1. Introduction
As the core component of electric vehicles, the battery management system (BMS) plays a pivotal role in monitoring battery status and optimizing battery usage. In electric vehicles, battery packs often suffer from inconsistencies among individual cells, which can significantly degrade overall performance and lifespan. Active balancing technology has emerged as a critical solution to address these inconsistencies, while off-line detection capabilities are essential for maintaining BMS functionality when the vehicle is not connected to the main power supply.
In traditional electric vehicle systems, BMS operations are heavily reliant on the vehicle’s main power source. When the vehicle is in a shutdown state, the BMS loses its ability to perform critical tasks such as fault diagnosis and performance evaluation. This limitation underscores the need for an independent off-line detection device that can operate autonomously, ensuring continuous maintenance and reliability of the battery system.
The objective of this study is to present the design and validation of an off-line detection device for active balancing in electric vehicle BMS. The following sections detail the hardware architecture, software implementation, and experimental results of the proposed device, with a specific focus on enhancing the consistency of battery packs and enabling efficient off-line maintenance.
2. Hardware Architecture of the Detection Device
The hardware design of the off-line detection device is centered around four key components: the core control unit, data acquisition module, active balancing circuit, and communication interface. Each component is meticulously engineered to ensure seamless integration and robust performance in off-line scenarios.
2.1 Core Control Unit
The core control unit serves as the brain of the detection device, responsible for coordinating all operational processes. We selected a high-performance microcontroller unit (MCU) with advanced processing capabilities to handle real-time data analysis and control algorithms. The MCU is equipped with multiple input/output ports to interface with other hardware modules, ensuring efficient data flow and command execution.
2.2 Data Acquisition Module
The data acquisition module is designed to accurately capture critical battery parameters, including voltage, current, and temperature. High-precision sensors are integrated into this module to ensure reliable measurements. For voltage sensing, we employed a differential amplifier circuit to minimize noise and improve measurement accuracy. The current sensor utilizes a hall-effect sensor to provide isolated and linear current measurements, while temperature sensors are strategically placed to monitor thermal conditions across the battery pack.
2.3 Active Balancing Circuit
The active balancing circuit is the core component for addressing battery cell inconsistencies. Unlike passive balancing methods that dissipate energy as heat, our design employs an active balancing approach based on a bidirectional DC-DC converter. This topology allows for the transfer of energy between cells, redistributing charge from higher-voltage cells to lower-voltage cells. The circuit is designed to operate with high efficiency, minimizing energy loss during the balancing process.
The balancing efficiency can be mathematically described by the following equation:\(\eta = \frac{E_{\text{transferred}}}{E_{\text{initial}}} \times 100\%\) where \(E_{\text{transferred}}\) is the energy transferred between cells, and \(E_{\text{initial}}\) is the initial energy imbalance in the battery pack.
2.4 Communication Interface
To enable seamless data exchange with external systems, the detection device is equipped with multiple communication interfaces, including CAN (Controller Area Network) and USB. The CAN interface allows for real-time communication with the vehicle’s BMS, while the USB interface supports data transfer to a central data center or maintenance terminal. These interfaces are designed to comply with industry-standard protocols, ensuring compatibility and reliability.
Table 1 summarizes the key hardware components and their specifications:
| Component | Specification | Function |
|---|---|---|
| Core Control Unit | 32-bit ARM Cortex-M7 MCU, 200 MHz clock speed | Coordinates system operations and controls |
| Voltage Sensor | 16-bit ADC, ±0.1% accuracy | Measures cell voltages |
| Current Sensor | Hall-effect sensor, ±0.5% accuracy | Measures charging/discharging currents |
| Temperature Sensor | Digital sensor, ±0.5°C accuracy | Monitors cell temperatures |
| Active Balancing Circuit | Bidirectional DC-DC converter, 95% efficiency | Redistributes energy between cells |
| Communication Interfaces | CAN 2.0B, USB 2.0 | Enables data exchange with external systems |
3. Software Implementation
The software framework of the detection device is developed using C/C++ for embedded systems, focusing on real-time data processing, control algorithm execution, and user interface management. The software is divided into several functional modules, each designed to address specific operational requirements.
3.1 Data Acquisition and Processing
The data acquisition module collects raw sensor data and processes it to remove noise and artifacts. A moving average filter is applied to voltage and current measurements to reduce high-frequency noise, while temperature data is compensated for environmental variations using a calibration algorithm. The processed data is then stored in a circular buffer for real-time analysis and historical record-keeping.
3.2 Active Balancing Control Algorithm
The active balancing control algorithm is the cornerstone of the software system. It continuously monitors the voltage distribution across the battery pack and initiates balancing operations when voltage discrepancies exceed a predefined threshold. The algorithm employs a fuzzy logic controller to determine the optimal balancing strategy, considering factors such as cell voltage, state of charge (SOC), and state of health (SOH).
