Fig 5 - uploaded by Johannes Buberger
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Battery modeling based on the second order ECM and Pulse characterization (fitting) based on the third order ECM and its relative error.
Source publication
![Fig. 1: BM3 system with valid switching states [20], [21]](profile/Johannes-Buberger/publication/361903998/figure/fig1/AS:1176614436110336@1657538150135/BM3-system-with-valid-switching-states-20-21_Q320.jpg)
This study shows a novel online approach for characterizing battery cells or modules in any type of reconfigurable battery system (RBS) for electric driving applications, which allows neighboring cells or modules to switch in parallel. Also, the different representations (time and frequency domain) of conventional battery modeling based on equivale...
Context in source publication
Context 1
... the generated information is characterized with simple second order ECM. Figure 5 shows the slight reduction in fitting accuracy, namely increasing the relative error from 2 × 10 − 7 to 5 × 10 − 5, but the behavior of the cell is still modeled to a large extent. As expected, the estimated dynamic parameters are not the same as for the tuned model, since the behavior of three RC is modeled by two RC. ...
Citations
... It helps to increase the safety of the system by avoid critical failures through early detection. It allows to use the pack's energy more efficiently an thoroughly to extend flight time and the lifetime of the batteries and it allows the implementation of adaptive charging functions [19], [20]. ...
... Especially in the context of battery analysis, as they are complex electrochemical systems with a challenging state estimation [6], [7], Cloud services can be used to over-This research is funded by dtec.bw -Digitalization and Technology Research Center of the Bundeswehr (Project MORE), which we gratefully acknowledge. ...
The electrification of the transport sector is crucial to achieve the environmental goals. The application of Internet of Things (IoT) and Cloud Computing can be part of the solution for improving efficiency and fault detection, and increasing the driving range for electric vehicles. The growing data generation and new data analysis methods demand high computing power and data storage capabilities. By combining IoT devices with Cloud Computing, the condition of a vehicle can be monitored in real time from anywhere in the world. In this paper, a new cloud architecture for the analysis of stationary and mobile measurements is presented. Additionally, a web application is developed for a detailed visualization and analysis of historical and real time data.
