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000753808 019__ $$a940438261
000753808 020__ $$a9783319297965$$q(electronic book)
000753808 020__ $$a3319297961$$q(electronic book)
000753808 020__ $$z9783319297958
000753808 0247_ $$a10.1007/978-3-319-29796-5$$2doi
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000753808 035__ $$aSP(OCoLC)938890916$$z(OCoLC)940438261
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000753808 049__ $$aISEA
000753808 050_4 $$aTL221.15
000753808 08204 $$a629.22/93$$223
000753808 1001_ $$aUnger, Johannes,$$eauthor.
000753808 24510 $$aEnergy efficient non-road hybrid electric vehicles$$h[electronic resource] :$$badvanced modeling and control /$$cJohannes Unger, Marcus Quasthoff, Stefan Jakubek.
000753808 264_1 $$aSwitzerland :$$bSpringer,$$c[2016]
000753808 264_4 $$c©2016
000753808 300__ $$a1 online resource :$$billustrations.
000753808 336__ $$atext$$btxt$$2rdacontent
000753808 337__ $$acomputer$$bc$$2rdamedia
000753808 338__ $$aonline resource$$bcr$$2rdacarrier
000753808 4901_ $$aSpringerBriefs in applied sciences and technology
000753808 504__ $$aIncludes bibliographical references.
000753808 5050_ $$aPreface; 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
000753808 5058_ $$a2.2.1 General Architecture and Structure of Local Model Networks2.2.2 Construction of LMN Using LOLIMOT; 2.2.3 Battery Cell Modeling Using LMN; 2.3 Optimal Model-Based Design of Experiments; 2.3.1 Optimization Criteria Based on the Fisher Information Matrix; 2.3.2 Formulation of the Constrained Optimization Problem; 2.3.3 Constrained Optimization; 2.3.4 Extensions on the Excitation Sequence; 2.4 Temperature Model of Battery Cells; 2.5 Battery Module Model Design; 2.5.1 Battery Cell Balancing in Battery Modules; 2.5.2 LMN-Based Battery Module Design; 2.6 State of Charge Estimation
000753808 5058_ $$a2.6.1 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
000753808 5058_ $$a3.4.3 Dynamic Accuracy of the LMN Battery Models3.5 Battery Cell Temperature Model Accuracy; 3.6 Battery Module Model Accuracy; 3.7 SoC Estimation Accuracy; 3.7.1 Battery Module SoC Estimation Results; 3.7.2 Battery Cell SoC Estimation Results; 4 Energy Management; 4.1 Introduction; 4.1.1 Challenges for Energy Management Systems; 4.1.2 State-of-the-Art; 4.1.3 Solution Approach; 4.2 Basic Concept of Model Predictive Control; 4.3 Cascaded Model Predictive Controller Design; 4.3.1 Architecture of the Control Concept; 4.3.2 System Models for Controller Design
000753808 5058_ $$a4.3.3 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
000753808 506__ $$aAccess limited to authorized users.
000753808 520__ $$aAnalyzing 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.
000753808 588__ $$aOnline resource ; title from PDF title page (viewed February 16, 2016).
000753808 650_0 $$aHybrid electric vehicles$$xDesign and construction.
000753808 650_0 $$aEarthmoving machinery$$xDesign and construction.
000753808 650_0 $$aRoad machinery$$xDesign and construction.
000753808 7001_ $$aQuasthoff, Marcus,$$eauthor.
000753808 7001_ $$aJakubek, Stefan,$$eauthor.
000753808 77608 $$iPrint version:$$aUnger, Johannes$$tEnergy Efficient Non-Road Hybrid Electric Vehicles : Advanced Modeling and Control$$dCham : Springer International Publishing,c2016$$z9783319297958
000753808 830_0 $$aSpringerBriefs in applied sciences and technology.
000753808 852__ $$bebk
000753808 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-29796-5$$zOnline Access$$91397441.1
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000753808 980__ $$aEBOOK
000753808 980__ $$aBIB
000753808 982__ $$aEbook
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