An accurate model of a cell’s OCV versus SOC relationship is critical for the proper function of BMS algorithms. Error as small as 1mV can cause 1% inaccuracy in SOC estimates. When attempting to find a faithful OCV model, we encounter three fundamental problems: the “missing data problem," the “inaccessible lithium problem," and the “observability problem." This talk shows how these problems can be addressed to develop high-fidelity OCV models.

Electric vehicle energy storage systems using lithium-ion batteries require careful monitoring to ensure safe and reliable vehicle performance. State-of-the-art battery management systems (BMS) rely on highly accurate battery models to produce accurate parameter estimates required for battery operation. New anode materials can increase energy density, but introduce large voltage hysteresis, which is difficult to model. This talk presents a comparative look at candidate hysteresis models and discusses their merits with respect to computational effort, accuracy, and implications for control.

Designing the Battery Packs with BMS is a purely tailor-made B2B business, which consumes a lot engineering resources. With the considerations of platform-izing the solutions across applications, it will not only optimize the utilization of the engineering resources and shorten the RTL time, and it will ensure the reliability and stability. Meanwhile, more engineering resources can focus on enriching BMS with more intelligence via advanced algorithms and data driven analytics. 

The classical cylindrical 18650 cell format is about to be replaced by the larger 21700 format. The differences are investigated regarding charge and discharge curves, C-rate tests with internal and external temperature sensors as well as long-term cycling tests. Estimations are made regarding larger cylindrical formats such as 30700.

The increased demand for portable devices operating for extended periods on a single charge is a great fit for both lithium primary (single-use) and lithium-ion (rechargeable) battery technologies. The benefits of these technologies include: 1) larger power and energy densities; 2) low self-discharge rates; and 3) a variety of form factors and chemistries that are compatible for different requirements. However, for both performance and safety, lithium battery technologies require active battery management. The battery management system (BMS) performs a number of functions.  Some safety-related functions include preventing cell overcharge, over discharge, and operation outside allowable temperatures. Some performance-related functions include estimating the battery’s state of charge (SOC), tracking the battery’s state of health (SOH), and electrical balancing of cells in strings (balancing is also important for safety). The purpose of this presentation is to explore the electrochemical and thermal phenomena behind system level requirements for lithium-ion batteries and to understand how these requirements vary over the lifespan of the battery system.

Industrial AI, Big Data Analytics, Machine Learning, and Cyber Physical Systems are changing the way we design product, manufacturing, and service systems. It is clear that as more sensors and smart analytics software are integrated in the EV systems, predictive technologies can further learn and autonomously optimize mobility and performance. This presentation will address the trends of Industrial AI for smart battery realization. First, Industrial AI systematic approach will be introduced. Case studies on advanced predictive analytics technologies for smart battery health management and mobility optimization will be discussed. In addition, issues battery data quality in future predictive mobility will be discussed