In the rapidly evolving landscape of electric vehicles (EVs), the safety and reliability of the EV battery pack are paramount. As the core power source, the lithium-ion battery pack is susceptible to thermal and mechanical stresses during charge-discharge cycles, which can lead to performance degradation or catastrophic failures like thermal runaway. This is especially critical in automotive experimental platforms, where EV battery packs are routinely tested under varying conditions. We have observed that temperature and pressure anomalies often exhibit a coupled effect: rising temperatures cause electrode expansion and gas generation, increasing internal pressure, while excessive pressure can impede heat dissipation, further elevating temperature. Therefore, developing a cost-effective, real-time monitoring and control system for multiple parameters of an EV battery pack is essential for both educational demonstration and industrial validation.
Existing thermal management techniques for EV battery packs, such as neural network-based temperature prediction or phase-change material (PCM) hybrid systems, often require substantial computational resources or exhibit response delays. Similarly, pressure control typically relies on mechanical relief valves, which lack precision. For state-of-charge (SOC) estimation, methods like ampere-hour integration, open-circuit voltage (OCV), or Kalman filtering face challenges in real-time accuracy, cumulative errors, or hardware costs. To address these gaps, we designed a multi-parameter monitoring and control system based on a 51 microcontroller, targeting EV battery packs in automotive experimental setups. Our system integrates sensors for current, voltage, temperature, and pressure, employs an improved composite SOC estimation algorithm, and implements safety actions like cooling, pressure relief, and low-SOC alarms. This approach provides a reusable, low-cost solution for safety protection while offering insights into thermal-mechanical coupling management for EV battery packs.
The overall system architecture is divided into functional modules to ensure comprehensive monitoring and control of the EV battery pack. As summarized in Table 1, the system comprises signal acquisition modules, a microcontroller unit, a display module, and execution modules. Each module plays a specific role in maintaining the EV battery pack within safe operational limits. The signal acquisition group collects real-time data on voltage, current, temperature, and pressure from the EV battery pack, while the microcontroller processes this information to drive displays and actuators. The display module presents key parameters for user observation, and the execution group responds to anomalies by activating a water pump for cooling, an electromagnetic valve for pressure relief, or a buzzer for low-SOC warnings. This modular design enhances flexibility and scalability, making it suitable for various EV battery pack configurations in experimental platforms.
| Module | Components | Function |
|---|---|---|
| Signal Acquisition | Voltage sensor, current sensor (ACS712), temperature sensor (DS18B20), pressure sensor (MPX4115) | Collect real-time data on EV battery pack parameters: voltage, current, temperature, pressure. |
| Microcontroller Unit | AT89C51 | Process sensor data, execute control algorithms, and manage outputs. |
| Display Module | LCD1602 | Display monitored parameters: voltage, current, temperature, pressure, SOC. |
| Execution Group | Water pump, electromagnetic valve, buzzer | Activate safety measures: cooling at >40°C, pressure relief at >113 kPa, alarm at SOC <20%. |
The hardware circuit design centers on the AT89C51 microcontroller, chosen for its low cost, reliability, and ease of use. The minimal system circuit includes a crystal oscillator, reset circuit, and power supply, ensuring stable operation. For temperature detection, we selected the DS18B20 digital sensor, which communicates via a one-wire interface with the microcontroller, eliminating the need for an analog-to-digital converter (ADC). Its connection involves a pull-up resistor on the data line to ensure signal integrity. Current sensing is achieved using the ACS712 Hall-effect sensor, which outputs an analog voltage proportional to the current. This signal is converted to digital via the ADC0832 chip, with the relationship given by:
$$V_{out} = 2.5 + 0.185 \times I_p$$
where \(V_{out}\) is the output voltage and \(I_p\) is the measured current. The voltage detection circuit uses a resistive divider (9 kΩ and 1 kΩ) to scale down the EV battery pack voltage for sampling by the ADC0832. Pressure monitoring employs the MPX4115 sensor, whose analog output is fed into the PCF8591 ADC with I²C interface. The pressure value is derived from the digital output using a linear transformation:
$$P = \frac{(115 – 15) \times X}{(243 – 13)} + 9.3$$
where \(X\) is the PCF8591 output and \(P\) is the pressure in kPa. Table 2 details the sensor specifications and their roles in safeguarding the EV battery pack.
| Sensor | Type | Output | Key Parameter | Role in EV Battery Pack Safety |
|---|---|---|---|---|
| DS18B20 | Digital temperature | Digital signal | Range: -55°C to 125°C | Monitors thermal state to prevent overheating. |
| ACS712 | Hall-effect current | Analog voltage | Sensitivity: 185 mV/A | Tracks charge-discharge current for SOC estimation. | MPX4115 | Analog pressure | Analog voltage | Range: 15 kPa to 115 kPa | Detects internal pressure buildup in EV battery pack. |
| Resistive Divider | Voltage sensor | Scaled analog voltage | Division ratio: 10:1 | Measures EV battery pack voltage for SOC and health. |
The execution modules are driven by transistor-based circuits with relays for isolation. When the microcontroller detects an anomaly—such as temperature exceeding 40°C—it outputs a low signal to turn on a PNP transistor, energizing the relay for the water pump. Similarly, for pressure above 113 kPa, the electromagnetic valve is activated, and for SOC below 20%, a buzzer sounds. Diodes are placed across relay coils to suppress back-EMF, protecting the transistors. The display module uses an LCD1602 screen connected to the microcontroller’s ports, with a potentiometer for contrast adjustment, showing all critical parameters of the EV battery pack in real time.
