Master’s in Field Battery Diagnostics and Data Analytics
Online master's programme of 1,900 hours in field battery diagnostics and data analytics. 12 modules covering real-world degradation, BMS data and telemetry, state estimation, fault diagnosis, data analysis, machine learning, digital twins, workshop diagnostics, state-of-health certification under Regulation (EU) 2023/1542, and fleet and storage applications, with an assigned technical tutor.
Master’s in Field Battery Diagnostics and Data Analytics — studying from Singapore
Singapore has a sizeable two-wheeler market, with motorcycles and scooters forming an important part of urban transport. Regulation of vehicle registration, emissions and safety falls under the Land Transport Authority and the National Environment Agency. Engineering work includes vehicle maintenance, electric scooter development, component design and compliance testing. Employers include dealership groups, service providers and companies supplying electric two-wheelers across the region.
Fees for Singapore: S$11,300 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body in Singapore. Vehicle engineering roles in Singapore are generally judged on employer requirements and relevant experience, and the Professional Engineers Board governs formal Professional Engineer registration. Recognition of a private qualification is decided by employers or the relevant body, so verify this before enrolling.
How might this training be used by employers in Singapore?
Singapore has a limited automotive manufacturing base, so 2-wheeler engineering opportunities are mainly in servicing, distribution, component supply, design support and motorsport-related work. An online private qualification may help you demonstrate knowledge, but it does not guarantee a role. Employers usually weigh hands-on experience and project evidence highly.
Can I study this alongside a full-time job, and can my employer help fund it?
The programme is online and taught in English, so you can study from home in Singapore with no student visa required. Workload is significant, with 800 to 1,900 hours depending on the programme, so plan around your job. Fees can be paid in instalments, and employer sponsorship is possible; ask your employer whether they fund job-related courses.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain degradation mechanisms in real-world use.
Extract and clean BMS and telemetry data.
Estimate SOC and SOH with advanced methods.
Diagnose cell and pack faults.
Apply machine learning to service-life prediction.
Develop battery digital twins.
Design analytics services for fleets and energy storage.
Who it's for
- Battery data analysts
- Diagnostics engineers
- Battery data scientists
- BMS engineers
- Electric fleet managers
- Digital twin specialists
- Engineering or science graduates looking to enter the electric vehicle sector
What you'll work with
- Regulation (EU) 2023/1542 (EU Battery Regulation and battery passport)
- ISO 15765 (diagnostics over CAN)
- ISO 14229 (Unified Diagnostic Services, UDS)
- Regulation (EU) 2024/1257 (Euro 7, including battery durability)
- IEC 62660-1 (performance testing of lithium-ion traction cells)
- SAE J1979 (OBD diagnostic test modes)
- UNECE GTR 22 (in-vehicle battery durability)
- Python
- MATLAB
- Apache Spark
- Grafana
- Vector CANalyzer
12 modules
1.1Ageing mechanisms1.2Stress factors in the field1.3Fleet usage profiles1.4Temperature and climate1.5Fast charging and its impact1.6Differences between laboratory and field1.7Cell failures1.8Health indicators1.9Documented degradation cases1.10Case study: degradation analysis of a fleet
2.1Variables logged by the BMS2.2Sampling frequency2.3CAN buses and diagnostics2.4Telematics and connectivity2.5UDS diagnostic protocols2.6Data quality2.7Cloud storage2.8Access to vehicle data2.9Privacy of vehicle data2.10Lab: decoding CAN frames
3.1SOC by coulomb counting3.2Kalman filters3.3SOH from capacity3.4SOH from resistance3.5Incremental capacity analysis3.6Differential voltage analysis3.7Estimation without a full charge3.8Accuracy of estimates3.9Hybrid physics-based and data-driven models3.10Practical: SOH estimation with a Kalman filter
4.1Cell imbalance4.2Internal micro-short circuits in cells4.3Contact faults4.4Sensor faults4.5Thermal runaway detection4.6Coolant leaks4.7Fault trees4.8Remote fleet diagnostics4.9Alert prioritisation4.10Case study: detection of a faulty cell
5.1Python and pandas5.2Cleaning time series5.3Aggregation by cycles5.4Visualisation of battery data5.5Descriptive fleet statistics5.6Correlations between usage variables5.7Big data with Spark5.8Time-series databases5.9Reproducible notebooks in Jupyter5.10Practical: analysis pipeline
6.1SOH regression6.2Remaining useful life prediction6.3Neural network models6.4Anomaly detection6.5Pattern clustering6.6Physics-informed models6.7Transfer between chemistries6.8Model validation6.9Model explainability6.10Lab: RUL prediction
7.1The digital twin concept7.2P2D electrochemical models7.3Equivalent-circuit models7.4Updating with data7.5Commercial digital twin platforms7.6Scaling to fleets7.7Scenario simulation7.8Usage optimisation7.9Integration with the BMS7.10Case study: digital twin of a bus battery pack
8.1Diagnostic tools8.2Capacity tests8.3Impedance measurements8.4Module inspection8.5Pack repair8.6Module replacement8.7Workshop safety8.8Documentation of the intervention8.9Repair cost8.10Practical: workshop diagnosis of a pack
9.1SOH certificates9.2Used vehicles9.3Durability regulations9.4Euro 7 and battery durability9.5Battery passport9.6Residual value of the vehicle9.7Battery capacity warranties9.8Arbitration between parties9.9Transparency for the buyer9.10Case study: SOH report for a used vehicle
10.1Electric fleet management10.2Charging optimisation10.3Replacement planning10.4Second-life packs10.5Stationary BESS10.6Performance warranties10.7Monitoring of storage plants10.8Insurance based on battery data10.9Predictive battery maintenance10.10Workshop: predictive maintenance plan
11.1Platform architecture11.2Cloud services11.3Data APIs11.4Business models11.5Customers and value propositions11.6Cybersecurity of data platforms11.7Platform scalability11.8Start-ups in the sector11.9Data regulation11.10Case study: proposal for an analytics service
12.1Problem definition12.2Inventory of available data12.3Data preprocessing and cleaning12.4Development of state models12.5Model validation12.6Case diagnosis12.7Service proposal12.8Evaluation of the proposed service12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
A 100% online programme on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python, MATLAB, Apache Spark, Grafana and Vector CANalyzer, in free, academic or evaluation versions. Practical work is based on real procedures: analysis of BMS data from real fleets, remaining useful life prediction models and workshop diagnosis of packs. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2023/1542, ISO 15765, ISO 14229, Regulation (EU) 2024/1257 (Euro 7), IEC 62660-1, SAE J1979, UNECE GTR 22.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python, MATLAB, Apache Spark, Grafana and Vector CANalyzer, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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