Battery Management System and its Applications E-Book

Xiaojun Tan

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 769

Veröffentlichungsjahr: 2022

Beschreibung

BATTERY MANAGEMENT SYSTEM AND ITS APPLICATIONS Enables readers to understand basic concepts, design, and implementation of battery management systems Battery Management System and its Applications is an all-in-one guide to basic concepts, design, and applications of battery management systems (BMS), featuring industrially relevant case studies with detailed analysis, and providing clear, concise descriptions of performance testing, battery modeling, functions, and topologies of BMS. In Battery Management System and its Applications, readers can expect to find information on: * Core and basic concepts of BMS, to help readers establish a foundation of relevant knowledge before more advanced concepts are introduced * Performance testing and battery modeling, to help readers fully understand Lithium-ion batteries * Basic functions and topologies of BMS, with the aim of guiding readers to design simple BMS themselves * Some advanced functions of BMS, drawing from the research achievements of the authors, who have significant experience in cross-industry research Featuring detailed case studies and industrial applications, Battery Management System and its Applications is a must-have resource for researchers and professionals working in energy technologies and power electronics, along with advanced undergraduate/postgraduate students majoring in vehicle engineering, power electronics, and automatic control.

Leseprobe

Battery Management System and its Applications

Xiaojun Tan

Sun Yat-sen University, China

Andrea Vezzini

University of Applied Science, Switzerland

Yuqian Fan

Henan Institute of Science and Technology, China

Neeta Khare

Iveco Group, Switzerland

You Xu

Guangdong Polytechnic Normal University, China

Liangliang Wei

Sun Yat-sen University, China

This edition first published 2023 by John Wiley & Sons Singapore Pte. Ltd under exclusive licence granted by China Machine Press for all media and languages (excluding simplified and traditional Chinese) throughout the world (excluding Mainland China), and with non-exclusive license for electronic versions in Mainland China.

© 2023 China Machine Press

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The right of Xiaojun Tan, Andrea Vezzini, Yuqian Fan, Neeta Khare, You Xu, and Liangliang Wei to be identified as the authors of the editorial material in this work has been asserted in accordance with law.

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Library of Congress Cataloging-in-Publication Data

Names: Tan, Xiaojun, author. Title: Battery management system and its applications / Xiaojun Tan [and five others]. Description: Hoboken, NJ : John Wiley & Sons, 2023. | Includes bibliographical references and index. Identifiers: LCCN 2022033684 (print) | LCCN 2022033685 (ebook) | ISBN 9781119154006 (hardback) | ISBN 9781119154037 (pdf) | ISBN 9781119154020 (epub) | ISBN 9781119154013 (ebook) Subjects: LCSH: Battery management systems. Classification: LCC TJ163.9 .T36 2023 (print) | LCC TJ163.9 (ebook) | DDC 621--dc23/eng/20220919 LC record available at https://lccn.loc.gov/2022033684LC ebook record available at https://lccn.loc.gov/2022033685

