chiller), and \(Q, R\) are weighting matrices.
Simulation results for a dynamic drive cycle showed:
| Control Strategy | Total BTMS Energy Consumed | Average Temperature Deviation from Setpoint | Time to Recover from Thermal Transient |
|---|---|---|---|
| Rule-Based (RB) Control | 1.0 (Baseline) | ±2.5 °C | ~10 minutes |
| MPC-based Intelligent Control (via BMS) | 0.72 (28% saving) | ±1.2 °C | ~4 minutes |
The intelligent battery management system (BMS) control achieves significant energy savings (22-30% across various cycles) while maintaining tighter temperature control and demonstrating faster response to sudden thermal loads, thereby improving overall vehicle energy efficiency.
Conclusion and Future Perspectives
This research presents a holistic solution for advanced traction battery thermal management. The novel modular pack architecture, featuring dual-side cold plates and integrated aerogel insulation, provides an excellent foundation for efficient heat removal and superior temperature homogeneity. The optimized thermal management system, with its intelligent coolant distribution and integrated heating, ensures robust performance across extreme climates.
The central role of an advanced battery management system (BMS) cannot be overstated. By leveraging real-time sensor data and implementing sophisticated control algorithms like MPC, the BMS transforms the BTMS from a passive, energy-intensive subsystem into an active, efficiency-optimizing component of the vehicle’s energy ecosystem. This synergy between innovative hardware design and intelligent software control via the battery management system leads to tangible benefits: reduced peak temperatures, minimized cell-to-cell variations, lower auxiliary energy consumption, and extended battery pack life.
Future work will focus on further integration, exploring the use of direct refrigerant cooling to reduce complexity, and incorporating AI/ML techniques within the BMS for adaptive, predictive thermal management that learns from driver behavior and route topography. The continuous evolution of the battery management system remains key to unlocking the full potential of next-generation battery packs for electric mobility.