The fuzzy logic controller uses the following input variables:
- Voltage difference (\(\Delta V\)) between cells
- Rate of voltage change (\(d\Delta V/dt\))
Based on these inputs, the controller outputs a control signal to the active balancing circuit, adjusting the energy transfer rate to achieve efficient balancing.
3.3 Battery State Estimation
Accurate estimation of battery states, including SOC and SOH, is critical for effective battery management. The SOC estimation employs a combination of the extended Kalman filter (EKF) and coulomb counting, while SOH is estimated using a neural network model trained on historical battery data. These algorithms provide real-time estimates that inform the balancing strategy and maintenance recommendations.
The EKF-based SOC estimation can be represented by the following state-space model:\(x_{k+1} = f(x_k, u_k) + w_k\)\(y_k = h(x_k) + v_k\) where \(x_k\) is the state vector (SOC), \(u_k\) is the input current, \(w_k\) and \(v_k\) are process and measurement noise, respectively, and f and h are the state and measurement functions.
3.4 User Interface and Remote Updates
The user interface is designed to provide intuitive access to system status and configuration parameters. A graphical LCD display shows real-time battery data, balancing progress, and diagnostic messages. Additionally, the device supports remote software updates via a wireless connection, allowing for easy deployment of new features and bug fixes without physical intervention.
Table 2 outlines the key software modules and their functionalities:
| Module | Functionality | Algorithm/Technology |
|---|---|---|
| Data Acquisition | Collects and preprocesses sensor data | Moving average filter, calibration algorithms |
| Active Balancing Control | Orchestrates energy redistribution between cells | Fuzzy logic controller |
| SOC/SOH Estimation | Estimates battery state of charge and health | Extended Kalman filter, neural network |
| Data Storage | Stores historical battery data | Circular buffer, flash memory |
| Remote Updates | Enables over-the-air software updates | HTTP/HTTPS protocol |
4. Experimental Testing and Results
To validate the functionality and reliability of the detection device, we conducted a series of experimental tests, including functional testing, performance testing, and long-term durability testing. The tests were performed on a prototype device connected to a lithium-ion battery pack consisting of 12 series-connected cells.
4.1 Functional Testing
Functional testing aimed to verify the core capabilities of the device, including active balancing, off-line detection, and communication stability.
4.1.1 Active Balancing Test
The active balancing test evaluated the device’s ability to reduce voltage discrepancies among cells. A battery pack with an initial voltage spread of 200 mV was subjected to the balancing process. Voltage measurements were taken before and after balancing using a high-precision multimeter.
Test Procedure:
- Measure initial cell voltages and record the maximum voltage difference (\(\Delta V_{\text{initial}}\)).
- Activate the active balancing function and allow the process to complete.
- Measure final cell voltages and record the maximum voltage difference (\(\Delta V_{\text{final}}\)).
- Calculate the voltage difference reduction ratio:\(\text{Reduction Ratio} = \left( 1 – \frac{\Delta V_{\text{final}}}{\Delta V_{\text{initial}}} \right) \times 100\%\)
Results:
- Initial voltage difference: 200 mV
- Final voltage difference: 30 mV
- Reduction ratio: 85% (exceeding the target of 80%)
4.1.2 Off-Line Detection Test
The off-line detection test assessed the accuracy of the device’s sensor measurements when operating independently of the vehicle’s main power supply. Voltage, current, and temperature measurements were compared against reference values obtained using laboratory-grade instruments.
Test Procedure:
- Disconnect the battery pack from the vehicle’s BMS and connect it to the detection device.
- Record sensor readings from the device and reference instruments simultaneously.
- Calculate measurement errors using the formula:\(\text{Error} = \frac{\text{Device Reading} – \text{Reference Value}}{\text{Reference Value}} \times 100\%\)
Results:
- Voltage error: ±0.5%
- Current error: ±0.8%
- Temperature error: ±0.6% All errors were within the acceptable range of ±1%.
4.1.3 Communication Test
The communication test evaluated the stability of data transmission between the detection device and a simulated vehicle BMS. Test data was transmitted over the CAN bus, and metrics such as data loss rate and latency were recorded.
Test Procedure:
- Configure the CAN bus parameters (baud rate: 500 kbps, data bits: 8, stop bits: 1).
- Transmit 10,000 data packets and record the number of lost packets and transmission latency.
- Calculate data loss rate and average latency.
