Master Thesis: Energy-efficient Battery SOC Prediction in Radios Base Stations

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Data Analysis

In short

This Master's thesis at Ericsson focuses on developing energy-efficient AI-based methods for predicting battery State of Charge (SOC) in Radio Base Stations (RBSs). The project involves a literature review, testing machine learning algorithms, implementing techniques, and evaluating a prototype to improve battery performance and energy utilization.

Responsibilities

Conduct a literature review of relevant concepts and algorithms for analysing battery data from RBS power systems.
Investigate and test suitable machine-learning and AI algorithms for SOC estimation.
Select, model, and implement appropriate techniques for different scenarios within the chosen use case.
Develop an end-to-end prototype or proof of concept.
Evaluate the resulting model using relevant battery and operational data.
Analyse the model’s accuracy, computational requirements, energy efficiency, and potential limitations.
Identify opportunities for improving battery management and energy utilisation in RBSs.

Requirements

MSc studies in Computer Science, Mathematics, Physics, Engineering, or a related field.
Excellent programming skills in Python.
Good knowledge of machine learning, including deep learning and unsupervised learning.
An interest in data analysis, prediction, and battery systems.
Experience with machine-learning libraries and frameworks such as TensorFlow, Keras, PyTorch, Scikit-Learn, or Spark.
Strong analytical and problem-solving skills.
An interest in developing end-to-end prototypes and practical concepts.
The ability to work independently while collaborating effectively with others.
Fluency in English.

Desired Qualifications

Knowledge of battery systems, time-series analysis, Docker containers, orchestration systems, or telecommunications is considered an advantage.

Benefits

Outstanding opportunity to use your skills and imagination to push the boundaries of what’s possible.
Build solutions never seen before to some of the world’s toughest problems.
Be challenged, but not alone.
Join a team of diverse innovators.
Work on challenging research problems with real-world impact in a global and innovative environment.
The scope can be adapted to the candidate’s interests and background.

Skills

Pythonmachine learningdeep learningunsupervised learningdata analysisTensorFlowKerasPyTorchScikit-LearnSpark

#Master Thesis#Energy-efficient#Battery#SOC Prediction#Radio Base Stations#AI#Machine Learning#Sweden#Luleå

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