Electric vehicle (EV) batteries, i.e., currently almost exclusively lithium-ion batteries, are removed from the vehicle once they no longer meet certain requirements. However, instead of being disposed of or recycled, the removed batteries can be used in another, less demanding application, giving them a “second life”. Research in the field of second-life batteries (SLBs) is still at an early stage and, to better understand the “second life” concept and the related challenges, potential second-life applications need to be identified first. Using a detailed study of the scientific literature and an interview with field experts, a list of potential second-life applications was drafted. Afterwards, a technical, economic, and legal evaluation was conducted to identify the most promising options. The findings of this research consisted of the identification of 65 different mobile, semi-stationary and stationary second-life applications; the applications selected as most promising are automated guided vehicles (AGVs) and industrial energy storage systems (ESSs) with renewable firming purposes. This research confirms the great potential of SLBs indicating that second-life applications are many and belong to a broad spectrum of different sectors. The applications identified as most promising are particularly attractive for the second-life use of batteries as they belong to fast-growing markets.
When determining the trajectory of the dummy head during a vehicle crash test, the head is not visible for all cameras during the whole movement, since it, e.g., dips into the airbag. Another possibility is to calculate the trajectory with acceleration and gyro sensor data. When using low-cost inertial measurement units, the calculated trajectory differs from the video analysis by 0.15 metres. The idea is to model possible electrical sensor errors and inaccurate known initial conditions and determine them in an optimisation process in which the video trajectory is taken as the target function. This paper deals with the optimisation process to determine if the optimisation function has a unique minimum. Based on that, several optimisation algorithms are compared and three of them are selected for a detailed comparison. The best of them is selected to show the calculation of a trajectory for a real world crash test.
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E.C. Klein et al.Keywords: dummy trajectory calculation; sensor parameter optimisation; optimisation function classification; optimisation algorithm comparison.Reference to this paper should be made as follows: Klein, E.C., Sinz, W., Moser, J., Greimel, R., Raguse, K., von Middendorff, C. and Steiner, C. (2016
Comparison of optimisation strategies
25Christina Steiner is currently finishing her PhD thesis, which deals with the development and application of an endoscope measurement system for high speed filming in narrow spaces. She received her MSc in 2009 from the University of Karlsruhe with her thesis about the implementation of a 3D video analysis system in the vehicle safety department at the AUDI AG. Her focus is on optical measurement technology and she is leading the team for the development of test-and measurement techniques.
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