Open models
Execute the engineering assumption.
MATLAB and Python references for battery thermal response and DC-link sizing, with tests and limits in view.
Open model libraryElectrical R&D · Power Electronics · Agentic Engineering
Mohammad Rezwan Khan
I turn battery thermal behavior, power-conversion constraints, and QA requirements into auditable engineering decisions.
Mohammad Rezwan Khan is an Electrical R&D Engineer and an Aalborg University PhD in Energy Technology. His work connects battery thermal systems, BESS QA/QC, FAT/SAT readiness, grid-forming control, and MATLAB/Simulink model validation.
- Battery systems
- Power electronics
- BESS assurance
- Agentic AI
Inspectable work
Start with something you can examine.
Models, quality artifacts, and AI workflows carry visible assumptions, boundaries, and source paths.
BESS QA/QC
Trace acceptance evidence.
A sanitized path from requirements through FAT/SAT, NCR closure, commissioning, and handover.
Open artifact libraryAgentic Engineering
Keep the agent accountable.
A curriculum draft for tool use, automated checks, visible failures, and explicit engineer approval.
Review the syllabusOpen engineering models
Equations are more useful when they can be executed and challenged.
The catalogue connects audited open-source projects and focused model views to pinned code, tests, research context, and explicit limits.
Battery · converters · BESS control · MATLAB/Simulink
MATLAB Simulink Energy Lab
RC → SOC-EKF → electro-thermal → BESS control
Explore the validated labBattery thermal · MATLAB/Python reference
Battery thermal response
Cth dT/dt = I²R − hA(T − Tamb)
Inspect thermal assumptionsPower electronics · MATLAB/Python reference
DC-link ripple and capacitance
Cmin = 2E / (V₁² − V₂²)
Inspect sizing assumptionsBattery degradation · analysis software · Python
Battery Cycle-Life Analyzer
capacity fade → model comparison → bounded EOL
Compare degradation modelsBattery thermal · research translation · Python
Battery Thermal Modeling Notes
source → equation → interval → engineering decision
Trace the thermal evidenceGrid storage · research benchmark · Python
VoltRL
statet + informationt → policy → value
Inspect the benchmark protocolBESS control · validated MATLAB/Simulink model
Grid-tied and grid-forming control
P, Q, V, f → modes, limits, verified transitions
Review the validated controllerGrid services · executable playbook · Python
Smart Grid Storage Playbook
P, Q, f, V, SoC → constraint-aware dispatch
Explore grid-service limitsBESS delivery · QA/QC toolkit · Python/JSON
BESS QA/QC Toolkit
requirement → evidence → gate → acceptance
Inspect the readiness gateExplore the complete open-engineering library Explore browser laboratories
Engineering decisions
What had to be decided, and what evidence made the decision possible.
Employer-and-date context stays visible; confidential project data stays private.
Utility-scale ESS/EPC review
- Situation
- Bidder assumptions and interfaces needed a common review baseline.
- Action
- Structured sizing inputs, technical responses, and risk ownership.
- Result
- A decision-ready review trail before procurement and execution.
BESS QA/QC and acceptance
- Situation
- Acceptance evidence was distributed across suppliers and milestones.
- Action
- Linked criteria, FAT/SAT evidence, NCR status, and commissioning records.
- Result
- A traceable handover path with visible ownership and closure status.
Human-supervised AI
AI should accelerate engineering work without obscuring responsibility.
The workflow exposes the objective, approved tools, generated artifacts, checks, stop conditions, and accountable reviewer.
Bounded input
Define what the agent may and may not decide.
Record the objective, system boundary, available evidence, prohibited actions, stop conditions, and approval authority.
Illustrative workflow scenario
Illustrative scenario: BESS thermal safety review
-
Proposed — The engine suggested a conservative cooldown profile and a revised current envelope for a candidate pack design.
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Illustrative check — In this hypothetical scenario, checks flag two edge cases outside acceptance bounds, including edge-zone hotspot growth in case 4; no test was run and no evidence package is implied.