Results:
- Data loss rate: 0.05% (below the target of 0.1%)
- Average latency: 80 ms (below the target of 100 ms)
Table 3 summarizes the functional test results:
| Test Case | Initial Condition | Test Result | Target |
|---|---|---|---|
| Active Balancing | Voltage spread: 200 mV | Reduction ratio: 85% | >80% |
| Voltage Detection | Reference: 13.52 V | Device: 13.50 V | ±1% error |
| Current Detection | Reference: 2.01 A | Device: 2.00 A | ±1% error |
| Temperature Detection | Reference: 25.2°C | Device: 25.0°C | ±1°C error |
| Data Loss Rate | 10,000 packets transmitted | 0.05% loss | <0.1% |
| Latency | – | 80 ms | <100 ms |
4.2 Performance Testing
Performance testing focused on evaluating the device’s efficiency, accuracy, and energy consumption under various operating conditions.
4.2.1 Balancing Efficiency Test
The balancing efficiency test was conducted on three battery packs with different initial voltage spreads: high (200 mV), medium (100 mV), and low (50 mV). The test measured the time required to reduce the voltage spread to below 20 mV and the energy consumed during the process.
Results:
- High 差异组 (200 mV):
- Balancing time: 30 minutes
- Energy consumption: 12 W·h
- Unit energy consumption: 0.06 W·h/mV
- Medium 差异组 (100 mV):
- Balancing time: 15 minutes
- Energy consumption: 6 W·h
- Unit energy consumption: 0.06 W·h/mV
- Low 差异组 (50 mV):
- Balancing time: 8 minutes
- Energy consumption: 3 W·h
- Unit energy consumption: 0.06 W·h/mV
The results indicate consistent unit energy consumption across different voltage spreads, demonstrating the device’s efficient energy management.
4.2.2 Detection Precision Test
The detection precision test verified the accuracy of the device’s sensors under steady-state conditions. Measurements were compared against reference values from calibrated instruments.
Results:
- Voltage: Error = -0.15% (device: 13.50 V, reference: 13.52 V)
- Current: Error = -0.50% (device: 2.00 A, reference: 2.01 A)
- Temperature: Error = -0.80°C (device: 25.0°C, reference: 25.2°C)
All errors were within the acceptable limits for battery management applications.
4.2.3 Response Time Test
The response time test measured the delay between receiving a command (detection or balancing) and the device’s initiation of the corresponding action.
Results:
- Detection command response time: 50 ms (average)
- Balancing command response time: 70 ms (average)
These results demonstrate the device’s ability to react quickly to user commands, ensuring timely maintenance and fault resolution.
4.2.4 Energy Consumption Test
The energy consumption test evaluated the device’s power usage in standby and active modes.
Results:
- Standby power: 0.5 W (average)
- Active balancing power: 15 W (average)
- Detection power: 5 W (average)
The low standby power ensures minimal energy drain during storage, while the active mode power consumption is within acceptable limits for portable maintenance devices.
Table 4 summarizes the performance test results:
| Test Case | Test Condition | Test Result | Evaluation |
|---|---|---|---|
| Balancing Efficiency | High voltage spread | 30 min, 12 W·h | Efficient |
| Medium voltage spread | 15 min, 6 W·h | Efficient | |
| Low voltage spread | 8 min, 3 W·h | Efficient | |
| Detection Precision | Voltage measurement | -0.15% error | Within tolerance |
| Current measurement | -0.50% error | Within tolerance | |
| Temperature measurement | -0.80°C error | Within tolerance | |
| Response Time | Detection command | 50 ms | Rapid |
| Balancing command | 70 ms | Rapid | |
| Energy Consumption | Standby mode | 0.5 W | Low |
| Active balancing | 15 W | Reasonable | |
| Detection mode | 5 W | Low |
5. Conclusion
In this study, we have presented the design and experimental validation of an off-line detection device for active balancing in electric vehicle BMS. The device addresses the critical need for autonomous battery maintenance in electric vehicles, overcoming the limitations of traditional BMS systems that rely on the vehicle’s main power supply.
Through detailed hardware and software design, we have demonstrated that the device can effectively reduce battery cell inconsistencies using active balancing technology, with voltage difference reduction ratios exceeding 80%. The off-line detection capabilities have been validated with measurement errors within ±1% for voltage, current, and temperature, ensuring reliable fault diagnosis and performance evaluation.
Performance tests have shown that the device operates efficiently across different battery conditions, with consistent energy consumption and rapid response times. The low standby power and robust communication capabilities further enhance its suitability for field maintenance applications in electric vehicles.
As the electric vehicle industry continues to grow, the development of advanced battery management technologies like the proposed off-line detection device is crucial for improving vehicle reliability, extending battery lifespan, and promoting the widespread adoption of electric mobility. Future work will focus on integrating machine learning algorithms to further optimize balancing strategies and enhance predictive maintenance capabilities.