To support quantitative functional safety (FuSa) concept development for an electro-mechanical brake (EMB) system, a fault injection (FI) method based on high-precision CarSim/Simulink co-simulation was employed to determine Automotive Safety Integrity Level (ASIL) ratings. The general development process of FuSa followed ISO 26262, which defines relevant items, and requires a comprehensive analysis of potential failure scenarios. In addition, failure mode analysis was performed using the HAZOP (Hazard and Operability Study) method, and fault tree analysis (FTA) was employed to investigate the root causes of braking function failures. For the risk assessment, thresholds for severity (S, measured by collision type and collision velocity difference), exposure (E, measured by scenario type and corresponding vehicle speed), and controllability (C, measured by time-to-collision, TTC) are defined using a parameterization method. The results shown that FI simulation can provide the real-time vehicle dynamic behavior for all hazardous events such as single or multiple wheel brake failures during EMB system braking, which are then compared against predefined thresholds to determine the severity (S), exposure (E), and controllability (C) levels for each operating condition, and ultimately the ASIL level and corresponding safety goals for EMB FuSa concept were generated. This quantitative assessment using the FI method could provide quantitative support for HARA (Hazard Analysis and Risk Assessment) analysis and subsequent functional safety development.
WangZZhangHWangB, et al. Trade-offs between direct emission reduction and intersectoral additional emissions: evidence from the electrification transition in China’s transport sector. Environ Sci Technol2023; 57(31): 11389–11400.
2.
ShuiBShafiqueMLuoX. Light-duty passenger vehicle electrification in China from 2021 to 2050 and associated greenhouse gas emissions: a dynamic fleet perspective. Transp Res D Transp Environ2024; 130: 104199.
3.
WanTFuHLiX, et al. Assessment of decarbonization pathway for Chinese road transport sector based on transportation-energy integration systems framework. Energy2025; 317: 134727.
4.
ZhangLZhangZWangZ, et al. Chassis coordinated control for full X-by-wire vehicles-a review. Chin J Mech Eng2021; 34(1): 42.
5.
HuaXZengJLiH, et al. A review of automobile brake-by-wire control technology. Processes2023; 11(4): 994.
6.
SchradeSNowakXVerhagenA, et al. Short review of EMB systems related to safety concepts. Actuators2022; 11(8): 214.
7.
ZhangLWangQChenJ, et al. Brake-by-wire system for passenger cars: a review of structure, control, key technologies, and application in X-by-wire chassis. eTransportation2023; 18: 100292.
8.
FangYWangWYangC, et al. Brake-by-wire architecture design and analysis in accordance with functional safety standard. Proc Inst Mech Eng D J Automobile Eng2024; 238(12): 3794–3808.
9.
LiCZhuoGTangC, et al. A review of electro-mechanical brake (EMB) system: structure, control and application. Sustainability2023; 15(5): 4514.
10.
BasuBBursiOSCasciatiF, et al. A European Association for the control of Structures joint perspective. Recent studies in civil structural control across Europe. Struct Control Health Monit2014; 21(12): 1414–1436.
11.
ZhangZLDaiRQZhouCF. The design and simulation test research of electrical mechanical brake. Adv Mater Res2011; 421: 526–531.
12.
YanZChenXYanM, et al. Design and optimization of a novel electronic mechanical brake actuator based on cam. Actuators2023; 12(8): 329.
JoCHwangSKimH. Clamping-force control for electromechanical brake. IEEE Trans Vehicular Technol2010; 59(7): 3205–3212.
15.
KimSHuhK. Fault-tolerant braking control with integerated EMBs and regenerative in-wheel motors. Int J Autom Technol2016; 17(5): 923–936.
16.
ShenYMaoJWuA, et al. Optimal slip ratio tracking integral sliding mode control for an EMB system based on convolutional neural network online road surface identification. Electronics2022; 11(12): 1826.
17.
LiJMaCJiangY. Fuzzy neural network PID-based constant deceleration control for automated mine electric vehicles using EMB system. Sensors2024; 24(7): 2129.
18.
KiYHLeeKJCheonJS, et al. Design and implementation of a new clamping force estimator in electro-mechanical brake systems. Int J Autom Technol2013; 14(5): 739–745.
19.
EumSChoiJParkS-S, et al. Robust clamping force control of an electro-mechanical brake system for application to commercial city buses. Energies2017; 10(2): 220.
20.
WangFChenDCaiY, et al. Nonlinear clamping force estimation and robust control of an electro-mechanical brake system for electric vehicles based on PWA-H∞. IEEE/ASME Trans Mechatron2025; 30: 4341–4352.
21.
ISO. 26262: road vehicles—functional safety. International Organization for Standardization; ISO, 2018.
22.