Software design is crucial for integrating hardware components and implementing control logic. The main program flow, as illustrated in Figure 10 (conceptual representation), begins with initialization of the microcontroller and peripherals. It then enters a continuous loop where sensor data is acquired, processed, and displayed. The core intelligence lies in the SOC estimation and threshold checks. For SOC, we developed an improved composite algorithm combining OCV and ampere-hour integration methods to balance accuracy and computational efficiency. Traditional ampere-hour integration is expressed as:
$$SOC = SOC_0 – \frac{1}{C} \int \eta i(t) dt$$
where \(SOC_0\) is the initial SOC, \(C\) is the nominal capacity, \(i(t)\) is the current (positive for discharge), and \(\eta\) is the efficiency. However, this method suffers from cumulative errors and dependency on initial conditions. To mitigate this, we use the OCV method at startup when the EV battery pack is open-circuit, referencing a pre-stored table that maps OCV to SOC at different temperatures. For intermediate temperatures, linear interpolation is applied. During operation, the ampere-hour integration is enhanced with correction factors for discharge rate and temperature:
$$SOC = SOC_0 – \frac{1}{\alpha \beta C} \int \eta i(t) dt$$
where \(\alpha\) is the discharge rate coefficient and \(\beta\) is the temperature coefficient, both determined empirically for the EV battery pack. This hybrid approach improves real-time SOC estimation without demanding excessive computation, making it suitable for microcontroller deployment.
The control logic thresholds are set based on safety standards for EV battery packs: temperature limit at 40°C, pressure limit at 113 kPa, and SOC warning at 20%. When temperature surpasses 40°C, the system triggers the water pump to circulate coolant; when pressure exceeds 113 kPa, the electromagnetic valve opens to vent gases; and when SOC drops below 20%, the buzzer alerts users to recharge. These actions are implemented in software with hysteresis to prevent rapid cycling, ensuring stable operation of the EV battery pack.
System simulation was conducted using Proteus software alongside Keil for code integration. The simulation modeled the EV battery pack with variable resistors to emulate current and voltage changes, while the DS18B20 and MPX4115 were simulated with parameter adjustments. As shown in Figure 11 (conceptual representation), the simulation demonstrated reliable operation under abnormal conditions. For instance, when the EV battery pack temperature reached 41°C, voltage 45.2 V, pressure 114 kPa, current 2.05 A, and SOC 86%, both the water pump and electromagnetic valve were activated simultaneously. Table 3 summarizes key simulation outcomes, validating the system’s responsiveness to multiple fault scenarios in the EV battery pack.
| Parameter | Normal State | Abnormal State | System Response | Effect on EV Battery Pack |
|---|---|---|---|---|
| Temperature | 35°C | 41°C | Water pump ON | Prevents thermal runaway. |
| Pressure | 100 kPa | 114 kPa | Electromagnetic valve OPEN | Relieves internal stress. |
| SOC | 50% | 18% | Buzzer ALARM | Warns of low charge. |
| Current | 1.5 A | 2.05 A | Monitored for SOC | Ensures accurate estimation. |
| Voltage | 48 V | 45.2 V | Displayed on LCD | Indicates EV battery pack health. |
The simulation confirmed that the hardware and software designs work cohesively to monitor and control the EV battery pack. The system successfully processed sensor inputs, estimated SOC, and executed safety actions within milliseconds, meeting real-time requirements. This simulation phase also allowed optimization of threshold values and control algorithms, ensuring robustness for actual EV battery pack deployments in experimental platforms.
In conclusion, we have designed and simulated an abnormal state monitoring system for EV battery packs based on a 51 microcontroller. This system provides comprehensive monitoring of voltage, current, temperature, pressure, and SOC, with active control measures to mitigate risks. The improved SOC estimation algorithm enhances accuracy while keeping computational overhead low, and the modular hardware design ensures cost-effectiveness and adaptability. Our work offers a practical solution for safety management in automotive experimental settings, contributing to the broader field of thermal-mechanical coupling in EV battery packs. Future directions include integrating wireless communication for remote monitoring, implementing machine learning for predictive maintenance, and testing with real EV battery packs under dynamic loads to further validate performance. Ultimately, this system underscores the importance of holistic monitoring in enhancing the safety and longevity of EV battery packs, paving the way for more resilient energy storage systems in electric vehicles.
The development of such monitoring systems is critical as EV battery packs become more complex and energy-dense. By addressing temperature and pressure anomalies proactively, we can reduce the likelihood of failures that could compromise not only experimental equipment but also vehicle safety. Our approach demonstrates that with careful design and integration, even microcontroller-based systems can provide reliable protection for EV battery packs. This aligns with industry trends toward smarter battery management systems (BMS) that incorporate multiple sensing modalities. As research continues, we anticipate further refinements in sensor accuracy, control algorithms, and integration with cloud-based analytics for EV battery packs, ultimately supporting the sustainable growth of electric mobility.