Development of a model-based controller prototype for a PEM fuel cell engine with ASCET-MD

Ji, Guangji; Chen, Fengxiang; Ma, Tiancai; Zhang, Chuangshen; Zhou, Yi; Zhou, Su

doi:10.1109/icca.2009.5410353

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Offline experiment is a non real-time simulation with the purpose of functionality verification. But it is useful for MiL (Model in Loop) simulation during the first phase of the Vcycle model for automotive ECU developments [3]. In this paper, a MPC-based controller and a real plant are modeled and MPC controller is validated through the offline experiments (see Fig.…”

Section: B Implementation In Ascet-mdmentioning

confidence: 99%

“…However, as far as the authors known, there is little work that makes the MPC algorithm be implemented under a real time operation system or in the automotive controller design. This contribution is motivated to use the software tool, ASCET-MD which is a dedicated software tool for automotive embedded control system [3], to implement a MPC-based controller for an industrial process, e.g. fuel cell air supply sub-system.…”

Section: Introductionmentioning

confidence: 99%

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MPC algorithm implementation and its offline experiments in ASCET-MD

Chen

Liu

et al. 2011

2011 International Conference on Electric Information and Control Engineering

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MPC (Model Predictive Control) algorithm has been widely applied in the chemical process control applications. In this paper, the ASCET-MD, which is an embedded controller development tool for automotive electronics, has been used to implement the MPC algorithm for a PEM (Proton Exchange Membrane) fuel cell air supply sub-system. A concept of receding update is forwarded and realized under a real time operation system. The proposed method provides a paradigm of MPC implementation for the design of automotive electronic controllers under ASCET-MD platform, and is validated in the manner of offline experiments.

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Section: B Implementation In Ascet-mdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MPC algorithm implementation and its offline experiments in ASCET-MD

Chen

Liu

et al. 2011

2011 International Conference on Electric Information and Control Engineering

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“…Some of these methods include indirect model reference adaptive control [12], model reference adaptive control with signal adaptation algorithm [13], and nonlinear control [14]- [20]. However, the dynamics of such systems is always slow compared to the dynamics of DC/DC converters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Peak Current Mode Controlled Boost Converter Supplied by Fuel Cell

Bjažić¹,

Ban²,

Perić³

2013

Journal of Power Electronics

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Adaptive control of a peak current mode controlled (PCM) boost converter supplied by a PEM fuel cell is described in this paper. The adaptive controller with reference model and signal adaptation is developed in order to compensate the deviation of the response during the change of the operating point. The procedure for determining the adaptive algorithm's weighting coefficients, based on a combination of the pole-zero placement method and an optimization method is proposed. After applying the proposed procedure, the optimal adaptive algorithm's weighting coefficients can be determined in just a few iterations, without the use of a computer, thus greatly facilitating the application of the algorithm in real systems. Simulation and experimental results show that the dynamic behavior of a highly nonlinear control system with a fuel cell and a PCM boost converter, can fairly accurately be described by the dynamic behavior of the reference model, i.e., a linear system with constant parameters.

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