Research on SOC Application of Lithium Battery Based on LTC6804-2

The state of charge (SOC) of a battery is a fundamental component of the battery management system (BMS). Accurate SOC estimation is crucial for providing users with real-time information about the battery’s energy level and serves as the foundation for charge/discharge control, balancing, and overall system management. Therefore, developing an accurate and reliable SOC measurement method is essential for efficient battery operation. In this paper, we present a lithium battery SOC monitoring system that integrates multiple sensors and microcontroller units. The LTC6804-2 chip is used for voltage acquisition in the battery pack, while a Hall sensor measures the charge and discharge current. A temperature sensor connected via I2C bus captures the battery case temperature. These components are controlled by the LPC2478 microcontroller to form a comprehensive SOC application system. **1. SOC Measurement System Principle** **1.1, Coulomb Counting Method** The coulomb counting method calculates SOC by integrating the current over time. The formula is as follows: $$ SOC(t) = SOC_0 + \frac{1}{C_N} \int_0^t I(\tau) d\tau $$ Where: - $ SOC_0 $: Initial SOC value - $ C_N $: Rated capacity of the battery - $ t $: Charge or discharge duration - $ I $: Charge or discharge current This method is straightforward and widely used. However, it relies heavily on accurate current measurements, and any small error in current sensing can accumulate over time, leading to significant inaccuracies. Additionally, timing errors can further affect the reliability of the results. **1.2, Open Circuit Voltage Method** The open circuit voltage (OCV) method is based on the strong correlation between OCV and SOC when the battery is in a stable state. This relationship is typically determined through experimental calibration. To use this method, the battery must be left idle for a period to allow the terminal voltage to stabilize. Once the OCV is measured, the SOC can be estimated from the pre-established OCV-SOC curve. This method offers high accuracy but requires long rest periods, making it unsuitable for real-time applications. It is often used in conjunction with other methods to improve overall performance. In our system, both the coulomb counting method and the open circuit voltage method are employed. During active charging or discharging, the coulomb counting method is used for real-time SOC estimation. When the battery is idle, the OCV method, combined with temperature data, is applied to correct the SOC value. This hybrid approach leverages the strengths of both techniques, enhancing the accuracy and reliability of the SOC estimation. **2. System Hardware Design** The system is built around the LPC2478 microcontroller, which acts as the central processing unit. The hardware includes 12 lithium battery cells, a voltage measurement circuit using the LTC6804-2, a Hall current sensor, a temperature sensor, and communication interfaces. The system architecture is designed to ensure accurate data acquisition and real-time processing. A block diagram of the system is shown in Figure 1, illustrating the integration of all components into a cohesive BMS platform. This design supports efficient SOC monitoring and contributes to the safe and effective operation of the battery system.

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