A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

Wang, Yanzi; Wang, Weida; Zhao, Yongsheng; Yang, Lei; Chen, Wenjun

doi:10.3390/en9010025

Cited by 57 publications

(37 citation statements)

References 17 publications

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“…In the test progress, the fuel and electricity consumption are obtained by the integral at different moments and converted into the consumption of 100 km. In addition, the electricity consumption is converted into fuel consumption by Equation (6), and the sum of fuel and electricity is as the comprehensive energy consumption. The result is shown in Table 11; the proposed control strategy could improve the energy consumption by 4.94% compared with the original rule-based control strategy, and its validity and practicability are verified.…”

Section: Hardware-in-the-loop Testmentioning

confidence: 99%

“…In [5], the rule-based strategy was used to allocate the output between the hybrid energy storage system (HESS) and the assistance power unit (APU), and the MPC was introduced to regulate the output between the battery and the ultracapacitor. Reference [6] predicted the electric power demand by the state transition probability matrices of the electrical power with different speeds, and the result was that the overall loss incurred by the whole HESS is reduced. Although the above control strategies have obtained some certain positive results, they rely on the standard driving cycles too much, resulting in a certain deviation with actual driving scenes.…”

Section: Introductionmentioning

confidence: 99%

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Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Gao

2019

Journal of Control Science and Engineering

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To exert fully the energy economy performance of plug-in hybrid electric buses (PHEBs) and enhance the adaptability to different drivers and driving cycles, a control strategy for PHEB based on actual driving cycle with driving style characteristic is proposed in this paper. Through the actual city bus driving data, collected in real time, 6 actual driving cycles with driving style characteristic are fitted by using Principal Component Analysis (PCA) and Cluster Analysis (CA). Based on the 6 driving cycles, the key parameters of rule-based control strategy are optimized and established by a combinatorial optimization algorithm in Isight. Then, an identification model to recognize the current condition based on the Learning Vector Quantization (LVQ) neural network has been built and trained offline, which is integrated in the control strategy for PHEB to invoke the corresponding optimized key control parameters in real time. A hardware-in-the-loop (HIL) test is conducted, and the result shows that the proposed strategy could improve the energy consumption by 4.94%, compared with the original rule-based control strategy, and its validity and practicability are fully verified.

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Section: Hardware-in-the-loop Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Gao

2019

Journal of Control Science and Engineering

View full text Add to dashboard Cite

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“…To eliminate the feedthrough, the linearized plant in Equation (13) is augmented by adding first-order filters with time constant T a to inputs, as shown in Equation (15). T a is chosen as one-tenth or smaller of the AMPC sampling time [37,38].…”

Section: Eliminating Direct Feedthroughmentioning

confidence: 99%

Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles

et al. 2017

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This paper deals with the energy management strategy (EMS) for an on-board semi-active hybrid energy storage system (HESS) composed of a Li-ion battery (LiB) and ultracapacitor (UC). Considering both the nonlinearity of the semi-active structure and driving condition uncertainty, while ensuring HESS operation within constraints, an adaptive model predictive control (AMPC) method is adopted to design the EMS. Within AMPC, LiB Ah-throughput is minimized online to extend its life. The proposed AMPC determines the optimal control action by solving a quadratic programming (QP) problem at each control interval, in which the QP solver receives control-oriented model matrices and current states for calculation. The control-oriented model is constructed by linearizing HESS online to approximate the original nonlinear model. Besides, a time-varying Kalman filter (TVKF) is introduced as the estimator to improve the state estimation accuracy. At the same time, sampling time, prediction horizon and scaling factors of AMPC are determined through simulation. Compared with standard MPC, TVKF reduces the estimation error by 1~3 orders of magnitude, and AMPC reduces LiB Ah-throughput by 4.3% under Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, indicating superior model adaptivity. Furthermore, LiB Ah-throughput of AMPC under various classical driving cycles differs from that of dynamic programming by an average of 6.5% and reduces by an average of 10.6% compared to rule-based strategy of LiB Ah-throughput, showing excellent adaptation to driving condition uncertainty.

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“…Thus electricity or fuel prices can be predicted in order to best manage the HESS [21]. A Fuzzy HESS power management strategy based on Markov random prediction was introduced in [22]. Thus, the management of HESS takes into account the forecasts of future load and can execute a division of powers more successfully.…”

Section: Introductionmentioning

confidence: 99%

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

et al. 2019

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Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

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A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

Cited by 57 publications

References 17 publications

Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

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