Steering System Dynamics Analyzer
Analyze and optimize steering dynamics, feedback, and torque vectoring effects
What You Can Do
You can analyze multi-dimensional steering system performance including torque characteristics, self-alignment torque, steering angle feedback, road feel transmission, and torque vectoring effects. Extract actionable insights from simulation and test data to identify root causes of steering feel issues, then develop tuning recommendations for steering gear ratios, EPS algorithms, suspension geometry effects, and torque vectoring control strategies.
Features
evaluate steering effort curves and assist mapping across speed ranges
quantify road feedback and on-center handling behavior
analyze transient response and linearity to inputs
predict handling balance changes from differential braking or active steering
review electronic power steering assist maps and damping tuning
determine how geometry changes affect steering performance
trace noise, vibration, and harshness to steering feedback transmission paths
produce prioritized engineering actions with expected outcomes
Example Output
Example 1: Steering Feel Complaint Analysis
Input: Simulation data showing heavy steering at low speeds, customer feedback on parking difficulty Output:
- Root cause identified: EPS assist curve insufficient below 10 km/h
- Recommendation: Increase assist gain by 15% in 0–15 km/h range
- Expected outcome: Parking effort reduced from 8 N·m to 5 N·m at 0 km/h
- Risk assessment: Slight instability risk on high-friction surfaces; recommend electronic stability control tuning review
Example 2: Torque Vectoring Impact Assessment
Input: Proposed torque vectoring control strategy with 500 N·m differential braking capability Output:
- Handling effect: +15% turn-in responsiveness, +8% mid-corner stability
- Steering feel impact: Reduced understeer gradient from 2.1 to 1.6 deg/g; on-center feel sharpened
- Tuning action: Adjust EPS return torque algorithm to prevent steering wheel oscillation during vectoring events
- Validation gates: Subjective testing on wet skidpad, high-speed lane-change stability assessment
What's Included
- SKILL.md instruction file: core methodology and analysis framework
- Steering dynamics simulation checklist: parameters to extract and validate from CAE models
- Torque vectoring tuning matrix: control logic mapping for differential braking/steering interventions
- EPS assist curve optimization template: structured approach to speed-dependent assist mapping
- On-center handling evaluation worksheet: quantitative metrics for steering feel assessment
Who It's For
- Vehicle Dynamics Engineers developing steering system performance specifications
- Calibration Engineers tuning electronic power steering and torque vectoring algorithms
- Suspension Engineers evaluating steering geometry effects on vehicle handling
- Simulation Engineers analyzing CAE steering dynamics data and translating results to physical testing
- Test Engineers diagnosing steering feel issues and validating tuning recommendations
Best For
- Steering system tuning before prototype validation and design freeze
- Root cause analysis of steering feel complaints and NVH issues
- Torque vectoring control strategy development and impact prediction
- EPS algorithm optimization across speed ranges and driving conditions
- Steering performance benchmarking across vehicle development iterations
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.