Vehicle electrification

Statistical and reliability aspects

Prenscia software delivers a range of software solutions for performing battery life analysis, understanding battery performance degradation, and identifying new failure modes in new design concepts of electrified vehicles.

Statistical and reliability aspects of vehicle electrification

Prenscia software delivers a range of software solutions for performing battery life analysis, understanding battery performance degradation, and identifying new failure modes in new design concepts of electrified vehicles.

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Battery life analysis

In order to improve the overall reliability of the battery system and avoid excessive warrantee exposure, it is important to calculate both the mean life and the statistical distribution of battery life.  ReliaSoft Weibull++ takes battery life data from laboratory tests or from a fleet of vehicles and calculates reliability information. This analysis is used to calculate the mean life, the B10 life, etc. as well as more advanced analysis such as “what proportion of the batteries will survive more than 1000 80%-to-20% cycles?”, or "having reached a life of 1000 cycles, what is the expected reliability of the batteries?"

Battery performance degradation modelling and analysis

An accurate predictive model for battery life is essential for the future economic success of Battery-powered Electric Vehicles (BEVs). It is necessary to estimate the statistical distribution in order to manage warrantee exposure and to design more reliable batteries and ReliaSoft Weibull++ offers a wide range of statistical life models with full regression analysis capabilities. These include mixed-mode models that represent multiple compounded failure modes. 

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With a strong correlation between vehicle usage and charge/discharge patterns, a battery deterioration model can be found.  By combining lab test results with real vehicle usage patterns from data monitored over CAN, a reliable estimate of the useful remaining life of a battery can be determined based on how it has actually been used. Designed to analyze huge quantities of vehicle CAN data, nCode GlyphWorks offers a range of cycle-counting algorithms suitable for charge/discharge analysis. Long CAN data sequences can be characterized into small and compact datasets for use in the damage models. New algorithms can also be added using MATLAB or Python and allows for multi-variate regression analysis using industry-standard tools such as scikit-learn and PyTorch.

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XFMEA allows you to quickly and easily report on outstanding risk identified in the FMEA, using RPN, SxO, Action Priority or customized risk criteria.

FMEA for new failure modes

Failure Mode and Effects Analysis (FMEA) and Failure Modes, Effects and Criticality Analysis (FMECA) are highly structured methodologies that are particularly useful for identifying potential failure modes in new design concepts where in-house experience may be limited. In high impact, complex systems such as EV batteries, FMEA provides an essential tool for mitigating risk and warranty exposure. ReliaSoft XFMEA supports all types of FMEA for design, process, system, and corrective action analysis and is fully compliant with industry standard approaches. It is also designed to support group working, capturing all the failure modes and easily sharing results between projects so that corporate knowledge is retained.

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