As the new energy vehicle industry rapidly evolves, the powertrain battery, serving as a critical energy storage component, sees its operational efficiency and lifespan heavily dependent on operating temperature. The battery thermal management system (BTMS) is essential for maintaining the battery within an ideal temperature range. However, current BTMS implementations often suffer from low energy utilization efficiency, which not only increases overall vehicle energy consumption but also hinders optimal battery performance. In this article, I will analyze the working principles and energy efficiency influencing factors of BTMS in new energy vehicles, explore strategies for efficiency enhancement, and provide theoretical support for improving BTMS performance, thereby promoting the driving range and safety of new energy vehicles. The battery management system (BMS) plays a pivotal role in monitoring and controlling these thermal processes, and its integration with the BTMS is crucial for achieving high efficiency.
The powertrain battery is a key component in new energy vehicles, and its operating temperature directly affects charging-discharging efficiency, capacity retention, and cycle life. Generally, the optimal working temperature range for these batteries is between 25°C and 40°C. When temperatures drop below 0°C, battery capacity significantly degrades, and charging efficiency plummets. Conversely, when temperatures exceed 50°C, internal side reactions accelerate, potentially leading to thermal runaway risks. The battery thermal management system (BTMS) employs active or passive regulation methods to achieve precise temperature control, uniform temperature distribution, and waste heat recovery, ensuring stable operation under ideal conditions. Nonetheless, current BTMS face notable challenges in energy efficiency: traditional air-cooling and liquid-cooling solutions consume increasing energy under extreme environments, shortening vehicle range, while inefficient heat transfer and inadequate temperature control precision further exacerbate energy losses. Therefore, exploring strategies to enhance BTMS efficiency is of great significance for the development of the new energy vehicle industry. The battery management system (BMS) is integral to this, as it provides real-time data for thermal regulation.
In this comprehensive discussion, I will delve into the working principles, efficiency factors, and advanced strategies for BTMS, emphasizing the synergy between the battery management system (BMS) and thermal management. By incorporating tables and mathematical formulations, I aim to provide a detailed analysis that supports engineering applications and future research.
Working Principles and Energy Efficiency Influencing Factors of BTMS in New Energy Vehicles
Working Principles
Based on heat transfer methods, battery thermal management systems can be classified into passive and active systems. Passive BTMS rely on natural convection, radiation, or phase change materials (PCM) that absorb and release latent heat to regulate temperature. Their advantage lies in zero additional energy consumption, but they are limited by temperature control ranges, making them more suitable for scenarios with low battery power density or stable climatic conditions. Active BTMS utilize power devices such as fans, water pumps, and refrigeration compressors to drive heat transfer media like air, coolant, or refrigerant in cycles, achieving precise and rapid battery thermal adjustment. However, these systems consume vehicle electrical energy, and their energy efficiency is closely tied to the overall range economy. Regardless of the type, the core logic of any BTMS revolves around “heat transfer, temperature control, and energy conservation”: maximizing heat transfer efficiency through rational flow path design and media selection; avoiding energy waste from excessive temperature control via accurate temperature monitoring and control algorithms; and enhancing overall system efficiency through waste heat recovery and energy reuse. The battery management system (BMS) is critical here, as it supplies data for these processes.
To illustrate, consider the heat transfer equation for a battery cell:
$$ q = h A (T_{\text{battery}} – T_{\text{coolant}}) $$
where \( q \) is the heat flux, \( h \) is the heat transfer coefficient, \( A \) is the surface area, \( T_{\text{battery}} \) is the battery temperature, and \( T_{\text{coolant}} \) is the coolant temperature. The efficiency of this process depends on design parameters that the BMS helps optimize.
| Feature | Passive BTMS | Active BTMS |
|---|---|---|
| Energy Consumption | Zero | High (requires power) |
| Temperature Control Precision | Low | High |
| Suitable Applications | Low power density, mild climates | High power density, extreme climates |
| Role of BMS | Limited monitoring | Active control and feedback |
Energy Efficiency Influencing Factors
Temperature Control Precision
Insufficient temperature control precision is a significant factor leading to energy efficiency losses in BTMS. Systems employing crude control strategies, such as simple threshold control, can cause large temperature fluctuations, with frequent device starts and stops generating extra energy consumption. Conversely, overly stringent temperature control beyond the battery’s actual precision requirements also results in unnecessary energy waste. Both extremes impact system efficiency, reducing energy utilization and failing to balance energy consumption and performance in the battery management system (BMS). This imbalance hinders the optimization of new energy vehicle range. For instance, if the temperature setpoint is not dynamically adjusted, energy is wasted in maintaining unnecessarily tight bounds.
