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E-raamat: Energy Efficient Non-Road Hybrid Electric Vehicles: Advanced Modeling and Control

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Energy Efficient Non-Road Hybrid Electric Vehicles: Advanced Modeling and ControlSuurem pilt
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This book analyzes the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications that work in continuous high dynamic operation. It also provides practical insights into maximizing the energy efficiency and drivability of such powertrains.





It introduces an energy-management control structure, which considers all the physical powertrain constraints and uses novel methodologies to predict the future load requirements to optimize the controller output in terms of the entire work cycle of a non-road vehicle. The load prediction includes a methodology for short-term loads as well as cycle detection methodology for an entire load cycle. In this way, the energy efficiency can be maximized, and fuel consumption and exhaust emissions simultaneously reduced.





Readers gain deep insights into the topics that need to be considered in designing an energy and battery management system for non-road vehicles. It also becomes clear that only a combination of management systems can significantly increase the performance of a controller.
1 Introduction
1(10)
1.1 Motivation
1(2)
1.2 Characteristic Applications of Non-Road Mobile Machines
3(1)
1.3 Configurations of Hybrid Electric Powertrains
4(1)
1.4 Challenges in Controlling Hybrid Electric Vehicles
5(1)
1.5 Proposed Concepts
6(2)
1.6 Main Contributions
8(3)
2 Battery Management
11(32)
2.1 Introduction
11(9)
2.1.1 Motivation
11(1)
2.1.2 Cell Chemistry-Dependent System Behavior of Batteries
12(1)
2.1.3 Challenges in Dynamic Battery Model Identification
13(1)
2.1.4 State of the Art
14(4)
2.1.5 Solution Approach
18(2)
2.2 Data-Based Identification of Nonlinear Battery Cell Models
20(6)
2.2.1 General Architecture and Structure of Local Model Networks
20(1)
2.2.2 Construction of LMN Using LOLIMOT
21(1)
2.2.3 Battery Cell Modeling Using LMN
22(4)
2.3 Optimal Model-Based Design of Experiments
26(7)
2.3.1 Optimization Criteria Based on the Fisher Information Matrix
27(2)
2.3.2 Formulation of the Constrained Optimization Problem
29(1)
2.3.3 Constrained Optimization
30(2)
2.3.4 Extensions on the Excitation Sequence
32(1)
2.4 Temperature Model of Battery Cells
33(2)
2.5 Battery Module Model Design
35(2)
2.5.1 Battery Cell Balancing in Battery Modules
35(1)
2.5.2 LMN-Based Battery Module Design
35(2)
2.6 State of Charge Estimation
37(6)
2.6.1 General Architecture of the SoC Observer Scheme
38(1)
2.6.2 SoC Fuzzy Observer Design
38(5)
3 Results for BMS in Non-Road Vehicles
43(24)
3.1 Generation of Reproducible High Dynamic Data Sets
43(3)
3.1.1 Measurement Procedures
44(1)
3.1.2 Test Hardware for Battery Cells
44(1)
3.1.3 Test Hardware for Battery Modules
45(1)
3.2 Battery Cells and Battery Module Specifications
46(1)
3.3 Training Data for Battery Cell Models
46(2)
3.4 Validation of Battery Cell Model Accuracy
48(9)
3.4.1 Battery Model Quality Improvement with Optimal DoE
48(3)
3.4.2 Comparison of Battery Cell Models with Different LMN Structures and Cell Chemistries
51(3)
3.4.3 Dynamic Accuracy of the LMN Battery Models
54(3)
3.5 Battery Cell Temperature Model Accuracy
57(1)
3.6 Battery Module Model Accuracy
58(3)
3.7 SoC Estimation Accuracy
61(6)
3.7.1 Battery Module SoC Estimation Results
62(2)
3.7.2 Battery Cell SoC Estimation Results
64(3)
4 Energy Management
67(30)
4.1 Introduction
67(3)
4.1.1 Challenges for Energy Management Systems
67(1)
4.1.2 State-of-the-Art
68(1)
4.1.3 Solution Approach
69(1)
4.2 Basic Concept of Model Predictive Control
70(2)
4.3 Cascaded Model Predictive Controller Design
72(18)
4.3.1 Architecture of the Control Concept
72(1)
4.3.2 System Models for Controller Design
73(4)
4.3.3 Structured Constraints for Controllers
77(2)
4.3.4 Slave Controller
79(4)
4.3.5 Master Controller
83(7)
4.4 Load and Cycle Prediction for Non-Road Machinery
90(7)
4.4.1 Short-Term Load Prediction
90(3)
4.4.2 Cycle Detection
93(4)
5 Application Example: Wheel Loader
97(10)
5.1 Hardware Configuration of the Hybrid Powertrain Test bed
97(1)
5.2 Energy Management in Wheel Loaders
98(9)
5.2.1 User-Defined Tuning of the Controller Penalties
99(1)
5.2.2 Simulation Results
99(2)
5.2.3 Experimental Results
101(6)
6 Conclusion and Outlook
107(2)
References 109
Prof. Stefan Jakubek is head of the Christian Doppler Laboratory (CDL) for Model Based Calibration Methodologies at the Vienna University of Technology with research goals that include the development of new and integrated methodologies for model based calibration of automotive systems (combustion engines, powertrain systems, hybrid components) and the implementation of these methodologies in essential calibration tasks.

Dr.techn. Johannes Unger, has research interests in the field of the control of the parallel hybrid electric powertrains as well as the modelling of the battery for the purpose of high accurate state of charge estimation with his research already published in high ranked scientific journals. Previously, Mr. Unger and Prof. Jakubek have collaborated in the field of order reduction with the aim of decreasing computational demands for controllers with their research awarded the NRW Young Scientist Award 2012.

Dr.-Ing. Marcus Quasthoff, is employed at Liebherr Machines Bulle SA, Switzerland and  is responsible for patent management and has responsibility for several research projects. In the Swiss Competence Center for Energy Research (SCCER) Dr.-Ing. Marcus Quasthoff is a board member, where he represents the topic chemical energy converters. Before he has joined Liebherr Machines Bulle SA, he was employed at MAN SE and has worked in the advanced development division in the department of alternative drive systems. 
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