Cover image: © petrmalinak/shutterstock

Cover design: Wiley

Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India

Contents

Cover

Title page

Copyright

Preface

About the Authors

Part I Introduction

1 Why Does a Battery Need a BMS?

1.1 General Introduction to a BMS

1.1.1 Why a Battery Needs a BMS

1.1.2 What Is a BMS?

1.1.3 Why a BMS Is Required in Any Energy Storage System

1.1.4 How a BMS Makes a Storage System Efficient, Safe, and Dependable

1.2 Example of a BMS in a Real System

1.2.1 LabView Based BMS

1.2.2 PLC Based BMS

1.2.3 Microprocessor Based BMS

1.2.4 Microcontroller Based BMS

1.3 System Failures Due to the Absence of a BMS

1.3.1 Dreamline Boeing Fire Incidences

1.3.2 Fire Accident at the Hawaii Grid Connected Energy Storage

1.3.3 Fire Accidents in Electric Vehicles

References

2 General Requirements (Functions and Features)

2.1 Basic Functions of a BMS

2.1.1 Key Parameter Monitoring

2.1.2 Battery State Analysis

2.1.3 Safety Management

2.1.4 Energy Control Management

2.1.5 Information Management

2.2 Topological Structure of a BMS

2.2.1 Relationship Between a BMC and a Cell

2.2.2 Relationship Between a BCU and a BMC

References

3 General Procedure of the BMS Design

3.1 Universal Battery Management System and Customized Battery Management System

3.1.1 Ideal Condition

3.1.2 Feasible Solution

3.1.3 Discussion of Universality

3.2 General Development Flow of the Power Battery Management System

3.2.1 Applicable Standards for BMS Development

3.2.2 Boundary of BMS Development

3.2.3 Battery Characteristic Test Is Essential to BMS Development

3.3 Core Status of Battery Modeling in the BMS Development Process

References

Part II Li-Ion Batteries

4 Introduction to Li-Ion Batteries

4.1 Components of Li-Ion Batteries: Electrodes, Electrolytes, Separators, and Cell Packing

4.2 Li-Ion Electrode Manufacturing

4.3 Cell Assembly in an Li-Ion Battery

4.4 Safety and Cost Prediction

References

5 Schemes of Battery Testing

5.1 Battery Tests for BMS Development

5.1.1 Test Items and Purpose

5.1.2 Standardization of Characteristic Tests

5.1.3 Some Issues on Characteristic Tests

5.1.4 Contents of Other Sections of This Chapter

5.2 Capacity and the Charge and Discharge Rate Test

5.2.1 Test Methods

5.2.2 Test Report Template

5.3 Discharge Rate Characteristic Test

5.3.1 Test Method

5.3.2 Test Report Template

5.4 Charge and Discharge Equilibrium Potential Curves and Equivalent Internal Resistance Tests

5.4.1 Test Method for Discharge Electromotive Force Curve and Equivalent Internal Resistance

5.4.2 Test Method for Charge Electromotive Force Curve and Equivalent Internal Resistance

5.4.3 Discussion of the Test Method

5.4.4 Test Report Template

5.5 Battery Cycle Test

5.5.1 Features of Battery Cycle Test

5.5.2 Fixed Rate Cycle Test Method

5.5.3 Cycle Test Schemes Based on Standard Working Conditions

5.5.4 Test Report Template

5.6 Phased Evaluation of the Cycle Process

5.6.1 Evaluation Method

5.6.2 Estimation of the Test Time

5.6.3 Test Report Template

References

6 Test Results and Analysis

6.1 Characteristic Test Results and Their Analysis

6.1.1 Actual Test Arrangement

6.1.2 Characteristic Test Results of the LiFePO4 Battery

6.1.3 Characteristic Test Results of the Li(NiCoMn)O2 Ternary Battery

6.1.4 Characteristics Comparison of the Two Battery Types

6.2 Degradation Test and Analysis

6.2.1 Capacity Change Rule During Battery Degradation

6.2.2 Internal Resistance Spectrum Change Rule During Battery Degradation

6.2.3 Impact of Storage Conditions on Battery Degradation

References

7 Battery Modeling

7.1 Battery Modeling for BMS

7.1.1 Purpose of Battery Modeling

7.1.2 Battery Modeling Requirement of BMS

7.2 Common Battery Models and Their Deficiencies

7.2.1 Non-circuit Models

7.2.2 Equivalent Circuit Models

7.3 External Characteristics of the Li-Ion Power Battery and Their Analysis

7.3.1 Electromotive Force Characteristic of the Li-Ion Battery

7.3.2 Over-potential Characteristics of the Li-Ion Battery

7.4 A Power Battery Model Based on a Three-Order RC Network

7.4.1 Establishment of a New Power Battery Model

7.4.2 Estimation of Model Parameters

7.5 Model Parameterization and Its Online Identification

7.5.1 Offline Extension Method of Model Parameters

7.5.2 Online Identification Method of Model Parameters

7.6 Battery Cell Simulation Model

7.6.1 Realization of Battery Cell Simulation Model Based on Matlab/Simulink

7.6.2 Model Validation

References

Part III Functions of BMS

8 Battery Monitoring

8.1 Discussion on Real Time and Synchronization

8.1.1 Factors Causing Delay

8.1.2 Synchronization

8.1.3 Negative Impact of Non-real-time and Non-synchronous Problems

8.1.4 Proposal on Solution

8.2 Battery Voltage Monitoring

8.2.1 Voltage Monitoring Based on a Photocoupler Relay Switch Array (PhotoMOS)

8.2.2 Voltage Monitoring Based on a Differential Operational Amplifier

8.2.3 Voltage Monitoring Based on a Special Integrated Chip

8.2.4 Comparison of Various Voltage Monitoring Schemes

8.2.5 Significance of Accurate Voltage Monitoring for Effective Capacity Utilization of the Battery Pack

8.3 Battery Current Monitoring

8.3.1 Accuracy

8.3.2 Current Monitoring Based on Series Resistance

8.3.3 Current Monitoring Based on a Hall Sensor

8.3.4 A Compromised Method

8.4 Temperature Monitoring

8.4.1 Importance of Temperature Monitoring

8.4.2 Common Implementation Schemes

8.4.3 Setting of the Temperature Sensor

8.4.4 Accuracy

References

9 SoC Estimation of a Battery

9.1 Different Understandings of the SoC Definition

9.1.1 Difference on the Understanding of SoC

9.1.2 Difference and Relation Between SoC and SoP as Well as SoE

9.2 Classical Estimation Methods

9.2.1 Coulomb Counting Method

9.2.2 Open Circuit Voltage Method

9.2.3 A Compromised Method

9.2.4 Estimation Methods Not Applicable for the Lithium-Ion Battery

9.3 Difficulty in an SoC Estimation

9.3.1 Difficulty in an Estimation Resulting from Inaccurate Battery State Monitoring

9.3.2 Difficulty in an Estimation Resulting from Battery Difference

9.3.3 Difficulty in an Estimation Resulting from an Uncertain Future Working Condition

9.3.4 Difficulty in an Estimation Resulting from an Uncertain Battery Usage History

9.4 Actual Problems to Be Considered During an SoC Estimation

9.4.1 Safety of the Electric Vehicle

9.4.2 Feasibility

9.4.3 Actual Requirements of Drivers

9.5 Estimation Method Based on the Battery Model and the Extended Kalman Filter

9.5.1 Common Complicated Estimation Method

9.5.2 Advantages of a Kalman Filter in an SoC Estimation

9.5.3 Combination of an EKF and a Lithium-Ion Battery Model

9.5.4 Implementation Rule of the EKF Algorithm

9.5.5 Experimental Verification

9.6 Error Spectrum of the SoC Estimation Based on the EKF

9.6.1 Estimation Error Caused by the Inaccurate Battery Model

9.6.2 Estimation Error Resulting from a Measurement Error of the Sensor

9.6.3 Factors Affecting SoC Estimation Accuracy

References

10 Charge Control

10.1 Introduction

10.2 Charging Power Categories

10.3 Charge Control Methods

10.3.1 Semi-constant Current

10.3.2 Constant Current (CC)

10.3.3 Constant Voltage (CV)

10.3.4 Constant Power (CP)

10.3.5 Time-Based Charging

10.3.6 Pulse Charging

10.3.7 Trickle Charging

10.4 Effect of Charge Control on Battery Performance

10.5 Charging Circuits

10.5.1 Half-Bridge and Full-Bridge Circuits

10.5.2 On-Board Charger (Level 1 and Level 2 Chargers)

10.5.3 Off-Board Charger (Level 3)

10.5.4 Fast Charger

10.5.5 Ultra-Fast Charger

10.6 Infrastructure Development and Challenges

10.6.1 Home Charging Station

10.6.2 Workplace Charging Station

10.6.3 Community and Highways EV Charging Station

10.6.4 Electrical Infrastructure Upgrades

10.6.5 Infrastructure Challenges and Issues

10.6.6 Commercially Available Charges

10.7 Isolation and Safety Requirement for EC Chargers

References

11 Balancing/Balancing Control

11.1 Balancing Control Management and Its Significance

11.1.1 Two Expressions of Battery Capacity and SoC Inconsistency

11.1.2 Significance of Balancing Control Management

11.2 Classification of Balancing Control Management

11.2.1 Centralized Balancing and Distributed Balancing

11.2.2 Discharge Balancing, Charge Balancing, and Bidirectional Balancing

11.2.3 Passive Balancing and Active Balancing

11.3 Review and Analysis of Active Balancing Technologies

11.3.1 Independent-Charge Active Balancing Control

11.3.2 Energy-Transfer Active Balancing Control

11.3.3 How to Evaluate the Advantages and Disadvantages of an Active Balancing Control Scheme (an Efficiency Problem of Active Balancing Control)

