With the rapid adoption of electric vehicles and increasingly stringent environmental regulations, the development of hybrid electric vehicles has become a focal point in automotive technology. The integration of new energy components, such as batteries, electric motors, and power control systems, into hybrid EV cars introduces unique challenges for On-Board Diagnostics systems. This paper addresses these challenges by designing an OBD system compliant with China’s National VI emission standards, which mandate comprehensive monitoring of new energy components in hybrid EV cars. The system architecture, fault diagnosis protocols, and remote monitoring capabilities are elaborated, with a focus on optimizing data collection and ensuring compatibility between OBD and Unified Diagnostic Services protocols. Extensive testing, including fault simulation and fleet trials, validates the system’s effectiveness in meeting regulatory requirements while maintaining vehicle performance and reliability.
The National VI emission standards, implemented in China since 2020, expand OBD requirements to include hybrid EV cars, specifically targeting components like battery management systems, electric drive units, and integrated power systems. Traditional OBD systems for internal combustion engines are insufficient for these EV car components, as they involve complex electrical and communication interfaces. This study leverages a hybrid EV car platform to develop an OBD system that integrates new energy components, ensuring real-time monitoring, fault detection, and data reporting. Key innovations include the fusion of OBD and UDS protocols, remote diagnostic capabilities, and a structured approach to fault management. The system not only complies with regulations but also enhances the diagnostic efficiency for EV cars in real-world driving conditions.
The OBD system architecture for hybrid EV cars is designed to minimize hardware changes while maximizing functionality. Based on the existing electronic architecture, the topology distributes new energy ECUs on a dedicated CAN network, connected via a gateway to external diagnostic tools and the engine management system. This setup allows for seamless communication between components, such as the battery management system and electric drive control unit, ensuring that all EV car-specific faults are monitored and reported. The gateway facilitates data exchange between networks, while the EMS acts as the central node for managing the malfunction indicator lamp based on inputs from various controllers. This architecture supports the platform’s scalability for future EV car models, reducing development costs and complexity.
Fault analysis for new energy components in hybrid EV cars involves identifying failure modes that could impact emissions or vehicle performance. Using Failure Mode and Effects Analysis, over 100 system-level and 500 subsystem-level faults were defined, covering sensor rationality, functional failures, communication errors, and I/O issues. For instance, faults in the battery system of an EV car, such as voltage deviations or thermal management failures, are critical due to their potential to cause unexpected engine starts or reduce emission control efficiency. The fault definition process ensures that all monitored parameters align with National VI requirements, focusing on failures that could lead to non-compliance. This comprehensive approach enhances the reliability of EV cars by preemptively addressing potential issues.
| Component | Fault Type | Description | Impact on EV Car |
|---|---|---|---|
| Battery Management System | Voltage Out-of-Range | Battery voltage exceeds safe limits | May trigger engine start or reduce efficiency |
| Electric Drive Unit | Communication Loss | Loss of CAN communication with controller | Could disable regenerative braking |
| Integrated Power System | Overcurrent | Current surge in power electronics | Risks component damage in EV car |
| Vehicle Control Unit | Sensor Rationality | Inconsistent sensor data readings | Affects overall EV car performance |
The OBD protocol development for hybrid EV cars involves defining diagnostic services and fault code management. Services such as $01 (current data), $03 (stored fault codes), and $09 (vehicle information) are implemented across controllers, with the EMS handling critical functions like MIL control. Fault codes are stored in non-volatile memory, with separate categories for pending, confirmed, and permanent faults. For example, a B-class fault in an EV car component requires two driving cycles for confirmation, ensuring that transient issues do not unnecessarily trigger warnings. The freeze frame data capture two sets: one at fault occurrence and one pre-failure, with a time interval t defined based on data transmission cycles. This dual capture enhances fault analysis efficiency for EV cars, as described by the equation for freeze frame storage: $$ \text{Freeze Frame} = \{ \text{Data}_{t-\Delta t}, \text{Data}_t \} $$ where $\Delta t$ is the configurable interval based on hardware constraints.
Integration of OBD and UDS protocols is crucial for hybrid EV cars to avoid redundancy and optimize resource usage. Both protocols share diagnostic IDs, such as 0x7DF for functional addressing, and fault code storage. The UDS service $14 (clear diagnostics) can erase OBD fault codes, while OBD service $04 is restricted to OBD-related codes, ensuring compatibility. The fault status mask combines bits from both protocols; for instance, bit 0 indicates test failure, bit 3 confirmed DTC, and bit 5 failure since last clear. This fusion simplifies software development for EV cars, as shown in the status bit usage table. The relationship between OBD and UDS fault codes is linear, with shared source codes but independent storage, facilitating platform-wide implementation for various EV car models.
| Service ID | OBD Protocol | UDS Protocol | Supported Controllers in EV Car |
|---|---|---|---|
| $01 | Current Data | Equivalent to $22 | EMS, VCU, BMS, etc. |
| $03 | Stored DTCs | Similar to $19 | All new energy ECUs |
| $09 | Vehicle Info | Custom in UDS | EMS only for VIN |
| $14 | N/A | Clear DTCs | Can clear OBD codes |
Remote OBD diagnosis for hybrid EV cars enables efficient data collection for regulatory compliance, such as In-Use Performance Ratio reporting. Using a Telematics Box, the system periodically reads OBD data like IUPR and calibration codes via service $09, transmitting it to a cloud platform. This approach eliminates the need for physical workshops, reducing costs for EV car manufacturers. The data acquisition process is triggered every 1000 km, with TBOX sending a broadcast request and caching responses from ECUs. The IUPR value, representing the frequency of diagnostic monitoring, is calculated as: $$ \text{IUPR} = \frac{\text{Number of Monitoring Completions}}{\text{Number of Driving Cycles}} $$ This metric ensures that EV cars meet the National VI requirement of representative data from at least 15 vehicles over 12 months, covering diverse driving conditions and temperatures.
Testing and validation of the OBD system in hybrid EV cars involve fault simulation, special condition tests, and fleet trials. Fault injection uses break-out boxes to simulate faults without damaging components, such as open circuits or signal deviations. For example, disconnecting a sensor in an EV car’s battery system triggers a fault code, which is verified through diagnostic tools. Fleet tests are conducted in varied environments—normal, high-temperature, low-temperature, and high-altitude—using routes based on China Light-duty Vehicle Test Cycle. The driving cycles include urban, suburban, and highway segments to replicate real-world usage of EV cars. Results show that the system detects over 300 faults across components, with optimized thresholds to prevent false MIL activations. The table below summarizes test outcomes, demonstrating the system’s robustness for EV cars in complying with emissions standards.
| Controller | Services Tested | Total DTCs | Tested DTCs | Pass Rate |
|---|---|---|---|---|
| EMS | $01, $03, $04, $07, $09, $0A | 162 | 38 | 100% |
| BMS | $01, $03, $04, $07, $09, $0A | 42 | 42 | 100% |
| VCU | $01, $03, $04, $07, $09, $0A | 144 | 125 | 100% |
| IPS | $01, $03, $04, $07, $09, $0A | 162 | 100 | 100% |
The development of the OBD system for hybrid EV cars under National VI standards highlights the importance of integrating new energy components. The architecture and protocol designs ensure compatibility and efficiency, while remote diagnostics facilitate cost-effective compliance. Testing confirms that the system meets all regulatory requirements without compromising vehicle performance. Future work could focus on enhancing predictive diagnostics for EV cars using machine learning, further improving the sustainability and reliability of hybrid electric vehicles. This approach sets a precedent for next-generation OBD systems in the evolving landscape of EV cars.