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2014 | Buch

Automotive Battery Technology

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SUCHEN

Über dieses Buch

The use of electrochemical energy storage systems in automotive applications also involves new requirements for modeling these systems, especially in terms of model depth and model quality. Currently, mainly simple application-oriented models are used to describe the physical behavior of batteries. This book provides a step beyond of state-of-the-art modeling showing various different approaches covering following aspects: system safety, misuse behavior (crash, thermal runaway), battery state estimation and electrochemical modeling with the needed analysis (pre/post mortem). All this different approaches are developed to support the overall integration process from a multidisciplinary point-of-view and depict their further enhancements to this process.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Holistic Safety Considerations for Automotive Battery Systems
Abstract
The objective of system safety engineering is to develop a system with no unreasonable risk. To this end, risks caused by the electrical and/or electronic (E/E) system that could potentially harm persons must be analyzed, and appropriate risk reduction measures have to be considered in an early phase of development. This requires a close collaboration between different engineering disciplines in order to specify a comprehensive description of risk reduction and mitigation measures—the safety concept. The international functional safety standard ISO 26262 has to be considered for the development of E/E systems within road vehicles up to 3.5 tons. This standard focuses on E/E measures and considers other non-E/E measures only after the specification of the safety concept. In contrast, this chapter proposes a workflow for the elaboration of an integrated safety concept including safety measures from different engineering disciplines. Two main lessons learned were that the consideration of all kinds of risk reduction measures in the concept phase improves the understanding of the safety of the overall system, and involving various fields of expertise enables the development of a clear safety concept. This approach will improve the development of the overall system, while complying with the requirements of ISO 26262 for the development of E/E systems. The applicability of the introduced approach is demonstrated on an automotive battery case study, where the influence of various safety measures on the Automotive Safety Integrity Level (ASIL) determination has been taken into account in order to reduce the costs of E/E system development.
Helmut Martin, Andrea Leitner, Bernhard Winkler
Chapter 2. Battery Modelling for Crash Safety Simulation
Abstract
Finite element battery models used for crash simulation are effective tools for designing safe, lightweight battery systems for electric and hybrid electric vehicles. This chapter describes the currently available methods for integrating batteries into full-vehicle crash models and discusses their limitations at the present state of implementation. Innovative modelling approaches are able to determine the specific battery failure modes, such as short circuits and (electrolyte-) leakage. These methods are discussed and evaluated here based on their future applicability in the vehicle design process.
Gernot Trattnig, Werner Leitgeb
Chapter 3. Thermal Runaway: Causes and Consequences on Cell Level
Abstract
Lithium-ion batteries play an ever-increasing role in our daily life. Therefore, it is important to understand the potential risks involved with these devices. In this work we demonstrate the thermal runaway characteristics of three types of commercially available lithium-ion batteries with the format 18650. The lithium-ion batteries were deliberately driven into thermal runaway by overheating under controlled conditions. Cell temperatures up to 850 \(^\circ \)C and a gas release of up to 0.27 mol were measured. The main gas components were quantified with gas-chromatography. The safety of lithium-ion batteries is determined by their composition, size, energy content, design and quality. This work investigated the influence of different cathode-material chemistry on the safety of commercial graphite-based 18650 cells. The active cathode materials of the three tested cell types were (a) LiFePO\(_4\), (b) Li(Ni\(_{0.45}\)Mn\(_{0.45}\)Co\(_{0.10}\))O\(_2\) and (c) a blend of LiCoO\(_2\) and Li(Ni\(_{0.50}\)Mn\(_{0.25}\)Co\(_{0.25}\))O\(_2\).
Andrey W. Golubkov, David Fuchs
Chapter 4. Application-Related Battery Modelling: From Empirical to Mechanistic Approaches
Abstract
Mathematical modelling and simulation has been an essential part of battery research and development ever since. Depending on the particular, objective several different approaches are feasible, each of which provides specific advantages, e.g. calculation speed or deep mechanistic insight. This chapter presents an overview of common battery model approaches and introduces the multi-scaling technique for the simulation of larger battery units.
Franz Pichler, Martin Cifrain
Chapter 5. Analytical Methods for Investigation of Lithium-Ion Battery Ageing
Abstract
One of the major issues battery research must address is the lifetime of a cell. This can be reduced by physical and chemical ageing processes that occur inside the cell and are influenced by both the operating strategy and the surrounding conditions (e.g. temperature). To understand battery ageing, it is necessary to analyze the materials used in a cell at the microscopic level and correlate the results with electrical measurement data. This chapter describes a strategy for performing an ageing experiment by using a combination of analytical methods.
Sascha Weber, Sascha Nowak, Falko Schappacher
Chapter 6. Bayesian Inference for Lithium-Ion Cell Parameter Estimation
Abstract
The optimization of lithium-ion cells is becoming increasingly important. Using models that reflect the fundamental electrochemical processes is advantageous for this purpose. These models are typically computationally expensive and hard to invert using optimization methods. Additionally, deterministic optimization methods do not yield information regarding parameter uncertainties in the presence of noise. To overcome this problem, it is possible to apply Bayesian methods. This chapter provides an overview of parameter estimation. After a brief introduction to the model, parameter selection and modelling of the prior is presented. Finally, we present the results of a synthetic fitting problem solved by a parallel adaptive Markov chain Monte Carlo method. We validate the approach and compare it to realistic noisy data and a separated method.
Matthias K. Scharrer, Heikki Haario, Daniel Watzenig
Chapter 7. Data-Driven Methodologies for Battery State-of-Charge Observer Design
Abstract
This chapter presents a data-based approach to nonlinear observer design for battery state of charge (SoC) estimation. The SoC observer is based on a purely data-driven model in order to allow for the application of the proposed concepts to any type of battery chemistry, especially when conventional physical modelling is not easily possible. In order to cope with the complex nonlinear dynamics of the battery, an integrated workflow for experiment design, model creation and automated observer design is proposed. The nonlinear battery model is constructed using a proven training algorithm based on the architecture of local model networks (LMNs). One important advantage of LMNs is that they offer local interpretability, which enables the extraction of local linear battery impedance models for automated nonlinear observer design. The proposed concepts are validated experimentally using real measurement data from a lithium-ion power cell.
Christoph Hametner, Stefan Jakubek
Erratum to: Application-Related Battery Modelling: From Empirical to Mechanistic Approaches
Franz Pichler, Martin Cifrain
Backmatter
Metadaten
Titel
Automotive Battery Technology
herausgegeben von
Alexander Thaler
Daniel Watzenig
Copyright-Jahr
2014
Electronic ISBN
978-3-319-02523-0
Print ISBN
978-3-319-02522-3
DOI
https://doi.org/10.1007/978-3-319-02523-0

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