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Engineer review — The engineer withholds acceptance because the illustrative residuals do not meet safety bounds and requests revised boundary conditions and another validation run.
Illustrative outcome: Human approval remains required; this is not an executed pilot, test result, or field outcome.
See the illustrative workflowPublished research
Research earns another life when its assumptions can be inspected in code.
The DOI establishes the scientific record. The implementation exposes selected equations, assumptions, tests, and engineering limits.
- DOI
- Question
- Equation
- Implementation
- Test
- Known limits
Research software
Inter-cell heat exchange → deterministic 3-D graph evidence
Inspect ThermoWeave-3DThermal systems
Spatial temperature evidence → reduced-order sensitivity model
Verify publisher recordState estimation
Measured behavior → online identification and SoC estimation
Verify publisher recordBESS control
Operating modes → controller requirements and scenarios
Verify publisher recordTechnical conversation
Bring one model, requirement, or review bottleneck.
Send the system boundary, available evidence, and decision deadline. I will reply with the most relevant model, case study, or technical next step.
Electrical R&D Engineer | Battery Systems | BESS QA/QC
Mohammad Rezwan Khan
I am Mohammad Rezwan Khan, an Electrical R&D Engineer with a PhD in Energy Technology from Aalborg University.
I connect battery measurements, control models, and QA records so teams can expose risk before design approval, FAT/SAT, or EPC handover.
Quick orientation
Engineering scope at a glance.
Problems I work on
- BESS QA/QC and acceptance readiness
- Battery thermal behavior and modeling
- Grid-forming BESS control validation
Public evidence
Where I plug in
- FAT/SAT and commissioning readiness
- EPC bidder and interface review
- MATLAB/Simulink model validation
Engineering proof
What the decision system looked like in practice.
The engineering decisions are visible; confidential project data stays protected.
Vestas · 2018-2019
Utility-scale ESS / EPC review
Make scope and bidder assumptions reviewable before commitment.
- Situation
- ESS sizing, interfaces, RFP responses, and LCoE trade-offs were distributed across review inputs.
- Action
- Structured requirements, assumptions, interfaces, and risk items for cross-functional bidder review.
- Result
- Clarified technical gaps and decision ownership before procurement and execution.
TotalEnergies · 2019-2022
BESS QA/QC / FAT-SAT / NCR
Connect acceptance criteria to inspection, closure, and handover.
- Situation
- FAT/SAT, NCR, punch-list, warranty, and handover records needed a common review path.
- Action
- Aligned evidence items, acceptance criteria, owners, status, and closure decisions.
- Result
- Created a repeatable QA/QC structure for multidisciplinary review.
Research translation
Models become useful when they change an engineering decision.
These visuals connect measured battery behavior and model-based development to cooling, control, validation, and review choices.
Published multiphysics model
Thermal evidence
Airflow sensitivity before pack-level cooling decisions.
A pouch-cell module model translates localized temperature behavior into evidence for cooling architecture and operating limits.
Open the publisher recordFeatured publications
Four research records with direct engineering relevance.
Each selection links a technical method to the battery, BESS, or control decision it can support.
Journal of Power Sources · 2014
Online state-of-charge determination using combined system identification.
- Method
- System identification and nonlinear optimization.
- Decision value
- More defensible real-time battery monitoring and control.
Batteries · 2017
Towards an ultimate battery thermal management system.
- Method
- Critical review of cooling and thermal-management methods.
- Decision value
- Selection criteria for battery safety, life, and performance.
IEEE IECON · 2022
Unified control for grid-tied and grid-forming BESS.
- Method
- Shared controller framework across operating modes.
- Decision value
- Clearer BESS control behavior across grid conditions.
IEEE EVER · 2016
Multiphysics thermal modeling of a pouch lithium-ion cell.
- Method
- Coupled heat-transfer and fluid-flow simulation.
- Decision value
- Pack cooling design grounded in spatial temperature evidence.
Impact & metrics
Research record with public source links.
