A Method for the Estimation of Cooling System and Driving Performance for Fuel Cell Vehicles Based on Customer Fleet Data

An efficient vehicle thermal management is essential to fulfil the requirements of fuel consumption and passenger comfort. Therefore, the design and dimensioning of the cooling system is under high scrutiny in new vehicle architectures. With increasing electrification, no longer just the load peaks define the design frame but also the dynamics of thermal loading and recovery. Consequently, electrified vehicle architectures such as plug-in hybrid fuel cell vehicles demand for alternative approaches regarding the design of cooling systems and the definition of the decisive criteria.

This article presents a new methodology for designing the cooling system related to its demands in customer operation. The recorded fleet data is first filtered for high load driving, using the so-called thermal load integral (LI) as a filter criterion. Application of the Markov Approach generates representative driving cycles out of the chosen driving sections, reducing the amount of data many times over again. Compared to other Markov models, this method also incorporates the road gradient, which is an essential information for thermal load cases. This enables the derivation of thermal load cases from fleet data. Combined with the time-weighted analysis method, a new methodology for customer-oriented cooling systems design is established.