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Record Nr. |
UNINA9910254250403321 |
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Autore |
Unger Johannes |
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Titolo |
Energy Efficient Non-Road Hybrid Electric Vehicles : Advanced Modeling and Control / / by Johannes Unger, Marcus Quasthoff, Stefan Jakubek |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (121 p.) |
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Collana |
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SpringerBriefs in Applied Sciences and Technology, , 2191-530X |
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Disciplina |
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Soggetti |
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Renewable energy resources |
Electronic circuits |
Automotive engineering |
Engines |
Machinery |
Renewable and Green Energy |
Circuits and Systems |
Electronic Circuits and Devices |
Automotive Engineering |
Engine Technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Preface; Contents; 1 Introduction; 1.1 Motivation; 1.2 Characteristic Applications of Non-Road Mobile Machines; 1.3 Configurations of Hybrid Electric Powertrains; 1.4 Challenges in Controlling Hybrid Electric Vehicles; 1.5 Proposed Concepts; 1.6 Main Contributions; 2 Battery Management; 2.1 Introduction; 2.1.1 Motivation; 2.1.2 Cell Chemistry-Dependent System Behavior of Batteries; 2.1.3 Challenges in Dynamic Battery Model Identification; 2.1.4 State of the Art; 2.1.5 Solution Approach; 2.2 Data-Based Identification of Nonlinear Battery Cell Models |
2.2.1 General Architecture and Structure of Local Model Networks2.2.2 Construction of LMN Using LOLIMOT; 2.2.3 Battery Cell Modeling Using LMN; 2.3 Optimal Model-Based Design of Experiments; 2.3.1 Optimization Criteria Based on the Fisher Information Matrix; 2.3.2 |
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Formulation of the Constrained Optimization Problem; 2.3.3 Constrained Optimization; 2.3.4 Extensions on the Excitation Sequence; 2.4 Temperature Model of Battery Cells; 2.5 Battery Module Model Design; 2.5.1 Battery Cell Balancing in Battery Modules; 2.5.2 LMN-Based Battery Module Design; 2.6 State of Charge Estimation |
2.6.1 General Architecture of the SoC Observer Scheme2.6.2 SoC Fuzzy Observer Design; 3 Results for BMS in Non-Road Vehicles; 3.1 Generation of Reproducible High Dynamic Data Sets; 3.1.1 Measurement Procedures; 3.1.2 Test Hardware for Battery Cells; 3.1.3 Test Hardware for Battery Modules; 3.2 Battery Cells and Battery Module Specifications; 3.3 Training Data for Battery Cell Models; 3.4 Validation of Battery Cell Model Accuracy; 3.4.1 Battery Model Quality Improvement with Optimal DoE; 3.4.2 Comparison of Battery Cell Models with Different LMN Structures and Cell Chemistries |
3.4.3 Dynamic Accuracy of the LMN Battery Models3.5 Battery Cell Temperature Model Accuracy; 3.6 Battery Module Model Accuracy; 3.7 SoC Estimation Accuracy; 3.7.1 Battery Module SoC Estimation Results; 3.7.2 Battery Cell SoC Estimation Results; 4 Energy Management; 4.1 Introduction; 4.1.1 Challenges for Energy Management Systems; 4.1.2 State-of-the-Art; 4.1.3 Solution Approach; 4.2 Basic Concept of Model Predictive Control; 4.3 Cascaded Model Predictive Controller Design; 4.3.1 Architecture of the Control Concept; 4.3.2 System Models for Controller Design |
4.3.3 Structured Constraints for Controllers4.3.4 Slave Controller; 4.3.5 Master Controller; 4.4 Load and Cycle Prediction for Non-Road Machinery; 4.4.1 Short-Term Load Prediction; 4.4.2 Cycle Detection; 5 Application Example: Wheel Loader; 5.1 Hardware Configuration of the Hybrid Powertrain Test bed; 5.2 Energy Management in Wheel Loaders; 5.2.1 User-Defined Tuning of the Controller Penalties; 5.2.2 Simulation Results; 5.2.3 Experimental Results; 6 Conclusion and Outlook; References |
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Sommario/riassunto |
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Analyzing the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications, which work in continuous high dynamic operation, this book gives practical insight in to how to maximize the energetic efficiency and drivability of such powertrains. The book addresses an energy management control structure, which considers all constraints of the physical powertrain and uses novel methodologies for the prediction of the future load requirements to optimize the controller output in terms of an entire work cycle of a non-road vehicle. The load prediction includes a methodology for short term loads as well as for an entire load cycle by means of a cycle detection. A maximization of the energetic efficiency can so be achieved, which is simultaneously a reduction in fuel consumption and exhaust emissions. Readers will gain a deep insight into the necessary topics to be considered in designing an energy and battery management system for non-road vehicles and that only a combination of the management systems can significantly increase the performance of a controller. |
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