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Battery management system (bms) algorithm developer

Leamington Spa
Atlas Engineering Services Limited UK
Algorithm developer
€55,000 a year
Posted: 16 April
Offer description

Battery Management System (BMS) Algorithm Developer

Location: Midlands, UK

Employment Type: Full-time / Contract / Hybrid / Remote


Overview

We are looking for a passionate and experienced BMS Algorithm Developer to lead the design, development, and validation of cutting-edge battery control algorithms for electric and hybrid vehicle applications. This role is integral to shaping the future of energy storage management across vehicle platforms, ensuring performance, safety, and compliance with global standards.


Key Responsibilities

* Develop robust algorithms for State of Charge (SoC), State of Health (SoH), State of Power (SoP), fault diagnostics, and cell balancing
* Design and implement protection logic, thermal management, and charging strategies
* Model and simulate battery behavior using MATLAB/Simulink and PLECS
* Write and optimize real-time embedded software in C/C++ for BMS microcontrollers
* Execute testing strategies including Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL)
* Collaborate with battery, thermal, system integration, and software engineering teams to ensure seamless vehicle deployment
* Ensure systems align with industry standards including ISO 26262 and AUTOSAR


Education

* Bachelor's or Master's degree in Electrical Engineering, Electronics, Mechatronics, or related fields


Must-Have Skills

* Hands-on experience in BMS algorithm development (e.g., SoC/SoH estimation, fault handling, thermal modeling)
* Strong programming skills in MATLAB, Simulink, Embedded C/C++, Python
* In-depth knowledge of battery chemistries such as Li-ion, LFP, NMC
* Proficient in control theory, including digital filters and Kalman filters (EKF/UKF)
* Familiarity with CAN protocols, UDS, and diagnostic tools


Preferred Skills

* Exposure to ISO 26262 functional safety processes
* Experience with machine learning for battery performance prediction
* Automotive development knowledge using ASPICE and V-model lifecycle
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