Using optimal control in concept evaluation and system optimization of diesel-electric hybrid construction machines

Frank, Bobbie

doi:10.1109/esars-itec.2016.7841323

Cited by 8 publications

(14 citation statements)

References 4 publications

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“…This should reduce the bias in concept evaluation during early development stages. Examples of such applications are found in [14] for diesel-electric hybrid wheel loaders and in [57] for hydraulic hybrid passenger vehicles.…”

Section: Offline Methodsmentioning

confidence: 99%

“…At the same time, the energy stored in the secondary source can be used at specific moments to reduce engine power requirements while meeting the necessary power demand [10]. In series hybrid wheel loaders, the decoupling between drivetrain and work functions is a major contribution for fuel savings [11]. This is because the power to each function can be controlled according to the respective demand.…”

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

“…This is because the power to each function can be controlled according to the respective demand. Examples of series hybrid mobile machines can be found in [12][13][14][15][16].…”

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

“…Hydraulic hybrid parallel systems are attractive for applications with frequent start-stop, which is the case for wheel loaders operating in short loading cycles. At least at the research level, parallel hybrid wheel loaders were studied in [10,14,[17][18][19]. A parallel hybrid system that can recover energy from work functions and drivetrain is presented in [20].…”

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

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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: Offline Methodsmentioning

confidence: 99%

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

“…This is because the power to each function can be controlled according to the respective demand. Examples of series hybrid mobile machines can be found in [12][13][14][15][16].…”

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

Section: Examples Of Machine Architecturesmentioning

confidence: 99%

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

Raduenz

2022

Linköping Studies in Science and Technology. Dissertations

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“…According to the definition of the International Electrotechnical Commission, a HEV is a vehicle with two or more forms of energy reserves, one of which can be converted into electric energy [10]. Like HEVs, DEH adopts a battery or ultracapacitor as ESS [6]. The use of fossil energy is reduced in DEH by combining hydraulic energy and electric energy during operation.…”

Section: Diesel-electric Hybridmentioning

confidence: 99%

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Zhang

Wang

et al. 2019

Energies

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Hybrid Construction Machinery (HCM), known as an effective and crucial solution for the issues of environment pollution and energy shortage, has aroused increasing attention from manufacturers and researchers. A suitable energy management strategy is the vital technology to determine the energy saving and emission reduction performance of HCM. In the present paper, the difference between construction machinery and automobiles is first analyzed from the perspective of configuration, and the energy-based HCM configuration classification method is introduced and analyzed. Second, the development of HCM energy management strategy is reviewed along with relevant references, and the HCM energy management strategies are classified and summarized. In the meantime, the characteristics of each strategy are compared and analyzed, and the application of HCM energy management strategy is analyzed based on the relevant research results. Lastly, the state, challenges facing and the trend of HCM energy management strategy are analyzed on the levels of theory, manufacturer and market. According to the analysis, though progress has been achieved in energy management technology of HCM driven by market and policy, many challenges and problems remain in the electrification and intellectualization of HCM and the testing, application and improvement of the strategy. The contribution of this paper can be identified in three points: First, it can be referenced to solve relevant engineering problems. Second, it lays the foundation for the proposal of new ideas. Third, it highlights the state-of-the-art trends and avoids what has already been done.

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Mobile Machine with Hydrostatic Hybrid Drive Train

Rădoi,

Dumitrescu,

Tudor

et al. 2023

Lecture Notes in Mechanical Engineering

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Using optimal control in concept evaluation and system optimization of diesel-electric hybrid construction machines

Cited by 8 publications

References 4 publications

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

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

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Mobile Machine with Hydrostatic Hybrid Drive Train

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