With the rapid growth of the electric vehicle industry, driven by policy support, technological advancements, and environmental awareness, functional safety has become a critical focus. The implementation of standards like GB/T 34590—2024 aligns with the unique characteristics of the three-electric systems—battery, motor, and electronic control—in electric vehicles, imposing stringent requirements throughout their lifecycle. As a key component in China EV development, ensuring the functional safety of these systems is essential for vehicle operation and occupant safety. This article analyzes technical points using methods like HAZOP and FMEA to identify failure modes and establish ASIL safety goals, providing theoretical support for engineering challenges.
In the context of electric vehicle innovation, the three-electric systems face risks such as overcharging, overheating, and torque interruptions. We explore hardware and software strategies, including redundancy and algorithmic controls, to mitigate these issues. For instance, in China EV applications, sensor redundancy has demonstrated significant improvements in failure rates, achieving ASIL-B compliance. This analysis not only addresses current industry norms but also sets the stage for future advancements in electric vehicle safety.
Battery System Functional Safety Analysis
The battery system in an electric vehicle is prone to hazards like overcharging, over-discharging, overheating, and short circuits. Utilizing Hazard and Operability Study (HAZOP), we systematically analyze potential deviations in the Battery Management System (BMS), such as voltage/current exceedances, temperature anomalies, and insulation failures. Failure Mode and Effects Analysis (FMEA) quantifies risks for components like battery cells and sensors, assigning ASIL-B/C/D levels. For example, cell overcharging may lead to lithium plating and short circuits, while sensor failures can delay protection responses.
In hardware design, the BMS employs a multi-layer protection architecture. High-precision sensors ensure voltage acquisition errors within $$ \pm 5 \text{mV} $$ and temperature resolution of $$ \pm 0.5^\circ\text{C} $$, coupled with 24-bit ADC for accurate cell monitoring. Isolation techniques, such as magnetic or optical coupling, achieve a common-mode rejection ratio exceeding 100 dB to mitigate electromagnetic interference. Redundant DC-DC converters and supercapacitor backup power sources enable safe shutdown within 30 seconds during main power failure, enhancing reliability in electric vehicle applications.
Software strategies incorporate state machine management, dividing operations into normal, warning, fault, and safe shutdown states. Fusion algorithms, like ampere-hour integration combined with Kalman filtering, estimate State of Charge (SOC) and State of Health (SOH) precisely. The Kalman filter can be represented as:
$$ x_k = F_k x_{k-1} + B_k u_k + w_k $$
$$ z_k = H_k x_k + v_k $$
where $$ x_k $$ is the state vector, $$ F_k $$ the state transition matrix, and $$ w_k $$ and $$ v_k $$ represent process and measurement noise, respectively. Dynamic power limiting modules adjust current thresholds based on cell temperature and cycle count; for instance, charging current reduces by 30% in high-temperature environments. A three-level fuse mechanism includes software thresholds for warnings, hardware comparators for passive balancing, and relay controls for high-voltage interlock disconnection, ensuring comprehensive safety in electric vehicle batteries.
| Failure Mode | Potential Hazard | ASIL Level | Mitigation Strategy |
|---|---|---|---|
| Overcharging | Lithium plating, short circuit | ASIL-C | Dynamic current limiting |
| Sensor failure | Delayed protection | ASIL-B | Redundant sensors |
| Insulation degradation | Leakage current | ASIL-D | Isolation monitoring |
Motor System Functional Safety Analysis
Motor systems in electric vehicles are susceptible to failures such as stall, overload, phase loss, and bearing faults. During stall conditions, rotor speed drops to zero, causing stator current to surge 3–5 times, which can lead to winding burnout. Overload beyond design thresholds for over 10 seconds may result in permanent magnet demagnetization. Phase loss, often due to IGBT open circuits, creates current imbalances exceeding 40%, increasing torque pulsation and mechanical vibration. Bearing failures manifest as lubrication issues or wear, generating noise above 80 dB and rotor eccentricity, potentially causing motor seizure.
Safety goals for motor systems target ASIL-B compliance. Torque limitation cuts power output within 10 ms during stall, while temperature monitoring triggers shutdown if winding temperature exceeds $$ 180^\circ\text{C} $$. Vibration threshold detection activates power reduction when acceleration surpasses $$ 15 \text{g} $$. Insulation monitoring alerts if resistance falls below $$ 20 \text{k}\Omega $$, and leakage protection disconnects high-voltage circuits for currents over $$ 30 \text{mA} $$, ensuring safe contact in electric vehicle operations.
Hardware fault tolerance adopts a “critical component redundancy + fault diagnosis” architecture. Parallel IGBT modules with independent drive circuits maintain 70% rated power during single-point failures, using current-sharing resistors with errors ≤5%. Redundant sensor designs include dual resolvers (angle deviation < $$ 0.1^\circ $$) and triple Hall sensors (speed error < $$ 0.5\% $$), employing a two-out-of-three voting algorithm to enhance rotor position detection reliability in China EV models.
