About this Research Topic
Batteries are highly nonlinear electrochemical systems. Consequently, their performance (e.g., capacity, internal resistance, output power, etc.) is vulnerable to the operating conditions. Moreover, the performance of batteries degrades over time, particularly during long-term operation. Consequently, to operate batteries with high efficiency, benefit from their advantages, and avoid costly downtime periods, comprehensive knowledge about battery states is essential. Therefore, the design of battery management systems (BMS) becomes a hot topic for researchers. The BMS should be efficient, reliable, accurate, cost-effective, etc. In addition, the BMS must be capable of estimating the battery states, such as state of charge, state of life, state of health, state of power, etc. It can also accurately predict the battery remaining useful life well before time to avoid degraded performance, operational impairment, or total failure.
With the increase in cloud computing capabilities and the availability of extensive real field and laboratory battery data, machine learning methods have been utilized as powerful and robust techniques for battery state estimation and RUL prediction. This Research Topic provides the platform to share the latest findings on battery management system design. The goal of this Research Topic is to encourage researchers to implement the machine learning algorithms, and smart control strategies to design the smart battery management system for smart cities and electric vehicles and benefit other researchers tackling such challenges and opportunities.
Both high-quality Original Research and Review articles are welcome regarding the latest progress and potential research applications in relevant areas, with particular interests in monitoring, modeling, control, and optimization of the battery storage system for management system design.
Keywords: battery management system, states estimation, modeling of lithium-ion battery, hybrid energy storage system
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