Carlos Jorques

Technical scope

System-level definition of embedded control functions, software boundaries, diagnostics, calibration hooks, and release-ready interfaces.

What I own

I define how control intent, embedded software structure, diagnostics, calibration, interfaces, and validation evidence fit together before implementation hardens around accidental boundaries.

Highlights

• Connected thermal control, battery functions, and charging interfaces into a vehicle-level control boundary.
• Extended propulsion architecture to torque allocation and actuator redundancy for autonomous haulers.
• Linked generated-code boundaries, diagnostics, and test evidence under real-time engine-control constraints.

Methods and constraints

• Requirements-driven functional design, control function decomposition, and architecture reviews.
• Interface definition across software, sensors, actuators, plant models, diagnostics, calibration, and test environments.
• Architecture choices shaped by timing loops, hardware constraints, release maturity, and traceability needs.
• Fallback, degradation, and diagnostic responses designed as part of the control architecture rather than late additions.

Technical scope

Real-time control, virtual sensing, observer design, calibration, model adaptation, and system identification for physical systems.

What I own

I connect models, measurements, calibration, and embedded implementation so estimation and control strategies produce dependable behavior in real machines.

Highlights

• Closed-loop combustion work reduced controlled-parameter dispersion by 25 to 75% in heavy-duty engine experiments.
• Pressure-based virtual sensing cut pilot fuel mass estimation error by 60% and enabled up to 96% misfire detection.
• PMSM thermal-model calibration reached approximately 98% accuracy for electric mobility software.

Methods and constraints

• Feedback control, observer design, virtual sensing, system identification, and model adaptation.
• Calibration and signal analysis under physical operating-envelope constraints.
• Stochastic detection and predictive models reduced to real-time methods that can be calibrated, tested, and explained.
• Experimental validation on engine test benches, prototype systems, and production-oriented embedded workflows.

Technical scope

Production-oriented implementation of embedded control software across generated code, C/C++ integration, calibration, debugging, and CI workflows.

What I own

I turn control concepts and software architecture into embedded software that teams can integrate, validate, calibrate, debug, and release.

Highlights

• Turned thermal-control behavior into software spanning batteries, auxiliaries, power electronics, and charging.
• Implemented traction and speed control across manual and autonomous operating modes.
• Bridged e-bike motor-control models with wireless-charging and DC/DC application interfaces.

Methods and constraints

• Model-based software development, generated C/C++ workflows, application-layer implementation, and ECU integration support.
• Git, GitLab, Jenkins, Jira, CI/CD, code review, debugging, ECU flashing, and issue-resolution workflows.
• Implementation shaped by real-time loops, hardware interfaces, calibration maturity, and production traceability.
• Software structured for testability across simulation, bench, HIL, vehicle, and field-data contexts.

Technical scope

Diagnostic and safety-aware control software for fault detection, controlled degradation, fallback behavior, and reliable physical-system operation.

What I own

My diagnostic work separates physical cause, signal symptom, control response, and degraded behavior, then uses evidence to decide what the system can still do safely and reliably.

Highlights

• Structured fault handling so thermal and battery functions could detect faults and move to defined fallback behavior.
• Applied safety-related monitoring to autonomous propulsion and speed-control functions.
• Used model-based diagnostics to distinguish engine, fuel, oil, and cooling-system faults.

Methods and constraints

• Fault detection and isolation, signal-health monitoring, fallback concepts, degradation logic, and diagnostic strategy design.
• Functional-safety-aware development, ISO 26262 context, A-SPICE traceability, and evidence-led diagnostic decisions.
• Use of empirical data, simulation, HIL, bench, vehicle, and field evidence to support robustness decisions.
• Diagnostic logic designed to support controlled behavior, not only fault reporting.

Technical scope

Validation planning, integration support, calibration maturity, and release evidence across simulation, bench, HIL, vehicle, and field contexts.

What I own

I connect requirements, software behavior, calibration, test environments, issue resolution, and system feedback so embedded control functions mature toward release.

Highlights

• Built a validation chain from model behavior to integrated vehicle evidence for electric truck thermal and BMS functions.
• Moved autonomous-hauler propulsion functions through unit, integration, SIL/HIL, and vehicle evaluation.
• Used bench experiments and data analysis to connect engine-control models with physical behavior.

Methods and constraints

• MIL, SIL, HIL, unit tests, integration tests, test bench, wind tunnel, test track, on-road validation, and field-data analysis.
• Calibration maturity tracking, debugging, issue tracking, ECU flashing, and cross-functional integration support.
• Validation choices shaped by system interfaces, safety-related constraints, hardware availability, and release-readiness evidence.
• Integration feedback used to refine requirements, interfaces, calibration, and software behavior.

Technical scope

Technical direction, mentoring, planning, architecture clarity, and cross-functional delivery support for embedded software teams.

What I own

I help teams make ambiguous engineering work visible: what is known, what is assumed, what must be validated, and which technical choices affect delivery, quality, and long-term maintainability.

Highlights

• Directed a team of 3 embedded software engineers while aligning architecture, planning, quality, and mentoring.
• Created shared decisions across systems, software, validation, suppliers, and stakeholders.
• Connected technical-risk and release-readiness discussions to concrete delivery actions.

Methods and constraints

• Team direction, mentoring, planning, architecture reviews, software-quality support, and delivery coordination.
• Practical trade-off framing across technical risk, validation effort, maintainability, and stakeholder expectations.
• Agile, Scrum, Kanban, SAFe, CI/CD, and client-delivery coordination in embedded automotive environments.
• Architecture and release-readiness discussions translated into concrete team actions.