This work extends a scalable, equivalent circuit model (ECM) with heat release from chemical decomposition to capture the electrical and thermal behavior of a cell during an external short. To capture the stranded energy and likelihood of thermal runaway, the modelshould accurately predict the final state-of-charge (SOC) and peak temperature. A key advantage of the proposed approach is that the ECM parameterization can be performed under normal operating conditions reducing the amount of hazardoustesting required. This can then be used to explore a controlled discharge by adjusting the external short resistance to avoid venting conditions.

Here we discuss effects in Li-ion batteries in which local nonuniformities in battery construction or mechanical stress can couple into the electrochemical processes of the system and lead to accelerated decay and safety concerns. We present the relevant mechanisms and discuss methods of mitigating these effects in real systems

This talk details a collaborative project between Sandia and Idaho National Laboratories to develop a diagnostic demonstration platform. As part of this, we describe the development and operation of a tool used to integrate battery failure diagnostics into battery modules and attempt to detect induced failures with various diagnostic tools. As a secondary goal we look to evaluate the ability of various diagnostic strategies to detect and arrest battery failure.

Si/C composite materials have attracted enormous research interest as the most promising candidates for the anodes of next-generation LIBs owing to their high energy density and mechanical buffering properties. We first investigate the Li diffusion behavior in Si/C composite material via DFT modeling and then establish a multiscale-multiphysics model to explore the behavior of Si/C anodes from the atomic level to cell level.

Traditionally, the method of tracking battery state functions, such as state of charge (SOC), state of health (SOH), is based on the capacity measured by the instrumentation. The accuracy and reliability of this methodology remain problematic to ensure system reliability and safety in managing performance and functionality. Here, a new perspective on tracking Li inventory in battery cells is proposed to provide an accurate account of the performance and function. The methodology of tracking Li inventory is described, validation provided, and prospects discussed. Why capacity is not a reliable variable for the state assessment will be emphasized.

TWS 3 tier On-Board Diagnostic System for Battery Management System enables the great internal visibility of the battery packs. The tools in this diagnostic systems provide means for engineers and customers to better understand the vital signs and key parameters of BMS and battery pack from visual to history  to current dynamic status. Aggregation of the diagnostic data with AI can further enable the capability of predictions on the battery. 

.During this talk we will introduce how ultrasound technology can enhance the performance of incumbent battery management systems (BMS) by monitoring a cell’s intrinsic inner parameters (lithium plating, SEI layer, and loss of active material) in real-time, resulting in greater performance, longer battery useful lifetime, and an early warning system for potential battery failure.

Several researchers have identified degradation of the SEI layer as a critical single-point cell failure. There is currently no sensor to monitor stresses in SEI during high-rate battery charge and discharge. Even the best BMS typically monitor the individual cell’s voltage and surface temperature. The Battery Internal Temperature Sensing BITS-BMS, described here, is designed to monitor in real time intrinsic cell parameters, including SEI, thus providing early warning for potential battery failure.

BMS require models of their cells to be able to compute estimates of state-of-charge, state-of-health, state-of-power, and state-of-energy. Physics-based models (PBMs) allow a BMS to control the cell to physical limits but it is difficult to estimate the parameter values for these PBMS. This talk proposes a non-destructive lab test that can identify a subset of the PBM parameters via the cell’s voltage response to ultra-short-duration current pulses.

Electric vehicle lithium-ion battery packs require careful monitoring to ensure safe and reliable performance. State-of-the-art battery management systems (BMS) rely on highly accurate battery models for the accurate parameter estimates needed for battery operation.  This talk presents an algorithmic framework built on physics-based principles of lithium-ion batteries and a robust nonlinear estimation scheme that can be used in computationally compact on-board BMS applications.

Although the electric vehicle market is witnessing an unprecedented evolution, the fast adoption of these vehicles requires a more thorough status analysis of the battery performance's functionality and reliability. This investigation aims at proposing a novel data-driven approach called data-driven prognosis (DDP) that estimates the relevant constitutive parameters in situ and captures deviations from the expected degradation dynamics of the Lithium-ion batteries (LIBs) in addition to precise modeling of the degradation and capacity models. This talk will present a new data-driven approach using statistical pattern recognition and machine learning tools to detect batteries' anomalies and failures.

We introduce a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately with as little as 100 cycles. Compared with single-task learning models, the model shows a significant prediction accuracy improvement and reduces 50% of the total computational cost.

At the core of any BMS is a battery model. The main limiting factor on how fast battery models can be developed is the experimental technique used for collection of data required for model parametrisation. Currently this is a very time-consuming process. We present a novel, fast parametrisation testing technique. GITT (galvanostatic intermittent titration technique) tests are typically used to parameterise ECMs (equivalent circuit models) for batteries. If done well, a GITT test can take up to 80 hours to complete for a given temperature. If this is repeated at multiple temperatures, a full parameter set can take few weeks or even months to compile a comprehensive data set. The ESE group at Imperial College London has developed an innovative experimental technique which takes less than 8 hours to complete for each test temperature. Models parameterised using AMPP (Accelerated Model Parametrisation Procedure) are as good as those models parametrised using GITT, however, AMPP is 90% faster.

At what price does an integrated solution come?  What is the impact on configurability, serviceability, and development time? This presentation will address the tradeoffs of two competing battery system architectures.