1. Introduction
Polymer Electrolyte Membrane Fuel Cells (PEMFCs) offer a promising way to achieve carbon dioxide-free transportation in the near future. A big step towards widespread application of PEMFCs in passenger cars has been taken with the launches of Hyundai’s ix 35 Fuel Cell and Toyota’s Mirai FCV in 2013 and 2014, respectively [
1,
2]. However, significant research is necessary for market breakthrough, to, e.g., increase power density and lifetime and reduce cost as formulated by the United States Department of Energy (DoE) [
3].
To achieve these requirements, understanding of water management in PEMFCs is crucial. The proton conductivity of the membrane is highly dependent on its water content [
4,
5,
6,
7]. For a high cell efficiency, one has to ensure a sufficiently high membrane water content during operation to minimize the membranes ohmic resistance. On the other hand, states in which liquid water blocks the reactant transport and affects the function of the catalyst layer have to be avoided as well. Both states, too dry and too wet, lead to a decrease in performance and lifetime [
8].
In automotive applications, the fuel cell systems face highly dynamic load profiles, which is represented in benchmarking cycles such as the Worldwide harmonized Light vehicles Test Cycle (WLTC). In addition, a customer expects a certain driving performance, for example, a certain top speed and acceleration. The exact values depend on the vehicle class. From the vehicle performance requirements, the Fuel Cell System (FCS) requirements in terms of steady-state net power and dynamics can be derived. The electrical net power requirements can be calculated from a vehicle simulation, by modeling air resistance, friction losses, motor and inverter losses for a selected vehicle type. Typically, fuel cell systems are combined with a low capacity battery for the fuel cell system start-up and for energy recuperation via the electrical motor during driving. In a fuel cell dominant approach, the steady-state power has to be produced by the fuel cell system. This leads to a relatively low capacity battery.
However, the fuel cell system must provide a high net power in a short time, to fulfill the acceleration requirements. If one would design a vehicle with a battery large enough to supply the whole net power necessary for this case, a high capacity battery had to be installed, as power-to-energy ratios (
) of batteries are limited. If the battery is only used for start-up and boost operation, the required energy content is not the dimensioning factor. For a fuel cell dominant vehicle, the maximum power becomes relevant. In this case, a high-power battery with a large P/E ratio is chosen over a high energy battery. The European Council for Automotive R&D (EUCAR) states a
of
for state-of-the-art Plug-in Hybrid Electric Vehicle (PHEV) batteries during a 10
peak load [
9]. Therefore, a cost advantage can be achieved when the fuel cell system can deliver the required power
during transients mainly on its own. It is known from Lohse-Busch et al. [
10] that in Toyota’s Mirai vehicle, the majority of the power demand during acceleration is provided by the fuel cell system. The Mirai’s battery is rather small in capacity (
), providing a maximum electrical power of only around 20
—a fraction of the fuel cell system’s power [
10].
An exemplary required power curve
is shown in
Figure 1. In this work, the net power of the FCS
, together with the battery’s power
, should add up to 90% of the FCS’s maximum net electric power within
. If the fuel cell system’s dynamic is enhanced, a lower capacity battery is sufficient to fill the remaining gap in power. In this example, by decreasing the fuel cell system’s response time by a factor of four from
to
, the battery’s capacity can be reduced by a factor of three. Reducing the battery’s capacity also reduces the overall system cost.
The required electric power changes dynamically during a typical passenger car driving cycle. Therefore, a fast adaption of the fuel cell system’s operating point becomes necessary. Every operating point requires a certain optimized set of stack conditions to achieve an ideal hydration state and high system efficiency [
11]. This makes a fast adaption of the stack conditions critical to achieve a highly dynamic fuel cell operation.
High potential is also a known factor that accelerates ageing [
12]. This can be avoided by so called voltage clipping, limiting the minimal current to approximately 5% of the rated current during operation. Therefore, the oxygen and hydrogen consumption changes by a factor of 20, between 5% and 100% of the consumption at the rated current. A fast increase in load requires a fast adaption of the gas supply mass flows to avoid reactant starvation, which would lead to a performance decrease and accelerated aging [
12]. To validate, e.g., a simulation model with highly dynamic, application-like experiments, the mass flow dynamics have to match the targeted fuel cell dynamics. If the mass flow adaption is significantly slower than the targeted fuel cell dynamics, one common way is to set the mass flow as constant [
13].
In the case of an upward load step, the mass flow would be set to satisfy the fuel cells’ consumption at the higher load. This leads to an unrealistically high stoichiometry at the initial, lower load and a possibly too-dry membrane: As the high mass flow combined with low water production rates reduces the local relative humidity, the membrane water content is decreased. Typical operational cathode stoichiometries lie between 1.2 and 6. If these operational stoichiometries are used, the fixed mass flow setup limits the achievable current step size to approximately a factor of five. This in turn makes it impossible to realistically validate a load step from idle to max load, where a step size factor of approximately 20 is typical, as described in the previous paragraph.
This is also the case for other stack conditions such as gas pressure and temperature, and coolant mass flow and temperature: If the adaption of these parameters does not match the targeted dynamics, at least one unrealistic operating point occurs. The impact of a delayed condition adaption on the cells’ voltage response to a current step has been shown by Loo et al. [
14] using a numerical fuel cell model. This finding highlights the relevance of the condition adaption dynamics when evaluating a fuel cell systems response to a load change.