11.4 Balancing Strategy Study

11.4.1 Balancing Time

11.4.2 Variable for Balancing

11.5 Two Active Balancing Control Strategies

11.5.1 Topologies of Two Active Balancing Schemes

11.5.2 Hierarchical Balancing Control Strategy

11.5.3 Lead-Acid Battery Transfer Balancing Control Strategy

11.6 Evaluation and Comparison of Balancing Control Strategies

11.6.1 Evaluation Indexes of Balancing Control Strategies

11.6.2 Comparison of Flows for Balancing Strategies

11.6.3 Comparison of Balancing Time

11.6.4 Comparison of Energy Consumption

11.6.5 Comparison of the Impact of Balancing on Battery Life

11.6.6 Comparison of the Capacity Utilization Ratio

11.6.7 Analysis of the Optimization Case

References

12 State of Health (SoH) Estimation of a Battery

12.1 Definition and Indices/Parameters of SoH

12.1.1 Relationship Between Battery Degradation and Battery Life

12.1.2 Relationship Between Battery Degradation and SoH of the Battery

12.1.3 Main Indicators to Describe Battery Degradation

12.2 Modeling of Battery Degradation (Aging) and SoH Estimation

12.2.1 Support Vector Regression

12.2.2 Battery Degradation Model Based on a Support Vector Regression Machine

12.2.3 Steps and Procedures for Evaluating Battery Degradation

12.3 Battery Degradation Diagnosis for EVs

12.3.1 Offline Degradation Diagnosis of the Power Battery

12.3.2 Online Degradation Diagnosis of the Power Battery

References

13 Communication Interface for BMS

13.1 BMS Communication Bus and Protocols

13.1.1 System Management Bus (SMBus)

13.1.2 BMS: Internal Data Communication

13.1.3 BMS: External Data Communication

13.2 Higher-Layer Communication Protocols

13.3 A Case Study: Universal CiA EnergyBus for a Low-Emission Vehicle (LEV)

References

14 Battery Lifecycle Information Management

14.1 Data Type of Power Battery

14.2 Vehicle Instrument Data Display

14.2.1 Battery Information Displayed on the Vehicle Instrument

14.2.2 Upgrade Based on a Traditional Instrument Panel

14.2.3 Design of the New Instrument Panel

14.3 Battery Data Transmission Mode

14.3.1 Hardware Implementation of Data Transmission

14.3.2 Control Flow of Data Transmission

14.3.3 Hierarchical Management of Power Battery Data

14.4 Information Concerning a Full-Power Battery Lifecycle

14.4.1 Database Structure of a Power Battery

14.4.2 Power Battery Data Volume Estimation

14.5 Storage and Analysis of Historical Information of a Battery

14.5.1 Necessity for Storage of Historical Information

14.5.2 Achievement of Historical Information Storage

14.5.3 Analysis and Processing of Historical Information

14.6 Battery Detection System Based on a Mobile Terminal

14.6.1 Server Program Design and Implementation

14.6.2 Design and Implementation of the Mobile Terminal

Reference

Part IV Case Studies

15 BMS for an E-Bike

15.1 Balancing

15.1.1 Passive Balancing

15.1.2 Active Charge Compensation

15.2 Battery Pack Design for an E-Bike

15.2.1 E-Bike Battery Pack Design Specifications

15.2.2 Testing

15.3 Methodology

15.4 Active Balancing Solutions

15.4.1 Structure of LTC3300

15.4.2 Discharging Procedure

15.4.3 Charging Process

15.5 Test Results

15.5.1 Measurements with Different Discharges

15.5.2 Comparison Between the Batteries

15.6 Possibility with Active Balancing

15.7 Results and Evaluation

Reference

16 BMS for a Fork-Lift

16.1 Lithium-Iron-Phosphate Batteries for Fork-Lifts

16.2 Battery Management Systems for Fork-Lifts

16.3 The LIONIC

®

Battery System for Truck Applications

16.4 Application

16.5 The Usable Energy Li-Ion Traction Batteries

Reference

17 BMS for a Minibus

17.1 Internal Resistance Analysis of a Power Battery System and Discharging Strategy Research of Vehicles

17.1.1 Internal Resistance Change Characteristic Research of a Power Battery

17.1.2 Internal Resistance Characteristic—Based Discharge Strategy

17.1.3 Research of a Charging Method for a Power Battery System Based on an Internal Resistance Characteristic

17.2 Consistency Evaluation Research of a Power Battery System

17.2.1 Analysis of a Battery Pack Maintenance Strategy and Performance Evaluation Index

17.2.2 Comparison of the Battery Pack Performance Evaluation Methods

17.2.3 Internal Resistance Characteristic-Based Consistency Evaluation Theory of the Battery Pack

17.2.4 Internal Resistance Characteristic-Based Consistency Evaluation of the Battery Pack

17.2.5 Internal Resistance Characteristic-Based Staged Consistency Evaluation Method for the Battery Pack

17.2.6 Internal Resistance Consistency Evaluation Test of the Battery Pack for a Pure Electric Vehicle

17.3 Safety Management and Protection of a Power Battery System

Index

End User License Agreement

List of Tables

CHAPTER 04

Table 4.1 Summary of Li-ion...

Table 4.2 Type of commercially available...

CHAPTER 05

Table 5.1 Discharge capacity of a...

Table 5.2 Energy, charge and discharge...

Table 5.3 Discharge rate characteristic of...

Table 5.4 Discretized GB/T 18386...

Table 5.5 Power spectrum of a...

Table 5.6 Electrical working condition for...

Table 5.7 Energy charged and discharged...

Table 5.8 Cycle test operation description...

Table 5.9 Time estimation of the...

Table 5.10 Time estimation of the...

Table 5.11 Time estimation of the...

Table 5.12 Time estimation of the...

Table 5.13 Time estimation of the...

Table 5.14 Time estimation of the...

Table 5.15 Energy (J) charged and...

Table 5.16 Capacity (Ah) diagnosis...

CHAPTER 06

Table 6.1 Battery characteristic test scheme...

Table 6.2 Parameters of the LiFePO4...

Table 6.3 Parameter of the Li...

Table 6.4 SoC estimation error of...

Table 6.5 Charge internal resistance curve...

Table 6.6 Discharge internal resistance curve...

Table 6.7 Charge internal resistance curve...

Table 6.8 Discharge internal resistance curve...

Table 6.9 SoC estimation error of...

Table 6.10 Cycle test arrangement of...

Table 6.11 Test arrangement for investigation...

Table 6.12 Test arrangement for investigation...

Table 6.13 Test arrangement for investigation...

Table 6.14 Test arrangement for investigation...

Table 6.15 Linear fitting of the...

Table 6.16 Comparison of the test...

Table 6.17 Function fitting effect of...

Table 6.18 Test arrangement for the...

Table 6.19 Fitting result of θ...

Table 6.20 The θ fitting results...

Table 6.21 Calendar life test arrangement...

CHAPTER 07

Table 7.1 Potential equation corresponding to...

Table 7.2 Charging and discharging efficiency...

Table 7.3 Errors of external characteristics...

Table 7.4 Hysteretic voltage Vh upon...

Table 7.5 Calculation equations for EMF...

Table 7.6 Error of the model...

Table 7.7 Error of the model...

Table 7.8 Error between the simulated...

Table 7.9 SoC estimation error of...

Table 7.10 Error between the simulated...

Table 7.11 SoC estimation error of...

CHAPTER 08

Table 8.1 Comparison of voltage monitoring...

Table 8.2 Available SoC range of...

Table 8.3 Temperature change at different...

CHAPTER 09

Table 9.1 Comparison of the actual...

Table 9.2 Discharge capacity of the...

Table 9.3 SoC estimation error caused...

Table 9.4 Composition error of the...

Table 9.5 Error statistics of different...

Table 9.6 Error caused by the...

Table 9.7 Impact of the systemic...

Table 9.8 Impact of a random...

Table 9.9 Impact of the system...

Table 9.10 Impact of the random...

Table 9.11 Impact of various factors...

CHAPTER 10

Table 10.1 Categories of a charging...

Table 10.2 Battery charging methods.

Table 10.3 Conventional AC charging solution...

Table 10.4 Summary table for commercial...

Table 10.5 Safety Standards for EV...

CHAPTER 11

Table 11.1 Classification of balancing control...

Table 11.2 Comparison of balancing topologies...

Table 11.3 Similarities of flows for...

Table 11.4 Difference of flows for...

Table 11.5 Comparison of the balancing...

Table 11.6 Comparison of balancing effect...