Software control optimizations utilize vector control algorithms with sliding mode observers, reducing torque control accuracy from $$ \pm 5\% $$ to $$ \pm 3\% $$. The sliding mode observer equation is:
$$ \hat{x} = A \hat{x} + B u + K \text{sign}(y – C \hat{x}) $$
where $$ \hat{x} $$ is the estimated state, and K is the observer gain. Dual watchdogs—a hardware watchdog with a 50 ms reset period and a software watchdog for task monitoring—ensure system reset within 20 ms if control programs deviate. Torque ramp control limits rate changes to $$ \leq 50 \text{N·m/s} $$, preventing abrupt power shifts and improving ride smoothness and safety in electric vehicles.
| Component | Redundancy Type | Performance Metric | Impact on Safety |
|---|---|---|---|
| IGBT Modules | Parallel with independent drives | 70% power retention | ASIL-B compliance |
| Sensors | Dual resolver + triple Hall | Voting algorithm reliability | Fault tolerance |
| Control Software | Sliding mode observer | Torque accuracy ±3% | Reduced parameter drift |
Electronic Control System Functional Safety Analysis
Electronic control system failures in electric vehicles often involve signal transmission issues, such as CAN/LIN bus bit error rates exceeding 0.1% or signal delays over 50 ms. Control logic anomalies, like state machine jump errors or priority scheduling混乱, and hardware faults, including MCU register flips or crystal oscillator failures, pose significant risks. For example, throttle pedal signal drift can cause unintended acceleration, with a risk probability of $$ 10^{-6} / \text{h} $$, highlighting the importance of robust design in China EV applications.
Redundancy architecture employs a “dual power + dual communication + dual controller” design. The power module integrates a main source (vehicle battery) and a backup (supercapacitor with capacitance $$ \geq 5 \text{F} $$), supporting a 300 ms safety window during main power failure. Communication systems use dual CAN buses (baud rate 500 kb/s), with signal voting algorithms reducing bit error rates from $$ 10^{-5} $$ to $$ 10^{-8} $$. Critical signals, like brake pedal travel, employ redundant encoding. Hardware optimization involves heterogeneous dual MCUs (e.g., AURIX TC397 and RH850), where the main controller handles real-time control and a monitor runs independent diagnostics, enabling switchover within 100 ms upon detecting faults like software stack timeouts >10 ms.
Fault diagnosis and recovery strategies adhere to ISO 14229, distinguishing temporary faults (stored for 20 drive cycles) from permanent ones (immediately illuminating warning lights). For critical faults, such as brake system communication loss, graded responses include: Level 1 faults entering limp-home mode (speed limited to 30 km/h with single-motor drive), and Level 2 faults activating safe parking procedures (applying 0.3 g braking within 300 ms and high-voltage shutdown in 500 ms). Recovery strategies feature a three-level safety chain—pre-diagnosis, degradation, and shutdown—with torque freeze maintaining output for 2 s to prevent power surges. Bus data from 10 s before and after faults, at 1 ms resolution, is recorded for root cause analysis, enhancing electric vehicle reliability.
| Redundancy Element | Specification | Safety Benefit | Application in Electric Vehicle |
|---|---|---|---|
| Power Supply | Main + supercapacitor backup | 300 ms operation window | Ensures continuous control |
| Communication | Dual CAN buses | Bit error rate reduction | Reliable signal transmission |
| Controllers | Heterogeneous dual MCUs | 100 ms switchover time | Fault tolerance and diagnostics |
Case Study: Pure Electric Commercial Vehicle
In early road tests of a pure electric commercial vehicle, resolver vibration signals caused torque interruptions in the drive motor. Based on ISO 26262 functional safety requirements, we implemented a low-cost redundancy strategy. The hardware solution combined a high-precision resolver (accuracy $$ \pm 0.01^\circ $$) with a Hall sensor (accuracy $$ \pm 1^\circ $$) as a backup, using signal fusion algorithms for real-time validation. Software included fault-switching logic: if the resolver signal deviation exceeded 5%, the system automatically switched to the Hall sensor, limiting torque to $$ \leq 150 \text{N·m} $$ and speed to 40 km/h for safe pull-over.
Hardware-in-the-loop (HIL) testing involved injecting faults like disconnections or drifts, resulting in a switch response time of $$ \leq 20 \text{ms} $$ and torque fluctuation below 10%. A 100,000 km durability test showed the sensor failure rate decreased from $$ 1.2 \times 10^{-4} / \text{h} $$ to $$ 3.5 \times 10^{-7} / \text{h} $$, meeting ASIL-B standards. This heterogeneous redundancy approach—pairing a high-precision main sensor with a low-cost backup—balances cost and performance, offering a scalable model for China EV commercial applications.
| Test Phase | Parameter | Value | Safety Outcome |
|---|---|---|---|
| HIL Testing | Switch response time | ≤20 ms | ASIL-B compliance |
| Durability Test | Failure rate reduction | From $$ 1.2 \times 10^{-4} / \text{h} $$ to $$ 3.5 \times 10^{-7} / \text{h} $$ | Enhanced reliability |
| Real-world Application | Torque and speed limits | 150 N·m, 40 km/h | Safe operational mode |
Conclusion
This analysis addresses failure risks in the three-electric systems of electric vehicles by establishing a functional safety framework through hardware redundancy, software fuses, and comprehensive testing. We have demonstrated how to mitigate issues like overcharging and torque interruptions, with case studies validating the effectiveness of heterogeneous redundancy in reducing failure rates. Future efforts should focus on cross-system coordination, data-driven diagnostics, and adaptation to extreme scenarios to advance the safety and reliability of electric vehicles. As the China EV market expands, these technologies will play a pivotal role in driving industry growth and ensuring high-quality development.
In summary, the integration of advanced sensors, redundant architectures, and algorithmic controls not only meets current standards but also paves the way for innovations in electric vehicle safety. By continuously refining these approaches, we can overcome technical bottlenecks and contribute to a sustainable automotive future.