Real-Time Predictive Control of Hybrid Fuel Cell Drive Trains

Bartholomaeus, Ralf; Fischer, Andreas; Klingner, Matthias

doi:10.3182/20070820-3-us-2918.00016

Cited by 4 publications

(8 citation statements)

References 3 publications

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“…Figures and give the simulation FCS net power, battery SOC, battery power, and friction braking power. The P fc , net has a maximum rate of increase of 15.8 kW/s on [36,37]s, below the 20 kW/s limit in . Also, the final SOC is 0.5826, a difference of 2.9% from the

{\overset{W}{true}}_{b a t, d e s}

of 0.6.…”

Section: Drive Cycle Resultsmentioning

confidence: 99%

“…For the NEDC, The maximum fuel cell power delivered is 41.0 kW with maximum rates of increase and decrease of 16.5 kW/s and −9.8 kW/s, respectively. Keeping the rates of power change bounded over both profiles reduces the rate of degradation of the fuel cell . The final UDDS battery SOC is 0.5950, a 0.8% undercharge from

{\overset{W}{true}}_{b a t, d e s} = 0.6

, energy that will have to be restored in the future.…”

Section: Drive Cycle Resultsmentioning

confidence: 99%

“…The net output power flow is approximated by a first‐order lag equation similar to the diesel engine power flow model adopted in :

\frac{d P_{f c, n e t} (t)}{d t} = \frac{1}{τ_{f c}} [- P_{f c, n e t} (t) + P_{f c, n e t}^{m a x} u_{f c} (t)]

where P fc , net is the fuel cell net power,

P_{f c, n e t}^{m a x}

is the maximum available output power at the operating conditions, u fc ∈ [0, 1] is the normalized actuation, and τ fc is the time constant of the power response. The controlled stack's power output in is estimated to have a time constant of 0.25 s but rapid changes in desired power promote fuel cell catalyst degradation and reduced lifetime . Two factors contribute to the choice of the time constant.…”

Section: Component and Switched System Modelsmentioning

confidence: 99%

“…where Pfc,net is the fuel cell net power, P fc net max , is the maximum available output power at the operating conditions, ufc ∈ [0, 1] is the normalized actuation, and tfc is the time constant of the power response. The controlled stack's power output in [4,12] is estimated to have a time constant of 0.25 s but rapid changes in desired power promote fuel cell catalyst degradation and reduced lifetime [1]. Two factors contribute to the choice of the time constant.…”

Section: Fuel Cell Systemmentioning

confidence: 99%

“…Here, for clarity and as a prelude to the forthcoming embedded model extension, the inputs for each mode's dynamics are given unique labels even when the dynamics for a particular component remain invariant across modes. For example, the FCS response, (1), is common to all modes and the input ufc is not presented as mode specific, but u fc 0 u fc 1 , or u fc 2 is applied in the respective mode. Again, these input distinctions are necessary to the embedded control problem (defined shortly).…”

Section: Supervisory Level System Dynamicsmentioning

confidence: 99%

See 4 more Smart Citations

Hybrid Model Predictive Power Management of A Fuel Cell‐Battery Vehicle

Meyer

DeCarlo

Meckl

et al. 2012

Asian Journal of Control

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This paper considers optimal power management of a fuel cell‐battery hybrid vehicle (FCHV) powertrain having three distinct modal configurations (modes): electric motor propelling/battery discharging, propelling/charging, and generating/charging. Each mode has a distinct set of dynamics and constraints. Using component dynamical/algebraic models appropriate to power flow management, the paper develops a supervisory‐level switched system model as an interconnection of subsystems. Given the model, the paper sets forth a hybrid model predictive control strategy based on a minimization of a performance index (PI) that trades off tracking and fuel economy in each operational mode. Specifically, the PI trades off velocity tracking error, battery state of charge variance, and electric drive and hydrogen fuel usages while penalizing frictional braking to encourage regenerative braking. The optimization is performed using an embedded system model and collocation with matlab's fmincon to compute mode switches and continuous time controls. The methodology avoids the computational complexity of alternate approaches based on, e.g., mixed integer programming. Projection methods for approximating the switched system solution from the embedded solution are empirically evaluated. To demonstrate the methodology, an example of a FCHV is simulated using three standard velocity driving profiles: a sawtooth profile with a hill climb, the EPA urban dynamic driving schedule, and the New European Driving Cycle. Also, drive cycle fuel usage is compared to that from the Equivalent Consumption Minimization Strategy.

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