Thesis: Machine Learning Based Estimation of Battery State of Health
Scania GroupRekommenderat
Sammanfattning
This temporary full-time position focuses on developing machine learning models to estimate the state of health (SOH) of lithium-ion batteries using laboratory test data. The role is based in Södertälje, Sweden, and involves analyzing voltage, temperature, impedance, and resistance measurements to improve estimation accuracy and generalization to various test conditions. Candidates will contribute to research that enhances battery performance assessment and maintenance planning.Jobbet i korthet
Arbetstid
heltid/deltid
Det här erbjuder vi
Opportunity to work on cutting-edge research in battery technology.Gain experience in machine learning applications in engineering.Collaborate with experts in the field.
Ansök senast: Öppet tillsvidare
Publicerad: 2026-11-12
Beskrivning
30hp - Machine Learning Based Estimation of Battery State of Health Using Voltage, Temperature, Impedance, and Resistance Measurements
Background and motivation
Reliable estimation of battery state of health (SOH) is important for assessing battery performance, planning maintenance, and supporting decisions about continued use and replacement. This proposal focuses on lithium-ion batteries tested under controlled laboratory conditions.
Battery testing produces electrical and thermal measurements that may contain useful information about degradation. These include voltage and current profiles, temperature measurements, internal resistance, and electrochemical impedance spectra. Machine learning offers a way to identify relationships between these measurements and battery health.
Problem statement
The proposed thesis will address the challenge of estimating battery SOH from test measurements collected under different operating and diagnostic conditions.
A central difficulty is distinguishing degradation from reversible changes associated with temperature, state of charge (SOC), and measurement conditions. A model may perform well on familiar test conditions while producing inaccurate estimates for an unseen battery or temperature range. The study will therefore evaluate both estimation accuracy and generalization to batteries and test conditions excluded from model training.
The research contribution will be an experimentally evaluated comparison of measurement combinations and machine learning methods, with particular attention to temperature effects, measurement requirements, and validation on unseen cells.
Aim and objectives
The aim is to develop and evaluate machine learning models that estimate battery SOH using laboratory test data, with emphasis on voltage, temperature, impedance, and resistance measurements.
The Objectives are to:
• Prepare a consistent dataset linking diagnostic measurements, test conditions,
and reference capacity measurements.
• Identify electrical and thermal features or health indicators associated with
battery degradation.
• Compare selected machine learning methods against simple baseline models.
• Quantify the additional value of voltage, temperature, resistance, and impedance
information.
• Evaluate model performance on unseen cells and test conditions.
• Assess prediction uncertainty and the practical measurement requirements of the
resulting models.
Research questions
1. How accurately can battery SOH be estimated from voltage, temperature,
impedance, and resistance measurements?
2. Does combining these measurements improve estimation compared with using
each measurement type separately?
3. How do measurement temperature, SOC, and ageing conditions affect model
accuracy?
4. How well do the models generalize to unseen batteries and operating conditions?
5. Which measurements provide the best balance between estimation, accuracy
and diagnostic effort?
Contact persons and supervisors
Traton Supervisor: Venu Gopal Prathimala venu-gopal.prathimala@scania.com
Application:
Your application must include a CV, personal letter and transcript.
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
Requisition ID: 35135
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country / Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: On-site
Background and motivation
Reliable estimation of battery state of health (SOH) is important for assessing battery performance, planning maintenance, and supporting decisions about continued use and replacement. This proposal focuses on lithium-ion batteries tested under controlled laboratory conditions.
Battery testing produces electrical and thermal measurements that may contain useful information about degradation. These include voltage and current profiles, temperature measurements, internal resistance, and electrochemical impedance spectra. Machine learning offers a way to identify relationships between these measurements and battery health.
Problem statement
The proposed thesis will address the challenge of estimating battery SOH from test measurements collected under different operating and diagnostic conditions.
A central difficulty is distinguishing degradation from reversible changes associated with temperature, state of charge (SOC), and measurement conditions. A model may perform well on familiar test conditions while producing inaccurate estimates for an unseen battery or temperature range. The study will therefore evaluate both estimation accuracy and generalization to batteries and test conditions excluded from model training.
The research contribution will be an experimentally evaluated comparison of measurement combinations and machine learning methods, with particular attention to temperature effects, measurement requirements, and validation on unseen cells.
Aim and objectives
The aim is to develop and evaluate machine learning models that estimate battery SOH using laboratory test data, with emphasis on voltage, temperature, impedance, and resistance measurements.
The Objectives are to:
• Prepare a consistent dataset linking diagnostic measurements, test conditions,
and reference capacity measurements.
• Identify electrical and thermal features or health indicators associated with
battery degradation.
• Compare selected machine learning methods against simple baseline models.
• Quantify the additional value of voltage, temperature, resistance, and impedance
information.
• Evaluate model performance on unseen cells and test conditions.
• Assess prediction uncertainty and the practical measurement requirements of the
resulting models.
Research questions
1. How accurately can battery SOH be estimated from voltage, temperature,
impedance, and resistance measurements?
2. Does combining these measurements improve estimation compared with using
each measurement type separately?
3. How do measurement temperature, SOC, and ageing conditions affect model
accuracy?
4. How well do the models generalize to unseen batteries and operating conditions?
5. Which measurements provide the best balance between estimation, accuracy
and diagnostic effort?
Contact persons and supervisors
Traton Supervisor: Venu Gopal Prathimala venu-gopal.prathimala@scania.com
Application:
Your application must include a CV, personal letter and transcript.
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
Requisition ID: 35135
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country / Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: On-site
Ansök till tjänsten
Thesis: Machine Learning Based Estimation of Battery State of Health
Rekommenderat
OM FÖRETAGET
Scania Group
