The proliferation of electric vehicles (EVs) and energy storage systems has placed stringent demands on the performance and safety of lithium-ion batteries. High-power charging, essential for user convenience, induces significant ohmic and electrochemical heat generation within battery cells. Effective heat dissipation is paramount, as elevated temperatures accelerate degradation mechanisms, increase internal resistance, and in extreme cases, can lead to thermal runaway. The core function of a modern battery management system (BMS) extends beyond state estimation to encompass sophisticated thermal management, ensuring cells operate within a narrow, optimal temperature window with minimal spatial variation. The design of the cooling architecture is therefore a critical component of the overall Battery Management System strategy. This study investigates the efficacy of a non-uniform, Z-type cooling channel design through integrated computational fluid dynamics (CFD) simulation and experimental validation, focusing on its ability to enhance temperature uniformity and, consequently, charging efficiency under demanding conditions.
The thermal management subsystem within a comprehensive BMS must address a key challenge: the inherent non-uniformity of heat generation within a battery module. During fast charging, the core regions of cells or modules typically experience higher current densities and greater heat accumulation compared to the edges. Traditional cooling plate designs with straight, parallel channels often struggle with this gradient. Coolant entering at a low temperature effectively cools the initial section but becomes progressively warmer as it flows, leading to a higher temperature at the outlet section and a corresponding longitudinal temperature delta across the battery pack. This uneven cooling forces the battery management system to derate performance to protect the hottest cells, compromising the overall system’s capability. The Z-type flow field proposed here is engineered to mitigate this issue. Its serpentine path, resembling a “Z” shape across the battery surface, is designed to create a more uniform thermal boundary condition. The principle involves directing cooler fluid from the inlet across areas of anticipated high heat flux and ensuring that no single cell is consistently exposed to pre-warmed coolant, thereby promoting a more homogeneous temperature distribution—a primary objective for any advanced Battery Management System.
Theoretical Foundation and Numerical Modeling
The design and analysis of the thermal management system are rooted in fundamental principles of fluid dynamics and heat transfer. The governing equations for the coolant flow and conjugate heat transfer within the battery and cooling plate are solved numerically. The conservation of mass, or continuity equation, for an incompressible coolant is given by:
$$\nabla \cdot \vec{v} = 0$$
where \(\vec{v}\) is the velocity vector field of the coolant. The conservation of momentum is described by the Navier-Stokes equations:
$$\rho \left( \frac{\partial \vec{v}}{\partial t} + \vec{v} \cdot \nabla \vec{v} \right) = -\nabla p + \mu \nabla^2 \vec{v} + \vec{F}$$
where \(\rho\) is the fluid density, \(t\) is time, \(p\) is pressure, \(\mu\) is the dynamic viscosity, and \(\vec{F}\) represents body forces. For simulating the heat transfer, the energy conservation equation is employed:
$$\rho c_p \left( \frac{\partial T}{\partial t} + \vec{v} \cdot \nabla T \right) = \nabla \cdot (k \nabla T) + \dot{q}_{gen}$$
Here, \(c_p\) is the specific heat capacity, \(T\) is temperature, \(k\) is the thermal conductivity, and \(\dot{q}_{gen}\) is the volumetric heat generation rate within the battery cell, a critical input from the electro-thermal models used by the BMS for state monitoring. For turbulent flow regimes expected at higher coolant velocities, the standard \(k\)-\(\epsilon\) turbulence model is utilized to close the Reynolds-averaged Navier-Stokes (RANS) equations.
The heat generation within a lithium-ion battery during charging is a complex function of its state of charge (SOC), current rate (C-rate), and internal resistance. A simplified but effective model for the heat generation rate \(\dot{q}_{gen}\) incorporates both irreversible (ohmic) and reversible (entropic) heating effects:
$$\dot{q}_{gen} = I (V_{OC} – V_t) + I T \frac{\partial V_{OC}}{\partial T} \approx I^2 R_{int} + I T \frac{\Delta S}{nF}$$
where \(I\) is the applied current, \(V_{OC}\) is the open-circuit voltage, \(V_t\) is the terminal voltage, \(R_{int}\) is the internal impedance, \(\Delta S\) is the entropy change of the electrode reactions, \(n\) is the number of electrons transferred, and \(F\) is Faraday’s constant. An effective battery management system estimates parameters like \(R_{int}\) in real-time, and this thermal model can be integrated into the BMS algorithms for predictive thermal control.
