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Table of Contents
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 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
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 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