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:
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.
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We design and development of embedded Observer and Control software with varied specifications including with GPU acceleration based on the Nvidia Cuda libraries.
We specialise in the design, development and implementation of Observer and Control software for both embedded and large scale systems.
We are capable of integrating our Control and observation software/hardware with practically all modern system infrastructure.
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.
We design, develop and integrate the latest control and automation algorithms for almost any system.
We provide ad hoc research & development as well as prototyping tailored to your needs.
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.