As an automotive researcher, I have extensively studied the keyless entry system in BYD EV models, focusing on the 2020 BYD Qin EV. This system represents a critical advancement in vehicle security and convenience, leveraging wireless communication technologies to enable seamless access and start-up. In this article, I will delve into the structural composition, control logic, and operational workflow of the keyless entry system in BYD cars, presenting a comprehensive methodology for fault diagnosis and maintenance. The BYD EV keyless system employs a hierarchical control strategy, utilizing CAN and LIN buses for data exchange among modules, and incorporates dynamic encryption mechanisms to ensure robust security. Through this analysis, I aim to provide valuable insights for diagnosing and resolving issues in BYD car anti-theft systems, enhancing both user experience and service efficiency.
The keyless entry system in BYD EV vehicles, often referred to as Kessy, integrates various components to facilitate automatic unlocking and starting. Based on my research, the system primarily relies on radio frequency identification (RFID) technology, with low-frequency (125 kHz) and high-frequency (433.92 MHz) signals for communication. The core elements include the smart key, low-frequency antenna arrays, the Body Control Module (BCM), the Keyless module, and start switches. In BYD cars, this setup ensures that authorized keys are detected within a range of approximately 1.5 meters around the doors and tailgate, with a height detection interval of 0.1 to 1.8 meters. The authentication process involves elliptic curve cryptography to generate temporary session keys, enhancing security against replay attacks. For instance, the Keyless module produces a random challenge value, and the smart key responds with a digital signature using a pre-set private key, dynamically updating session keys via hash-based key derivation functions.
To better understand the components of the BYD EV keyless entry system, I have summarized them in the following table. This breakdown highlights the key elements and their functions, which are essential for grasping the system’s operation and troubleshooting.
| Component | Function | Key Specifications |
|---|---|---|
| Smart Key | Transmits encrypted high-frequency signals for authentication and remote control. | Operates at 434 MHz; includes buttons for lock, unlock, trunk release, and start. |
| Low-Frequency Antenna Arrays | Emits 125 kHz signals to wake up the smart key and detect its presence. | Detection range: ~1.5 m; height: 0.1-1.8 m; includes door and trunk antennas. |
| Body Control Module (BCM) | Processes authentication signals and controls door lock actuators via CAN bus. | Integrated CAN architecture; manages central locking and security functions. |
| Keyless Module | Coordinates LF and RF communication, verifies key codes, and authorizes access. | Located in trunk; uses dynamic encryption; interfaces with BCM via start subnet CAN. |
| Door Handles and Start Switches | Trigger wake-up signals and initiate the authentication process when touched. | Micro-switch based; part of the user interface for seamless entry. |
The control principle of the BYD EV keyless entry system involves a multi-step process that ensures secure and efficient operation. When a user approaches the driver’s door with the smart key, touching the door handle micro-switch activates the system. The Keyless module sends a 125 kHz low-frequency signal to wake up the smart key, which then responds with a 434 MHz high-frequency signal containing an encrypted key code. This signal is verified by the Keyless module, and upon successful authentication, an authorization signal is sent to the BCM via the start subnet CAN bus. The BCM subsequently controls the door lock controller to perform the unlocking action. This process can be modeled using signal transmission equations, where the signal strength $S$ at a distance $d$ from the antenna is given by the formula: $$S = \frac{P_t G_t G_r \lambda^2}{(4\pi d)^2 L}$$ Here, $P_t$ is the transmitted power, $G_t$ and $G_r$ are the gains of the transmitting and receiving antennas, $\lambda$ is the wavelength, and $L$ represents losses. For BYD cars, the low-frequency signal typically has a peak voltage of 18-22 V, ensuring reliable wake-up within the specified range.
In terms of security, the BYD EV keyless system employs a dynamic encryption mechanism to prevent unauthorized access. The authentication process uses a challenge-response protocol based on elliptic curve cryptography. Let $K_{priv}$ be the private key stored in the smart key and $K_{pub}$ the corresponding public key in the Keyless module. When a challenge $C$ is generated randomly by the Keyless module, the smart key computes a response $R = \text{Sign}(K_{priv}, C)$, which is then verified by the Keyless module using $K_{pub}$. The session key $K_s$ for the communication is derived using a hash chain: $$K_s = H(K_{prev} || C)$$ where $H$ is a cryptographic hash function and $K_{prev}$ is the previous session key. This ensures forward security, making it difficult for attackers to compromise past or future sessions. In BYD car systems, this approach mitigates risks such as relay attacks, where adversaries attempt to intercept and amplify signals to gain access.
