About Us
Battery Management Analytics
We develop highly accurate Battery Management Analytics that can accurately estimate both the State of Charge of Lithium-Ion batteries and the State of Health degradation.
Combined with industry leading prognosis of End of Discharge, State of Maximum Power and End of Usable Life.
We are currently developing our systems for primarily the Electric Vehicle market but, our technology can be applied equally to:
- Satelites
- Electric Vehicles
- Unmaned Aerial Vehicles
Who Are We?
Altilium is a collective of dedicated researchers and engineers who specialise in Battery Monitoring Technology focused on the Satellite, Electric Aircraft/UAV and Electric Vehicle industries.
We strive to deliver cutting edge technology, combined with world class research in an environment that encourages independent thought.
Get to know our founder:
Our Services
Hardware Design
We design and development of embedded Observer and Control software with varied specifications including with GPU acceleration based on the Nvidia Cuda libraries.
Software Design
We specialise in the design, development and implementation of Observer and Control software for both embedded and large scale systems.
System Integration
We are capable of integrating our Control and observation software/hardware with practically all modern system infrastructure.
Exploratory Analytics
We contract for one off and long term analysis of systems as well as exploratory analytics for improving process control as well as fault detection.
Control & Automation
We design, develop and integrate the latest control and automation algorithms for almost any system.
Research & Development
We provide ad hoc research & development as well as prototyping tailored to your needs.
Our Blog
The Impact of Lithium-Ion Battery Polarising Impedance Modelling on End-of-Discharge Prognosis Accuracy
In Lithium-Ion batteries, the polarising impedance is an important characteristic that has been shown to be a complex function of, among others, both the state of charge and the demanded current. Therefore within a prognostic framework, which typically solely relies on the a prior modelling of the hidden state evolution, the correct characterisation of the functional surface with respect to state of charge and current impacts the accuracy of the predicted end-of-discharge probability density function. This is important in critical systems that rely solely on the Lithium-Ion as a power source and require an unbiased prediction of the end-of-discharge time. This paper demonstrates how a correctly modelled polarising surface can improve prediction accuracy over the state of the art models found in literature.