liionpack: A Python package for simulating packs of batteries with PyBaMM
Thomas G. Tranter, Robert Timms, Valentin Sulzer, Ferran Brosa Planella, Gavin Wiggins, Suryanarayana Karra, Priyanshu Agarwal, Saransh Chopra, Srikanth Allu, Paul R. Shearing, Dan J. L. Brett
Abstract
Electrification of transport and other energy intensive activities is of growing importance as it \nprovides an underpinning method to reduce carbon emissions. With an increase in reliance on \nrenewable sources of energy and a reduction in the use of more predictable fossil fuels in both \nstationary and mobile applications, energy storage will play a pivotal role and batteries are \ncurrently the most widely adopted and versatile form. Therefore, understanding how batteries \nwork, how they degrade, and how to optimize and manage their operation at large scales is \ncritical to achieving emission reduction targets. The electric vehicle (EV) industry requires \na considerable number of batteries even for a single vehicle, sometimes numbering in the \nthousands if smaller cells are used, and the dynamics and degradation of these systems, as well \nas large stationary power systems, is not that well understood. As increases in the efficiency \nof a single battery become diminishing for standard commercially available chemistries, gains \nmade at the system level become more important and can potentially be realised more quickly \ncompared with developing new chemistries. Mathematical models and simulations provide a \nway to address these challenging questions and can aid the engineer and designers of batteries \nand battery management systems to provide longer lasting and more efficient energy storage \nsystems.