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Designing and Testing the Endurance of Structural Components for Electric Vehicles

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Electrical and Signal Post-Processing Techniques for AC Power Analysis in Electric Vehicles

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Applying Statistical and Reliability Techniques for Determining Battery Life in Electric Vehicles

Fatigue analysis, vibration testing and reliability for electrified vehicle insights

Engineers are mobilizing at a rapid pace to meet the demands and challenges presented by the shift to more widely adopted battery powered devices and transportation. Prenscia has partnered with customers and researchers to deliver insights into the mechanical and durability aspects, electrical and signal processing aspects, as well as statistical and reliability aspects of these highly advanced vehicles for a more in-depth understanding of how the range and overall efficiency of the vehicle can be improved.

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Mechanical and durability aspects

Like their thermal-engine counterparts, electric vehicles are susceptible to structural fatigue failures. The mechanical complexity of the battery structure and its mountings also give rise to significant additional fatigue failure issues. Insights into these structural and vibration-induced failures enable engineers to eliminate the risk of fatigue failure, improve the durability of electric-engines, and increase vehicle reliability.

  • Fatigue design of battery packs 
  • Accelerated vibration testing of battery packs 
  • Fatigue analysis of vehicle structures 

Electrical and signal processing aspects

The theoretical and real-world range of an electric vehicle may vary significantly. In order to maximize the range and overall efficiency of the vehicle, it is necessary to understand and characterize how the vehicle will be used and determine through meticulous measurement and analysis where the losses occur.

  • Electric motor efficiency and loss mapping
  • Power measurement and analysis
  • Real-world battery usage and vehicle efficiency assessment

Statistical and reliability aspects

All electric vehicle batteries degrade overtime. Its performance, however, varies by model and external conditions such as usage, temperature, and charging methods. In order to improve the overall reliability of the battery system and avoid excessive warrantee exposure, it is important to understand both the mean life and the statistical distribution of lives for the battery. Furthermore, understanding how the battery degrades over time will lead to significant improvements in battery design, reliability and vehicle efficiency.

  • Battery life analysis
  • Battery performance degradation modeling and analysis
  • FMEA for new failure modes