Medium- and heavy-duty fuel cell electric vehicles (FCEV) have gained attention
over the battery electric vehicles, offering long vehicle range, fast refueling
times, and high payload capacity. However, FCEVs face challenges of high upfront
system cost and fuel cell system durability. To address the cost sensitivity of
the fuel cell powertrain, it is imperative to maximize the operating efficiency
of the energy and thermal management system while meeting the fuel cell
durability requirements. This article presents an advanced adaptive control
strategy for each of the energy and thermal management systems of a FCEV to
maximize operating efficiency as well as vehicle performance.
The proposed adaptive energy management strategy builds upon a real-time
equivalent consumption minimization strategy (ECMS), which is updated based on a
horizon prediction algorithm using GPS and navigation data of the route. The
algorithm predicts the battery state of charge (SOC) for a defined horizon,
which is used to predict the target SOC for the real-time ECMS strategy to
minimize hydrogen consumption. For a long-haul heavy-duty truck application, the
proposed adaptive ECMS strategy showed 1.8% and 1% improvements in fuel
efficiency when compared to rule-based and baseline ECMS strategies through
model-in-loop (MiL) evaluation.
In addition, this article presents an adaptive thermal management strategy that
integrates predictive and real-time control approaches, such as the adaptive
ECMS. The predictive control strategy leverages GPS and navigation data to
forecast component temperatures over a predefined horizon prediction. The
predicted component temperatures are then utilized to adjust the target
component temperatures for the real-time linear quadratic regulator (LQR)
control algorithm. LQR is deployed to minimize the energy consumption of the
thermal management system while ensuring that component temperatures are
maintained within limits during aggressive duty cycles. Lastly, MiL evaluations
were conducted on a validated plant model to verify the developed adaptive
thermal management control strategy.
