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

Abstract

Ultrasonic analysis was used to predict the state of charge and state of health of lithium-ion pouch cells that have been cycled for several hundred cycles. The repeatable ultrasonic trends are reduced to two key metrics: time of flight shift and total signal amplitude, which are then used with voltage data in a supervised machine learning technique to build a model for state of charge (SOC) prediction. Using this model, cell SOC is predicted to ∼1% accuracy for both lithium cobalt oxide and lithium iron phosphate cells. Elastic wave propagation theory is used to explain that the changes in ultrasonic signal are related to changes in the material properties of the active materials (i.e., elastic modulus and density) during cycling. Finally, we show the machine learning model can accurately predict cell state of health with an error ∼1%. This is accomplished by extending the data inputs into the model to include full ultrasonic waveforms at top of charge. A key component of an electric vehicle is the battery management system (BMS), which is responsible for controlling the operating conditions on a given battery cell (or stack of cells) in order to optimize the performance and lifetime of the full battery system. The most effective battery management systems must be able to track battery state of charge (SOC), state of health (SOH) and cell failure, including early prediction of catastrophic failure. Despite the importance of this task, being able to reliably determine SOC, SOH and failure at low cost still presents a significant challenge. A range of methods exist at present, however the simplest methods can prove inaccurate, and more complex methods are not suitable for low-cost, in-operando SOC determination. 1-3For instance, in its most common implementation, SOC prediction consists of voltage monitoring (direct measurement) combined with coulomb-counting (book-keeping).1 This can present challenges for a variety of reasons. First, for voltage measurements, the flatness of voltage readings over the majority of battery capacity, especially for lithium iron phosphate (LFP) cells, presents difficulties. 4 Furthermore, voltage fade, changing cell impedances, and varying discharge rates impact measured voltage, obscuring true SOC. Second, coulomb counting is also an inexact science, as discharge rate, environmental factors such as temperature, and cell degradation can all impact the actual capacity for any given discharge. This can lead to a cycle of abuse, whereby discharge conditions lead to an incorrect estimate of SOC, and therefore the cell becomes inadvertently over-discharged. This causes damage to the cell, which leads to further inaccuracy in the SOC prediction, resulting in continued over-discharging and cell damage. Effectively, a battery "death-spiral" ensues.One method to further increase the accuracy of battery management systems is to introduce a technique that can directly measure the physical state of the battery to enhance the determination of SOC, SOH, and cell failure, especially when applied i...

Ultrasonic Health Monitoring of Lithium-Ion Batteries

“…Figure 3c shows that the TOF basically increases as the battery degrades. Increasing of TOF indicates decreasing of the ultrasound velocity, which is a result of the changes in the electrode densities and modulus (i.e., the lithium content changes in each electrode) as a battery degrades [28,31]. Note that an increase of TOF could also indicate an increase in the thickness.…”

“…Note that an increase of TOF could also indicate an increase in the thickness. However, changes of thickness are ignored in this case as they are not the main contributor to TOF changes according to [28]. In addition, the signal amplitude, in Figure 3c, generally increases before cycle 50, followed by a fluctuation after cycle 50.…”

“…As we can see, the TOF decreased during the charging process, while the TOF increased during the discharging process. Referring to [28], as the battery discharges, lithium de-intercalates from the graphite and intercalates into LiCoO2, which decreases the elastic moduli for To further explain the change in the TOF with the battery SOH, Figure 6 shows the evolution of the TOF during a single cycle. As we can see, the TOF decreased during the charging process, while the TOF increased during the discharging process.…”

A Novel State of Charge Estimating Scheme Based on an Air-Gap Fiber Interferometer Sensor for the Vanadium Redox Flow Battery

“…Note that VRFB electrolyte conductivity is highly dependent on temperature, and errors can happen because of side reactions. It was also observed that ultrasonic waves propagate through the electrolytes with distinctly different velocities according to different vanadium ion compositions [10,11]. Open-circuit voltage (OCV) measurement was used to estimate VRFB's SOC in [12].…”

Application of Artificial Intelligence to State-of-Charge and State-of-Health Estimation of Calendar-Aged Lithium-Ion Pouch Cells

“…In addition to SOC, state-of-health (SOH) of the battery also needs to be monitored by BMS. 52 The SOH demonstrates a battery energy storage and delivery capability compared to its original condition. When the battery is fresh, SOH equals to 100% and decreases as the battery ages.…”