In the pursuit of sustainable transportation, electric vehicles (EVs) have emerged as a pivotal solution to mitigate environmental pollution and address energy scarcity. Central to EV performance is the power battery, whose efficiency, safety, and longevity are profoundly influenced by thermal conditions. During high-power DC charging, batteries generate significant heat, which, if not managed properly, can lead to reduced charging efficiency, accelerated degradation, and even safety hazards. This underscores the critical role of an advanced battery management system (BMS) in regulating temperature. In this study, we propose a novel thermal management system integrated with charging infrastructure to enhance charging performance under extreme temperatures. Through comprehensive numerical simulations, we analyze the thermal characteristics and charging duration, demonstrating the efficacy of our approach in optimizing the battery management system for fast-charging applications.
The battery management system is essential for maintaining optimal battery operation. Traditional BMS designs often rely on onboard systems that may lack the responsiveness needed for high-power charging. Our research introduces a charging-pile-integrated heat pump system, which preconditions the coolant externally, thereby reducing the thermal inertia typically associated with onboard systems. This innovation allows for rapid temperature control, enabling batteries to reach ideal charging temperatures more quickly. By shifting part of the thermal management to the charging infrastructure, we enhance the overall efficiency of the BMS, making it more adaptive to varying environmental conditions.
To understand the thermal behavior of lithium-ion batteries during charging, we begin with a fundamental heat balance equation. The temperature change within a battery is governed by heat generation and dissipation mechanisms. The general energy balance can be expressed as:
$$c_p m \frac{dT}{dt} = Q – h_s A (T – T_f) \pm Q_w$$
where \(c_p\) is the specific heat capacity, \(m\) is the mass, \(T\) is the battery temperature, \(t\) is time, \(Q\) is the total heat generation rate, \(h_s\) is the convective heat transfer coefficient, \(A\) is the surface area, \(T_f\) is the ambient temperature, and \(Q_w\) represents internal heat sources. For battery applications, \(Q\) is primarily derived from electrochemical reactions. According to Bernardi’s model, the heat generation rate during charging or discharging is given by:
$$Q = I \left[ (U_{ocv} – U) \pm T \frac{\partial U_{ocv}}{\partial T} \right]$$
Here, \(I\) is the current, \(U_{ocv}\) is the open-circuit voltage, \(U\) is the terminal voltage, and \(\frac{\partial U_{ocv}}{\partial T}\) is the entropy coefficient. The sign depends on whether the battery is charging (+) or discharging (-). This equation highlights the interplay between electrical and thermal dynamics in a BMS. Simplifying further by substituting \(U_{ocv} – U = IR\), where \(R\) is the internal resistance, we obtain:
$$Q = I^2 R \pm I T \frac{\partial U_{ocv}}{\partial T}$$
The internal resistance \(R\) varies with state of charge (SOC) and temperature, as shown in Table 1, which summarizes key battery parameters used in our simulations. Accurate modeling of \(R\) is crucial for the BMS to predict heat generation and implement effective thermal control strategies.
| Parameter | Value | Unit |
|---|---|---|
| Battery Type | NCM Prismatic Cell | – |
| Nominal Capacity | 155 | Ah |
| Nominal Voltage | 3.7 | V |
| Total Pack Energy | 55 | kWh |
| Density, \(\rho\) | 2000 | kg/m³ |
| Specific Heat, \(c_p\) | 1138 | J/(kg·K) |
| Thermal Conductivity, \(\lambda_x\) | 19 | W/(m·K) |
| Thermal Conductivity, \(\lambda_y\) | 9.8 | W/(m·K) |
| Thermal Conductivity, \(\lambda_z\) | 5.9 | W/(m·K) |
| Operating Temperature Range | -30 to 60 | °C |
Heat conduction within the battery is modeled using the three-dimensional heat diffusion equation, accounting for anisotropic thermal properties due to the layered structure of battery components:
$$\rho c_p \frac{\partial T}{\partial t} = \lambda_x \frac{\partial^2 T}{\partial x^2} + \lambda_y \frac{\partial^2 T}{\partial y^2} + \lambda_z \frac{\partial^2 T}{\partial z^2} + Q$$
This partial differential equation is solved numerically with boundary conditions that incorporate convection from the battery surface to the coolant or environment:
$$\lambda \frac{\partial T}{\partial n} = h (T – T_f)$$
where \(\lambda\) is the thermal conductivity in the normal direction \(n\), and \(h\) is the convective heat transfer coefficient. In our BMS design, the coolant flows through cold plates attached to the battery modules, enhancing heat removal. The effectiveness of this liquid cooling system is pivotal for the BMS to maintain temperature uniformity and prevent hot spots.
