Scania • Deltid • Södertälje
Accurate estimation of a battery pack’s State of Charge (SOC) and State of Health (SOH) is essential for optimizing vehicle performance, enabling predictive maintenance, and assessing total cost of ownership and residual value of aged battery packs.
However, achieving high accuracy in SOC and SOH estimation is challenging. A deeper understanding of the sources of estimation errors can significantly improve the performance of the BMS. By analysing large volumes of time-series data collected from battery electric trucks and buses operating under various real-world conditions, error models can be developed to explain and quantify major sources of inaccuracy in SOC and SOH estimations.
Target:To quantify and understand the errors in onboard SOC and SOH estimations, and to identify correlations between estimation errors and operating conditions.
Assignment:The assignment is divided into these sub-tasks:
1. Develop a solid understanding of SOC and SOH estimation algorithms and all related parameters.
2. Process large-scale time-series data to extract relevant parameters from selected operational periods.
3. Quantify estimation errors and analyse their relationship to vehicle operating conditions and influencing factors.
Education:Master (civilingenjör) in computer science, robotics, engineering physics, electrical engineering, or applied mathematics. Knowledge of Python and SQL programming are a strong advantage. Background in data science, statistics, signal processing, or engineering mathematics is beneficial.
Number of students: 1
Start date: January 2026
Estimated time needed: 20 weeks
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