Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Chen, Luming; Liao, Zili; Ma, Xiaofu

doi:10.1109/access.2020.3036644

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…In the MPC of energy management, it is important to predict some data of the system. A non-linear MPC system based on random power prediction methods was proposed to achieve the best performance in heavy HEV that lack navigation support [101]. The data-driven prediction method is used to obtain high-precision ultra-short-term power prediction, and then find the optimal numerical solution through nonlinear MPC in real-time.…”

Section: Ideep Reinforcement Learning In Simulationmentioning

confidence: 99%

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Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Huang,

Xiao,

Gao

et al. 2023

Energies

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The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques may not meet these requirements. Driven by the ability to achieve intelligent decision-making by exploring data, artificial intelligence (AI) and digital twins (DT) have gained much interest within the transportation sector. Currently, research on EMS for OBMGs primarily focuses on AI technology, while overlooking the DT. This article provides a comprehensive overview of both information technology, particularly elucidating the role of DT technology. The evaluation and analysis of those emerging information technologies are explicitly summarized. Moreover, this article explores potential challenges in the implementation of AI and DT technologies and subsequently offers insights into future trends.

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Section: Ideep Reinforcement Learning In Simulationmentioning

confidence: 99%

“…Control [101][102][103] Inherent ability to tackle con-straints on input, output, andstates; Real-time optimization Depends on prediction accuracy;Seldom achieves globaloptimal solution…”

Section: Model Predictivementioning

confidence: 99%

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Huang,

Xiao,

Gao

et al. 2023

Energies

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“…However, since the model is non-linear, the optimization problem is solved using forward DP. In [29], it is presented a nonlinear MPC for a heavy-duty hybrid electric vehicle (HEV) based on a random power prediction method. The solution is based on a grey Markov chain model that predicts the future load power demand, followed by a model linearization, which is used to regulate the SoC of the battery pack and the super capacitor bank, while the fuel consumption is minimized.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control-Based Energy Management System for a Hybrid Electric Agricultural Tractor

Curiel-Olivares,

Johnson,

Escobar

et al. 2023

IEEE Access

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In this work, a model predictive control (MPC) based energy management system (EMS) is designed and implemented to control the powertrain power flow of a series hybrid electric agricultural tractor. The proposed MPC-based EMS considers the battery SoC regulation and the fuel consumption minimization subject to power sources constraints. In addition to the control objectives established in the MPC, the analysis of the results shows the influence of the proposed scheme on the battery state of health (SoH), the battery temperature, the fuel economy and the engine performance based in the break specific fuel consumption (BSFC) map. For comparison purposes, a conventional rule-based (RB) EMS is also implemented. The results show that the MPC-EMS can achieve the main objectives with 7.2% fuel reduction while satisfactorily managing the systems constraints, and positively impacting the battery SoH and temperature in comparison with the RB-EMS.INDEX TERMS energy management system, vehicle power management, power management system, model predictive control, hybrid electric vehicles, series hybrid electric vehicle, agricultural tractor.

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“…It controlled mainly the state of charge of the battery, DC Bus voltage, and UC voltage by using PI control. These control schemes could be tuned easily and it could be done by online tuning for better tracking (Tian et al, 2019;Chen et al, 2020;Lee et al, 2020;Li et al, 2020;Ostadian et al, 2020;Niu et al, 2022). Here, the power of the load was exchanged predominantly to provide steady-state demand of load by using the fuel cell system.…”

Section: Introductionmentioning

confidence: 99%

An energy management scheme for improving the fuel economy of a fuel cell/battery/supercapacitor-based hybrid electric vehicle using the coyote optimization algorithm (COA)

Mounica,

Obulesu

2023

Front. Energy Res.

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This study describes a multi-input power system that is suited for fueling electric automobiles, InterCitys, and airplanes, particularly in situations with significant fluctuating load demand. The dual framework utilizes fuel cells (FC), batteries, and super capacitors (SCs). An energy management system (EMS) remains a critical aspect of lowering overall hydrogen consumption and minimizing the degradation of FC functionality. A novel EMS that has been suggested focused on a novel optimization method known as the Coyote optimization algorithm (COA), and it considers the fact that the total load is adequately supplied within the limitations of each power source. To minimize the hydrogen consumption. By maximizing the power generated by the energy storage devices, the energy acquired from the FC is reduced. In comparison to other optimization methods, the COA would be a practical, effective, and relatively straightforward optimizer that only involves a limited number of controlling factors to be set. The framework application MATLAB/Simulink is used to create the proposed method. In order to show the effectiveness of the proposed methodology, a study with several different conventional techniques is performed, which includes the classic proportional-integral control mechanism, the frequency decoupling with state machine (FDSM) controlling technique, the equivalent consumption minimization scheme (ECMS), and the external energy minimization scheme (EEMS). The efficacy of the algorithm and the FC’s aggregate H2 usage serve as the focal points for comparison in this work. The outcomes demonstrate that the recommended COA strategy is superior and more effective than the alternative approaches.

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Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Cited by 8 publications

References 55 publications

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Model Predictive Control-Based Energy Management System for a Hybrid Electric Agricultural Tractor

An energy management scheme for improving the fuel economy of a fuel cell/battery/supercapacitor-based hybrid electric vehicle using the coyote optimization algorithm (COA)

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