Mathematically, the energy wasted due to imprecision can be modeled as:
$$ E_{\text{waste}} = \int_{0}^{t} P_{\text{cooling/heating}} \cdot \mathbb{I}_{|T – T_{\text{opt}}| > \delta} \, dt $$
where \( E_{\text{waste}} \) is the wasted energy, \( P_{\text{cooling/heating}} \) is the power for cooling or heating, \( T \) is the actual temperature, \( T_{\text{opt}} \) is the optimal temperature, \( \delta \) is the tolerance band, and \( \mathbb{I} \) is an indicator function. The BMS can minimize this by adaptive control.
System Integration Level
Currently, some BTMS operate independently from the vehicle’s overall thermal management system, leading to obvious defects in energy utilization. When subsystems run independently without effective coordination mechanisms, energy cannot flow and distribute reasonably between systems. In low-temperature conditions, the battery pack requires heating to maintain performance, but waste heat from motor operation cannot be effectively recovered, resulting in energy waste. In high-temperature conditions, the air conditioning system consumes substantial electrical energy to cool the battery, yet fails to coordinate with the motor cooling system to optimize散热 efficiency. This lack of synergistic energy use makes it difficult to improve overall vehicle energy efficiency, affecting the performance and range of new energy vehicles. Integrating the BMS with other thermal systems is key to addressing this.
| Scenario | BTMS Action | Waste Source | Potential BMS Solution |
|---|---|---|---|
| Low Temperature | Electric heating | Unrecovered motor waste heat | Integrate heat exchangers |
| High Temperature | Air conditioning cooling | Lack of coordination with motor cooling | Unified coolant loops |
| Fast Charging | Aggressive cooling | Overcooling during transient states | Predictive control via BMS |
Efficiency Enhancement Strategies for Battery Thermal Management Systems
To overcome these challenges, I propose multi-faceted strategies focusing on structural design, heat transfer media, control algorithms, and novel technologies, all leveraging advancements in the battery management system (BMS).
Optimizing System Structural Design to Reduce Thermal and Flow Resistance
Optimizing Flow Path Layout
For liquid-cooling and air-cooling systems, traditional series flow path architectures should be abandoned in favor of distributed flow path designs. This scheme utilizes multi-branch flow path networks to promote balanced distribution of heat transfer media within the battery pack, effectively avoiding local temperature anomalies. For example, inside battery modules, designing flow paths as serpentine or parallel multi-channel structures allows each cell to fully contact the heat transfer media, reducing temperature gradients. Through numerical simulations like computational fluid dynamics (CFD), optimizing flow path cross-sectional areas and inlet-outlet positions can minimize flow dead zones and reduce flow resistance, thereby decreasing energy consumption of pumps or fans. The BMS can aid in this by providing temperature data for CFD validation.
The pressure drop \( \Delta P \) in a flow path can be approximated by:
$$ \Delta P = f \frac{L}{D} \frac{\rho v^2}{2} $$
where \( f \) is the friction factor, \( L \) is the length, \( D \) is the hydraulic diameter, \( \rho \) is density, and \( v \) is velocity. Optimizing \( D \) and layout reduces \( \Delta P \), saving pump energy.
Optimizing Contact Thermal Resistance
To reduce contact thermal resistance between individual battery cells and heat transfer components (e.g., liquid-cooling plates, thermal pads), flexible thermal conductive media filling or integrated molding processes can be employed to eliminate contact gaps. For instance, laying graphene thermal pads between cells and cooling plates, with thermal conductivity of 50–100 W/(m·K) compared to traditional silicone pads’ 1–5 W/(m·K), significantly enhances heat conduction efficiency. Using integrated casting processes for batteries and cooling plates further reduces thermal resistance, lowering system temperature control energy consumption. The BMS monitors interface temperatures to ensure these materials perform as expected.
