Battery Management Systems (eBook)

Table of contents 7
List of abbreviations 10
List of symbols 12
Chapter 1 Introduction 20
1.1 Battery Management Systems 20
1.2 State-of-Charge definition 22
1.3 Goal and motivation of the research described in this book 23
1.4 Scope of this book 25
1.5 References 26
Chapter 2 State-of-the-Art of battery State-of-Charge determination 29
2.1 Introduction 29
2.2 Battery technology and applications 29
2.3 History of State-of-Charge indication 34
2.4 A general State-of-Charge system 41
2.5 Possible State-of-Charge indication methods 42
2.6 Commercial State-of-Charge indication systems 56
2.7 Conclusions 59
2.8 References 60
Chapter 3 A State-of-Charge indication algorithm 64
3.1 An introduction to the algorithm 64
3.2 Battery measurements and modelling for the State-of-Charge indication algorithm 64
3.3 States of the State-of-Charge algorithm 69
3.4 Main issues of the algorithm 71
3.5 General remarks on the accuracy of SoC indication systems 76
3.6 Conclusions 76
3.7 References 77
Chapter 4 Methods for measuring and modelling a battery’s Electro-Motive Force 79
4.1 EMF measurement 79
4.2 Voltage prediction 85
4.3 Hysteresis 99
4.4 Electro-Motive Force modelling 102
4.5 Conclusions 109
4.6 References 109
Chapter 5 Methods for measuring and modelling a battery’s overpotential 111
5.1 Overpotential measurements 111
5.2 Overpotential modelling and simulation 119
5.3 Conclusions 124
5.4 References 125
Chapter 6 Battery aging process 126
6.1 General aspects of battery aging 126
6.2 EMF measurements as a function of battery aging 129
6.3 Overpotential dependence on battery aging 147
6.4 Adaptive systems 152
6.5 Conclusions 156
6.6 References 157
Chapter 7 Measurement results obtained with new SoC algorithms using fresh batteries 159
7.1 Introduction 159
7.2 Implementation aspects of the algorithm 160
7.3 Results obtained with the algorithm using fresh batteries 165
7.4 Uncertainty analysis 169
7.5 Improvements in the new SoC algorithm 178
7.6 Comparison with Texas Instruments’ bq26500 SoC indication IC 188
7.7 Conclusions 192
7.8 References 193
Chapter 8 Universal State-of-Charge indication for battery-powered applications 195
8.1 Introduction 195
8.2 Implementation aspects of the overpotential adaptive system 196
8.3 SoC=f(EMF) and adaptive system 197
8.4 Results obtained with the adaptive SoC system using aged adaptive system 199
8.5 Uncertainty analysis 202
8.6 Results obtained with other Li-based battery 203
8.7 Practical implementation aspects of the SoC algorithm 214
8.8 Conclusions 232
8.9 References 233
Chapter 9 General conclusions 235
References 237

"Chapter 3

A State-of-Charge indication algorithm (p. 47-48)

As discussed in chapter 2, many advances have been made in State-of- Charge (SoC) indication in recent years, both through continued improvement of the SoC algorithms and through the development of more accurate hardware systems. Nevertheless, there is still no ""ideal"" SoC system that gives accurate indications under all realistic user conditions. The ""ideal"" SoC system is obviously one that is not expensive, can handle all battery chemistries, can operate over a wide range of load currents and can deal with the aging effect. Leading semiconductor companies (e.g. Philips [1]–[3], NXP Research, Texas Instruments [4]–[6], Microchip [7], [8] Maxim [9], [10], etc.) are paying more and more attention to accurate State-of-Charge indication in attempts to find that ideal system.

A SoC algorithm that combines some form of adaptivity with direct measurement and book-keeping systems was developed and implemented by Bergveld et al. in 2000 [1]–[3]. By implementing the mathematical models described in [1], this algorithm was found to be the most sophisticated and accurate [11], [12]. This chapter will give a complete description of this algorithm, which serves as the starting point of this book. This chapter is organised as follows.

An introduction to the algorithm is given in section 3.1. Section 3.2 describes the models and states of the SoC indication system. The main aspects of the algorithm are given in section 3.3. The focus in section 3.4 is on accuracy problems. Section 3.5 presents concluding remarks.

3.1 An introduction to the algorithm

The SoC indication algorithm presented by Bergveld et al. in [1]–[3] aims to eliminate the main drawbacks and combine the advantages of the direct measurement and book-keeping methods described in Chapter 2. The basis of the SoC algorithm is Electro-Motive Force (EMF) measurement during equilibrium and current measurement and integration during charge and discharge. During discharge, in addition to simple Coulomb counting, the effect of the overpotential is also considered [1]. A method has also been developed for updating the value of the maximum capacity for coping with capacity loss due to the aging effect. The algorithm will be described below for a Panasonic CGR17500 Li-ion battery, but the basis of the algorithm holds for other types of Li batteries, too. The rated capacity of this battery is 720 mAh.

3.2 Battery measurements and modelling for the State-of-Charge indication algorithm

The battery model applied in the developed SoC indication algorithm describes the battery EMF and overpotential behaviour, neither of which can be measured directly. The EMF and overpotential curves have been measured with an accurate battery tester and implemented in the Battery Management System (BMS) using mathematical-function approximations [1], [13]. Both the measurement and the implementation method contribute to the final accuracy of the SoC indication.
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