Current steps lead to a voltage overshoot in the case of a current decrease and a voltage undershoot in the case of a current increase, as experimental and simulation literature data suggests [
13,
14,
15,
16,
17,
18,
19,
20,
21]. A similar behavior is reported for step changes in voltage and the current response [
22,
23]. Wang and Wang linked it to the transient behavior of membrane hydration, electrochemical double-layer capacitance and reactant diffusion [
15]. The time constants of the double-layer capacitance effect is, however, too low to explain the behavior observed in the later sections. In the opinion of the authors, membrane hydration and reactant supply dynamics should be in focus. To investigate this properly, this paper is based on experiments with realistic coolant temperature and reactant supply response times, other than the most common literature experiments.
Tang et al. [
24] conducted dynamic experiments with a
fuel cell system. While it is shown that the mass flow adaption works sufficiently fast (<2 s) to allow a dynamic duty cycle, the stack temperature reacts quite slowly (>100 s). This temperature effect dominates the stacks voltage response. In typical automotive systems, a thermal system would be able to adapt significantly faster to the change in heat production, leading to a lower voltage response time constant as shown later in
Section 3.4.
As described beforehand, most research either focuses solely on simulation approaches, which are hard to validate without experiments, or on experiments with constant or low-dynamic stack boundary conditions. However, the realistic adaption of stack boundary conditions as well as system or test rig limitations greatly influence the voltage response of a fuel cell stack in highly dynamic operation, as the response in each case is dominated by different effects. To evaluate and optimize control strategies for automotive application and to validate simulation models, realistic experiments are necessary. This work compares typical simplified experiments from the literature with two sophisticated, application-like operated fuel cell systems, highlighting the pitfalls of commonly-used setups regarding transferability towards automotive usage. The key contribution of this paper is the systematic comparison of these experimental setups in dynamic operation, as system level experimental data are especially sparse in the common literature.
This paper is structured as follows:
Section 2 introduces the key variables for the experiments, the experimental systems and test rigs, as well as three selected test cases. In
Section 3, the impact of load-dependent stack boundary condition adaption, system architecture choice and test rig behavior on the dynamic voltage response is compared.
Section 4 focuses on the different root causes dominating the voltage response in the selected test cases. In
Section 5, the results are summarized, highlighting their scientific and practical importance. Additionally, topics with further need for research are proposed.
4. Discussion
The process chain from
Figure 2 is broken down to the dominating effects for three selected cases according to the results presented in this paper, see
Figure 8. Test case 1 shows two different voltage relaxation modes, which can be distinguished by their dominating effect: With adaptive boundary conditions, the stack voltage reacts with a time constant similar to the ones of the air supply. The ohmic resistance is almost constant, thereby suggesting only a small change in membrane humidity. The change in voltage is therefore linked to the local partial pressure dynamics of the reactants.
If the gas supply is kept constant, the voltage response is dominated by the membrane humidification and dry-out, resulting in varying ohmic resistance. The time constants of the HFR signal and the stack voltage match well. The loss in membrane conductivity is linked to dry-out: while the air mass flow remains high, the current and the resulting water production are reduced by ≈60%. Additionally, the HFR does not return completely to the stationary value from case 1a within 5 . A slow component of the rehydration process with a time constant above 5 prevents the voltage from reaching the initial steady-state again. The initially higher voltage after a current decrease is caused by the higher reactant partial pressure, but is then overcompensated by the increase in ohmic resistance. After the dry phase, the undershoot increases in depth as expected. This can be prevented with an adapted operating strategy that ensures minimal variation in membrane water content.
Analyzing the HFR time constants, the rehydration process is approximately four times faster than the dehydration process. This is not in line with results from Didierjean et al. [
26], which calculated water desorption kinetics to be five times faster than the adsorption kinetics from the gas phase for Nafion 117. Furthermore, they modeled and measured significantly lower time constants well above 100
. It is likely that the different experimental setup caused this discrepancy. Didierjean et al. [
26] used a membrane in a controlled environment, without an active fuel cell reaction, to measure the sorption kinetics. In the experiments described in this work, the fuel cell reaction is ongoing. Another possible explanation might be that the adsorption process is dominated by liquid water uptake. Zawodzinski et al. [
5] measured lower time constants when immersing a Nafion 117 membrane in liquid water compared to water uptake from the gas phase [
26].
Different system architectures and components limit the achievable set points and dynamics for pressure and stoichiometry. The operating strategy may need adaption to the actual system and vice versa. The mass flow dynamics is especially one key limitation to the achievable current dynamics. Deeper undershoots occur in System 2, caused by its lower mass flow dynamics. In the case of a reactant starvation, a significant voltage reduction is observed. This state has to be avoided to satisfy the power demand and reduce aging [
12].
Typical short stack test rigs are limited in the achievable gas supply dynamics and the thermal control response time. A significantly slower voltage response time of ≈100 s was measured. The time constant analysis suggests that this response is dominated by the slow thermal control, possibly in combination with a membrane humidification process. The latter can only be presumed based on the other experiments, as no HFR measurement is available from this specific test.
The transient voltage response of the stack can be influenced via the operating strategy, system architecture and/or the test rig. Three dominating effects are identified from the experiments:
Air supply (pressure and stoichiometry) with typical time constants between and ;
Membrane hydration and dehydration with typical time constants of ≈10 s and ≈45 s, respectively;
The coolant inlet temperature dynamics, which have typical settling times of ≈7– in realistic automotive systems, but time constants around on a common short stack test rig.
The impact of the anode loop on the observed dynamic response characteristics is assumed to be negligible, as the HyS reacts fast to changes in the set points. In addition, the anode mass flows and their resulting impact on membrane humidification are small compared to the cathode side. During the case 1b experiment, the cathode dry-out caused a decreasing relative humidity within the anode loop, which did not occur in case 1a. A possible impact of changing gas composition within the anode loop on the voltage response could not be observed.