Public sources linked
Research reach
20,879+Reads listed on the public ResearchGate profile
Public profile link for battery thermal management, BESS, smart-grid, and SoC-estimation work.
Source checked 9 July 2026.
Citation signal
648Google Scholar citations
Refreshed nightly through the SerpApi Google Scholar Author API; the linked Scholar profile remains the source of truth.
Cached Scholar snapshot; open the source for the current count.
Publication base
17Publications listed on ResearchGate
Includes calorimetry, pouch-cell thermal modeling, BTMS design, SoC estimation, and grid-forming BESS control.
Source checked 9 July 2026.
Profile trail
Public profiles for cross-checking authorship and research scope
Use these links to verify works, citations, co-authors, and research themes without relying on the site alone.
Quick facts
Engineering value, clearly stated.
Four direct answers for engineering leaders, recruiters, and research collaborators.
What do I engineer?
I develop and validate battery, BESS, and smart-grid systems. My focus includes thermal management, model-based control, MATLAB/Simulink validation, FAT/SAT readiness, and QA/QC traceability.
What decisions do I make easier?
I turn fragmented evidence into reviewable decisions. I connect requirements, test cases, interfaces, NCR ownership, and handover records across R&D and EPC delivery.
How can you verify my work?
Use the public source that matches your review. Verify authorship through ORCID, citations through Google Scholar, and publication context through ResearchGate or DOI-backed records.
How can we work together?
I am available for senior R&D, BESS QA/QC, validation, and technical-review missions. Share the decision, evidence gap, and timing.
Engineering perspective
Why I build traceable decision paths.
The hardest project friction I have seen rarely begins with a missing calculation. It begins when assumptions, test evidence, owners, and acceptance decisions stop connecting.
I structure engineering records so the next reviewer can understand what changed, why it changed, and what evidence supports the decision without reconstructing the project from memory.
Human impact: clearer handovers reduce avoidable rework and help multidisciplinary teams make accountable decisions under delivery pressure.
Technical philosophy & research
Battery systems need test data before scale-up decisions.
My work uses calorimetry, multiphysics thermal modeling, SoC estimation, FMEA, and SIL/HIL validation to make battery behavior testable before scale-up.
THERMAL MANAGEMENT
BATTERY SAFETY
REAL-TIME SOC ESTIMATION
POWER ELECTRONICS
THERMAL MANAGEMENT
BATTERY SAFETY
REAL-TIME SOC ESTIMATION
POWER ELECTRONICS
GRID-FORMING STORAGE
AUTONOMOUS ENGINEERING
HYBRID POWERTRAIN
DIGITAL TWINS
GRID-FORMING STORAGE
AUTONOMOUS ENGINEERING
HYBRID POWERTRAIN
DIGITAL TWINS
CALORIMETRIC MEASUREMENT
MODEL-BASED CONTROL
RENEWABLE INTEGRATION
MISSION-DRIVEN R&D
CALORIMETRIC MEASUREMENT
MODEL-BASED CONTROL
RENEWABLE INTEGRATION
MISSION-DRIVEN R&D
Profile
Battery R&D, delivery discipline, and traceable technical work.
Mohammad Rezwan Khan is an Electrical R&D Engineer working across battery systems and model-based validation. He was awarded a PhD in Energy Technology by Aalborg University, Denmark, for research on thermal management of battery systems for electric-vehicle and smart-grid applications. He is preparing to launch an educational app development company in the UK; the venture is planned and not yet operating.
Predict and improve performance before it becomes reliability risk in EV and grid-connected battery systems.
Shape BESS control logic that can be reviewed, tested, and documented across operating modes.
Turn R&D outputs into delivery choices across hybrid powertrain, renewables, and power electronics.
Achievement stories
Challenge, action, result.
Selected examples framed the way engineering managers evaluate work: what was unclear, what I did, and what became easier to decide.
Battery research
Thermal behavior before scale-up
- Challenge
- Battery packs need thermal evidence before design teams commit to cooling architecture, control limits, or lifetime assumptions.