Table 11.7 List of parameters...

CHAPTER 12

Table 12.1 Sample battery capacity...

Table 12.2 Capacity loss of sample...

Table 12.3 Influence of different input...

Table 12.4 Error analysis of the...

Table 12.5 Equilibrium potential prediction result...

Table 12.6 Equilibrium potential prediction result...

CHAPTER 13

Table 13.1 Summary table for BMS...

CHAPTER 14

Table 14.1 Information of cells in...

Table 14.2 Information of the battery...

Table 14.3 General catalogue of the...

Table 14.4 Storage format of cell...

Table 14.5 Storage format of battery...

CHAPTER 15

Table 15.1 Battery.

Table 15.2 Cell.

Table 15.3 Test setup details.

Table 15.4 History of the three test batteries.

Table 15.5 Three test battery packets.

CHAPTER 17

Table 17.1 Comparison of charge and...

Table 17.2 Comparison of charge and...

Table 17.3 Inflection point of the...

Table 17.4 Effect comparison of discharge...

Table 17.5 Inflection point of resistance...

Table 17.6 Comparison of results achieved...

List of Illustrations

CHAPTER 01

Figure 1.1 Block diagram...

Figure 1.2 Schematic diagram...

Figure 1.3 Schematic block...

Figure 1.4 Block diagram...

CHAPTER 02

Figure 2.1 Basic functions of...

Figure 2.2 Structure of one...

Figure 2.3 Structure of one...

Figure 2.4 Star connection of...

Figure 2.5 Bus type connection...

CHAPTER 03

Figure 3.1 General BMS development...

Figure 3.2 Core status of...

CHAPTER 04

Figure 4.1 Systematic approach in...

Figure 4.2 Anode and cathode...

Figure 4.3 Cell assembly steps...

Figure 4.4 Safety operation temperatures...

CHAPTER 05

Figure 5.1 Discharge voltage and...

Figure 5.2 Discharge rate characteristic...

Figure 5.3 Charge and discharge...

Figure 5.4 GB/T 18386...

Figure 5.5 Capacity curve obtained...

Figure 5.6 Charge and discharge...

Figure 5.7 Some results of...

Figure 5.8 Diagnostic test curve...

Figure 5.9 Diagnostic test curve...

Figure 5.10 Diagnostic test curve...

Figure 5.11 Diagnostic test curve...

Figure 5.12 Diagnostic test curve...

CHAPTER 06

Figure 6.1 Absolute capacity and...

Figure 6.2 Charge and charge...

Figure 6.3 Comparison of the...

Figure 6.4 Comparison of the...

Figure 6.5 Comparison of the...

Figure 6.6 DC equivalent internal...

Figure 6.7 Temperature characteristic of...

Figure 6.8 Absolute capacity and...

Figure 6.9 Li(NiCoMn)O2...

Figure 6.10 Comparison of the...

Figure 6.11 Charge and discharge...

Figure 6.12 Hysteretic difference of...

Figure 6.13 Hysteretic difference of...

Figure 6.14 Capacity loss of...

Figure 6.15 Impact of the...

Figure 6.16 Impact of SoCstart...

Figure 6.17 Impact of the...

Figure 6.18 Impact of T...

Figure 6.19 Impact of the...

Figure 6.20 Impact of DOD...

Figure 6.21 Impact of SoCstart...

Figure 6.22 Phased evaluation result...

Figure 6.23 Schematic diagram for...

Figure 6.24 Change of characterization...

Figure 6.25 Impact of test...

Figure 6.26 Impact of the...

Figure 6.27 Impact of SoCstart...

Figure 6.28 Impact of DOD...

Figure 6.29 Charge internal resistance...

Figure 6.30 Impact of the...

Figure 6.31 Impact of the...

Figure 6.32 Impact of the...

Figure 6.33 Relationship between the...

Figure 6.34 Relationship between the...

Figure 6.35 Comparison of battery...

CHAPTER 07

Figure 7.1 Typical artificial neural...

Figure 7.2 Battery model based...

Figure 7.3 Equivalent circuit model...

Figure 7.4 PNGV equivalent circuit...

Figure 7.5 Circuit model with...

Figure 7.6 Equilibrium potential curve...

Figure 7.7 Relation curve of...

Figure 7.8 Battery equivalent voltage...

Figure 7.9 Composition structure of...

Figure 7.10 Voltage curve at...

Figure 7.11 The n-order...

Figure 7.12 Simple equivalent circuit...

Figure 7.13 New power battery...

Figure 7.14 Equivalent voltage source...

Figure 7.15 Rebound voltage curve...

Figure 7.16 Equivalent impedance model...

Figure 7.17 Rebound voltage characteristic...

Figure 7.18 Battery discharging–...

Figure 7.19 Equivalent circuit of...

Figure 7.20 Voltage response of...

Figure 7.21 Rebound voltage curves...

Figure 7.22 Multidimensional extension of...

Figure 7.23 Model parameter online...

Figure 7.24 Results of the...

Figure 7.25 Results of the...

Figure 7.26 Structural diagram of...

Figure 7.27 Fifteen working conditions...

Figure 7.28 Simulated external characteristics...

Figure 7.29 SoC estimation result...

Figure 7.30 UDDS.

Figure 7.31 Simulated external characteristics...

Figure 7.32 SoC estimation result...

CHAPTER 08

Figure 8.1 Functional block diagram...

Figure 8.2 Electrical schematic diagram...

Figure 8.3 Functional block diagram...

Figure 8.4 Differential operational amplifier...

Figure 8.5 Battery voltage monitoring...

Figure 8.6 Voltage reading vs...

Figure 8.7 Change in available...

Figure 8.8 Current monitoring scheme...

Figure 8.9 Current monitoring scheme...

Figure 8.10 Current monitoring scheme...

Figure 8.11 Operating curve of...

Figure 8.12 A compromised current...

CHAPTER 09

Figure 9.1 Structure diagram of...

Figure 9.2 Relationship between SOC...

Figure 9.3 SoC–EMF...

Figure 9.4 Schematic diagram of...

Figure 9.5 Voltage waveform at...

Figure 9.6 Current waveform flowing...

Figure 9.7 Spectrogram of voltage...

Figure 9.8 Validity verification of...

Figure 9.9 Voltage rebound curve...

Figure 9.10 Hysteretic voltage curve...

Figure 9.11 Spectrum of the...

Figure 9.12 The EMF–...

Figure 9.13 Fitting result of...

Figure 9.14 SoC estimation error...

Figure 9.15 Fitting result of...

Figure 9.16 SoC estimation error...

Figure 9.17 Comparison of SoC...

Figure 9.18 SoC estimation error...

Figure 9.19 Comparison of the...

Figure 9.20 Error spectrum of...

Figure 9.21 Comparison of the...

Figure 9.22 Error spectrum of...

Figure 9.23 Comparison of the...

Figure 9.24 SoC estimated by...

Figure 9.25 Comparison of the...

CHAPTER 10

Figure 10.1 On/off board...

Figure 10.2 Level 1 chargers...

Figure 10.3 Level 2 charger...

Figure 10.4 Siemens VersiCharge EVSE...

