Smart Cells for Embedded Battery Management

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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Fiber Optic Sensing Technologies for Battery Management Systems and Energy Storage Applications

Sensors ◽

10.3390/s21041397 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1397

Author(s):

Yang-Duan Su ◽

Yuliya Preger ◽

Hannah Burroughs ◽

Chenhu Sun ◽

Paul Ohodnicki

Keyword(s):

Energy Storage ◽

Fiber Optics ◽

Fiber Optic ◽

Gas Sensing ◽

Fiber Optic Sensors ◽

Management Systems ◽

Practical Implementation ◽

Battery Management ◽

Battery Management Systems ◽

Battery Systems

Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

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Design of a Modular Battery Management System for Electric Motorcycle

Energies ◽

10.3390/en14123532 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3532

Author(s):

Hung-Cheng Chen ◽

Shin-Shiuan Li ◽

Shing-Lih Wu ◽

Chung-Yu Lee

Keyword(s):

Temperature Measurement ◽

Management System ◽

Battery Management System ◽

Battery Management ◽

Voltage Measurement ◽

Mixed Signal ◽

Discharge Temperature ◽

Charging Current ◽

Battery Module ◽

Signal Processor

This paper proposes a modular battery management system for an electric motorcycle. The system not only can accurately measure battery voltage, charging current, discharging current, and temperature but also can transmit the data to the mixed-signal processor for battery module monitoring. Moreover, the system can control the battery balancing circuit and battery protection switch to protect the battery module charging and discharging process safety. The modular battery management system is mainly composed of a mixed-signal processor, voltage measurement, current measurement, temperature measurement, battery balancing, and protection switch module. The testing results show that the errors between the voltage value measured by the voltage measurement module and the actual value are less than 0.5%, about 1% under the conditions of different charging and discharging currents of 9 A and 18 A for the current measuring module, less than 1% for the temperature measurement module; and the battery balancing in the battery management system during the charging process. When the module is charged at 4.5 A for about 805 s, each cell of the battery has reached the balancing state. Finally, the testing results validate that the modular battery management system proposed in this paper can effectively manage the battery balancing of each cell in the battery module, battery module overcharge, over-discharge, temperature protection, and control.

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