Abstract The rapid growth of electric vehicles (EVs) has heightened the need for efficient battery management systems (BMS), particularly to address voltage inconsistency in power batteries caused by production variations. This paper explores active balancing strategies as a superior alternative to traditional passive methods, focusing on their design, optimization, and performance evaluation. Through systematic analysis of existing solutions, a cost-effective active balancing approach is proposed, validated by experimental data to enhance energy efficiency and extend battery lifecycle in EVs.
Keywords: electric vehicle; power battery; battery management system; active balancing; energy efficiency
1. Introduction
The proliferation of electric vehicles (EVs) has reshaped the automotive industry, with global sales driven by environmental concerns and technological advancements. In 2023, China alone produced 9.587 million EVs and sold 9.495 million, accounting for over 60% of worldwide sales . Central to EV performance is the power battery system, which faces critical challenges due to inherent voltage and state-of-charge (SoC) discrepancies among battery cells. These inconsistencies, arising from manufacturing tolerances and cyclic charging/discharging, can lead to overcharging, over-discharging, and thermal runaway risks .
Traditional passive balancing strategies, while simple and low-cost, dissipate excess energy as heat through resistors, limiting 均衡电流 to ≤300 mA and resulting in slow balancing speeds . In contrast, active balancing transfers energy between cells using storage elements (e.g., capacitors, inductors, transformers), achieving higher efficiency (up to 90%) and faster balancing (currents of 3–5 A) . This paper systematically evaluates existing active balancing techniques, proposes an optimized design, and demonstrates its feasibility through experimental validation.
2. Passive vs. Active Balancing: Core Differences
2.1 Passive Balancing: Principles and Limitations
Passive balancing relies on resistive energy dissipation. When a cell’s voltage exceeds a threshold, a resistor is activated to discharge the cell until voltage uniformity is achieved. Key characteristics include:
- Advantages: Simple circuitry, low initial cost, minimal control complexity.
- Disadvantages: High energy loss (up to 30% of total energy), significant heat generation, and slow response times due to limited current (≤300 mA) .
2.2 Active Balancing: Energy Transfer Mechanisms
Active balancing mitigates passive limitations by redistributing energy 而非 dissipating it. This is achieved via:
- Energy Storage Elements: Capacitors (for short-range transfers), inductors, or transformers (for long-range, isolated transfers).
- Higher Efficiency: Reduced energy loss (typically <10%) and faster balancing due to higher currents (3–5 A) .
Table 1: Comparative Analysis of Passive and Active Balancing
| Feature | Passive Balancing | Active Balancing |
|---|---|---|
| Energy Handling | Dissipative (resistive) | Non-dissipative (transfer) |
| Typical Current | ≤300 mA | 3–5 A |
| Energy Loss | High (20–30%) | Low (<10%) |
| Heat Generation | Significant | Minimal |
| Balancing Speed | Slow (hours) | Fast (minutes) |
| Circuit Complexity | Low | High |
| Cost | Low | Medium-High |
3. Active Balancing Architectures: Isolated vs. Non-Isolated
3.1 Isolated Active Balancing
Isolated systems use transformers or isolated DC-DC converters to transfer energy between any two cells, regardless of their position in the battery pack.
3.1.1 System Design
- Key Components:
- Switch Matrix: Selects cells for energy transfer.
- Bidirectional DC-DC Transformer: Facilitates energy transfer between cells and a relay power supply (e.g., 12 V/24 V) .
- Relay Power Supply: Temporarily stores energy during transfer.
3.1.2 Advantages
- Flexibility: Enables energy transfer between any cells in the pack.
- Low Loss: Single-stage energy transfer with minimal resistance.
- Parallel Operation: Supports simultaneous charging/discharging of multiple cells .
3.1.3 Disadvantages
- Complexity: High component count (transformers, relays) increases failure rates.
- Cost: Expensive due to isolated components and strict voltage tolerance requirements.
- Size: Large transformers occupy significant space .
Derivative Designs: Some variants omit the relay power supply, using the battery pack’s total voltage for energy transfer. Unidirectional designs (e.g., cell-to-total voltage or vice versa) reduce complexity and cost .
3.2 Non-Isolated Active Balancing
Non-isolated systems transfer energy between adjacent cells using capacitors or inductors, avoiding transformers and isolated circuits.
3.2.1 System Design
- Capacitor-Based: Uses flying capacitors to shuttle energy between adjacent cells (e.g., Cell A → Capacitor → Cell B) .
- Inductor-Based: Employs Buck-Boost converters to transfer energy via inductive coupling .
