M.Sc. Electrical & Information Technology · KIT 2025
Electrical engineer working across battery diagnostics, electrochemical modelling, embedded systems, and physics-guided machine learning. My work focuses on extracting health, operational history, and future degradation behaviour from the electrochemical evidence a battery leaves behind.
Every measurement is influenced by chemistry, temperature, operating history, instrumentation, and modelling assumptions. Understanding a battery therefore requires more than prediction. It requires understanding the chain of physical processes that produced the measurement in the first place.
That means following one signal across the full vertical: the electrochemistry that produced it, the hardware that captured it, the firmware that processes it, and the models, both physics-based and machine-learned, that try to explain it. That range isn't scatter. It's the depth a single noisy measurement actually demands.
Disagreement between measurement and prediction is rarely random. It usually points toward missing assumptions, incomplete models, or physical effects that have not yet been accounted for.
Models are only useful where they fail predictably.
Every measurement carries assumptions. Find them before you trust it.
Uncertainty should be quantified, not concealed.
From the molecules inside a cell to a diagnosis running in the cloud. These are layers of the same system, not separate interests.
Every role I took was an answer to a question the last one couldn't close.
How does a real circuit betray its own schematic?
B.Tech Electronics & Instrumentation · Techno India College, Kolkata
Hardware never quite behaves the way the textbook promises, and that gap is where the learning lives. Built robotics, chased the difference between theory and bench, published a first paper before graduating.
Reliability is rarely determined by a single design decision. It emerges from thousands of small ones.
Electrical Design Engineer · Powertech Engineers, India
Schematics, control diagrams, industrial automation, all verified against safety standards. The difference between "functional" and "reliable" became something close to an obsession.
A degree that kept dismantling what I thought I already knew.
M.Sc. Electrical & Information Technology · KIT, Karlsruhe
Every semester raised a question the previous one couldn't answer. It ended in a machine-learning thesis validated inside a real automotive production environment, the framework underneath everything else on this page.
the spine of the storyRace cars are remarkably efficient at exposing bad engineering.
HV-BMS Design · KA-RaceIng (Formula Student), KIT
Designed the high-voltage BMS for an electric race car from scratch: PCB hardware, protection circuitry, embedded C++ firmware, CAN integration, on-track commissioning. Shipped something that had to be right under load, not in a slide.
components → system behaviourThe challenge was no longer measurement. The challenge was determining which measurements could be trusted.
Research Assistant · Energy Storage Systems (IAM-ESS), KIT
First sustained exposure to real electrochemical data: noisy, ambiguous, and refusing to match the model cleanly. Built Python pipelines to chase the disagreement between model and reality.
where measurement & model first disagreedDoes the battery actually do what the spec promises?
e-Drive & BMS Validation · Mercedes-AMG, Affalterbach
System-level HiL / SiL validation of safety-critical BMS and e-drive systems. An exploratory thread, estimating thermal state from impedance spectroscopy with no extra sensor, taught me more about electrochemical measurement than any course.
validation → sharper questionsImpedance spectroscopy became a language rather than a measurement technique.
Battery Algorithms · Mercedes-Benz · Electrochemical Technologies (IAM-ET), KIT
Built ECM-based power and resistance prediction directly from impedance spectra, then ran EIS and polarisation campaigns across batteries and PEM fuel cells. The chemistry changed; the core analytical question did not.
impedance as a languageWhere do physics-based models break, and what does that boundary tell us?
Master's Thesis · Battery SOH, Degradation & RUL · Mercedes-Benz
A hybrid physics-and-ML framework for SOH, degradation-trajectory and RUL estimation across large automotive lifecycle data. The key finding: cells with identical cumulative usage aged differently depending on their history. Path matters. >98% model-measurement correlation, <4% error, inside an ISO 26262 confidence framework.
the answer to everything since IAM-ESSIf a cell arrives with no history at all, can we reconstruct the life it lived?
Retrograde · Independent Research
The thesis proved that path determines a cell's future degradation trajectory. Retrograde asks the inverse, and harder, question: with no documentation and no baseline, can you reason backwards from the cell's present electrochemical state to the operational history that produced it?
Electrochemical forensics for batteries with no past.
The battery may not have a logbook.
That doesn't mean it has no history.
Two batteries can exhibit identical state-of-health estimates while possessing fundamentally different degradation trajectories. Capacity alone does not reveal how a battery arrived at its current condition.
Fast charging, thermal stress, storage behaviour, and cycling intensity may all leave distinct electrochemical signatures despite producing similar present-day health estimates. Every existing diagnostic assumes a known, continuous history. Salvaged cells don't have one.
A salvaged cell isn't a system with missing data; it's a physical record of prior operating conditions. Every stress event left a trace. Some signatures emerge from SEI growth, others from lithium inventory loss, impedance growth, or prolonged thermal exposure. Retrograde explores whether parts of that history can be reconstructed from present-day electrochemical measurements alone.
State of health tells us where a battery is. It says nothing about how it got there, and how it got there determines its future degradation trajectory.
Extract electrochemical fingerprints via HPPC and incremental capacity analysis. Run abductive inference over a physics-scored hypothesis space to reconstruct probable operational history.
Differentiate batteries that appear identical today but will age differently tomorrow. Confidence-bounded output, so the system knows when it doesn't know.
A cell in active observation. Pulse test, ECM extraction, physics scoring: every change in the residual is a question.
Validating against cells with known ground-truth histories, Retrograde correctly recovered cycling, calendar, and shallow-use archetypes. But one cell, CS2_34, was genuinely ambiguous, and the system returned low confidence across every hypothesis rather than forcing a tidy story onto it. Outputs are framed as degradation phases, not precise dates, to keep every claim defensible.
// built with
Each of these teaches the same temperament I bring to engineering: precision, structure, and a refusal to settle for "close enough."
Competing at the Landesmeisterschaft Baden-Württemberg. Olympic precision shooting is its own kind of systems engineering: the nervous system, the breath, the trigger finger, all tuned to fire within a tolerance most instruments can't measure.
Ten metres, one shot, zero room for a hopeful guess.
Writing and performing in indie / alternative. Composition is pattern recognition under constraints, the same instinct that reads a noisy impedance spectrum and hears something useful in it.
Structure you can feel before you can explain it.
Visual communication of complex systems, including this site. Making something technically dense immediately legible is one of the hardest problems I know.
Making the complicated legible is its own kind of engineering.
ESP32 sensor systems, LLM agent integration, and workflow automation pipelines. The constraints of a weekend build are where the most honest engineering decisions get made.
Weekends are where I test the questions the week couldn't answer.
Location Based in Karlsruhe, Germany.
Relocation Open to anywhere in Germany and across Europe.
I am particularly interested in battery diagnostics, battery systems, validation, embedded engineering, and technologies at the intersection of physical systems and intelligent software. If you are working on difficult problems in any of those domains, I would be glad to hear from you.
Write to mearnalisaha@gmail.com→