Abstract

Electric vehicles are set to be the dominant form of transportation in the near future and Lithium-based rechargeable battery packs have been widely adopted in them. Battery packs need to be constantly monitored and managed in order to maintain the safety, efficiency and reliability of the overall electric vehicle system. A battery management system consists of a battery fuel gauge, optimal charging algorithm, and cell/thermal balancing circuitry. It uses three non-invasive measurements from the battery, voltage, current and temperature, in order to estimate crucial states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power fade, and remaining useful life. These estimates are important for the proper functioning of optimal charging algorithms, charge and thermal balancing strategies, and battery safety mechanisms. Approach to robust battery management consists of accurate characterization, robust estimation of battery states and parameters, and optimal battery control strategies. This paper describes some recent approaches developed by the authors towards developing a robust battery management system.

Highlights

  • Automobiles powered by gasoline engines account for nearly 25% of the global energy consumption [1]

  • Battery packs used in electric vehicles are expected to be replaced when they reach about 80% of their original capacity [11], since range is an important quality in EVs

  • The importance of battery management system (BMS) evaluation is discussed in Reference [59]; in Reference [60], the need to minimize power dissipation and extend battery run-time for portable devices is discussed; the advantages of hardware-in-the-loop (HIL) testing to validate a BMS under various failure conditions was motivated in Reference [61]; and a HIL test to validate the Battery fuel gauge (BFG) using a multi-cell battery pack was proposed in References [62,63]

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Summary

Introduction

Automobiles powered by gasoline engines account for nearly 25% of the global energy consumption [1]. The need to fast-charge the battery, which is important in electric vehicle applications, increases the possibility of thermal runaway and safety issues [5,6]. There are wide ranging issues affecting the efficiency of energy storage in batteries; electric vehicle applications strive to improve efficiency in every possible way. The present manuscript is written in the form of an expository paper detailing the many solutions developed by the authors in the recent past in order to address specific challenges in battery management systems.

State of Charge Estimation
3: SOC SOC tracking
Real-Time State of Health Estimation
Optimal Charging
Fast Characterization
Battery Reuse
Universality
Self Evaluation
Solutions Through Model Based Algorithms
Normalized Open Circuit Voltage Characterization
Equivalent Circuit Model Identification
Real-Time Battery Capacity Estimation
Optimized Charging
Adaptive Algorithms for Universality
Approaches to BMS Evaluation
Findings
Conclusions
Full Text
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