An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming

Qin, Feiyan; Liu, Weimin; Hu, Yue; Xu, Guoqing

doi:10.3390/a11030033

Cited by 9 publications

(5 citation statements)

References 17 publications

(17 reference statements)

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“…Based on comparisons, the RBFNN-based NDP EMS supports the efficiency of the CWNN and MRWNN. Qin et al (2018) [56] introduced online energy management control of hybrid electric vehicles based on NDP to optimize battery state of charge and fuel economy at the same time. In the proposed NDP method, the action network was a conventional wavelet neural network, and the critic network was a multi-resolution wavelet neural network.…”

Section: Wnnmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Wnnmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Under specific driving conditions, the researchers were able to reduce energy consumption by up to 5.77%. Qin et al [19] employed neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state-of-charge (SOC). Mathur et al [20] developed a robust online scheduling framework that utilizes stochastic optimization within a modelbased feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters.…”

Section: Scheduling Approachesmentioning

confidence: 99%

“…Constraints (19) ensures that the super capacitor is either charging, supplying, or unused at each time step k.…”

Section: Battery Modelmentioning

confidence: 99%

Energy Management of a Multi-Source Power System

2021

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This work focuses on energy management for a system operated by multiple energy sources which include batteries, super capacitors, a hydrogen fuel cell, and a photovoltaic cell. The overall objective is to minimize the power consumption from all sources needed to satisfy the system’s power demand by optimizing the switching between the different energy sources. A dynamic mathematical model representing the energy sources is developed taking into account the different constraints on the system, i.e., primarily the state-of-charge of the battery and the super capacitors. In addition to the model, a heuristic approach is developed and compared with the mathematical model. Both approaches were tested on a multi-energy source ground robot as a prototype. The novelty of this work is that the scheduling of an energy system consisting of four different types of sources is compared by performing analysis via dynamic programming, and a heuristic approach. The results generated using both methods are analyzed and compared to a standard mode of operation. The comparison validated that the proposed approaches minimize the average power consumption across all sources. The dynamic modeling approach performs well in terms of optimization and provided a superior switching sequence, while the heuristic approach offers the definite advantages in terms of ease of implementation and simple computation requirements. Additionally, the switching sequence provided by the dynamic approach was able to meet the power demand for all simulations performed and showed that the average power consumption across all sources is minimized.

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“…Similar to BP-NN or RBF-NN, the adaptation of weights and parameters of both networks can be achieved by error back propagation and a gradient descent algorithm. A neuro-dynamic programming (NDP) method using MRWNN as the critic network and the CWNN as the action network was detailed in [111], and it was able to optimize fuel economy for online application and optimize SOC without previewing future traffic information. In addition, a correction model for the output of the action network was used to discretize the continuous gear ratio to the real vehicle gear ratio of the vehicle.…”

Section: Endmentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming

Cited by 9 publications

References 17 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energy Management of a Multi-Source Power System

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

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