- Action
- Used calorimetry, multiphysics modeling, SoC estimation, and FMEA to connect measured behavior with design decisions.
- Result
- Created research outputs and public publications visitors can verify through ResearchGate, Google Scholar, and ORCID.
Utility-scale ESS
Procurement decisions with fewer blind spots
- Challenge
- EPC bidder reviews become risky when scope, interfaces, assumptions, and LCoE trade-offs are scattered across documents.
- Action
- Supported ESS sizing, RFP review, due diligence, technical specifications, and bidder comparison for hybrid energy projects.
- Result
- Made scope gaps, supplier inputs, and decision points easier to review before procurement and delivery commitments hardened.
BESS QA/QC
Inspection records that help handover
- Challenge
- FAT/SAT, NCRs, punch lists, commissioning gates, and warranty feedback can disconnect when quality is treated as paperwork.
- Action
- Structured inspection records, acceptance criteria, NCR status, review cadence, and handover-ready documentation.
- Result
- Linked the case study to a sanitized traceability matrix so visitors can inspect the working method, not only the claim.
Expertise pillars
Battery, grid, and power electronics work grounded in testable methods.
Hybrid Electric Powertrain
Optimizing subsystem architecture and energy flow for next-generation electric mobility.
Smart Grid
Deploying intelligent control and resilient storage strategies for grid-forming BESS.
Renewable Energy Systems
Maximizing PV-battery integration and storage flexibility for utility-scale delivery.
Power Electronics
Engineering robust converters and fault-detection systems for uncompromised power quality.
AI for Energy Systems
Applying digital twins and predictive modeling to optimize battery thermal behavior and grid response.
Core capabilities
Skillset
BESS roadmap, QA/QC, FAT/SAT, NCR tracking, MATLAB/Simulink, and project controls for energy-system delivery.
Strategic Project Leadership
- Portfolio and program management
- Cross-functional technical leadership
- International stakeholder coordination
- BESS roadmap and specification planning
- CAPEX/OPEX and quality-cost awareness
- Risk assessment and mitigation
- KPI tracking and decision logs
- Change and escalation management
Technical Innovation & R&D
- Hybrid electric powertrain systems
- BMS, BTMS, and battery safety logic
- MATLAB/Simulink and model-based design
- RCP, SIL/PIL/HIL validation workflows
- Power electronics and converter systems
- Smart-grid and renewable integration
- AI-assisted energy-system analytics
- Systems engineering and prototyping
Project Management Tools
- Jira and Confluence delivery boards
- MS Project and milestone planning
- PLM and PDM collaboration systems
- GitHub and version-control workflows
- Simulation and test documentation tools
- Requirements and traceability matrices
- Dashboard, reporting, and KPI packs
- Microsoft Office technical documentation
End-to-End Project Excellence
- Agile, waterfall, and V-model methods
- FMEA and quality-assurance frameworks
- FAT/SAT and commissioning readiness
- QA/QC inspection and NCR systems
- Supplier interface and warranty feedback
- Technical handover documentation
- Regulatory and standards alignment
- Cross-disciplinary problem solving
BESS QA/QC lead asset
Start with the checklist. Request the full matrix when you need depth.
Download the one-page readiness checklist without sharing an email. For a working CSV structure covering FAT/SAT evidence, NCR ownership, closure, and handover, use the professional request.
Instant PDF · No email required BESS QA/QC readiness checklist One page of pre-FAT/SAT checks for requirements, test evidence, NCR closure, commissioning, and handover. Download checklist
Full matrixBESS QA/QC traceability structureSanitized fields for inspection and handover control.
Featured case studies
Utility-scale ESS execution and BESS QA/QC under real project constraints.
Two focused examples tied to the career timeline: EPC bidder review and QA/QC traceability for battery energy storage work.
Work-email access
Sanitized BESS QA/QC traceability matrix
Open the access request for the FAT/SAT, NCR, owner, closure, and handover sample.
Sanitized CSVWork-email requestNo background tracking
Representative schematic
01
Utility-scale EPC Project Leadership
Coordinating high-stakes energy delivery from system intent to execution readiness.