Our proposed battery management system leverages a charging-pile-integrated heat pump to precondition the coolant. Unlike conventional onboard systems, where the heat pump cycles between heating and cooling modes via a four-way valve, our design incorporates a separate water tank loop that interfaces with the charging pile’s heat pump. This allows the coolant to be preheated or precooled to a target temperature (e.g., 40°C for heating or 15°C for cooling) before being circulated through the battery cold plates. When the EV is connected to the charging pile, valves switch to direct this preconditioned coolant into the battery loop, enabling immediate thermal management. This setup reduces the response time significantly, as the onboard system no longer needs to gradually adjust the coolant temperature. The BMS continuously monitors battery temperature and SOC to regulate the coolant flow and charging current, ensuring optimal conditions.
The charging strategy is integral to the BMS. We employ a multi-stage constant current (MSCC) method, where the charging current is adjusted based on real-time battery temperature and SOC. This approach maximizes charging speed while keeping thermal rise in check. The current map, derived from experimental data, defines the charging rate at different SOC and temperature levels. For instance, at low temperatures, the BMS initiates a preheating phase to raise the battery to a suitable temperature (e.g., 20°C) before applying high charging rates. This proactive thermal management is a key feature of our advanced BMS, distinguishing it from conventional systems that may delay high-rate charging until temperatures stabilize.
To validate our design, we developed simulation models in AMESim software, comparing the performance of the charging-pile-integrated BMS against a traditional onboard BMS. The battery pack consists of 12 modules, each with 8 cells in series, arranged in two rows. Each module is cooled by an aluminum cold plate with microchannels. We simulated five ambient temperatures: -20°C, -10°C, 0°C, 20°C, and 40°C, representing a wide range of climatic conditions. The initial SOC is set to 0%, and we track the charging process until 100% SOC, recording temperature profiles and charging times.
The thermal response during charging is critical for BMS performance. At low temperatures, the battery management system must heat the battery efficiently to enable fast charging. Our results show that the charging-pile-integrated BMS achieves this much faster than the onboard system. For example, at -20°C, the time to reach 20°C—the threshold for high-rate charging—is reduced by 45.75% with our system. This is attributed to the direct injection of preheated coolant at 40°C, whereas the onboard system relies on gradual heat exchange with the ambient air. The temperature evolution follows the heat generation equation, where \(Q\) initially includes heating from the coolant, then transitions to internal heat generation during charging. The BMS dynamically adjusts the charging current based on temperature feedback, as per the MSCC strategy.
At moderate temperatures like 20°C, both BMS designs perform similarly, as the battery starts at an ideal temperature. However, at high temperatures such as 40°C, the cooling capability becomes paramount. Our BMS provides superior cooling by circulating precooled coolant, limiting the peak temperature to 46°C, compared to 49.5°C with the onboard system. This is achieved through enhanced heat dissipation described by the convection term in the heat balance equation. The BMS also modulates the charging current to reduce heat generation when temperatures approach safety limits, demonstrating its integrated control over electrical and thermal aspects.
Charging time is a direct measure of BMS efficiency. Table 2 summarizes the total charging times for both systems across different temperatures. The charging-pile-integrated BMS consistently reduces charging duration, especially under extreme conditions. This reduction is primarily due to shortened preheating or precooling phases, which are managed more effectively by the external heat pump. The BMS ensures that the battery enters high-rate charging sooner, thereby optimizing the overall charging curve.
| Ambient Temperature (°C) | Onboard BMS Charging Time (min) | Charging-Pile BMS Charging Time (min) | Time Reduction (%) |
|---|---|---|---|
| -20 | 54.4 | 41.0 | 24.6 |
| -10 | 45.9 | 37.7 | 17.9 |
| 0 | 37.8 | 33.8 | 10.6 |
| 20 | 29.4 | 29.0 | 1.4 |
| 40 | 35.1 | 33.0 | 6.0 |
To delve deeper, we analyze the charging time breakdown by SOC intervals. Table 3 details the time spent in three key SOC ranges: 0-20%, 20-80%, and 80-100%. The charging-pile-integrated BMS significantly shortens the 0-20% phase across all low-temperature scenarios, highlighting its rapid thermal conditioning. This phase is where preheating or initial cooling occurs, and our BMS excels due to its external preconditioning capability. In contrast, the onboard BMS requires more time to adjust temperatures internally. The later charging stages show less variation, as thermal management becomes more about maintaining rather than altering temperature. This underscores the importance of a responsive BMS in the early charging phase to unlock faster overall charging.
