Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

Guo, Hongwei; Liu, Congzhi; Yong, Jia-Wang; Cheng, Xingqun; Muhammad, Fahad

doi:10.1109/access.2019.2919684

Cited by 25 publications

(17 citation statements)

References 46 publications

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“…The closed-loop stability of the proposed controller is demonstrated based on the Lyapunov stability theory [44]. DefiningW(t) = W * (t)−W(t) as the weight estimation error of disturbance, then the positive-definite Lyapunov function candidate can be chosen as:…”

Section: B Controller Developmentmentioning

confidence: 99%

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Chen

2019

IEEE Access

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Robot-assisted rehabilitation therapy has become an important technology applied to recover the motor functions of disabled individuals. In the present paper, an adaptive admittance control strategy combined with a neural-network-based disturbance observer (AACNDO) is developed for a therapeutic robot to provide upper extremity movement assistance. Firstly, a comprehensive overview of the robot hardware and real-time control system is introduced. Then, the dynamics-based adaptive admittance controller is designed to improve human-robot interaction compliance and induce the active participation of the patients during rehabilitation training. A disturbance observer with a radial basis function network is designed to guarantee the control performance with external uncertainties and dynamics error. Besides, an adaption law is integrated into the admittance model to adjust the interaction compliance in different working areas based on the motion intention and recovery phase of the patient. Further experimental investigations, including sinusoidal trajectory tracking experiments, circular trajectory tracking experiments with admittance adjustment, and intention-based resistive training experiments, are conducted by three volunteers. Finally, the experimental results validate the feasibility and effectiveness of the rehabilitation robot and AACNDO scheme in providing patient-passive and patient-cooperative rehabilitation training.

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Section: B Controller Developmentmentioning

confidence: 99%

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Chen

2019

IEEE Access

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“…In addition, ILC is applied to an uninterruptible power supply 6 and for optimal residential load scheduling 44 . In building automation, data-driven methods for demand response in the residential building sector are taken into account 45 ; ILC also addresses frequency control with high penetration of wind integration 46 ; it is further applied to energy management in electric vehicles 47 . Hence, most of the literature combining energy management and ILC focus on single nodes in a grid without emphasis on the overall grid perspective.…”

Section: B Energy Managementmentioning

confidence: 99%

A multiplex, multi-timescale model approach for economic and frequency control in power grids

Strenge

Schultz

Kurths

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Power systems are subject to fundamental changes due to the increasing infeed of decentralised renewable energy sources and storage. The decentralised nature of the new actors in the system requires new concepts for structuring the power grid, and achieving a wide range of control tasks ranging from seconds to days. Here we introduce a multiplex dynamical network model covering all control timescales. Crucially, we combine a decentralised, self-organised lowlevel control and a smart grid layer of devices that can aggregate information from remote sources. The safety-critical task of frequency control is performed by the former, the economic objective of demand matching dispatch by the latter. Having both aspects present in the same model allows us to study the interaction between the layers. Remarkably, we find that adding communication in the form of aggregation does not improve the performance in the cases considered. Instead, the self-organised state of the system already contains the information required to learn the demand structure in the entire grid. The model introduced here is highly flexible, and can accommodate a wide range of scenarios relevant to future power grids. We expect that it is especially useful in the context of low-energy microgrids with distributed generation.Highly decentralised power grids, possibly in the context of prosumer systems, require new concepts for their stable operation. We expect that both self-organised systems as well as intelligent devices with communication capability that can aggregate information from remote sources will play a central role. Here we introduce a multiplex network model that combines both aspects, and use it in a basic scenario and uncover surprising interactions between the layers.

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“…3 However, it is still a difficult question to achieve optimal distribution of the demand power between the fuel-based and electricity-based power sources. 4 Furthermore, the engine of HEVs cannot always work in high-efficiency range. Therefore, further research of energy management strategy (EMS) which enables the single-shaft parallel HEV to work efficiently during complex driving cycles is challenging.…”

Section: Introductionmentioning

confidence: 99%

A stochastic optimal energy management strategy considering battery health for hybrid electric bus

Cui

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The problem of battery health coupled with energy management brings a considerable challenge to the hybrid electric bus. To address this challenge, three contributions are made to realize optimal energy management control while prolonging battery life. First, a semi-empirical aging model of lithium iron phosphate battery is built and identified by the data fitting method, based on the battery cycling test. Besides, a severity factor map is constructed by employing the proposed aging model to characterize the relative aging of the battery under different operating conditions. Second, to make the driver demand torque more appropriate for statistical prediction, a Markov chain is formulated to predict driving behavior and also a stochastic vehicle mass estimation method is proposed to assist the prediction of required torque. Then, a stochastic multi-objective optimization problem is formulated by taking the severity factor map as a battery degradation criterion, where minimized battery degradation and fuel consumption can be simultaneously realized. Finally, a stochastic model predictive control strategy that considers battery health is established. Both simulation and hardware-in-loop tests are performed. The results demonstrate that fuel economy and battery degradation can be improved by 16.73% and 13.8% compared with rule-based strategy, respectively.

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Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

Cited by 25 publications

References 46 publications

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

A multiplex, multi-timescale model approach for economic and frequency control in power grids

A stochastic optimal energy management strategy considering battery health for hybrid electric bus

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