Figure 10.5 Level 3 charger...

Figure 10.6 CHAdeMO connector. Credit...

Figure 10.7 Li-ion battery...

Figure 10.8 Semi-constant current...

Figure 10.9 Constant current charging...

Figure 10.10 Basic constant control...

Figure 10.11 Constant voltage.

Figure 10.12 Comparison between (a)...

Figure 10.13 Bidirectional chargers: (a...

Figure 10.14 Interleaved unidirectional charger...

Figure 10.15 Single-phase multilevel...

Figure 10.16 Combined on-board...

Figure 10.17 Three-phase diode...

Figure 10.18 CHAdeMO sequence circuit...

Figure 10.19 Buffered EV charging...

CHAPTER 11

Figure 11.1 Battery pack formed...

Figure 11.2 Expression by battery...

Figure 11.3 Expression by battery...

Figure 11.4 Initial capacity of...

Figure 11.5 After the charge...

Figure 11.6 After discharge without...

Figure 11.7 After a charge...

Figure 11.8 After discharge with...

Figure 11.9 Electromotive force curve...

Figure 11.10 SoC of four...

Figure 11.11 Comparison with and...

Figure 11.12 Typical centralized balancing...

Figure 11.13 Typical distributed battery...

Figure 11.14 Discharge balancing mode...

Figure 11.15 Charge balancing mode...

Figure 11.16 Balancing control scheme...

Figure 11.17 Balancing control by...

Figure 11.18 Balancing control by...

Figure 11.19 Capacitor switching balancing...

Figure 11.20 Buck-boost-based...

Figure 11.21 Active balancing process...

Figure 11.22 Balancing at the...

Figure 11.23 Discharge process without...

Figure 11.24 Discharge process with...

Figure 11.25 Balancing control in...

Figure 11.26 Equivalent circuit model...

Figure 11.27 Comparison of balancing...

Figure 11.28 Relation between SoC...

Figure 11.29 Difference between the...

Figure 11.30 Schematic diagram of...

Figure 11.31 LLB topology of...

Figure 11.32 Topology of lead...

Figure 11.33 Schematic diagram of...

Figure 11.34 Hierarchical two-way...

Figure 11.35 LLB control strategy...

Figure 11.36 Balancing operation....

Figure 11.37 Bidirectional balanced control....

Figure 11.38 Schematic diagram of...

Figure 11.39 Simulation results of...

Figure 11.40 Simulation results of...

Figure 11.41 Simulation results of...

Figure 11.42 Relative time consumption...

Figure 11.43 Comparison of the...

Figure 11.44 Relative time consumption...

Figure 11.45 Relative energy consumption...

Figure 11.46 Relative energy consumption...

Figure 11.47 Comparison of balancing...

Figure 11.48 Comparison of the...

CHAPTER 12

Figure 12.1 Accelerated aging of...

Figure 12.2 Broader SoH covers...

Figure 12.3 Test the battery...

Figure 12.4 Characteristic curves for...

Figure 12.5 AC impedance of...

Figure 12.6 Equivalent circuit diagram...

Figure 12.7 DC equivalent internal...

Figure 12.8 Capacity loss of...

Figure 12.9 DC equivalent internal...

Figure 12.10 Capacity loss of...

Figure 12.11 DC equivalent internal...

Figure 12.12 Capacity loss of...

Figure 12.13 Degradation evaluation effect...

Figure 12.14 Effect of degradation...

Figure 12.15 Effect of degradation...

Figure 12.16 Degradation evaluation method...

Figure 12.17 Block diagram of...

Figure 12.18 Data processing and...

Figure 12.19 Capacity loss-accumulated...

Figure 12.20 Data processing and...

Figure 12.21 Online diagnosis hardware...

Figure 12.22 Whole online diagnosis...

Figure 12.23 Voltage rebound curve...

Figure 12.24 Schematic diagram of...

Figure 12.25 Charge equilibrium potential...

Figure 12.26 Discharge equilibrium potential...

Figure 12.27 Relative capacity loss...

Figure 12.28 Regression result and...

Figure 12.29 Prediction results and...

Figure 12.30 Error analysis.

Figure 12.31 Training result and prediction result.

CHAPTER 13

Figure 13.1 BMS system architecture...

Figure 13.2 Communication between BMS...

Figure 13.3 Universal serial communication...

Figure 13.4 RS232–RS485...

Figure 13.5 CAN and CANopen...

Figure 13.6 CANopen internal device...

Figure 13.7 Energy Bus connector...

Figure 13.8 Energy Bus framework...

Figure 13.9 E-bike ST2...

CHAPTER 14

Figure 14.1 Data type of...

Figure 14.2 An old-fashioned...

Figure 14.3 Electric vehicle instrument...

Figure 14.4 Electric vehicle instrument...

Figure 14.5 Different ways to...

Figure 14.6 Hardware structure of...

Figure 14.7 Overall flow of...

Figure 14.8 Data hierarchical management...

Figure 14.9 Hierarchical management of...

Figure 14.10 Local view of...

Figure 14.11 Global view of...

Figure 14.12 Real-time information...

Figure 14.13 Historical information database...

Figure 14.14 Implementation scheme for...

Figure 14.15 Analysis flow diagram...

Figure 14.16 Travel data report...

Figure 14.17 Remote power battery...

Figure 14.18 Overall architecture of...

Figure 14.19 Data reception and...

Figure 14.20 Diagnostic program implementation...

Figure 14.21 Enquiry service implementation...

Figure 14.22 Client interaction and...

Figure 14.23 Schematic diagram of...

CHAPTER 15

Figure 15.1 Passive charge compensation...

Figure 15.2 Passive and active...

Figure 15.3 E-bike battery...

Figure 15.4 Evaluator B FuelCon...

Figure 15.5 Test setup.

Figure 15.6 (a) Discharge curve...

Figure 15.7 (a) Strong and...

Figure 15.8 (a) Discharge cut...

Figure 15.9 (a) Remaining capacity...

Figure 15.10 Structure of LTC3300...

Figure 15.11 Discharging with LTC3300...

Figure 15.12 Charging with LTC3300...

Figure 15.13 Test results of...

Figure 15.14 (a) Battery NEW...

Figure 15.15 Battery NEW –...

Figure 15.16 Middle age battery...

Figure 15.17 Middle age battery...

Figure 15.18 Old age battery...

Figure 15.19 Old age battery...

Figure 15.20 Voltage drift of...

Figure 15.21 Voltage drift between...

Figure 15.22 Residual capacity of...

Figure 15.23 Residual capacity of...

Figure 15.24 Possibility of extracting...

Figure 15.25 Possibility with active...

CHAPTER 16

Figure 16.1 Discharge behavior of...

Figure 16.2 Charge behavior of...

Figure 16.3 Task of the...

Figure 16.4 LIONIC

®

Smart...

Figure 16.5 Capacity curve of...

Figure 16.6 Annual energy consumption...

Figure 16.7 Discharge curve for...

Figure 16.8 Discharge curve of...

Figure 16.9 Usable energy with...

Figure 16.10 Boost voltage as...

CHAPTER 17

Figure 17.1 V model of...

