Browse Topic: Battery management systems (BMS)
A power battery parameter acquisition device was designed and developed with STM32 as the core, featuring the functions of a battery management system (BMS) to ensure the safety and stability of the battery pack during operation. The device includes functions such as battery charge and discharge management, battery safety protection, and battery status monitoring, enabling real-time monitoring of cell parameters. The hardware design covers the power circuit, charge and discharge cycle circuit, battery acquisition circuit, communication module circuit, and single-cell balancing circuit. The software part completes the design and development of each functional module. This paper addresses issues in battery management systems, such as low accuracy in battery parameter acquisition, inconsistencies between individual cells, and weak BMS balancing capabilities. The developed acquisition device can collect parameters for 15 series-connected power batteries, and conduct sampling tests of cell
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
In the realm of low-altitude flight power systems, such as electric vertical take-off and landing (eVTOL), ensuring the safety and optimal performance of batteries is of utmost importance. Lithium (Li) plating, a phenomenon that affects battery performance and safety, has garnered significant attention in recent years. This study investigates the intricate relationship between Li plating and the growth profile of cell thickness in Li-ion batteries. Previous research often overlooked this critical aspect, but our investigation reveals compelling insights. Notably, even during early stage of capacity fade (~ 5%), Li plating persists, leading to a remarkable final cell thickness growth exceeding 20% at an alarming 80% capacity fade. These findings suggest the potential of utilizing cell thickness growth as a novel criterion for qualifying and selecting cells, in addition to the conventional measure of capacity degradation. Monitoring the growth profile of cell thickness can enhance the
Tracking of energy consumption has become more difficult as demand and value for energy have increased. In such a case, energy consumption should be monitored regularly, and the power consumption want to be reduced to ensure that the needy receive power promptly. Our objective is to identify the energy consumption of an electric vehicle from battery and track the daily usage of it. We have to send the data to both the user and provider. We have to optimize the power usage by using anomaly detection technique by implementing deep learning algorithms. Here we are going to employ a LSTM auto-encoder algorithm to detect anomalies in this case. Estimating the power requirements of diverse locations and detecting harmful actions are critical in a smart grid. The work of identifying aberrant power consumption data is vital and it is hard to assure the smart meter’s efficiency. The LSTM auto-encoder neural network technique is used here for predicting power consumption and to detect anomalies
Nowadays, Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are becoming popular globally due to increasing pollution levels in the environment and expensive conventional non-renewable fuels. Li-ion battery EV’s have gained attention because of their higher specific energy density, better power density and thermal stability as compared to other cell chemistries. Performance of the Li-ion battery is affected by temperatures of the cells. For Li-ion cells, optimum operating temperature range should be between 15-35 °C [1]. Initially, small battery packs which are cooled by air were used but nowadays, large battery packs with high power output capacities being used in EV’s for higher vehicle performance. Air based cooling system is not sufficient for such batteries, hence, liquid coolant based cooling systems are being introduced in EV’s. Computational Fluid Dynamics (CFD) simulation can be used to get better insight of cell temperature inside battery. But it is complex, time
Batteries for eVTOL aircraft need to deliver high power for efficient takeoff and landing, as well as high energy for the cruise period. To meet these demands, designers must consider the power-energy tradeoff of batteries and integrate a reliable battery management system into the overall design. Multiphysics simulation can be used to evaluate this tradeoff and consider all design requirements in a way that is comprehensive and saves time. In recent years, more and more organizations have announced their development of electric vertical take-off and landing (eVTOL) systems and, in some cases, are even showing previews of systems that are intended to hit the market in just a few years. As new design ideas emerge, there is one important question that needs to be asked: To keep up with the developments in eVTOL aircraft, what design requirements need to be considered for the batteries that power them?
Lithium-ion cells operate under a narrow range of voltage, current, and temperature limits, which requires a battery management system (BMS) to sense, control, and balance the battery pack. The state of power (SOP) estimation is a fundamental algorithm of the BMS. It operates as a dynamic safety limit, preventing rapid ageing and optimizing power delivery. SOP estimation relies on predictive algorithms to determine charge and discharge power limits sustainable within a specified time frame, ensuring the cell design constraints are not violated. This paper explores various approaches for real-time deployment of SOP estimation algorithms for a high-power lithium-ion battery (LIB) with a low-cost microcontroller. The algorithms are based on a root-finding approach and a first-order equivalent circuit model (ECM) of the battery. This paper assesses the practical application of the algorithm with a focus on processor execution time, flash memory and RAM allocation using a processor-in-the
This paper proposes a novel reconfigurable battery balancing topology and reinforcement learning-based intelligent balancing management system. The different degradations cause a significant loss of battery pack available capacity, as the pack power output relies on the weakest cell due to the relevant physical requirements. To handle this capacity drop issue, a reconfigurable battery topology is adopted to improve the usability of the heterogeneous battery. There are some existing battery reconfigurable topologies in the literature. However, these studies rely on the limited options of topology designs, and there is a lack of study on the reconfigurability of these designs and other possible new designs. Also, it is rare to find an optimal management system for the reconfigurable battery topology. To fill these research gaps, this paper explores existing battery reconfigurable topology designs and proposes a new reconfigurable topology for battery balancing. Besides, the battery
The capacity of a lithium-ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms associated with unwanted side reactions that occur in these batteries. The same reactions occur during overcharge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. As the battery ages the accuracy of state of charge prediction decreases and vulnerability to persistent overcharge increases. Moreover, as the battery ages, its tolerance to such unintended overcharge changes. This tolerance depends on the nature of the history of cycle and calendar aging. A map of this tolerance in the BMS can provide awareness of the factor of safety due to overcharge as battery ages. Signatures of early warning signs of incipient thermal runaway due to overcharge can also be very useful features in a BMS. The SwRI EssEs-I consortium conducted aging of two commercially available LMO cell types with different
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