Steering Dynamics Analysis Framework
Diagnose steering system performance issues using dynamics principles and simulation data
What You Can Do
You can diagnose complex steering performance issues by applying vehicle dynamics principles to interpret simulation data, correlate hardware test results, and identify root causes across mechanical geometry, electrical actuation, and software algorithm layers. This skill guides you through structured troubleshooting of steering feel complaints, EPS calibration validation, and active steering integration challenges, helping you develop data-driven tuning solutions that work across speed ranges and load conditions.
Features
evaluate mechanical steering geometry effects on linearity and feel
diagnose motor response lag, assist curve tuning, and friction compensation issues
compare CarMaker, Carsim, and PreScan predictions against dyno and track test data
optimize steering effort curves, on-center stiffness, and self-centering behavior across drive modes
isolate steering kickback, harshness, and resonance across speed ranges
troubleshoot active steering coordination with base steering and vehicle dynamics control
validate motor torque, gear ratio, and column stiffness specifications against performance targets
Example Output
Example 1: Heavy Steering Complaint Diagnosis
Input: Test driver reports steering feels heavy above 80 km/h; simulation predicts light feel
Output:
- Ackermann compensation analysis shows 2° toe-in drift over steering angle range
- EPS assist curve validation reveals motor current limiting at highway speeds due to undersized motor
- Recommendation: Increase motor rated torque from 3.5 Nm to 4.2 Nm or reduce gear ratio by 8%
- Correlation: Dyno test data confirms 15% motor current reduction post-tuning
Example 2: On-Center Instability Root Cause
Input: Vehicle oscillates ±0.5° steering angle at 60 km/h; Carsim model stable
Output:
- Column stiffness measurement shows 5 N·m/° vs. 8 N·m/° specification
- Software friction compensation analysis indicates insufficient centering force
- Hardware issue identified: bearing preload drift
- Solution: Adjust bearing preload tolerance and increase friction compensation by 0.8 N·m in software
Example 3: Torque Vectoring Integration
Input: Rear axle steer conflicts with ESP, creating jerky feel in corners
Output:
- Coordination matrix shows rear steer command phase lag of 80 ms relative to base steering
- Recommendation: Reduce rear steer authority below 2 Hz yaw frequency by 40% or implement feed-forward algorithm
- Track validation: Lateral acceleration response improves 1.2° peak yaw angle reduction
What's Included
- SKILL.md: complete steering dynamics analysis framework with diagnostic methodologies
- Steering Diagnostics Checklist: structured workflow for isolating steering complaints (feel, response, stability issues)
- EPS Calibration Validation Template: comparison matrix for assist curves, friction models, and speed-dependent tuning
- Simulation Correlation Worksheet: protocols for matching CarMaker/Carsim/PreScan outputs to dyno and track test results
- Active Steering Integration Guide: torque vectoring coordination parameters and conflict resolution strategies
Who It's For
- Steering Dynamics Engineers — diagnose and tune steering system performance across mechanical, electrical, and software domains
- Vehicle Dynamics Test Engineers — correlate simulation models with hardware dyno and track test data
- EPS Calibration Specialists — validate and optimize electronic power steering assist curves and control algorithms
- Hardware Integration Engineers — select motor, gear ratio, and column specifications based on performance targets
- Active Chassis Engineers — troubleshoot torque vectoring and rear axle steering coordination with base steering systems
Best For
- Diagnosing steering feel complaints (heavy, light, dead center, instability)
- Validating EPS calibration and assist curve optimization across speed ranges
- Correlating steering simulation models against dyno and track test results
- Troubleshooting steering response lag, kickback, shimmy, and vibration issues
- Integrating active steering (torque vectoring, rear axle steer) with base steering and vehicle stability control
You might also like
You can systematically analyze suspension geometry, load paths, and component interactions to validate design decisions and identify optimization opportunities. Claude acts as your collaborative design partner to map force distributions, explore how geometry changes affect performance characteristics, compare suspension architectures, and reconcile conflicting engineering constraints in independent double-wishbone, multi-link, and other suspension systems.
You can transform vague customer NVH complaints into actionable diagnostic protocols by systematically mapping vibration sources, transfer paths, and receiver responses. The framework guides you through symptom normalization, operational condition analysis, and isolation strategy development—reducing diagnostic iteration cycles and accelerating root cause identification across powertrain, chassis, interior, and aerodynamic domains.
You can pinpoint the exact source of sensor fusion failures—whether they stem from misaligned camera/LiDAR/radar extrinsic calibration, timestamp desynchronization, data validation gaps, or algorithmic issues. This skill decomposes multi-modal perception errors into sensor-specific contributions and generates quantifiable diagnostic reports that directly guide calibration and testing teams toward fixes rather than blind troubleshooting.
You can efficiently diagnose complex NVH concerns by working through a structured framework that correlates customer symptoms with potential powertrain failures. This skill helps you narrow diagnostic possibilities, recommend targeted testing sequences, distinguish between component failures and tuning issues, and substantiate warranty claims with technical evidence.
You can model transient and steady-state thermal behavior in EV power electronics, calculate junction temperature rise during driving cycles, and design cooling strategies that meet automotive thermal specifications. This skill helps you size heat sinks, evaluate thermal interface materials, compare active versus passive cooling approaches, and validate designs against AEC-Q200 and ISO 16750-3 standards.
You can systematically analyze thermal performance of battery pack designs using heat generation physics, thermal resistance calculations, and boundary condition modeling. The skill helps you identify cooling system inadequacies, assess failure modes like temperature runaway and hot-spot formation, and generate optimized design recommendations across passive air, active liquid, hybrid, and advanced cooling strategies for cylindrical, pouch, and prismatic cell formats.
You can decode SAE J2012 standard CAN diagnostic trouble codes (P, B, C, and U codes) with manufacturer-specific extensions, analyze multi-frame fault sequences and temporal patterns across multiple modules, and generate structured diagnostic hypotheses that differentiate between electrical, communication, and algorithmic failures. This skill transforms raw DTC data into actionable investigation vectors, reducing diagnostic analysis cycles from hours to minutes while improving root cause accuracy.
You can transform raw accelerometer data, sound pressure measurements, and frequency spectra into structured diagnostic insights. Claude helps you organize test data, interpret Campbell diagrams and waterfall plots, quantify source contributions through transfer path analysis, and correlate physical measurements with customer complaints. This systematic approach replaces intuitive troubleshooting with hypothesis-driven test plans and targeted solutions.