State-of-Charge Determination in Lithium-Ion Battery Packs Based on Two-Point Measurements in Life

Abstract

The state-of-charge (SOC) estimation is of extreme importance for the reliability and safety of battery operation. How to estimate SOC for an assembly of cells in a battery pack remains a subject of great interest. Here a viable method for SOC determination and tracking for multi-cell assemblies is proposed and validated. Using 3S1P (three in series and one in parallel) strings as an example, an inference of SOC is illustrated in a battery assembly based on a correct open pack voltage (OPV) versus SOC (i.e. OPV = f (SOC)) function. The proposed method only requires the measurements of the rest cell voltages of the single cells at two distinct occasions. Rechargeable lithium-ion batteries (LIB) continue being considered viable choices for mobile power and energy storage applications. Yet, a reliable deployment of LIB in powertrains remains very challenging, primarily due to the requirements for reliable multi-cell assemblies to provide high energy and power. Better capability to characterize battery pack performance, identify aging mechanism, and perform state-of-charge (SOC) estimation is desired to achieve great efficiency.1,2 In our previous work, we devoted substantial effort to understand the behavior of cells in a pack and the impact of cell variability on pack performance. 3,4 We also reported a diagnostic and prognostic approach to identify and quantify cell-aging mechanisms in the course of cycle aging for a number of cell chemistries.5-11 To enable these methodologies, the SOC determination is the most vital component among all for accurate operation of the battery management system (BMS).1 In our latest work, 12 we showed that the most accurate method to obtain the SOC of a battery pack is either by measuring the residual capacity (Q res ) against the maximum pack capacity (Q max ); i.e. pack SOC = Q res /Q max , or by measuring the rest pack voltage (RPV) to infer SOC based on a SOC versus open pack voltage (OPV) function; i.e. pack SOC = f -1 (OPV). Both methods, however, suffer from their inoperability in practical applications, due to (1) in the case of capacity measurements, the uncertainty related to Q res in a duty cycle, (2) the fade in the Q max over lifetime, and (3) in the case of voltage-based measurements, the need for accurate OPV across the full SOC range. Therefore, a simple and practical method for SOC estimation remains very desirable.For a single cell (SC), the open circuit voltage (OCV) versus SOC function is often preferred for SOC determination because in principle, and at the beginning-of-life (BOL) of the cell, the sc SOC = f -1 (OCV) function is universal for cells of the same chemistry, disregarding size or geometry.3 Therefore, this OCV = f(SOC) function only needs to be determined once and for all from a single cell of a specific design. Upon aging, uncertainties to this OCV = f(SOC) function are introduced due to aging-pathway dependence.6 Such variations could be predicted as a function of duty cycle characteristics using the mechanistic model reported in our prior work. 5...

State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

“…Natural variation in cell performance 2,11 and noise in the signal measurements makes direct linear regression challenging for accurate state of charge prediction. For instance, in Figures 4b and 4d, note the slight variations in the ToF shift and amplitude trends versus SOC between cycles.…”

“…This manifests itself as a change in the properties of the electrode materials as a whole, which leads to shifts in the transmitted ultrasonic signal at MHz frequencies. Despite cell variability 2,3,11 and measurement noise, we demonstrate that practical determination of state of charge is possible, beyond a proof of concept. This technique relies on an ultrasonic library of similar cells being generated, with the data then fed into a machine learning algorithm.…”

Cell-balancing currents in parallel strings of a battery system

“…These cells were made by a brand name manufacturer. The cell chemistry has been studied in our laboratory in a series of investigations on subjects from cell variability [2], cycle aging and degradation mechanism [3], capacity fading in temperature excursions [4], a peculiar capacity fade phenomenon in the c-PE associated with overdischarge [5], to the methodology in determining state-of-charge (SOC) and state-of-health (SOH) of the cells and multi-cell strings [6,7]. Here, we discussed the transient aspects of parallel string configurations and the string balancing issues.…”

Discharge Capacity Estimation for Lithium–Ion Battery Packs with Cells in Parallel Connection Based on Current Prediction of In‐Pack Cells

“…Recently, Dubarry et al. categorized cell variations into three types: different initial SOCs, different capacity rations, and different SOHs . By using three in series and one in parallel (3S1P) strings as an example, they derived the relationship between open pack voltage and the SOC of the pack.…”

“…[3] Recently,Dubarry et al categorized cell variations into three types:d ifferent initialS OCs,d ifferent capacity rations,a nd different SOHs. [4] By using three in series and one in parallel (3S1P)s tringsa sa ne xample,t hey derived the relationship between open pack voltage and the SOC of the pack. Zheng et al studied cells in series and investigated the evolution of capacityl oss of battery packs by analyzingt he aging mechanismo fc ells.…”