Simulating a Variable-structure Model of an Electric Vehicle for Battery Life Estimation Using Modelica/Dymola and Python

Strict operating boundaries on commercial lithium ion cells are defined to mitigate the effect of aging (loss of capacity and increase in internal resistance), as well as avoiding safety hazards, like the appearance of lithium plating during charge, which can lead to internal short circuit and subsequent thermal runnaway. Therefore, to develop fast charge algorithms that maximize charging speeds, electrochemical and thermal constraints must be considered. Most studies so far have focused on the single cell problem, whereas pack-level fast charge challenge has been tackled directly by the industry. The reason is that the temperature difference between cells within a battery pack is often considered small, and therefore that optimal charging profiles can be extrapolated from single cell investigations. In practice, temperature spread can reach up to 10 K from coldest to warmest points in the pack, and at least 5 K between same position of different cells. With this in mind, a Nonlinear Model Predictive Control (NLMPC) scheme is proposed that considers both electrochemical and thermal constraints at pack level, establishing, at least on a theoretical basis, the practical limits of fast charge. We demonstrate how active thermal management, i.e., controlling the fluid inlet temperature, is critical to reducing charging times below 40 min (from 0% to 80% state of charge), and discuss some challenges when using online optimization-based control techniques

Section: Solving the Fast Charge Problemmentioning

confidence: 99%

Fast Charge Algorithm Development for Battery Packs under Electrochemical and Thermal Constraints with JModelica.org

Romero,

Angerer

2023

“…This command has been applied for example within the Hybrid Decomposition method in combination with DY-MOLA-external tools like PYTHON and MATLAB as described in (Mehlhase, 2015) and (Stüber, 2017). For these methods, the online evaluation of the results are indispensable, since the chosen long time simulations depend on event-based information about the end-point of the simulation.…”

Section: Simulateextendedmodel Functionmentioning

confidence: 99%

Proceedings of the 2nd Japanese Modelica Conference Tokyo, Japan, May 17-18, 2018

2019

Model based product design requires both intuitive and effective user interface and powerful computing power. The presentation will contain a brief history of Modelica evolution and current status including some applications. Some new needs that are currently not covered will be discussed. New technical possibilities will be introduced, such as web apps for intuitive and effective user interaction and easy access, domain specific language extensions for advanced modeling capabilities and cloud computing for large scale simulation deployment..

“…In the Modelica environment (Modelica Association 2017) it is especially challenging to simulate applications such as tool changers since structural variability is not possible, limited to special cases (Stüber 2017) or requires additional effort (Tinnerholm, Pop, and Sjölund 2022). To our knowledge, there has been no work in the area of simulating a tool changer based on Modelica models.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of dynamically constrained objects for limited structurally variable systems in Modelica

Reiser,

Reiner

2023

This work introduces a new solution for the modeling and simulation of dynamically constrained objects for limited structurally variable systems purely in Modelica. A combination of a collision detection algorithm, the limitation of collisions, and a method to constrain objects based on forces leads to a constraint network in Modelica. It allows a stable and accurate simulation of applications such as robot tool changers in a flexible way without the need for predefined connections in the model.