Figure 17.2 Voltage resilience after...

Figure 17.3 Battery internal resistance...

Figure 17.4 Battery internal resistance...

Figure 17.5 Battery internal resistance...

Figure 17.6 Battery internal resistance...

Figure 17.7 Test steps to...

Figure 17.8 Battery internal resistance...

Figure 17.9 Battery internal resistance...

Figure 17.10 Ideal discharge current...

Figure 17.11 Stepping depth of...

Figure 17.12 Relationship between the...

Figure 17.13 Comparison of the...

Figure 17.14 Current output delay...

Figure 17.15 Electric minibus.

Figure 17.16 Sustainable maximum discharge...

Figure 17.17 Internal resistance characteristic...

Figure 17.18 Variable current charging...

Figure 17.19 Comparison of results...

Figure 17.20 Division of the...

Figure 17.21 Consistency curve of...

Figure 17.22 Consistency curve of...

Figure 17.23 Energy change curve...

Figure 17.24 Discharge internal resistance...

Figure 17.25 Charge internal resistance...

Figure 17.26 Charge internal resistance...

Figure 17.27 Discharge internal resistance...

Figure 17.28 Charge internal resistance...

Figure 17.29 Discharge internal resistance...

Figure 17.30 Charge internal resistance...

Figure 17.31 Discharge internal resistance...

Figure 17.32 Over-temperature control...

Figure 17.33 Lower-temperature control...

Guide

Cover

Title page

Copyright

Table of Contents

Preface

About the Authors

Begin Reading

Index

End User License Agreement

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Preface

We have known each other since the autumn of 2005 and have been engaged in the research of electric vehicles together. We often worked in each other’s laboratories in the form of short-term visits. In China and Switzerland, we have a group of good partners. We trusted each other and always shared information together.

In 2015, two of us discussed writing down our knowledge and thoughts in the field of BMS and publishing a book to share with researchers and engineers around the world. However, the work of editing and publishing is huge. Our teams on both sides have added many young researchers to participate in the organization and editing of the manuscript. With everyone’s efforts, we had completed the basic compilation of the manuscript by the end of 2019.

Due to the influence of COVID-19, our final editing work has been affected. Finally, we were glad to see that in the winter of 2021, we were able to send the manuscript of this book to the publishing house.

In this book, Chapters 1, 4, 10, 15, and 16 were mainly written by Andrea Vezzini, with the assistance of Neeta Khare. Chapters 2, 3, 5, 6, 7, 8, 9, 11, 12, 13 and 14 were mainly written by Xiaojun Tan, with the assistance of Yuqian Fan. Chapter 17 was written by You Xu. Liangliang Wei helped to revise all the text and mature the figures and tables.

Many thanks are due to the faculties and students from both research teams in China and Switzerland, who gave valuable help to this book.

Xiaojun Tan and Andrea Vezzini

About the Authors

Professor Dr. Xiaojun Tan received his PhD degree from Sun Yat-sen University, China, in 2005. He has worked for Sun Yat-sen University since 2005 and is now a professor at the School of Intelligent Systems Engineering, Sun Yat-sen University. At the same time, he is the Director of the Electric Vehicle Research Center which is an engineering laboratory of Guangdong Province. He has worked on electric vehicles for more than 17 years. His research interests include battery management systems and intelligent driving.

The research fields of the Electric Vehicle Research Center include the safe battery system as well as the lightweight vehicle body. Since 2005, Sun Yat-sen University began to collaborate with BFH on the area of battery management systems and driving motors.

Professor Dr. Andrea Vezzini received his PhD in electrical engineering from ETH Zurich in 1996 and successfully completed the Mastering Technology Enterprises (MTE) programme at IMD Lausanne in 2002. In 1996 he became a professor at the Bern University of Applied Sciences.

Professor Dr. Andrea Vezzini heads the BFH Centre for Energy Storage in Biel and is the head of Innosuisse’s flagship project “CircuBAT – Swiss Circular Economy Model for Lithium Car Batteries.”

Since 2020, he has also been president of “iBAT,” the Swiss innovation platform for companies and research institutions on the topic of batteries.

He has been a member of the Swiss Federal Energy Research Commission (CORE) since 2015 and an official member of the Scientific Advisory Board of AEE Suisse, the umbrella organization of the renewable energy and energy efficiency industry, since 2018.

Professor Dr. Yuqian Fan received the PhD degree in Intelligent Transportation Engineering from Sun Yat-sen University, China, in 2019, an ME degree in Electronics and Communication Engineering from Wuhan University, Wuhan, China, in 2011, and a BE degree in Communication Engineering from Harbin Engineering University, Harbin, China, in 2009.

From 2020 to 2022, he was a postdoc with the School of Intelligent Systems Engineering, Sun Yat-sen University. Since 2022, he has been a Professor at Henan Institute of Science and Technology. Dr. Fan was the author of more than 10 scientific publications published on the Web of science. His research interests include intelligent control and optimization design for power battery systems, battery thermal management and thermal safety, and battery state of health prediction, etc.

Neeta Khare has 19-plus years of experience in developing BMS and battery pack for EV and Energy storage systems. Dr. Khare’s broad work experience covers a wide range of battery technologies (Li-ion, LFP, LTO, NMC-G, lead-acid and zinc hybrid), failure analysis, diagnostics, and optimized solutions for energy storage and hybrid power systems. Her core expertise is in aging algorithms of a battery/cell using AI and adaptive algorithms, Battery Pack, Battery Management System (BMS) development, multistring controller, EMS, innovative Battery Health Monitoring, and an optimized power controller for Hybrid Power Systems for Electric Vehicles.

Currently, Dr. Khare is serving as Director (Battery and Fuel Cell) with the Iveco Group. Prior to this position, she served as CTO/CIO in Green Cubes Technology (GCT-EU), Uster and Vice President of Technology in Leclanche, Switzerland.

Dr. Khare acquired her doctoral degree on “Intelligent Battery Monitoring” from Banasthali University in India and served as post doc and in the research faculty in Villanova University in USA. She claims multiple patents and international publications to her credit.

You Xu received his BSc degree and PhD degree from Sun Yat-sen University in 2006 and 2011 respectively. He is now an associate professor in Guangdong Polytechnic Normal University, where he has been engaged in power battery systems and precision reverse equipment.

Dr. Xu was the author of more than 20 scientific publications, among which, 12 papers were published or accepted in Engineering Village or Web of Science. His research interests include battery management for electrical vehicles and accuracy of multi-joint robot and visual measurement systems, etc. Dr. Xu has presided over one general project of Guangdong Provincial Natural Science Foundation, one sub-project of Guangdong Provincial Department of science and technology, one project of Guangdong Provincial Department of education, one research and development project of Guangdong Provincial Key Laboratory, won third prize of Guangdong Provincial Scientific and technological progress in 2018, won second prize of Dongguan municipal scientific and technological progress in 2017, and second prize of Guangdong Provincial Science and technology award of machinery industry.

Liangliang Wei received BS and PhD degrees in Electrical Engineering from the Wuhan University, Wuhan, China, in 2012 and 2017 respectively.