Designing Waste Heat Recovery Channels
Integrating BTMS with the vehicle’s overall thermal management system and designing dedicated waste heat recovery channels enable synergistic energy use. For example, adding heat exchangers between the motor cooling loop and battery cooling loop allows waste heat from motor operation to be transferred to the battery loop, preheating the battery in low-temperature environments and reducing extra electrical energy needed for heating. Additionally, integrating battery heat exchange modules into the air conditioning refrigerant cycle can directly remove battery heat via refrigerant phase change latent heat. Such integrated designs reduce independent operation time of the battery cooling system, optimizing overall vehicle thermal management efficiency. The BMS coordinates these interactions by managing heat flow based on real-time demands.
| Design Aspect | Improvement | Efficiency Gain | BMS Role |
|---|---|---|---|
| Distributed Flow Paths | Balanced coolant distribution | ~15% lower pump energy | Temperature uniformity feedback |
| Graphene Thermal Pads | Higher thermal conductivity | ~30% faster heat transfer | Monitor hotspot reduction |
| Waste Heat Recovery | Heat exchanger integration | ~20% less heating energy | Control heat diversion valves |
Optimizing Heat Transfer Media to Enhance Heat Transfer Performance and Adaptability
The performance of heat transfer media directly determines heat transfer efficiency and system energy consumption. Based on BTMS type and application scenarios, selecting or developing high-performance media is essential to balance heat transfer efficiency and flow resistance.
Optimizing Liquid-Cooling System Media
Traditional water-glycol solutions have limited thermophysical properties; alternatives like nanofluids or specialized coolants can be used. Nanofluids, which disperse nanoparticles (e.g., Al₂O₃, CuO) in base fluids, increase thermal conductivity by 20%–50%, significantly improving convective heat transfer efficiency. Additionally, precise regulation of nanoparticle concentration and size parameters can control viscosity increases, preventing excessive pump energy consumption due to high loads. Simultaneously, focusing on coolant performance optimization, developing specialized coolants with low freezing points and high boiling points broadens their applicable temperature range, reduces viscosity decay in low-temperature conditions, and enhances overall system efficiency. The BMS can adjust flow rates based on media properties to optimize performance.
The effective thermal conductivity \( k_{\text{eff}} \) of a nanofluid can be estimated as:
$$ k_{\text{eff}} = k_{\text{base}} \left(1 + \frac{3\phi (k_{\text{np}} – k_{\text{base}})}{k_{\text{np}} + 2k_{\text{base}} – \phi (k_{\text{np}} – k_{\text{base}})}\right) $$
where \( k_{\text{base}} \) is the base fluid conductivity, \( k_{\text{np}} \) is the nanoparticle conductivity, and \( \phi \) is the volume fraction. Optimizing \( \phi \) maximizes \( k_{\text{eff}} \) while minimizing viscosity rise.
Optimizing Phase Change Materials
For PCM in passive BTMS, composite modifications can enhance thermal and mechanical properties. For example, adding expanded graphite or carbon fibers to paraffin-based PCM increases thermal conductivity from 0.2 W/(m·K) to 5–10 W/(m·K), addressing low conductivity issues, while improving shape stability to prevent leakage during phase change. Furthermore, combining PCM with different phase change temperatures creates a gradient PCM system. This system’s phase change temperature range can precisely match the optimal working temperature interval of power batteries, effectively extending passive temperature control duration, reducing active system activation frequency, and ultimately lowering energy consumption. The BMS can track PCM state to predict when active cooling is needed.
Optimizing Air-Cooling System Media
Traditional air-cooling systems use air as the heat transfer medium, but due to relatively low heat transfer efficiency, methods like air humidification or inert gas substitution can improve performance. For example, in dry environments, moderately humidifying air entering the system utilizes water vapor’s high specific heat capacity to enhance换热 efficiency. In high-power-density battery packs, using inert gases like helium (with thermal conductivity six times that of air) significantly improves convective heat transfer efficiency, reducing fan energy consumption. The BMS can modulate fan speed based on media properties to save energy.
| Media Type | Thermal Conductivity (W/(m·K)) | Advantages | Challenges | BMS Integration |
|---|---|---|---|---|
| Water-Glycol | ~0.4 | Low cost, stable | Limited efficiency | Basic temperature control |
| Nanofluid (Al₂O₃) | ~0.6 (20% increase) | Higher heat transfer | Potential clogging | Adjust for viscosity changes |
| PCM with Graphite | 5–10 | Passive, high latent heat | Weight and cost | Monitor phase change status |
| Helium Gas | ~0.15 (6× air) | Excellent convection | Leakage risks | Control gas circulation pumps |
Optimizing Control Strategies for Precise Temperature Control and Energy-Efficient Operation
Control strategies are key to enhancing BTMS efficiency. By combining battery operating states and environmental conditions with intelligent, adaptive control algorithms, over-control and frequent starts/stops can be avoided, balancing efficiency and temperature control.
