Battery lockout circuit and battery pack using same

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

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Model-based Health Diagnosis for Lithium-ion Battery Pack in Space Applications

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2020.3045745 ◽

2020 ◽

pp. 1-1

Author(s):

Yuchen Song ◽

Yu Peng ◽

Datong Liu

Keyword(s):

Lithium Ion Battery ◽

Lithium Ion ◽

Battery Pack ◽

Space Applications ◽

Model Based ◽

Health Diagnosis

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Effect of dynamic loads and vibrations on lithium-ion batteries

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/14613484211008112 ◽

2021 ◽

pp. 146134842110081

Author(s):

Xia Hua ◽

Alan Thomas

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion Battery ◽

Experimental Studies ◽

Lithium Ion ◽

Battery Pack ◽

Dynamic Loads ◽

Electrical Performance ◽

Mechanical Degradation ◽

Energy Storage Devices ◽

Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

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A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021994349 ◽

2021 ◽

pp. 095440702199434

Author(s):

Wei Yue ◽

Cong-zhi Liu ◽

Liang Li ◽

Xiang Chen ◽

Fahad Muhammad

Keyword(s):

Fractional Order ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Open Circuit ◽

Design Criterion ◽

Observer Design ◽

Nonlinear Observer ◽

Battery Pack ◽

Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

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Task Planner for Robotic Disassembly of Electric Vehicle Battery Pack

Metals ◽

10.3390/met11030387 ◽

2021 ◽

Vol 11 (3) ◽

pp. 387

Author(s):

Martin Choux ◽

Eduard Marti Bigorra ◽

Ilya Tyapin

Keyword(s):

Computer Vision ◽

Electric Vehicles ◽

Measurement Errors ◽

Vision System ◽

Lithium Ion ◽

Battery Pack ◽

Computer Vision System ◽

Electric Vehicle Battery ◽

Robotic Disassembly ◽

Total Average

The rapidly growing deployment of Electric Vehicles (EV) put strong demands on the development of Lithium-Ion Batteries (LIBs) but also into its dismantling process, a necessary step for circular economy. The aim of this study is therefore to develop an autonomous task planner for the dismantling of EV Lithium-Ion Battery pack to a module level through the design and implementation of a computer vision system. This research contributes to moving closer towards fully automated EV battery robotic dismantling, an inevitable step for a sustainable world transition to an electric economy. For the proposed task planner the main functions consist in identifying LIB components and their locations, in creating a feasible dismantling plan, and lastly in moving the robot to the detected dismantling positions. Results show that the proposed method has measurement errors lower than 5 mm. In addition, the system is able to perform all the steps in the order and with a total average time of 34 s. The computer vision, robotics and battery disassembly have been successfully unified, resulting in a designed and tested task planner well suited for product with large variations and uncertainties.

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