Multi-input model predictive speed control of lean-burn natural gas engine in range-extended electric vehicles

Xiong, Wei; Ye, Jie; Gong, Qichangyi; Han, Feng; Xu, Jinbang; Shen, Anwen

doi:10.1016/j.energy.2021.122165

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The simulation results demonstrated the effectiveness and robustness of the proposed control scheme, outperforming the PI controller with Smith predictor under different operating conditions. It is feasible to extend the similar works that is mentioned above [15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 84%

An elite approach to re-design Aquila optimizer for efficient AFR system control

Izci,

Ekinci,

Hussien

2023

PLoS ONE

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Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAO’s outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average ± standard deviation range of 0.6823±0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p<0.001) between ImpAO and other algorithms. ImpAO also outperforms competitors in terms of elapsed time, with an average of 43.6072 s per run. Transient response analysis reveals that ImpAO achieves a lower rise time of 1.1845 s, settling time of 3.0188 s, overshoot of 0.1679%, and peak time of 4.0371 s compared to alternative algorithms. The algorithm consistently achieves lower error-based cost function values, indicating more accurate control. ImpAO demonstrates superior capabilities in tracking the desired input signal compared to other algorithms. Comparative assessment with recent metaheuristic algorithms further confirms ImpAO’s superior performance in terms of transient response metrics and error-based cost functions. In summary, the simulation results provide strong evidence of the exceptional performance and effectiveness of the proposed ImpAO algorithm. It establishes ImpAO as a reliable and superior solution for optimizing the FF mechanism-supported PI controller for the AFR system, surpassing state-of-the-art algorithms and recent metaheuristic optimizers.

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Section: Introductionmentioning

confidence: 84%

An elite approach to re-design Aquila optimizer for efficient AFR system control

Izci,

Ekinci,

Hussien

2023

PLoS ONE

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“…Even though this controller omits the interaction between the two control loops, it still demonstrates the capability of achieving stable engine operation and meeting the NOx emission requirements. In Xiong et al, 10 a multi-input model predictive controller is proposed to regulate the speed of a lean-burn natural gas engine in range extended vehicles. It is revealed from simulated and experimental results that the proposed model predictive controller can significantly improve the anti-interference ability and response speed of the engine system under heavy load.…”

Section: Introductionmentioning

confidence: 99%

Control design-oriented modeling and µ-synthesis-based robust multivariate control of a turbocharged natural gas genset engine

Qiu

Rayasam

Shaver

et al. 2023

International Journal of Engine Research

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This paper describes a comprehensive framework for the development and validation of a model-based robust multivariate controller, which is used to regulate the gas exchange processes of an advanced turbocharged natural gas genset engine. The natural gas genset engine involved in this study features a multi-input and multi-output structure and is highly nonlinear, making it a challenge for which to design a controller. A control design-oriented model describing the dynamics of the studied engine is first developed and validated, using modeling techniques commonly employed for modeling of turbocharged engines. A novel robust model-based multivariate controller is then synthesized to incorporate the significant coupling between the engine actuators (throttle valve, bypass valve, and fuel valve) and the desired control objectives (engine speed, throttle differential pressure, and air-to-fuel ratio). To verify the effectiveness of the synthesized controller, it is compared to a benchmark production controller using an experimentally-validated simulation model. Simulation results indicate the merits of the synthesized robust multivariate controller in terms of better tracking performance (robust multivariate controller reduces steady-state error of engine speed, throttle differential pressure, and AFR at most by 100%, 58.9%, and 100% respectively, compared to benchmark production controller), faster transient response (robust multivariate controller reduces settling time of engine speed, throttle differential pressure, and AFR at most by 74.5%, 47.8%, and 71.2% respectively, compared to the benchmark production controller), and finer coordination among multiple interacted actuators when faced with multifacted control objectives.

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Feedforward-Compensated PI Controller Design for Air–Fuel Ratio System Control Using Enhanced Weighted Mean of Vectors Algorithm

2023

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Multi-input model predictive speed control of lean-burn natural gas engine in range-extended electric vehicles

Cited by 10 publications

References 21 publications

An elite approach to re-design Aquila optimizer for efficient AFR system control

An elite approach to re-design Aquila optimizer for efficient AFR system control

Control design-oriented modeling and µ-synthesis-based robust multivariate control of a turbocharged natural gas genset engine

Feedforward-Compensated PI Controller Design for Air–Fuel Ratio System Control Using Enhanced Weighted Mean of Vectors Algorithm

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