Battery Cell Model Engineer (SoX Algorithms)

Ultraviolette Automotive; Bengaluru Urban; On-site; INR 1,200,000 - 2,100,000; Full time

Benefits offered by this job

Restaurant d'entreprise

Job summary

Ultraviolette Automotive in Bengaluru seeks a Cell Model Engineer to design, implement and validate advanced BMS algorithms for SOC, SOH, SOP, and SOE estimation. You will translate lithium-ion degradation phenomena into robust mathematical models and production-ready code.

The role involves ECM/PBM development, Kalman filter-based state estimation, MIL/HIL testing, and close collaboration with BMS software teams to ensure real-time execution on low-power microcontrollers.

Qualifications

• Background in battery management systems or related electrochemical modeling.
• Experience with SOC/SOH/SOP estimation and ECM/PBM concepts.
• Strong programming skills in C/C++ for embedded platforms.

Responsibilities

• Develop and implement SOC, SOH, and SOP estimation algorithms.
• Design ECM or PBM to model cell dynamics and aging.
• Translate models into production-ready code and perform MIL/HIL validation.

Skills

SOC/SOH/SOP

Kalman Filtering

RLS & ML

C/C++ embedded

Education

Bachelor's in Electrical/Electronics Eng

Tools

Simulink

C/C++

Embedded tooling

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.