About Expertise Projects Posts Contact
bakikocabasa@gmail.com
Ph.D. Candidate at Volkswagen AG

Hi, I'm Baki Kocabasa

Researcher and engineer at the intersection of battery technologies, data science, and computational modeling. Building the future of electric mobility at Volkswagen AG & TU Braunschweig.

View My Work Resume
0+ Years Research
0+ Technologies
0+ Publications
Baki Kocabasa
About Me

Driven by curiosity.
Powered by data.

I'm a Ph.D. candidate at Volkswagen AG in collaboration with TU Braunschweig, focusing on battery field data analysis and the development of non-destructive, mechanistic models to identify the root causes of battery degradation. Through this work, I aim to predict, understand, and ensure battery quality for the future of the automotive industry. My research lies at the intersection of electrical engineering, method development, and computational modeling.

With a Master's in Computational Sciences in Engineering and a Bachelor's in Electrical Engineering, I bring a multidisciplinary approach to solving complex problems in energy systems and machine learning.

Based in Braunschweig, Germany, I'm passionate about leveraging computational methods to advance sustainable transportation and energy technologies.

Education M.Sc. Computational Sciences (TU Braunschweig)
Focus Battery Data Analysis & Modeling
Location Braunschweig, Germany
Expertise

Skills & Technologies

A blend of engineering fundamentals, programming proficiency, and domain expertise in energy systems and data science.

Data Science & ML

Python, TensorFlow, scikit-learn, numerical analysis, statistical modeling, and deep learning for engineering applications.

Python TensorFlow Pandas NumPy

Battery Technologies

Field data analysis, state estimation, degradation modeling, and battery management systems for electric vehicles.

BMS SOC/SOH EV Systems Modeling

Software Engineering

Full-stack development with modern languages and frameworks. Clean architecture, design patterns, and version control.

Java MATLAB Simulink Git

Computational Methods

Numerical analysis, finite element methods, optimization algorithms, and simulation-based engineering design.

FEM Optimization Simulation HPC

Computer Vision

Object detection with YOLO architectures, image processing, and visual recognition systems for industrial applications.

YOLOv5 OpenCV PyTorch CNNs

Energy Systems

Energy management systems, pricing models, renewable integration, and smart grid optimization for sustainable mobility.

EMS Smart Grid Renewables EV
Projects

Featured Work

Selected research projects and engineering work spanning machine learning, energy systems, and computational methods.

Family Tree App
Web App

Interactive Family Tree Application

Bilingual (English/Turkish) interactive family tree visualization. Privacy-focused, offline-capable, built with D3.js.

Explore App
YOLOv5 Object Detection
Deep Learning

YOLOv5 Object Detection on DIOR Dataset

Custom-trained YOLOv5 model for aerial image object detection. Full pipeline from data preparation to evaluation.

Read Case Study
Energy Management System
Optimization

EV Energy Management with Genetic Algorithms

Genetic algorithm-based optimization for electric vehicle energy management. Balancing performance, efficiency, and battery longevity.

View on GitHub
Numerical Methods
Numerical Analysis

Numerical Methods for Value Approximation

Implementation of various numerical methods for engineering value approximation problems. Comparing convergence rates and efficiency.

View on GitHub
Blog

Latest Posts

Thoughts on engineering, technology, and the occasional deep dive into topics I'm passionate about.

Web Dev

Building an Interactive Family Tree

How I built a bilingual, offline-capable interactive family tree application.
Deep Learning

Working with Remote Sensing Data

Spectral band visualization, pansharpening, and NDVI vegetation index computation from satellite imagery.
Deep Learning

MLP for Image Classification

Multi-Layer Perceptron for Fashion MNIST — overfitting, data generators, and spatial invariance limits.
Deep Learning

CNNs for Image Classification

Vanilla CNN architectures on Fashion MNIST — effects of padding, depth, and kernel size on generalization.
Deep Learning

CNN Training: Optimizers, Initializers & Augmentation

Systematic comparison of optimizers, weight initializers, and data augmentation on UC Merced land use.
Deep Learning

Transfer Learning with VGG16

Frozen feature extraction vs fine-tuning for aerial land use classification on UC Merced.
Deep Learning

Explainable AI: Grad-CAM Visualizations

Visualizing what a CNN sees when classifying aerial imagery — opening the black box of deep learning.
Deep Learning

Semantic Segmentation with U-Net

Pixel-wise segmentation on ISPRS Vaihingen aerial imagery with custom DataGenerator and IoU evaluation.
Deep Learning

DeepLabV3+ vs U-Net: Advanced Segmentation

Comparing architectures with data augmentation and focal loss on the ISPRS Vaihingen benchmark.
Deep Learning

Single-Class Object Localization with VGG16

Bounding box regression using a frozen VGG16 backbone for airplane localization on Caltech101.
Deep Learning

Multi-Class Object Detection

Dual-head CNN for simultaneous classification and bounding box regression on Caltech101.
Deep Learning

Custom Object Detection with YOLOv5

Training YOLOv5 on the DIOR remote sensing dataset for aerial image object detection.
Engineering

Numerical Analysis Approaches in Engineering

Data-driven numerical methods for solving complex engineering problems with practical examples.
Lifestyle

The Art of Brewing Excellent Coffee

From Turkish coffee to V60 pour-over: brewing methods and what makes a truly great cup.
Get in Touch

Let's work
together.

Whether you're interested in collaboration, have a question about my research, or just want to connect — I'd love to hear from you.

bakikocabasa@gmail.com LinkedIn GitHub Instagram

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