The accurate state of charge (SOC) is required for the battery for hybrid electric vehicles (HEV). Because HEV batteries, a widely fluctuating current profile; partial recharging phases (regenerative braking); inadequate lifetimes to date; expanding market and strong competition from other battery technologies is a major drawback in this application. Though as lead acid batteries are improved as well, they are widely used for micro hybrid vehicles, and the types of lead acid batteries, which are used in micro hybrid cars are quite similar to starter batteries. The motivation of this thesis work is to eliminate the drawback and determine the SoC of the energy storage system (lead acid battery) in the HEV. Because the conventional SoC determination method (e.g. coulomb counting, chemical method, voltage method etc) cannot measure the SoC correctly. In this thesis, I studied, analysed and compared different published research works and proposed a new approach by using electromotive force (EMF) to calculate SoC, which was successfully developed and validated.
Table of Contents
1. Introduction
1.1 Motivation of the thesis work
2. Fundamentals of Lead Acid Battery
2.1 The galvanic cell
2.2 Thermodynamic Parameters
2.3 Open Circuit Voltage
2.4 Effect of electrolyte concentration
2.5 Temperature coefficient
2.6 Butler–Volmer Equation
2.7 Overvoltage due to concentration gradients
2.8 The lead acid battery
2.9 Capacity and State of Charge
2.9.1 Effect of high discharge current
2.9.2 Effect of capacity on temperature
2.10 Battery Types
2.11 Battery aging
2.11.1 Sulfation
2.11.2 Stratification
2.11.3 Corrosion
2.11.4 Water loss
3. Classic SoC indication methods
3.1 Chemical Method
3.2 Voltage Method
3.3 Current integration method
3.4 New SoC indication method
3.4.1 Introduction of the first algorithm (EMF= a.OCVrest + b.(dv/dt) – c.T- d )
3.4.2 Introduction of the second algorithm (OCV(t) = EMF – a/ (tb . logc(t)))
3.4.3 Introduction of the new algorithm
3.5 Validation and Analysis of the methods
3.5.1 Data used for the validation
3.5.2 Result analysis for the first algorithm
3.5.3 Result analysis for the second algorithm
3.5.4 Result analysis for the new algorithm
3.6 Results from driving profiles
4. Online operation of the new algorithm
4.1 Algorithm’s calculation method
4.2 Working principle
4.3 Design proposal of the new algorithm
5. Discussion
6. Scope of further development
7. Conclusion
Objectives and Research Focus
The primary objective of this thesis is to address the limitations of conventional State of Charge (SoC) determination methods for lead-acid batteries in hybrid electric vehicles by developing and validating a new, more accurate estimation algorithm based on electromotive force (EMF).
- Analysis of fundamental electrochemical properties of lead-acid batteries.
- Critical review of classic SoC indication methods, including chemical, voltage, and current integration approaches.
- Development of a new SoC estimation model considering small current discharge and internal resistance.
- Experimental validation of algorithms using laboratory measurement data and real-time driving profiles.
- Performance evaluation based on error rate analysis across different battery relaxation conditions.
Excerpts from the Book
3.4.3 Introduction of the new algorithm
A new model is developed to calculate the EMF of the battery (9). The model describes the battery relaxation behavior and determines the EMF. The models described above do not contain the small current discharge part inside the battery which is also included in this new model along with the ohmic overpotential part, because this small current or quiescent current discharge, decreases the capacity of the battery over time. The new algorithm is described in equ. (15),
U(t) = A1 * exp (- t/tau1) + A2 * exp (- t/tau2) - ∫ I dt + EMFraw.....(26)
In this model, the double exponential part describes the voltage relaxation part of the battery voltage, because one exponential function is not enough to describe the relaxation behavior of the battery for the long period of time (6hr) (Fig. 17, 18) and the small current discharge inside the battery is described as integration of the current and can be modeled by a linear part with the slope m (m*t). A1, A2 are parameter and tau1, tau2 are time constants of the exponential parts. This equation should maintain the lookup table for these parameter values for different SoC, temperature and discharge current to calculate the EMF values.
Summary of Chapters
1. Introduction: Presents the motivation behind the study, emphasizing the need for reliable SoC estimation in micro hybrid vehicles due to the dynamic nature of battery usage.
2. Fundamentals of Lead Acid Battery: Discusses the electrochemical principles, thermodynamic parameters, and factors influencing battery performance and aging.
3. Classic SoC indication methods: Reviews traditional SoC methods and introduces new algorithm concepts, including mathematical models for voltage relaxation.
4. Online operation of the new algorithm: Proposes the integration of the developed algorithm into Battery Management Systems (BMS) for real-world application.
5. Discussion: Evaluates the experimental results, comparing the accuracy of the new algorithm against existing methods.
6. Scope of further development: Identifies potential improvements, such as parameter adaptation, to enhance estimation precision under varying operating conditions.
7. Conclusion: Summarizes the findings, highlighting the success of the new algorithm in achieving more accurate SoC estimation compared to standard models.
Keywords
Lead-acid battery, State of Charge, SoC, Electromotive force, EMF, Hybrid Electric Vehicles, HEV, Battery Management System, BMS, Voltage relaxation, Algorithm development, Ohmic resistance, Current integration, Coulomb counting, Battery aging.
Frequently Asked Questions
What is the core focus of this research?
The work primarily focuses on improving State of Charge (SoC) estimation for lead-acid batteries used in micro hybrid vehicles by utilizing electromotive force (EMF) during voltage relaxation.
What are the main thematic areas?
The research covers electrochemical fundamentals, a critique of classic SoC methods, the development of new mathematical algorithms for EMF prediction, and the validation of these models via lab data and driving profiles.
What is the primary research goal?
The goal is to develop an algorithm that accurately estimates SoC by accounting for factors often ignored by traditional methods, such as small quiescent current discharge and internal battery resistance during idle periods.
Which scientific methods are applied?
The study uses experimental laboratory measurements combined with mathematical curve fitting and regression analysis to define and validate new estimation models.
What topics are covered in the main section?
The main part covers the fundamentals of lead-acid chemistry, the mathematical derivation of new algorithms using double exponential functions, and a comparative analysis of the accuracy of these models.
Which keywords characterize this work?
The work is characterized by terms like State of Charge (SoC), Electromotive Force (EMF), lead-acid battery, hybrid electric vehicle, and voltage relaxation modeling.
How does the new algorithm handle battery relaxation?
The new algorithm uses a double exponential function to model the non-linear voltage recovery and incorporates a linear correction factor for the quiescent current discharge that occurs during the relaxation period.
Why are standard Coulomb counting methods insufficient for this application?
Standard Coulomb counting requires an accurate initial SoC value and frequent recalibration, which is problematic for starter batteries in hybrid vehicles that experience frequent, dynamic start-stop cycles.
- Quote paper
- Md Navid Bin Anwar (Author), 2013, Development of Algorithms for Battery SoC Estimation in Hybrid Vehicles, Munich, GRIN Verlag, https://www.grin.com/document/299026
