With the global shift toward sustainable energy and the implementation of carbon neutrality strategies, the optimization of charging technologies has become a pivotal aspect of the electric vehicle industry. Traditional charging systems often rely on microcontrollers and sophisticated power management chips, leading to high complexity and cost. In this study, we propose a novel hardware-centric control architecture that leverages voltage divider circuits, multi-stage comparators, and RS flip-flops to achieve energy balance and stability during charging. By eliminating software dependencies, our design reduces hardware costs by over 50% while maintaining precision and reliability. This approach is particularly relevant for the rapidly expanding China EV market, where cost-effective and scalable charging infrastructure is essential.
The proliferation of electric vehicles worldwide, especially in regions like China, underscores the need for efficient charging solutions. Conventional battery management systems (BMS) incorporate complex circuitry that increases manufacturing expenses and maintenance requirements. Our research addresses these challenges by introducing a distributed control mechanism that operates independently of microcontrollers. The core innovation lies in the dynamic threshold adjustment and fault-tolerant design, which enable multi-level voltage detection and mode switching with minimal latency. This paper details the system architecture, module design, experimental validation, and performance metrics, emphasizing the applicability to electric vehicle charging networks.
The charging process for electric vehicles involves multiple stages to ensure battery health and efficiency. Our system divides these stages into trickle, small-current, and large-current modes, each triggered by specific voltage thresholds. The hardware components include a voltage divider network, high-speed comparators, bistable RS flip-flops, and switching circuits. Below, we present the mathematical foundation of the voltage divider circuit, which generates reference signals for comparator operations:
$$ V_{ref} = V_{in} \times \frac{R_{lower}}{R_{total}} $$
where \( V_{in} \) is the input DC voltage (30V), \( R_{lower} \) is the resistance below the node, and \( R_{total} \) is the sum of all resistances in the divider. For instance, the 5.54V reference at node A is calculated as:
$$ V_A = 30 \times \frac{R108 + R109 + R110}{R107 + R108 + R109 + R110} $$
Similarly, the other nodes yield 5.25V and 5.15V, with tolerances within ±1%. This precision is critical for accurate mode transitions in electric vehicle charging systems.
To illustrate the resistor values and their roles, Table 1 summarizes the voltage divider configuration:
| Node | Resistor Combination | Output Voltage (V) | Tolerance |
|---|---|---|---|
| A | R107-R108 | 5.54 | ±0.08V |
| B | R108-R109 | 5.25 | ±0.08V |
| C | R109-R110 | 5.15 | ±0.08V |
The comparator circuit employs LM339 devices, which feature a response time of less than 1 microsecond. Each comparator compares the battery voltage \( V_{bat} \) with a reference voltage \( V_{ref} \). The output \( V_{out} \) is given by:
$$ V_{out} = \begin{cases}
V_{CC} & \text{if } V_{bat} > V_{ref} \\
0 & \text{otherwise}
\end{cases} $$
This binary output drives the RS flip-flops, which utilize Schmitt triggers (74HC14) to provide hysteresis and prevent oscillations. The hysteresis voltage \( V_h \) is typically 0.5V, ensuring stable state transitions even in noisy environments common to electric vehicle charging stations.
For the switching circuit, we model the current flow using Ohm’s law. The small-current path via transistor Q101 delivers:
$$ I_{small} = \frac{18 – V_{bat}}{R102} $$
where \( R102 = 10\Omega \), resulting in approximately 0.5A. The large-current path via MOSFET VT101 provides:
$$ I_{large} = \frac{18 – V_{bat}}{R103} $$
with \( R103 = 5\Omega \), yielding about 1.8A. The total current during overlapping modes is the sum of these contributions, crucial for managing energy distribution in electric vehicle batteries.
Efficiency analysis is vital for assessing the system’s performance. The overall efficiency \( \eta \) is defined as:
$$ \eta = \frac{P_{out}}{P_{in}} \times 100\% $$
where \( P_{out} \) is the power delivered to the battery and \( P_{in} \) is the input power. Our measurements indicate \( \eta = 92\% \), with losses attributed to resistor dissipation (3%) and transistor voltage drops (5%). This high efficiency supports the adoption of such systems in China EV infrastructure, where energy conservation is a priority.
Experimental validation involved simulating a battery charging cycle from 4.8V to 5.5V. The test setup included an RC model with R=0.1Ω and C=5000mF to emulate electric vehicle battery characteristics. Key results are summarized in Table 2:
| Parameter | Threshold Voltage (V) | Response Time (ms) | Current Output (A) |
|---|---|---|---|
| Trickle Mode | 5.15 | 9.2 | 0.5 |
| Small-Current Mode | 5.25 | 8.5 | 1.0 |
| Large-Current Mode | 5.54 | 5.0 | 2.8 |
The voltage detection accuracy remained within ±1.5%, and mode switching delays were under 10ms, meeting the design specifications. Overvoltage protection activated at 5.45V, rapidly reducing the current to 0.5A to prevent battery damage. These findings demonstrate the system’s robustness for electric vehicle applications, particularly in dynamic charging environments.
In conclusion, our hardware-based control architecture offers a cost-effective and reliable solution for electric vehicle charging systems. By leveraging analog components and logic circuits, we achieve energy balance and stability without software overhead. The design’s simplicity and high efficiency make it suitable for mass deployment in the China EV sector, supporting the global transition to sustainable transportation. Future work will focus on integrating this system with renewable energy sources and enhancing scalability for fast-charging networks.