3.2.2 Advantages
- Simplicity: No isolated components; self-controlled via voltage thresholds.
- Low Power: Operates without external power, reducing standby loss.
- Compact Design: Smaller footprint due to fewer components .
3.2.3 Disadvantages
- Limited Range: Only adjacent cells can be balanced, leading to reduced efficiency in long cell strings.
- Inter-Pack Complexity: Difficult to balance cells across multiple battery packs.
- Cable Resistance: Voltage drops in wiring can affect current accuracy .
Table 2: Isolated vs. Non-Isolated Active Balancing
| Feature | Isolated | Non-Isolated |
|---|---|---|
| Energy Transfer Range | Any cells | Adjacent cells only |
| Storage Element | Transformer | Capacitor/inductor |
| Control Complexity | High (requires MCU) | Low (self-controlled) |
| Scalability | Good (suitable for large packs) | Poor (challenging for long strings) |
| Cost per Cell | High | Low |
| 典型均衡电流 | 3–5 A | 3–5 A |
4. Optimized Active Balancing Strategy for EVs
To address the trade-offs between performance and cost, we propose an optimized isolated active balancing scheme centered on a battery cell acquisition analog front-end (AFE) chip.
4.1 Design Principles
- MCU-Free Architecture: Eliminates microprocessors and associated peripherals, reducing cost by ~30% .
- Simplified Switch Matrix: Uses I2C-controlled I/O chips (16 channels each) to drive 12 V relay-based switches, enabling precise cell selection .
- Unidirectional Total Voltage Charging: Employs a flyback DC-DC converter to transfer energy from the pack’s total voltage to individual cells, with self-adaptive closed-loop control .
- Integrated Power Supply: Generates 12 V and 5 V rails from the pack’s total voltage using a single DC-DC chip, powering all low-voltage components .
- Comprehensive Sensing: Expands AFE analog inputs to monitor cell temperatures alongside voltages, enabling precision balancing .
4.2 Mathematical Formulation
The balancing efficiency (η) is defined as the ratio of useful energy transferred to total energy consumed:\(\eta = \frac{E_{\text{transferred}}}{E_{\text{input}}} \times 100\%\) Where \(E_{\text{transferred}} = \sum I \cdot V \cdot t\) (energy moved between cells) and \(E_{\text{input}} = \text{energy drawn from the pack}\).
For the unidirectional design, the 均衡电流 (I_balance) is constrained by the DC-DC converter’s capacity and thermal limits:\(I_{\text{balance}} \leq \frac{P_{\text{DC-DC}}}{V_{\text{cell}}}\) Where \(P_{\text{DC-DC}}\) is the converter’s power rating and \(V_{\text{cell}}\) is the target cell voltage.
4.3 Experimental Validation
Setup: A 10-cell lithium-ion battery module (target voltage: 3.3 V) was tested using the optimized scheme.
Results:
- Balancing Speed: Achieved full balance within 100 seconds, with peak current of 3 A (Figure 1) .
- Stability: Maintained balance across 8 discharge cycles, with voltage deviation <1% (Figure 2) .
- Cycle Life: After 485 charge-discharge cycles, the active balancing group showed 20% less capacity degradation compared to passive balancing (Figure 3) .
Table 3: Performance Metrics of the Optimized Scheme
| Parameter | Value | Comparison to Passive |
|---|---|---|
| 均衡电流峰值 | 3 A | 10× higher |
| Full Balance Time (10 cells) | 100 s | 5× faster |
| Energy Loss per Cycle | <5% | 80% reduction |
| Component Cost | $50/cell | 2× higher (but justified by efficiency) |
5. Challenges and Future Directions
While the optimized scheme improves cost-efficiency, challenges remain:
- Thermal Management: High currents in non-isolated systems require better heat dissipation.
- Scalability: Extending to >100 cells may necessitate hybrid isolated/non-isolated topologies.
- AI Integration: Machine learning could optimize balancing paths in real time, reducing energy loss.
Future work will focus on integrating predictive algorithms and exploring solid-state energy storage for faster transfers.
6. Conclusion
Active balancing is a critical enabler for high-performance EV battery systems, offering superior energy efficiency and lifecycle extension compared to passive methods. Our optimized scheme, by leveraging AFE-based control and cost-effective components, achieves a balance between performance and affordability, with demonstrated 3 A balancing currents and 20% reduced capacity fade. As EVs transition to higher energy densities, such innovations will be pivotal in maximizing battery utility and supporting sustainable mobility.