| Temperature (°C) | BMS Type | Time 0-20% SOC (min) | Time 20-80% SOC (min) | Time 80-100% SOC (min) |
|---|---|---|---|---|
| -20 | Onboard | 28.4 | 11.8 | 14.2 |
| Charging-Pile | 15.7 | 11.2 | 14.1 | |
| -10 | Onboard | 20.1 | 11.7 | 14.1 |
| Charging-Pile | 12.4 | 11.2 | 14.1 | |
| 0 | Onboard | 12.3 | 11.4 | 14.1 |
| Charging-Pile | 8.7 | 11.0 | 14.1 | |
| 20 | Onboard | 3.2 | 12.2 | 14.0 |
| Charging-Pile | 3.2 | 11.8 | 14.0 | |
| 40 | Onboard | 4.0 | 17.1 | 14.0 |
| Charging-Pile | 3.8 | 15.0 | 14.2 |
The thermal performance of the BMS is further quantified by peak temperatures during charging. In all cases, the charging-pile-integrated BMS maintains the battery below 50°C, a critical threshold for safety and longevity. The maximum temperature \(T_{max}\) can be expressed as a function of heat generation and cooling capacity:
$$T_{max} = T_f + \frac{Q_{net}}{h_s A}$$
where \(Q_{net}\) is the net heat generation after accounting for cooling. Our BMS minimizes \(Q_{net}\) by efficient coolant circulation and adaptive current control. Under high-temperature conditions, the cooling effect is enhanced by the precooled coolant, which increases the temperature gradient \(T – T_f\) in the convection term, thereby boosting heat dissipation. This proactive approach is a hallmark of an advanced battery management system.
Another key aspect is the uniformity of temperature across the battery pack. Hotspots can lead to localized degradation, so the BMS must ensure even cooling. Our design uses cold plates with optimized microchannels that distribute coolant uniformly. The temperature variation \(\Delta T\) within the pack is modeled using the thermal diffusion equation, and simulations show that \(\Delta T\) is kept below 5°C in most scenarios, meeting industry standards. This uniformity is achieved by the BMS regulating flow rates based on real-time temperature sensors embedded in the modules.
The integration of the BMS with charging infrastructure also offers scalability. As charging power increases, thermal management becomes more challenging. Our system can be extended to ultra-fast charging stations by scaling up the heat pump capacity and coolant reservoir. The BMS would coordinate with the station’s power electronics to adjust charging profiles dynamically. This synergy between the BMS and external systems represents a paradigm shift in EV charging, where thermal management is no longer solely the vehicle’s responsibility but a shared function with the grid.
From an energy efficiency perspective, the charging-pile-integrated BMS reduces parasitic losses. Onboard systems often consume battery power for heating or cooling, which can detract from driving range. By offloading this to the charging pile, which is grid-connected, the overall energy utilization improves. The coefficient of performance (COP) of the heat pump can be analyzed to quantify this benefit. For heating at low temperatures, the COP is typically greater than 1, meaning more heat is delivered than electrical energy consumed. This efficiency gain is captured by the BMS through optimized control algorithms.
Future developments in BMS technology could incorporate machine learning for predictive thermal management. By analyzing historical charging data and weather forecasts, the BMS could pre-condition the battery even before plug-in, further reducing charging time. Additionally, advancements in materials, such as phase change materials or advanced coolants, could be integrated into the BMS to enhance thermal buffering. Our research lays the groundwork for such innovations by demonstrating the value of infrastructure-integrated thermal management.
In conclusion, the battery management system is a cornerstone of EV performance, especially during high-power DC charging. Our proposed charging-pile-integrated thermal management system significantly enhances the BMS by providing rapid temperature control, reducing charging times, and maintaining safe operating conditions. Through detailed simulations, we have shown that this approach outperforms traditional onboard systems across a wide temperature range. The key formulas governing heat generation and dissipation, combined with adaptive charging strategies, enable the BMS to optimize both speed and safety. As EV adoption grows, such advanced BMS designs will be crucial for enabling fast, reliable, and efficient charging, ultimately contributing to the widespread acceptance of electric mobility.
The continuous evolution of battery management systems will drive further improvements in EV technology. By integrating thermal management with charging infrastructure, we can overcome one of the major bottlenecks in fast charging. This study highlights the importance of a holistic approach to BMS design, where electrical, thermal, and control systems are seamlessly coordinated. We envision future BMS platforms that are not only reactive but also predictive, leveraging real-time data and artificial intelligence to achieve unprecedented levels of efficiency and reliability. The journey toward sustainable transportation relies heavily on innovations in the battery management system, and our work represents a significant step in that direction.