Fault diagnosis in the BYD EV keyless entry system requires a systematic approach, which I refer to as the “signal traceability, segmental troubleshooting, and data verification” method. This three-stage diagnostic strategy helps isolate issues efficiently, whether they stem from power supply problems, signal interference, or module failures. Based on my experience, common faults in BYD cars include key battery depletion, antenna signal degradation, CAN bus communication errors, and software conflicts. The following table outlines the three stages of this diagnostic method, along with key actions and tools used.
| Diagnostic Stage | Key Actions | Tools and Measurements |
|---|---|---|
| Signal Traceability | Verify signal transmission from smart key to antennas and modules; check for wake-up signals and RF responses. | Oscilloscope for LF/RF waveforms; multimeter for voltage and resistance; key indicator observation. |
| Segmental Troubleshooting | Isolate faults by testing individual components (e.g., antennas, Keyless module, BCM) and their interconnections. | Resistance checks (e.g., antenna coils: 1.2-1.8 Ω); CAN bus terminal resistance (60 Ω ±5%); power supply tests (11-14 V). |
| Data Verification | Validate communication data on CAN buses; confirm authentication success and module responses via diagnostic tools. | CAN analyzer for message logs; oscilloscope for differential voltages (CAN_H: 2.5-3.5 V, CAN_L: 1.5-2.5 V). |
To illustrate the application of this diagnostic method, I will describe a real-world fault case involving a 2020 BYD Qin EV. The vehicle exhibited a failure in the keyless entry system, where touching the driver’s door handle did not unlock the doors, and the remote key functions were inactive. Initially, I performed basic functional tests: pressing the smart key buttons showed that the indicator light flashed normally, suggesting that the key’s battery and RF transmission were operational. This ruled out simple issues like a depleted CR2032 battery, which is a common culprit in BYD EV keyless problems. Next, I moved to signal traceability, using an oscilloscope to measure the low-frequency antenna signals. The expected 125 kHz sine wave with a peak voltage of 18-22 V was absent at the door antennas, indicating a potential issue in the LF wake-up path.
Segmental troubleshooting involved checking the Keyless module and its connections. I measured the power supply at the KG25(A) connector: pin 1 showed a stable 12 V, and pins 9-10 had a ground resistance below 0.5 Ω, confirming adequate power. However, when I examined the start subnet CAN bus between the Keyless module and BCM, the oscilloscope revealed an anomaly in the CAN_L signal. The differential voltage between CAN_H and CAN_L, which should be $$V_{diff} = V_{CAN_H} – V_{CAN_L} \approx 1.0 \pm 0.2 \text{ V}$$ was distorted, with CAN_L showing irregular waveforms. Using a multimeter, I measured the resistance along the CAN_L line from the Keyless module’s KG25(B) pin 6 to the BCM connector and found an open circuit (infinite resistance), pointing to a break in the wiring. This fault aligned with the data verification stage, as the CAN communication was disrupted, preventing authorization signals from reaching the BCM.
To resolve this, I repaired the broken wire in the CAN_L circuit, ensuring proper connectivity. After reassembly, I re-tested the system: touching the door handle now resulted in successful unlocking, and the oscilloscope displayed correct CAN waveforms with $V_{CAN_H}$ at 2.7 V and $V_{CAN_L}$ at 2.3 V, yielding a stable $V_{diff}$ of 1.0 V. This case underscores the importance of the three-stage diagnostic method for BYD car systems, where systematic checks can quickly pinpoint issues that might otherwise lead to broader system failures. In general, the failure rate of keyless entry systems in BYD EV models is low, but when faults occur, they often involve power supply issues, signal interference, or communication errors, which can be efficiently addressed through this structured approach.
In conclusion, my analysis of the BYD EV keyless entry system highlights its sophisticated control principles and the effectiveness of the proposed diagnostic methodology. The integration of hierarchical control, dynamic encryption, and CAN/LIN bus communication in BYD cars ensures high security and reliability. However, as with any electronic system, faults can arise, and the “signal traceability, segmental troubleshooting, and data verification” method provides a robust framework for maintenance. This approach not only enhances the diagnostic process for technicians but also contributes to the overall safety and user satisfaction of BYD EV vehicles. Future advancements in BYD car technology, such as integration with biometric authentication or OTA updates, may further refine these systems, but the foundational principles discussed here will remain relevant for fault diagnosis and system optimization.