SongXGriessnigG (eds). Automotive functional safety standardization status and outlook in China. In: Systems, software and services process improvement. Cham: Springer Nature, 2023, pp. 113–124.
23.
LeeKJKiYHCheonJS, et al. Approach to functional safety-compliant ECU design for electro-mechanical brake systems. Int J Autom Technol2014; 15(2): 325–332.
24.
PengJWuTChuL, et al. Electro-mechanical brake system architectural design and analysis based on functional safety of vehicles. Actuators2024; 13(9): 346.
25.
GuzekMJackowskiJJureckiR, et al. Electric vehicles—an overview of current Issues—Part 2—infrastructure and road safety. Energies2024; 17(2): 495.
26.
MuhammadEBehnamgolVValiA, et al. Adaptive MIMO sliding mode control for enhanced vehicle stability with coordinated AFS and DYC systems. Asian J Control2025; 27(3): 1442–1454.
27.
DuKMaBLiJ, et al. Low-complexity control for uncertain time-varying SbW systems with input nonlinearity and dual-channel event-triggering communication. IEEE Trans Intell Transp Syst2025; 26(6): 8992–9003.
28.
LvFHeWZhaoL. An improved nonlinear multibody dynamic model for a parafoil-UAV system. IEEE Access2019; 7: 139994–140009.
29.
MiunskeTHolzapfelCBaumgartnerE, et al. (eds). A new approach for an adaptive linear quadratic regulated motion cueing algorithm for an 8 DoF full motion driving simulator. In: 2019 International Conference on Robotics and Automation (ICRA), 2019. IEEE.
30.
BuiVLeV. Improving ride comfort, stability, and safety of electric vehicles using FFOPID controller. Proc Inst Mech Eng K J Multi-body Dyn. Epub ahead of print 26 September2025. https://doi.org/10.1177/14644193251378235
31.
MashadiBMajidiM. Integrated AFS/DYC sliding mode controller for a hybrid electric vehicle. Int J Veh Des2011; 56(1/2/3/4): 246–269.
32.
MengQSunZChenC. A homogeneous domination control method based on sampled-data output feedback for lateral stability of an intelligent electric vehicle. Asian J Control2023; 25(3): 1851–1865.
33.
HongGWeiWWeiX, et al. Design of electrical/mechanical hybrid 4-redundancy brushless DC torque motor. Chin J Aeronaut2010; 23(2): 211–215.
34.
LeuK-LHuangHChenY-Y, et al. An intelligent brake-by-wire system design and analysis in accordance with ISO-26262 functional safety standard. In: 2015 International Conference on Connected Vehicles and Expo (ICCVE), 2015, pp.150–156.
35.
WuTWangJRongJ, et al. Braking reconstruction control for fault-tolerance in electro-mechanical brake actuator failures. Proc Inst Mech Eng D J Automobile Eng2025; 239: 6455–6470.
36.
DunjóJFthenakisVVílchezJA, et al. Hazard and operability (HAZOP) analysis. A literature review. J Hazard Mater2010; 173(1-3): 19–32.
37.
KabirS. An overview of fault tree analysis and its application in model based dependability analysis. Expert Syst Appl2017; 77: 114–135.
38.
SAE Standard J2980. Considerations for ISO 26262 ASIL hazard classification. SAE International, 2023.
39.
HobbsCAMcDonoughPJ. Development of the European new car assessment programme (Euro NCAP). Regulation1998; 44(3): 2439–2453.
40.
KhastgirSBirrellSDhadyallaG, et al. Towards increased reliability by objectification of hazard analysis and risk assessment (HARA) of automated automotive systems. Saf Sci2017; 99: 166–177.
41.
MaYSunCChenJ, et al. Verification and validation methods for decision-making and planning of automated vehicles: a review. IEEE Trans Intell Vehicles2022; 7(3): 480–498.
42.
DogruguvenEHUstogluI. Augmented Markov model for enhancing hazard rate result of safety critical systems possessing diverse redundant architecture. IEEE Trans Vehicular Technol2021; 70(9): 8606–8616.
43.
KrammerMArmengaudEBourrouilhQ (eds). Method library framework for safety standard compliant process tailoring. In: 2011 37th EUROMICRO conference on software engineering and advanced applications, 2011. IEEE.
44.
TelawiSHayekABorcsokJ. Safe detection of wheels spinning and sliding in vehicles. IEEE Trans Vehicular Technol2022; 71(9): 9410–9421.
45.
Verband Der Automobilindustriee. Situationskatalog E-parameter Nach ISO 26262-3. Technical Report VDA 702. Verband Der Automobilindustriee. V. (VDA), 2015.
46.
KornackiAWawrzosekJBochniakA, et al. Critical values of driver response time and its impact on reducing reliability and safety in road traffic. Eksploat Niezawodn2017; 19(1): 142–148.