- Situation
- EPC review becomes fragile when sizing assumptions, supplier interfaces, RFP responses, and LCoE trade-offs are not traceable.
- Task
- Energy storage specialist supporting utility-scale ESS sizing, due diligence, technical specifications, and bidder proposal review.
- Action
- Structured EPC review around requirements, interfaces, risk registers, assumptions, and decision logs for cross-functional review.
- Result
- Gives engineering managers a clear review trail before procurement, site execution, and commercial commitments harden.
Representative schematic
02
QA/QC Systems Implementation
Building inspection workflows for FAT/SAT, NCR, and handover control.
- Situation
- BESS and regulated energy projects need traceable records across FAT/SAT, commissioning gates, punch lists, and warranty feedback.
- Task
- Quality-system builder connecting inspection discipline with engineering feedback loops and supplier accountability.
- Action
- Standardize inspection records, NCR tracking, acceptance criteria, review rituals, and handover-ready documentation.
- Result
- Connects inspections, NCR status, punch-list closure, and handover files in one review trail.
- Artifact
- Sanitized BESS QA/QC traceability matrix available through the work-email access request above.
Career journey
Mapping the Projects, Missions, and Partnerships that Shape My Career.
A CV-derived view of R&D, BESS portfolio strategy, utility-scale EPC evaluation, QA/QC systems, and battery-grid research milestones.
June 2022 - Present
Electric Engineer, France
Advancing model-based design for hybrid power systems through RCP architecture, HMI development, simulation-based testing, and CI/CD workflows for MATLAB, Simulink, and RCP environments.
November 2019 - June 2022
R&D Engineer, TotalEnergies France
Built strategic BESS portfolio intelligence across standards, market requirements, customer needs, technical specifications, warranty cases, quality cost drivers, sizing, O&M, and decarbonization roadmaps.
May 2018 - May 2019
Energy Storage Specialist, Vestas Wind A/S Denmark
Led energy storage engineering inside hybrid solutions, covering degradation modelling, utility-scale ESS sizing, technical specifications, EPC bidder proposal review, RFP support, due diligence, and LCoE-driven value engineering.
2017 - 2018
Senior Engineer, Beyonder AS Norway
Led BMS and BTMS technology work for electric vehicle, heavy transport, and maritime storage applications, combining ISO 26262 functional safety, HIL verification, quality assurance, and partner-backed project planning.
2013 - 2017
PhD Researcher in Energy Technology, Aalborg University Denmark
Awarded a PhD in Energy Technology for research on thermal management of battery systems for EV and smart-grid applications, including cell characterization, multiphysics modelling, SIL/HIL testing, and FMEA.
June 2011 - August 2013
Research Employee, VITO Belgium
Developed battery-grid integration research, online SoC estimation, nonlinear parameter estimation, Kalman filtering, battery characterization, regression test campaigns, and multidisciplinary QA practices for battery systems.
Visual engineering briefs
Four perspectives on research, systems work, and industrial delivery.
Each face connects a career chapter to the engineering methods, decisions, and outcomes behind it.
01 / 04
BESS strategy
TotalEnergies BESS strategy
Market intelligence, product strategy, technical operations, and cross-functional leadership.
Technical philosophy
How I think when systems get complex.
I prefer requirement IDs, test logs, acceptance criteria, and versioned handover files over broad claims.
Point of view 01
Battery performance is measured across load, heat, and time.
Thermal management, BMS logic, power electronics, enclosure design, and operating context have to be evaluated as one coupled risk picture.
Point of view 02
QA/QC is engineering memory.
A strong inspection system connects deviations, NCR owners, punch-list closure, and handover files.
Point of view 03
AI should improve diagnosis, sizing, forecasting, or simulation.
I look for AI use cases that improve diagnosis, sizing, forecasting, simulation, or traceability, not surface-level automation.
Next step
Put one difficult engineering decision on the table.
Send the system context, available evidence, and decision deadline. I will reply with the relevant method, case, or next question.