Power allocation smoothing strategy for hybrid energy storage system based on Markov decision process

Li, Guidan; Yang, Zhe; Li, Bin; Bi, Huakun

doi:10.1016/j.apenergy.2019.03.001

Cited by 26 publications

(12 citation statements)

References 30 publications

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“…Moreover, Eq. (36) indicates that the iso-efficiency surface of the motor is fitted by a polynomial surface about the fourth power of the motor torque and the fourth power of the speed. 4 4 Table 2 presents the coefficients of Eq.…”

Section: ) Binary Polynomial Fitting Based On the Least Square Methodsmentioning

confidence: 99%

“…Eq. (36) indicates that when the required torque of the driver is predicted based on the Markov model, the engine torque and the motor torque should always meet the equality constraints on the driver's required torque. All output parameters in the prediction area can be obtained by solving Eq.…”

Section: ) Mpcmentioning

confidence: 99%

“…Meanwhile, online computational expenses of this energy management strategy are relatively small when the comparison is made with the global optimization. On the other hand, MPC outperforms the rule-based strategy from the optimization efficiency viewpoint [36]- [38]. Luo et al considered the nonlinearity of the engine and motor efficiency and proposed a control strategy based on the nonlinear control theory, which greatly improved the safety and fuel economy of engines [39].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Yang

Huang

et al. 2021

IEEE Access

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Section: ) Binary Polynomial Fitting Based On the Least Square Methodsmentioning

confidence: 99%

Section: ) Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Yang

Huang

et al. 2021

IEEE Access

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“…Markov chain (MC) models are well-suited to represent the uncertainty in velocity, power demand ... etc., which can lower both the information required for implementation and the on-board computing burden [37], [38]. To improve the prediction accuracy of the MC, some papers use the second-order or even higher-order Markov algorithm [39], [40], but the calculation amount of the algorithms will double or many times.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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“…This is despite the fact that several supercapacitor‐based control strategies have been proposed to protect the battery power supply. The earlier strategies have been schemed according to the trial‐and‐error and empirical procedures for example, filter‐based power segregation technique, 38 rule‐based fuzzy logic technique 39 model predictive technique, 40 while the later ones have been optimally upgraded for example, dynamic programming technique 41 multi‐objective optimization technique 42 and Markov decision technique 43 …”

Section: Introductionmentioning

confidence: 99%

Robust fractional‐order super‐twisting sliding mode control to accurately regulate lithium‐battery / super‐capacitor hybrid energy storage system

Falehi

Torkaman

2021

Int J Energy Res

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Summary One of the main challenges in front of the inventors and researchers to compensate the power quality problems is the existence of an efficient and reliable electric energy source for dynamic voltage restorer (DVR). Recent advances in the technologies of lithium‐battery (LB) and super‐capacitor (SC) have made them possible to be used as hybrid energy storage system (HESS) for DC‐power supply of DVR. But then, a robust and efficient controller must be essentially designed to compensate the intricate correlation between system components, unknown nonlinearities, parameter uncertainties related to the HESS. Hence, fractional‐order super‐twisting sliding mode control (FOSTSMC) is proposed for LB/SC‐HESS to smoothly and accurately smoothly track the current reference and control the DC‐link voltage of the asymmetric half‐cascaded multilevel inverter of DVR during different compensation conditions. As the primary controller, FOSTSMC has compensated the unknown nonlinear parts and parameter uncertainties related to LB/SC‐HESS through the real‐time disturbance estimation using sliding mode state and disturbance observer (SMSDO). Both the battery current and DC‐link voltage due to the physical measurability are chosen to design the control scheme based on the proposed FOSTSMC. The proposed controller has been compared with the proportional integral derivative and the sliding mode control under different probable scenarios to evaluate and validate its observability and controllability. To better present the effects of FOSTSMC, AHCMLI, and HESS‐based DVR, they have been quantified as follows: Three real‐time stability benchmarks that is, overshoot, undershoot and settling time are respectively attained for FOSTSMC: 0.6, 0.4, and 0.03; for sliding mode control: 1.1, 0.7 and 0.019; for proportional integral derivative: 0.9, 2, and 0.117. That is to say, FOSTSMC can smoothly and accurately track the current reference and control the DC‐link voltage during different compensation conditions as compared with other controllers. Asymmetric half‐cascaded multilevel inverter can provide 33 steps with consideration of 12 unidirectional switches, whereas binary, trinary, and other multilevel structures can provide less than 27 steps with consideration of 12 switches. The quasi‐AC voltage synthesizer‐based DVR can completely and accurately compensate the voltage sag and swell with THD of 2.12 and 2.35, respectively. The THD of mal‐compensation voltage sag and swell using the conventional DVR are respectively 11.57 and 13.86.

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Power allocation smoothing strategy for hybrid energy storage system based on Markov decision process

Cited by 26 publications

References 30 publications

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

Robust fractional‐order super‐twisting sliding mode control to accurately regulate lithium‐battery / super‐capacitor hybrid energy storage system

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