From 2017 to 2018, he was a postdoc in Kyoto University, where he has been engaged in permanent magnet machines and renewable energy generation. From 2018 to 2020, he became an Assistant Professor in Kyoto University. Since 2020, he has been an Associate Professor in control science and engineering at Sun Yat-Sen University. Dr. Wei was the author of more than 20 scientific publications, among which, 10 papers were published or accepted in IEEE Transactions or Web of Science. His research interests include electrical machines and motor control for electrical vehicles and renewable energy generation, battery management systems, and health condition monitoring, etc.

Part I Introduction

1 Why Does a Battery Need a BMS?

1.1 General Introduction to a BMS

1.1.1 Why a Battery Needs a BMS

A battery management system (BMS) is an essential part of any energy storage system. It controls battery charging and discharging, manages optimum operating conditions, governs the safety limits, runs the battery charge and health algorithms, monitors battery parameters, and communicates with other associated devices [1, 2]. A BMS or similar monitoring and control system is strongly recommended for other electrical energy systems, such as a fuel cell, supercapacitor, superbat capacitor, or other hybrid combinations of electrical energy storage systems. A BMS allows the system to be efficient and to use an application for stored energy up to the safe operating limit [3]. It makes energy storage cost effective for short-term applications such as consumer electronics. With an efficient control over optimum charge and discharge ranges, the BMS adequately extends the life of energy storage. The increased life makes the energy storage economically viable for long-term applications such as grid, automotive, and stationary applications [4].

1.1.2 What Is a BMS?

A BMS is a control system that ensures optimum use of the battery energy in powering any portable or non-portable system. This is achieved by monitoring and controlling the battery’s charging and discharging processes along with careful control over the surrounding environment. The BMS becomes essential in all storage systems to prevent the risk of damaging the battery by misuse. The features of a BMS design should include:

Charge control

Battery capacity and efficiency calculations

Remaining run-time information

Cycle counting

Battery life prognosis

Thermal management

Prediction of battery failure

Safety and alarm indications for over the limit usage

An effective BMS can protect the battery from damage, ensure safety, predict battery life, and maintain the battery operation in order to keep efficiency high.

A general block diagram of a BMS is shown in Figure 1.1. The battery-charger charges the battery from the mains. A protection integrated circuit (IC) connected to the battery indicates the unsafe condition of the battery. A protection IC specifically deals with the over/under-voltage protection, over current protection, imbalance of cells, and thermal runaway. In addition, protection circuits also include a blocking diode, each of which is outfitted with a series string that prevents parallel strings from discharging through a battery with an unforeseen short circuit [5]. Researchers, such as Kim et al. [6], have also proposed more robust circuits capable of mitigating the electrical impacts of a single cell failure. Manufacturers of large battery systems typically integrate a proprietary control system as well, in order to control issues such as cell balance, cell temperature, and an estimation of the battery life.

Figure 1.1 Block diagram of a battery management system.

The battery state indicates the current state and future prediction of the battery by using the State of Charge (SoC) and State of Health (SoH). The processor runs the battery management algorithms that compute the SoC, SoH, and property parameters [7]. The subsequent parts in the book will discuss prognostic and diagnostic approaches for determination of the SoC and SoH. Finally, to establish communication between the BMS and other devices, most commonly used interfaces are I2C, Modbus, and CAN ports and protocols.

1.1.3 Why a BMS Is Required in Any Energy Storage System

The demand for an energy storage system is increasing day by day with exponential growth in the area of consumer electronics, portable devices, and e-mobility. In addition, a budding urge for clean energy usage in order to address the challenge of reducing carbon footprints makes energy storage more popular than other stationary applications. At present, stationary applications, such as a grid-connected energy storage, are aggressively being tested around the world. Grid-connected electrical energy storage is a potential candidate for load shifting, PV smoothing, stabilizing the grid, etc. The most popular solution for electric energy storage is a battery pack due to its high energy density, long life, and cost-effective features. However, challenges lie with its optimum performance and safety. The requirement for a BMS controller with energy storage is quite obvious when considering the increasing challenge regarding safety and optimum utilization together with high efficiency. A BMS allows energy storage to function within the safety limits and provides high-performance capabilities.

1.1.4 How a BMS Makes a Storage System Efficient, Safe, and Dependable

An important aspect of BMS functions is to control the battery charging and usage within safe limits. A BMS recommends relevant parameters to the battery charger and commands it to use the most effective charging algorithm. A charging algorithm helps to reduce the charging time, offers a long battery life, and maintains high efficiency, while keeping the operation within given safety limits of voltages, temperature, current, and SoC. The BMS monitors real-time electrical parameters such as terminal voltage, charging and discharging current, temperature, impedance, and number of cycles [8]. Further, it calculates compensation factors, estimates the SoC and SoH, and determines other performance characteristic parameters such as energy efficiency, capacity, and remaining life time. The SoC and SoH are the most critical parameters for maintaining the operation under safe conditions [9, 10]. Monitoring battery health is one of the prime factors affecting the system reliability.

A BMS helps energy storage in the following three ways:

Increases efficiency by

Compensating cut-off voltage with temperature variations, C-rate charge and discharge, and aging.

Selecting appropriate charging current to maintain the current density limit at the electrode surfaces.

Controlling and compensating the SoC range for charging and discharging over the operating range in order to maintain coulombic efficiency.

Keeping all cell voltage and SoC balanced to increase its operating range.

Thermal controlling the pack in order to maintain the optimum temperature range.

Increases battery life time by

Saving the battery from abuses of over-charging. Over-charging causes heating and out-gassing that reduces the life of the battery.

Preventing deep discharging by limiting the discharge at the end of the discharge cut-off voltage. Metal plating is a major cause of shortening the battery age when operating below the end of discharge cut-off voltage.

Maintaining current density to prevent electrode surfaces from damage.

Keeping the SoC within the operating range that provides a balance between capacities in and out at various operating conditions.

Cell balancing prevents under-charging of good cells and over-charging of weak cells, which increase the overall age of the pack.

Provides safety and reliability by

Maintaining and controlling operations within the safety limits.

Indicating safety alarms for events beyond the operating condition.

Shutting down the operation during a critical safety threat.

Employing a thermal controlling system to prevent any thermal runaway conditions

Giving an indication of the remaining battery life and thus facilitating timely action taken proactively in alarming conditions, reducing the risk of running into a disaster.

1.2 Example of a BMS in a Real System

1.2.1 LabView Based BMS

A LabView (Laboratory Virtual Instrument Engineering Workbench) based BMS provides easy execution on a PC. A LabView from the National Instruments Corporation is a software development application that uses a graphical programming language to create programs in a block diagram. Since a LabView includes libraries of functions for data acquisition, serial instrument control, data analysis, data presentation, and data storage, it is recommended for the BMS application. A BMS designed using LabView offers higher flexibility and much better graphic tools for data visualization. The central unit of a LabView based BMS consists of the following blocks:

Data Processing

Parameter Adaptation

Monitoring

Management

The central unit and input/output interfaces have been recognized as a LabView application.

