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.

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.

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application.

Battery management systems depend on open circuit voltage (OCV) characterization for state of charge (SOC) estimation in real time. The traditional approach to OCV-SOC characterization involves collecting OCV-SOC data from sample battery cells and then fitting a polynomial model to this data. The parameters of these polynomial models are known as the OCV-parameters, or OCV-SOC parameters, in battery management systems and are used for real-time SOC estimation. Even though traditional OCV-SOC characterization approaches are able to abstract the OCV-SOC behavior of a battery in a few parameters, these parameters are only applicable in high precision computing systems. However, many practical battery applications do not have access to high-precision computing resources. The typical approach in a low-precision system is to round the OCV-parameters. This paper highlights the perils of rounding the OCV parameters and proposes an alternative OCV-SOC table. First, several existing OCV-SOC parameters are compared in terms of their expected system requirements and accuracy losses due to rounding. Then, a systematic optimization-based approach is introduced to create an OCV-SOC table that is robust to rounding. A formal performance evaluation metric is introduced to measure the robustness of the resulting OCV-SOC table. It is shown that the OCV-SOC table obtained through the proposed optimization approach outperforms the traditional parametric OCV-SOC models with respect to rounding.

Real-time identification of electrical equivalent circuit models (ECMs) is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order ECMs. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this article, we analyze the performance of a linear recursive least squares (RLS) approach to ECM identification and show that the LS approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that when the signal-to-noise ratio is low-resembling the case in many practical applications-the LS estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on the total LS method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total LS method. The resulting total Kalman filter (TKF) is shown to be both unbiased and efficient in ECM identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this article in identifying higher-order electrical ECMs is explained.