Energy Management Controller for Fuel Cell Hybrid Electric Vehicle Based on Sat‐Nav Data

Abstract

The hybridization of fuel cells (FC) and battery in electric vehicles necessitates designing an energy management system (EMS) for optimal energy use of the two power sources. The EMS represents a high‐level controller (HLC) calculating the optimal power split between the two power sources, using a prediction model for the vehicle's electric load over a planned journey trajectory. However, the instantaneous actual power demand of the vehicle is likely to deviate from the prediction due to varying traffic circumstances. This power offset needs to be compensated for whilst the optimality is still considered. A control approach is proposed here that converts the optimal power split into a dimensionless power split ratio (PSR). This ratio is passed to a low‐level controller (LLC) to be implemented as a set‐point. The LLC is responsible for simultaneously controlling the power of the FC and the battery using solely one control element, which is the DC‐DC boost converter. The PSR will be maintained whatever the actual power demand of the vehicle is. This approach will result in a fully controlled optimal power utilization, so that the high efficiency of the battery and the extended range enabled by the FC system are used to best effect.

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

“…A number of strategies have been developed by several researchers for the EMS of hybrid energy storage system. In previous studies, some researchers have used a long-term EMS, a power split control, and a loss minimization strategy in hybrid power system [14][15][16]. The performance improvements resulting from energy management have been investigated in these works.…”

A multi‐input and single‐output voltage control for a polymer electrolyte fuel cell system using model predictive control method

“…Due to its high complexity, strong nonlinearity, and especially series of constraints on the operation parameters, 17 the PEFC system requires much more advanced and robust control algorithms to ensure its reliable and safe operation. As an advanced process control method, model predictive control (MPC) controller usually excels in solving control schemes with multi input variables and a bunch of constraints, [18][19][20] which makes it especially suitable for PEFC system's control application. MPC controllers have been widely used in PEFC system industry thanks to its high robustness and optimal performance.…”