Due to the flexible design of the LabView BMS, the system is able to perform control and surveillance activities for any kind of battery application and battery technology (e.g. Pb, VRLA, NiCd, NiMH, etc.) [11].

1.2.2 PLC Based BMS

The PLC (programmable logic controller) plays an important role in the field of industrial automation because of its excellent performance. Its multiple functions include logic arithmetic, calculation, communication, noise resistance, and stability. A PLC based BMS design is shown in Figure 1.2. The analog and digital data from the battery were passed to PLC on real time. This system controls the battery charging and discharging.

Figure 1.2 Schematic diagram of a PLC based BMS.

It can be seen that the PLC controls the action of relays and delivers the signal to the sensor for judging whether charging is being done or not. The development cost of a PLC based BMS is high and offers only limited functions. Thus, it does not match the demand in price.

1.2.3 Microprocessor Based BMS

The microprocessor based BMS consists of (a) a data acquisition unit (DA); (b) an ampere-hours counter unit (AhC) with a battery current measuring unit, a battery voltage measuring unit, and a battery ambient temperature measuring unit; (c) a cell or mono blocks voltage measuring unit (CV); (d) a modem; and (e) a personal computer (PC). In this BMS, AhC and CV units are used to measure the battery parameters. All measured data are read out in a definite time period with the help of a DA unit, and are then stored in an internal memory of the system. Figure 1.3 represents the use of the central unit – a microprocessor 80C535 – in a DA unit to estimate the battery state.

Figure 1.3 Schematic block diagram of a DA unit of a microprocessor based BMS.

Personal computer (PC) with utility program is used to read saved data from system memory and to transfer it in appropriate database files. Data were transmitted from Battery Monitoring System to personal computer PC via modem.

1.2.4 Microcontroller Based BMS

In this BMS design, an 80C196 KB microcontroller was used for developing the system hardware to estimate the battery SoC and SoH. The general block diagram of the microcontroller based BMS is shown in Figure 1.4.

Figure 1.4 Block diagram of a microcontroller based BMS implementation.

The five battery parameters, which include the current drawn, terminal voltage, temperature, internal resistance, and time, are supplied as inputs to the microcontroller, which was programmed according to the Neuro Fuzzy process model. The weights, biases, and battery history were stored in EPROM. The microcontroller processed the input parameters and gave the output as SoC and SoH [12]. In the complete setup of the microcontroller real-time battery, the parameters were extracted by the interfacing circuitry. These parameters were applied to a programmed microcontroller as inputs and finally the SoC and SoH were displayed.

1.3 System Failures Due to the Absence of a BMS

The internal state information of the battery is one of the most important factors used to protect the system from failure. There are a large number of examples of system failure due to the absence of a BMS or non-accurate BMS algorithms or malfunctioning in BMS control.

In the recent past, there have been major EV and energy storage failures highlighted in the media. The following incidences are a few examples.

1.3.1 Dreamline Boeing Fire Incidences

During 2013 and 2014, a series of incidents of smoke and fire in the battery pack of the Boeing 787 of Dreamline Airlines were detected, including at Boston airport a jet fire accident and at Narita airport smoke detected by the maintenance crew. The airline used lithium-ion (Li-ion) batteries to deliver power for its energy-hungry electrical systems.

Various agencies including NTSB and US Japanese Joint team carried out investigations and found a few possible causes:

It was found that electrolytes, a flammable battery fluid, had leaked from the main Li-ion battery pack and the entire system was damaged.

The Wall Street Journal

reported on February 12, 2013: a possible theory is that the formation of microscopic structures known as dendrites inside the Boeing Co. 787’s Li-ion batteries played a role in twin incidents of fire and smoke in the battery pack of the Boeing.

NTSB declared that overvoltage was not the cause of the Boston incident, as voltage did not exceed the battery limit of 32 V and the charging unit passed tests. The battery had signs of short-circuiting and thermal runaway.

Based on the data and the recorded events, there were several factors that suggest a problem with integration of the battery system into the plane. Two smoke detectors in the electrical/electronic bay, where the APU battery is located, failed to trigger an alarm.

These include problems that may arise from poor systems integration between the engine indicating and crew alerting system (EICAS) and the battery management system.

Another theory said that it is possible that the fail-safe devices in the battery management system, the charger, and the battery cells functioned properly and prevented the short circuit from becoming a catastrophic failure [

13

]. However, the reboot of the APU by a different subsystem in the plane could have caused the final surge in the current that led to the fire.

1.3.2 Fire Accident at the Hawaii Grid Connected Energy Storage

Electricity storage using a Li-ion battery with the BMS and system integration caused fire in 2011 and 2012 at Kahuku, Hawaii. This 15 MW battery energy storage pilot project was connected to a 30 MW wind power plant and was among the first of a few utility scale projects. The project was developed by First Wind, the Kahuku facility with a 15 MW battery from VC-funded Xtreme Power and sells power to the island utility, HECO. Dynapower had supplied the power inverter. Although there were disputes about the cause of the fire, one theory claimed that both fires were attributed to ECI capacitors in inverters from Dynapower. Xtreme, who designed the BMS later, sued Dynapower for malfunctioning of the power controller. Besides legal battles between the companies, the BMS design and safety alarms were always questioned. It was found that the BMS should shut down the process in any extreme conditions and disconnect the AC/DC switchgears for safety.

1.3.3 Fire Accidents in Electric Vehicles

1. The Chevrolet Volt

The Chevrolet Volt recorded the first famous fire when obliterated by the NHSTA (National Highway Traffic Safety Administrative). A fire occurred in a parking lot due to a failure to discharge the battery after crash testing. NHTSA found the Volt to meet its five-star crash rating. After the test, they stashed the mangled Volt outside and three weeks later the vehicle’s battery pack shorted and caught fire – an incident. As a result, NHTSA has opened an inquiry into not only the Volt’s battery pack performance but also the post-crash performance of all hybrid vehicle battery packs [3]. The prevailing theory explaining the battery fire is that the coolant lines serving the battery were probably severed during the crash, leading to a short or eventual overheating condition. However, this has not yet been confirmed. GM contends that the fire happened because the prototype test vehicle’s programming was incomplete. All production Volts have programming for depowering the battery after a crash, dissipating any remaining charge and rendering the battery inert [14].

2. Tesla S model battery fire

There has been more than one incidence where Tesla Model S caught fire. A few happened when the driver ran into something due to high speed at turning or after the driver ran over a chunk of metal on the road.

One theory claimed that the fire did not spread quickly to the whole battery pack and, as a result, no one was hurt. In a L-ion battery, fire spreads quickly throughout the battery, as the cells within the battery ignite their neighboring cells. Tesla’s CTO, J. B. Straubel, said that the company had engineered the pack to prevent fires from spreading and this prevented the fire from spreading throughout the whole battery, and, according to Tesla, it did not enter the passenger compartment.

There are a couple of schools of thought among battery experts about the causes of fire. In a battery fire, the main thing that burns is the liquid electrolyte, which burns easiest when it is exposed to air. One school of thought is that even in the absence of air there are other oxidants within the battery that can create and sustain a fire. It is thought that the battery electrodes themselves can release oxygen, fueling the fire from within.