Study on Power Management Strategy of HEV using Dynamic Programming

Lee, Heeyun; Kang, Changbeom; Park, Yeong-il; Won, Suk

doi:10.3390/wevj8010274

Cited by 14 publications

(9 citation statements)

References 3 publications

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“…They are heuristic strategies implemented as "if-then-else" rules. Human expertise, intuition, operation boundaries, mathematical models, and safety considerations determine such rules [49,58].…”

Section: Deterministic Rule-basedmentioning

confidence: 99%

On Machine Learning-Based Control for Energy Management in Construction Machines

Raduenz

2022

Linköping Studies in Science and Technology. Dissertations

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High energy efficiency is a key requirement for modern construction machinery. This is because of stricter environmental targets, electrification, and reduction of operation costs. To meet this requirement, the powertrain architectures of the machines are becoming increasingly complex, for example through hybridisation of drivetrain and work functions, or with improved hydraulic systems. However, the more complex the architecture is, the harder the management of splitting power between different sources and consumers. The number of work functions, operating environments, and tasks these machines engage in, along with the added degrees of freedom with respect to how energy can be recovered, exchanged, and reused, makes them unique. Therefore, the development of control strategies for energy management in such machines requires specific research and development with their architecture and application in focus. This doctoral thesis presents an analysis of two methods for the development of machine learning-based energy management strategies for construction machines. One is based on supervised learning and the other on reinforcement learning. The methods use optimisation to find optimised solutions for the control problem of the systems and machine learning for learning and implementing the control decisions. In both methods, models of the physical systems are used for the learning and training. The thesis highlights and confirms, with experimental results, the potential of such methods to derive control strategies for these machines. The studied methods can learn and implement improved control decisions in the real systems that result in the potential for increased efficiency. At the same time, their robustness is shown in the application to unseen scenarios during training, although that does not eliminate the need for further training in the real systems after deployment. The thesis also increases the comprehensiveness on energy management for construction machines. The thesis was completed in a double-degree format between the

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Section: Deterministic Rule-basedmentioning

confidence: 99%

On Machine Learning-Based Control for Energy Management in Construction Machines

Raduenz

2022

Linköping Studies in Science and Technology. Dissertations

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“…Typically, we select several sets of battery parameters as the input layer of the neural network. Reference [105], for example, employs neural networks to control battery power. Reference [106] inputs voltage, capacity, and internal resistance (as input parameters) into a backpropagation (BP) neural network model.…”

Section: Neural Network Algorithmmentioning

confidence: 99%

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.

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“…For the optimal control strategy of a PHEV, rule-based control for the CD and CS modes has been widely used based on heuristic data [6,7]. Rule-based control has the advantage of easy on-line application, but cannot guarantee optimality for real driving cycles [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, since these control strategies do not consider the effect of the present battery SOC, they cannot guarantee global optimality for the CD and CS mode. To ensure global optimality, rule-based control was proposed by implementing an optimization tool, such as dynamic programming (DP) [6,13,14] or Pontryagin's minimum principle (PMP) [15][16][17]. In a previous study [2], a rule-based mode control (RBC) strategy was obtained for the CS mode using DP with power electronics (PE) and drivetrain losses.…”

Section: Introductionmentioning

confidence: 99%

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

et al. 2018

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This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9-3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.

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Study on Power Management Strategy of HEV using Dynamic Programming

Cited by 14 publications

References 3 publications

On Machine Learning-Based Control for Energy Management in Construction Machines

On Machine Learning-Based Control for Energy Management in Construction Machines

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

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