Fuzzy PID Control Strategy
Traditional PID control has limitations when facing system parameter fluctuations,容易 causing overshoot or oscillation. Fuzzy PID control innovatively combines the nonlinear adjustment advantages of fuzzy control with the precise regulation characteristics of PID control. It dynamically optimizes control parameters like proportional gain, integral time, and derivative time based on multidimensional parameters such as battery temperature change rate, ambient temperature, and charging-discharging power. For example, when battery temperature approaches the upper limit of the optimal range (e.g., 38°C), the system automatically reduces cooling intensity to avoid over-cooling; when the battery is in fast-charging mode (high heat generation rate), it increases temperature control response speed to ensure stability, thereby reducing energy consumption while meeting control needs. The battery management system (BMS) implements this strategy by processing sensor data.
The fuzzy PID output can be expressed as:
$$ u(t) = K_p e(t) + K_i \int e(t) dt + K_d \frac{de(t)}{dt} $$
where \( u(t) \) is the control signal, \( e(t) \) is the error, and \( K_p, K_i, K_d \) are adjusted dynamically via fuzzy rules based on BMS inputs like \( \frac{dT}{dt} \).
Predictive Control Strategy
Building accurate battery heat generation models and combining them with environmental parameter prediction data, forward-looking control algorithms dynamically regulate BTMS, effectively avoiding energy losses from traditional passive control modes. For instance, the battery management system (BMS) collects real-time data such as charging-discharging current, state of charge (SOC), and ambient temperature, then uses a heat generation model to predict battery temperature trends over the next 5–10 minutes. If a temperature exceedance is predicted, the system pre-activates cooling devices at lower power to maintain stability, preventing high-power cooling after sudden temperature rises, significantly reducing energy consumption.
The heat generation model can be based on the Bernardi equation:
$$ \dot{Q} = I (V_{\text{oc}} – V) + I T \frac{dV_{\text{oc}}}{dT} $$
where \( \dot{Q} \) is the heat generation rate, \( I \) is current, \( V_{\text{oc}} \) is open-circuit voltage, \( V \) is terminal voltage, and \( T \) is temperature. The BMS uses this for predictions.
Multi-Mode Switching Control Strategy
Based on different battery conditions like idle, charging-discharging, and fast-charging, along with ambient temperature changes, a flexible multi-mode switching control strategy is formulated to select the optimal thermal management mode. For example, in常温 environments (25–30°C) with low battery heat generation, only passive BTMS (e.g., PCM) is activated, consuming no electrical energy. When the battery is in fast-charging mode (high heat generation) or ambient temperature exceeds 35°C, switch to active liquid-cooling mode to ensure temperature stability. In low-temperature environments (below 0°C), prioritize waste heat recovery mode, using motor or air conditioning waste heat to preheat the battery; if insufficient, activate electric heating mode to minimize额外 energy consumption. The BMS orchestrates these mode transitions seamlessly.
| Strategy | Key Mechanism | Energy Savings | BMS Requirements |
|---|---|---|---|
| Fuzzy PID | Dynamic parameter tuning | ~15% vs. traditional PID | Real-time temperature and derivative data |
| Predictive Control | Model-based foresight | ~20% by avoiding peaks | Accurate heat generation models |
| Multi-Mode Switching | Context-aware mode selection | ~25% by minimizing active cooling | Integrated sensor fusion and logic |
Integrating Novel Technologies to Break Traditional Efficiency Bottlenecks
Integrating novel technologies like heat pipes, heat pumps, and smart materials is a vital途径 to address traditional BTMS efficiency bottlenecks, significantly improving heat transfer efficiency and energy utilization.