The Z-type channel geometry is parameterized to optimize flow distribution. A key design variable is the channel’s cross-sectional area or its hydraulic diameter \(D_h\), which influences pressure drop and heat transfer coefficient. The convective heat removal is governed by Newton’s law of cooling:
$$q” = h (T_{cell} – T_{coolant})$$
where \(q”\) is the heat flux, \(h\) is the convective heat transfer coefficient, and \(T_{cell}\) and \(T_{coolant}\) are the surface and bulk fluid temperatures, respectively. The coefficient \(h\) is strongly dependent on flow velocity and regime, scaling approximately with \(v^{0.8}\) in turbulent flow for a given geometry. This underpins the experimental hypothesis that increasing coolant velocity will significantly enhance cooling performance, a lever that the thermal Battery Management System can actuate via a variable-speed pump.
CFD Simulation Methodology and Setup
A three-dimensional CFD model was constructed to simulate the thermal and fluid dynamic behavior of the battery module integrated with the Z-type cooling plate. The model geometry included detailed representations of multiple pouch cells, the aluminum cooling plate with the engraved Z-channel, and the interface materials (e.g., thermal interface pads). A polyhedral mesh was generated, with refinement applied near the channel walls and cell-cooling plate interfaces to resolve boundary layers and thermal gradients accurately. A mesh independence study was conducted to ensure solution accuracy was not compromised by discretization. The results, summarized below, validated the chosen mesh density.
| Mesh Scheme | Number of Elements (Million) | Max. Cell Temp. (°C) | Relative Comp. Time |
|---|---|---|---|
| Coarse | 0.5 | 51.8 | 0.6 |
| Medium (Selected) | 1.0 | 49.9 | 1.0 |
| Fine | 1.5 | 49.7 | 1.8 |
The boundary conditions were set to replicate experimental conditions. A 50/50 ethylene glycol-water mixture was specified as the coolant with temperature-dependent properties. Inlet conditions included mass flow rates corresponding to velocities of 2, 3, 4, and 5 m/s at a fixed temperature of 25°C. A constant heat flux boundary condition, calculated based on a 3C constant-current charging protocol for a 100 Ah cell, was applied to the battery cell volumes to simulate internal heat generation. The external surfaces of the module were assigned a convective heat transfer boundary condition to account for natural convection to the ambient air at 25°C.
Experimental Design and Validation
To validate the numerical models and obtain practical performance data, a bespoke test bench was established. The core of the setup was a custom-built battery module equipped with the Z-type cold plate. The module incorporated both Lithium Iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC) pouch cells to assess performance across chemistries with different thermal characteristics and energy densities. The cooling system comprised a reservoir, a variable-speed centrifugal pump, a liquid-to-air heat exchanger, and precise flow meters. The thermal battery management system prototype controlled the pump speed based on preset velocity profiles.
An array of T-type thermocouples (accuracy ±0.1°C) was embedded at strategic locations: on the surface of selected cells (central and edge), within the coolant at the inlet and outlet of the cold plate, and at intermediate points along the flow path. A high-precision battery cycler was used to administer the 3C charging protocols while simultaneously measuring terminal voltage and current. Electrical data (current, voltage, accumulated charge) and thermal data from all sensors were synchronized and recorded at 1 Hz using a National Instruments data acquisition system. This integrated data stream allowed for the direct calculation of charging efficiency metrics correlated with real-time thermal states, providing a holistic view of the Battery Management System‘s thermal regulation effectiveness.
The experimental matrix systematically varied two key parameters: Coolant Inlet Velocity (2, 3, 4, 5 m/s) and Ambient Temperature (25°C, 45°C). For each test condition, the module underwent a standardized charge cycle while all data was logged. The primary metrics extracted for analysis were:
1. Maximum cell temperature (\(T_{max}\)).
2. Temperature uniformity, defined as the maximum difference between any two measurement points on the cells (\(\Delta T_{max}\)).
3. The estimated area of the cell surface exceeding a threshold temperature (e.g., 45°C), indicative of “hot spot” magnitude.
4. Charging Conversion Efficiency (CCE), calculated as the ratio of energy stored in the battery to the total energy input during the constant current (CC) phase.
5. Apparent internal resistance, estimated from the instantaneous voltage jump upon current interruption at a high SOC.
Results and Discussion: Thermal Performance
The experimental results conclusively demonstrate the superior thermal regulation capability of the Z-type cold plate, a finding well-correlated by the CFD simulations. The impact of increasing coolant velocity on key thermal metrics is summarized in the table below.
