A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus

He, Tian; Lu, Ziwang; Wang, Xu; Zhang, Xinlong; Huang, Yong; Tian, Guangyu

doi:10.1016/j.apenergy.2016.05.086

Cited by 57 publications

(23 citation statements)

References 33 publications

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“…In the online control of plug-in electric bus [68], the trip information was represented by the length ratio, and this mapping relationship was learned by a neural network. Reference [69] studied the pump speed control to optimize the waste heat recovery of internal-combustion engines.The authors used the dynamic programming and supervised learning methods, whose inputs were specially designed according to several physical differential equations.…”

Section: Category 3 Surrogate Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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Section: Category 3 Surrogate Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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“…As for the predetermined bus route, [27,[92][93][94] proposed a length ratio-based neural network energy management strategy for online control of PHEB to reduce the computational time and storage capacity of the micro-controller and to achieve approximate optimal control performance. The length ratio representing the space domain was chosen as the input variable of the neural network module to represent trip information, which consists of four parameters: trip length, trip duration, current driving length and current driving time.…”

Section: Neural Network-dynamic Programming (Nn-dp)mentioning

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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“…As the message of driving cycle is previously given, dynamic programming (DP) could obtain theoretically global optimal control. For example, a novel efficient neural network module structure is compared with DPbased controls to declare its optimality in [5]. However, its online effectiveness cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

2018

IEEE Trans. Ind. Inf.

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In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse the future power demand information into transition probability matrix (TPM) computation. Furthermore, the Kullback-Leibler (KL) divergence rate is employed to decide the alteration of control strategy. The lower-level computes the relevant energy management strategy, based on the updated TPM and a model-free reinforcement learning (RL) technique. Simulation results illustrate that the vehicular energy efficiency in the proposed scheme exceeds the common RL control by tuning the KL divergence value. Comparative results also show that the developed control strategy outperforms the common RL one, in terms of energy efficiency and computational speed.Index Terms-Energy management, hybrid tracked vehicle (HTV), Kullback-Leibler (KL) divergence rate, power demand prediction, reinforcement learning (RL).

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A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus

Cited by 57 publications

References 33 publications

Review of Learning-Assisted Power System Optimization

Review of Learning-Assisted Power System Optimization

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

A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

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