Bayesian Network for Reliability Predictions of Automotive Battery Cooling System

The working principle of EVs is mainly based on the rechargeable battery packs which provide the power to the vehicle. Keeping these battery packs within optimal temperature is very much essential to achieve the best performance, gain autonomy (higher mileage) and preserve the battery components from deterioration. Therefore, the battery cooling system plays a fundamental role in optimizing the thermal management of battery packs. It also has implications for vehicle safety, because a failure of battery coolers may result in hazardous conditions. Thus, the reliability of battery coolers becomes an important and unavoidable task for the industries leading towards mass production manufacturing of EVs. The reliability analysis of automotive parts is usually performed at component level. In the case of battery coolers, the reliability predictions need to take into account the system-wise structure of the part, which is made by different coolers with different design. Indeed, this poses many challenges, since all possible wear-out failure modes (FMs) may have conditional dependability on each other. For example, corrosion phenomena are dependent on the position of the coolers (inside or outside the battery pack), and in the case of vibration loading how each cooler, pipes and connections are integrated within the battery pack. In the present work we have conducted an analysis considering the predominant FMs of battery coolers as dependent and modelled them through a Bayesian network (BN) approach to calculate the probability of field failure (part per million, PPM) in case of extension of the warranty period. The results obtained through the BN analysis are then compared with a simplified PPM calculator tool, based on an empirical approach, used within the company for preliminary risk assessment during the early stages of product development. We also illustrate how BN method can be applied to the risk assessment by considering the proposed design, its integration within the battery pack, and the severity of the validation tests requested by the customers. The final results show that the PPM calculator (the present method) overestimates the PPM whereas the BN provides more realistic PPM figures since it considers the dependability of FMs. The study also shows that the empirical method is simple but brings more conservative results. In conclusion, a risk assessment based on the BN has proved to be a modern and robust method to predict the reliability of mechanical components undergoing wear-out field failures.