Battery Cell Model Engineer (SoX Algorithms)
Battery Cell Model Engineer (SoX Algorithms)
Refined job description
ROLE
– Analyze large datasets from battery cyclers to extract electrochemical parameters and aging trends.
– Correlate lab-based data with real-world field performance to improve model accuracy.
– Collaborate with BMS software teams to optimize algorithms for real-time execution on low-power microcontrollers.
REQUIREMENTS
– Deep understanding of lithium‑ion battery degradation mechanisms.
– Experience developing battery management system algorithms and models.
– Proficiency in designing SOC, SOH, SOP estimation algorithms.
– Knowledge of Equivalent Circuit Models and Physics‑Based Models.
– Strong data analysis skills for large battery cycler datasets.
– Ability to translate models into production‑ready C/C++ code or generate code from Simulink.
– Experience with Model‑in‑the‑Loop and Hardware‑in‑the‑Loop testing.
– Ability to work with low‑power microcontroller constraints and real‑time execution.
Full job description
Job Description
Role Overview
We are seeking a
Cell Model Engineer
to design, implement, and
validate
advanced Battery Management System (BMS) algorithms. You will focus on developing
SoX
frameworks
(SOC, SOH, SOP, SOE) that ensure the safety, longevity, and performance of lithium-ion battery packs. This role requires a deep understanding of lithium-ion degradation mechanisms and the ability to translate complex physical phenomena into robust mathematical models.
Key Responsibilities
1. Algorithm & Model Development
Develop and implement
State of Charge (SOC)
,
State of Health (SOH)
, and
State of Power (SOP)
estimation algorithms.
Design Equivalent Circuit Models (ECM) or Physics-Based Models (PBM) to
represent
cell dynamics.
Utilize advanced estimation techniques such as
Kalman Filters (EKF, UKF)
, Recursive Least Squares (RLS), or Machine Learning-based approaches for time-series battery data.
2. Characterization & Testing
Define and oversee cell-level testing protocols (HPPC, Capacity Fade, Cycle Life) to parameterize models.
Analyze large datasets from battery cyclers to extract electrochemical parameters and aging trends.
Correlate lab-based data with real-world "field" performance to improve model accuracy.
3. Software Integration & Validation
Translate models into production-ready code (C/C++ or Auto-generated code from Simulink).
Perform
Model-in-the-Loop (MIL)
and
Hardware-in-the-Loop (HIL)
testing to
validate
algorithm robustness across various temperatures and aging states.
Collaborate with BMS software teams to optimize algorithms for real-time execution on low-power microcontrollers.
I'm interested