A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed

With increased usage, individual batteries within the battery pack will begin to show disparate voltage and State of Charge (SOC) profiles, which will impact the time at which batteries become balanced. Commercial battery management systems (BMSs), used in electric vehicles (EVs) and microgrids, typically send out signals suggesting removal of individual batteries or entire packs to prevent thermal runaway scenarios. To reuse these batteries, this paper presents an analysis of an off-the-shelf Orion BMS with a constrained cycling approach to assess the voltage and SOC balancing and thermal performances of such near-to-second life batteries. A scaled-down pack of series-connected batteries in 6s1p and 6s2p topologies are cycled through a combination of US06 drive and constant charge (CC) profiles using an OPAL-RT real-time Hardware-in-the-loop (HIL) simulator. These results are compared with those obtained from the Matlab/Simulink model to present the error incurred in the simulation environment. Results suggest that the close-to-second life batteries can be reused if operated in a constrained manner and that a scaled-up battery pack topology reduces incurred error.

Design of an Embedded Energy Management System for Li–Po Batteries Based on a DCC-EKF Approach for Use in Mobile Robots

In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components.

Low Power Modular Battery Management System with a Wireless Communication Interface

The paper concerns the design and development of large electric energy storage systems made of lithium cells. Most research advances in the development of lithium-ion battery management systems focus solely on safety, functionality, and improvement of the procedures for assessing the performance of systems without considering their energy efficiency. The paper presents an alternative approach to the design and analysis of large modular battery management systems. A modular battery management system and the dedicated wireless communication system were designed to analyze and optimize energy consumption. The algorithms for assembly, reporting, management, and communication procedures described in the paper are a robust design tool for further developing large and scalable battery systems. The conducted analysis of energy efficiency for the exemplary 100S15P system shows that the energy used to power the developed battery management system is comparable to the energy dissipated due to the intrinsic self-discharge of lithium-ion cells.