KR-20260064671-A - Smart battery management system for electric motorcycles

Abstract

The present invention relates to a smart battery management system for electric two-wheeled vehicles. The system comprises a cell voltage, current, and temperature measurement unit, an active cell balancing circuit based on a flyback transformer, an SOC estimation module combining Coulomb counting and a Kalman filter, an SOH estimation module based on capacity fade rate, internal resistance, and self-discharge rate, a three-stage independent protection circuit, and a CANbus/UART communication module. Through active balancing, energy loss is reduced by more than 60% compared to the passive method, and battery life and safety are improved through high-precision SOC estimation within ±2% and SOH-based replacement timing guidance.

Inventors

• 이상호

Assignees

• 주식회사 핸디라이프

Dates

Publication Date

20260507

Application Date

20260417

Claims (1)

1. In a smart battery management system for managing battery packs installed in electric two-wheeled vehicles, A cell voltage measuring unit (100) that individually measures each cell voltage of a battery pack with a resolution of 1mV or less; A current measuring unit (200) including a Hall effect current sensor for measuring the total charge/discharge current of the pack; NTC thermistor-based temperature measuring unit (300) installed at three or more locations within the battery pack; An active cell balancing circuit (400) that transfers energy from a high-voltage cell to a low-voltage cell using a flyback transformer-based energy shuttle method when a voltage imbalance between cells of 20mV or more is detected; A dual correction method SOC estimation module (500) that updates the SOC by Coulomb counting while driving and corrects the accumulated error by measuring the OCV after the vehicle stops; SOH estimation module (600) that estimates battery SOH by analyzing cumulative charge/discharge capacity, internal resistance increase rate, and self-discharge rate; A multi-stage protection circuit (700) that cuts off the FET switch when overcharging (cell voltage exceeding 4.25V), over-discharging (cell voltage less than 2.5V), overcurrent (discharge exceeding 80A and charge exceeding 40A), overheating (exceeding 60°C), or short circuit occurs; and A smart battery management system for an electric two-wheeled vehicle, characterized by including a communication module (800) that transmits SOC, SOH, cell voltage, current, and temperature data in real time to an external system via CANbus 2.0B and UART interfaces.

Description

Smart battery management system for electric motorcycles The present invention relates to a battery management system (BMS) that manages the charge and discharge status of a lithium-ion battery pack mounted on an electric two-wheeled vehicle in real time, and in particular, to a smart battery management system that integrates active cell balancing, high-precision State of Charge (SOC) estimation, State of Health (SOH) estimation, and battery protection functions. The lithium-ion battery pack of an electric two-wheeler consists of a structure in which multiple cells are connected in series and parallel, and imbalances continuously occur due to variations in electrochemical characteristics between cells. When voltage imbalances between cells accumulate, the overall capacity utilization rate of the pack decreases, and overcharging and over-discharging of specific cells are induced, rapidly shortening the battery life. Conventional passive balancing methods dissipate excess energy as heat through resistance, resulting in significant energy loss and serious heat generation issues. In particular, the decrease in balancing efficiency is pronounced in electric two-wheeler environments where charging and discharging are frequent. In addition, the existing Coulomb counting method for SOC estimation has a problem where accuracy deteriorates over long-term use due to the accumulation of current sensor errors, and it was not possible to accurately determine the time for battery replacement because there was no SOH estimation function. Accordingly, there is a technical need for a smart BMS that integrates an active balancing circuit that minimizes energy loss and a high-precision SOC/SOH estimation function that combines multiple estimation algorithms. FIG. 1 is an overall block diagram of a smart battery management system according to the present invention. 1. System Configuration Overview The smart BMS of the present invention is designed with a structure in which a cell voltage measurement IC, a current sensor, a temperature sensor, an active balancing circuit, an SOC/SOH calculation engine, a FET-based protection circuit, and a communication module are integrated on a single PCB, centered around an ARM Cortex-M4-based MCU. The total power consumption is designed to be 5mW or less in standby mode to minimize battery consumption due to self-discharge. 2. Design of Active Cell Balancing Circuit The flyback transformer-based energy shuttle method stores excess energy from a cell in the primary winding of the transformer and then releases it to the entire pack bus through the secondary winding to be used for charging other cells. Unlike passive methods that consume energy as heat, this method recycles energy with a conversion efficiency of over 85%. The balancing current is set to a maximum of 500mA, and the operation automatically starts when the voltage difference between cells is 20mV or more and ends when it is equalized to 10mV or less. The balancing operation time takes from a few minutes to tens of minutes depending on the cell capacity and the degree of imbalance. 3. SOC Estimation Algorithm Coulomb counting is used as the basic algorithm during driving. The discharge capacity is accumulated by integrating the current measurement value at a 1-second interval, and the SOC is calculated by dividing this by the rated capacity. Auto-Zero calibration is performed in the zero-current range to compensate for the current sensor offset error. After 10 minutes or more have passed since the vehicle stopped, the Open Circuit Voltage (OCV) is measured, and the SOC is recalibrated using a pre-established OCV-SOC lookup table and an extended Kalman filter. To reflect the OCV-SOC characteristics, which have a high temperature dependency, lookup tables for different temperature ranges (0°C, 25°C, 45°C) are applied. 4. SOH Estimation Algorithm SOH estimation is implemented by weighting and summing three indicators. First, the capacity fade rate is calculated by dividing the actual discharge capacity measured during a full charge-discharge cycle by the initial rated capacity. Second, the internal resistance increase rate is calculated from the voltage drop measured during a pulse discharge test (10 seconds, 0.5C rate). Third, the self-discharge rate is calculated from the difference in SOC before and after 72 hours of standing following a full charge. The weights for the three indicators are set differently for each battery type, with default values of 60% for the capacity fade rate, 30% for the internal resistance increase rate, and 10% for the self-discharge rate. 5. Multi-stage protection circuit Primary software protection involves the MCU monitoring the measurement value at a 1ms interval and shutting off the charge or discharge FET when a threshold is exceeded. Secondary hardware protection involves an analog comparator operating independently of the MCU and shutting off the FET within 100μs. Tertiary physical fuse permanently