Disturbance-Immune and Aging-Robust Internal Short Circuit Diagnostic for Lithium-Ion Battery

Hu, Jian; He, Haibo; Wei, Zhongbao; Li, Yang

doi:10.1109/tie.2021.3063968

Cited by 92 publications

(27 citation statements)

References 41 publications

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“…The BMS Master performs the current sensing, measures the battery total voltage, monitors the insulation impedance of the high voltage circuit, takes part in the high voltage interlock circuit (HVIL), controls redundant power switches for battery isolation and protection and, finally, communicates with external electronic control units (ECUs). Advanced estimation algorithms, such as SoC, SoH and SoP [6][7][8], or internal shor circuit detection algorithms [32], are highly desirable diagnostics for battery misuse pre vention as long as they are accurately tuned [33] for the used cell. Consequently, the should be allocated to either the BMS Master or an external ECU like an energy manage ment system (EMS) or shared between both.…”

Section: Development Phase: Functional/technical Requirements and Designmentioning

confidence: 99%

Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries

et al. 2021

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The increasing use of lithium batteries and the necessary integration of battery management systems (BMS) has led international standards to demand functional safety in electromobility applications, with a special focus on electric vehicles. This work covers the complete design of an enhanced automotive BMS with functional safety from the concept phase to verification activities. Firstly, a detailed analysis of the intrinsic hazards of lithium-based batteries is performed. Secondly, a hazard and risk assessment of an automotive lithium-based battery is carried out to address the specific risks deriving from the automotive application and the safety goals to be fulfilled to keep it under control. Safety goals lead to the technical safety requirements for the next hardware design and prototyping of a BMS Slave. Finally, the failure rate of the BMS Slave is assessed to verify the compliance of the developed enhanced BMS Slave with the functional safety Automotive Safety Integrity Level (ASIL) C. This paper contributes the design methodology of a BMS complying with ISO 26262 functional safety standard requirements for automotive lithium-based batteries.

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Section: Development Phase: Functional/technical Requirements and Designmentioning

confidence: 99%

Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries

et al. 2021

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“…A common approach used in LIB applications is the extended Kalman Filter (EKF) [3,9]. It is widely used to estimate the state of charge [17,18], state of health [1], and cell capacity [6,19]. It can also be used for online parameter estimation [20].…”

Section: Introductionmentioning

confidence: 99%

Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

2021

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The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

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“…In addition to the use in SOC estimation, ECM parameter estimation has other important applications in a battery management system. The literature review suggests that there are several model-based SOC estimation methods that exist, which are already explored by different researchers [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

et al. 2021

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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

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Disturbance-Immune and Aging-Robust Internal Short Circuit Diagnostic for Lithium-Ion Battery

Cited by 92 publications

References 41 publications

Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries

Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries

Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

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