Hardware-in-the-Loop Simulation of Robust Mode Transition Control for a Series–Parallel Hybrid Electric Vehicle

Zhang, Hu; Zhang, Yong; Yin, Chengliang

doi:10.1109/tvt.2015.2486558

Cited by 94 publications

(39 citation statements)

References 30 publications

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“…Zhang et al conducted several tests to verify the stability and adaptability of the controller, as shown in Figure . Case 1, case 2, and case 3 referred to the driving conditions of HEV from 0 to 8.89 m/s, 11.67 m/s, and 13.89 m/s in 12 seconds, respectively, while model uncertainties (1), model uncertainties (2) meant the condition response when HEV model parameters change to 95% and 105% of the standard value.…”

Section: Research On Robustness Of Dccsmentioning

confidence: 99%

Review on multi‐power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process

et al. 2020

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Summary Aiming at the problem of torque coordinated control between motor with high‐frequency response and engine with low‐frequency response of hybrid electric vehicle (HEV) during mode transition process, the mechanism of its coordinated control problem is deeply explored, and the essence of dynamic coordinated control strategy (DCCS) through torque coupling analysis is also revealed, namely, motor torque compensation control. Then a series of domestic and international researches on multi‐power sources coordinated control are systematically summarized from the establishment of electromechanical transmission (EMT) system model, mode switching process analysis, engine‐clutch‐motor coordinated control strategy based on torque estimation and speed feedback tracking, as well as the controller robustness analysis under system parameter perturbation and external disturbance. Finally, the key issues on the rapidity, adaptability, and experiment of torque coordination control which need to be solved in the subsequent researches are pointed out clearly.

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Section: Research On Robustness Of Dccsmentioning

confidence: 99%

Review on multi‐power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process

et al. 2020

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“…After decades of development, HILS has been deeply favored by scientific research units and manufacturers all over the world. In China, many manufacturers (CRRC, China's State Grid, China First Automobile) and higher teaching institutions (Shanghai Jiaotong University, National University of Defense Technology, Harbin Institute of Technology) have carried out scientific research in related fields based on HILS, and have achieved fruitful research results [41][42][43][44]. ATO has both optimization and control functions: the main processor unit (MPU) uses an optimization algorithm to optimize the train's ideal target speed distance curve based on constraints such as line condition and optimization objectives such as expected running time.…”

Section: The Relevant Data and Simulation Platform Of The Practical Ementioning

confidence: 99%

Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Wang

Liu

et al. 2019

Energies

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Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

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“…Due to the situation of the actual automatic train operation experiment, it is difficult to implement, and traditional simulation based on pure software environment cannot truly reflect the actual automatic train operation process; hardware-in-the-loop simulation (HILS) is often used in automatic train operation due to its characteristics [25,26]. At present, plenty of HILS-related products are researched, developed, and applied in various fields of rail vehicles, traction control system, hybrid electric vehicles, and electric cars, and numerous relative research findings have been achieved [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

Wang

Sheng

et al. 2020

Energies

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In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

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Hardware-in-the-Loop Simulation of Robust Mode Transition Control for a Series–Parallel Hybrid Electric Vehicle

Cited by 94 publications

References 30 publications

Review on multi‐power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process

Review on multi‐power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process

Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

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