Heat Pipe Technology
Heat pipes are highly efficient heat transfer elements with thermal conductivity up to 1,000 times that of copper, enabling rapid heat transfer via phase change of internal working fluids. Integrating heat pipe technology into BTMS allows constructing heat pipe-liquid cooling composite systems: using heat pipe arrays penetrating between cells creates efficient heat transfer channels, enabling localized heat to quickly conduct to liquid cooling散热 modules, effectively balancing temperature distribution within the battery pack. Relying on heat pipes’ self-circulating heat transfer mechanism without external drive can significantly reduce operating time of active cooling equipment, effectively controlling system energy consumption. For example, in power battery modules, employing flat heat pipes integrated with liquid cooling plates, where heat pipes directly contact cells, rapidly conduct heat to cooling plates, improving efficiency by 20%–30% compared to traditional liquid cooling. The BMS can monitor heat pipe performance to ensure optimal operation.
The heat transfer capability of a heat pipe is given by:
$$ Q_{\text{max}} = \frac{\rho_l h_{fg} \sigma}{8 \mu_l} \left( \frac{2}{r_c} \right) A_w $$
where \( \rho_l \) is liquid density, \( h_{fg} \) is latent heat, \( \sigma \) is surface tension, \( \mu_l \) is liquid viscosity, \( r_c \) is capillary radius, and \( A_w \) is wick area. Designing for high \( Q_{\text{max}} \) enhances BTMS efficiency.
Heat Pump Technology
Heat pump technology transfers heat from low-temperature to high-temperature environments by consuming small amounts of electrical energy, with a coefficient of performance (COP) of 2–4, far exceeding traditional electric heating (COP=1). Integrating heat pumps into BTMS forms heat pump-BTMS协同 systems. These systems offer bidirectional regulation: in low-temperature conditions, heat pumps extract heat from ambient air to heat the battery; in high-temperature conditions, they discharge excess battery heat to the environment, achieving effective cooling. Such integrated systems not only reduce battery heating energy consumption but can also replace traditional air conditioning compressor cooling, improving overall vehicle thermal management efficiency. For example, at -10°C, using heat pumps for battery heating reduces energy consumption by 50%–60% compared to electric heating, while extending vehicle range by 10%–15%. The BMS manages the heat pump cycle based on temperature demands.
Smart Material Technology
Smart materials like shape memory alloys and temperature-sensitive thermal materials automatically adjust properties with temperature changes. Incorporating them into BTMS enables self-adaptive regulation, reducing manual control energy consumption. For instance, using temperature-sensitive thermal materials as filler between batteries and heat transfer components allows thermal conductivity to automatically increase when battery temperature rises, accelerating heat transfer; in low-temperature conditions, conductivity自主 decreases to limit heat transfer rates for passive insulation, reducing active system activation frequency. Simultaneously, leveraging the thermal deformation特性 of shape memory alloys to create smart flow path control valves: when battery temperature exceeds a threshold, the alloy deforms due to heat, opening the valve to activate active cooling; once temperature returns to the ideal range, the valve automatically closes, precisely avoiding unnecessary energy consumption. The BMS can leverage these materials for autonomous adjustments.
| Technology | Efficiency Improvement | Key Mechanism | BMS Synergy |
|---|---|---|---|
| Heat Pipes | 20–30% better heat spreading | Passive two-phase heat transfer | Monitor temperature uniformity |
| Heat Pumps | 50–60% less heating energy | High COP heat transfer | Control refrigerant cycles |
| Smart Materials | ~15% reduced control energy | Self-adaptive conductivity/valves | Provide temperature triggers |
Conclusion
Enhancing the energy efficiency of battery thermal management systems in new energy vehicles has become a crucial命题 for industry advancement. The breakthrough lies in leveraging system architecture innovation, heat transfer media iteration, intelligent control algorithm optimization, and cutting-edge technology integration to achieve multi-dimensional协同 optimization goals of efficient heat transfer, precise temperature control, and energy recycling. The strategies I proposed demonstrate that: through distributed flow path design and waste heat recovery channel integration, system thermal and flow resistance can be reduced; through high-performance nanofluids and composite phase change materials, the adaptability and heat transfer efficiency of media can be improved; through combining fuzzy PID and predictive control strategies, precise temperature control and energy-efficient operation can be realized; and through integrating heat pipes, heat pumps, and smart materials, traditional system efficiency bottlenecks can be broken. Throughout these efforts, the battery management system (BMS) serves as the brain, coordinating data and actions to maximize efficiency. Future work should focus on further refining BMS algorithms for real-time adaptability and exploring hybrid systems that combine multiple strategies for even greater gains. As the new energy vehicle industry continues to grow, ongoing research in BTMS efficiency will be vital for achieving sustainable mobility with extended range and enhanced safety.