| Coolant Velocity (m/s) | Max. Cell Temp., \(T_{max}\) (°C) | Temp. Uniformity, \(\Delta T_{max}\) (°C) | High-Temp. Area (>45°C) (mm²) |
|---|---|---|---|
| 2.0 | 50.2 | 5.2 | 102 |
| 3.0 | 45.1 | 3.4 | 78 |
| 4.0 | 40.6 | 2.1 | 63 |
| 5.0 | 38.3 | 2.0 | 49 |
At the lowest velocity of 2 m/s, the cooling capacity was insufficient to counteract the 3C heat generation, leading to a maximum temperature of 50.2°C, which approaches critical limits for long-term health. The temperature spread of 5.2°C indicates significant gradients, and a substantial hot spot area was observed. As velocity increased to 4 m/s, a dramatic improvement was recorded: \(T_{max}\) was suppressed to a safe 40.6°C, \(\Delta T_{max}\) was reduced to an excellent 2.1°C, and the high-temperature area shrunk by approximately 38%. This meets a common design target for a high-performance Battery Management System—maintaining cell temperatures below 40-45°C with a gradient under 5°C, and ideally under 3°C. The further improvement at 5 m/s was marginal in terms of uniformity but offered a lower peak temperature. The CFD flow field analysis revealed that the Z-path effectively redistributes the coolant, preventing the formation of persistent stagnant or overheated zones on the battery surface compared to a baseline parallel-channel design. This inherent advantage makes the Z-type system a robust component for the thermal battery management system.
Results and Discussion: Impact on Charging Efficiency
The thermal performance directly and indirectly influences the electrical charging efficiency. A cooler, more uniform temperature profile mitigates the rise in internal impedance and reduces polarization losses. The experimental data quantifying this relationship is presented below.
| Coolant Velocity (m/s) | Charging Conversion Efficiency, CCE (%) | Energy Loss per Cycle (Wh) | Estimated Internal Resistance, \(R_{int}\) (mΩ) |
|---|---|---|---|
| 2.0 | 84.8 | 12.5 | 82.0 |
| 3.0 | 87.6 | 9.3 | 61.0 |
| 4.0 | 91.9 | 6.2 | 49.0 |
| 5.0 | 93.7 | 5.1 | 39.0 |
The trend is unequivocal: enhanced cooling directly translates to higher efficiency. As velocity increased from 2 m/s to 4 m/s, the CCE improved from 84.8% to 91.9%, representing a substantial recovery of energy that would otherwise be wasted as heat. Concurrently, the perceived internal resistance dropped from 82.0 mΩ to 49.0 mΩ. This reduction in \(R_{int}\) has a quadratic effect on ohmic losses (\(\dot{q}_{irrev} = I^2 R_{int}\)), creating a virtuous cycle: better cooling lowers resistance, which in turn reduces heat generation, further easing the thermal load on the battery management system. The data at 5 m/s shows a continued but diminishing return in CCE, suggesting an optimal point where the energy cost of running the pump at high speed may begin to offset the gains in charging efficiency—a crucial trade-off for the system-level Battery Management System to optimize.
The performance was consistent across both LFP and NMC chemistries, though the absolute temperatures for NMC were slightly higher under identical conditions due to its different thermal properties and heat generation profile. This underscores the need for the BMS algorithms to be chemistry-aware. Furthermore, tests at an elevated ambient temperature of 45°C confirmed the system’s robustness. While absolute temperatures increased universally, the Z-type system maintained its relative advantage in uniformity, and the efficiency gains from active cooling were even more pronounced compared to a passive or poorly cooled scenario.
Conclusion and Outlook for BMS Integration
This investigation comprehensively demonstrates that a Z-type channel cold plate architecture significantly enhances the thermal management of lithium-ion battery modules during fast-charging operations. Through validated CFD models and controlled experiments, it was shown that increasing coolant flow velocity within this optimized geometry effectively reduces peak cell temperatures, improves spatial temperature uniformity to within ±2°C, and markedly shrinks the extent of localized hot spots. These thermal benefits directly yield tangible electrical advantages, notably an increase in Charging Conversion Efficiency from 84.8% to over 93% and a corresponding drop in internal resistance, confirming the intrinsic link between thermal and electrical management governed by the Battery Management System.
The findings advocate for the adoption of such directed-flow cooling designs in next-generation high-power battery packs. The Z-type system presents a compelling balance between performance, complexity, and manufacturability. For practical implementation, the logic of the thermal battery management system must evolve. Rather than simple on/off control, an advanced BMS should employ model-predictive or adaptive strategies to dynamically regulate coolant flow rate (via pump speed) based on real-time estimates of heat generation, cell temperatures, and the desired charging rate. The setpoint could optimize for the trade-off between cooling efficacy and parasitic pump power, maximizing overall system efficiency. Future work will focus on integrating this physical cooling model into the state estimation algorithms of the BMS, enabling proactive thermal control, exploring the use of advanced coolants or nanofluids, and extending the analysis to full pack-level thermal interactions and lifetime degradation studies under real-world driving cycles. The ultimate goal is a seamlessly integrated, intelligent battery management system where thermal safety, performance, and longevity are co-optimized in real-time.