Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

Microcontroller-Based Lead-Acid Battery Balancing System for Electric Vehicle Applications

In application of lead-acid batteries for electrical vehicle applications, 48 V of four 12 V batteries in a series configuration are required. However, the battery stack is repeatedly charged and discharged during operation. Hence, differences in charging and discharging speeds may result in a different state-of-charge of battery cells. Without proper protection, it may cause an excessive discharge that leads to premature degradation of the battery. Therefore, a lead-acid battery requires a battery management system to extend the battery lifetime. Following the LTC3305 balancing scheme, the battery balancing circuit with auxiliary storage can employ an imbalance detection algorithm for sequential battery. It happens by comparing the voltage of a battery on the stack and the auxiliary storage. In this paper, we have replaced the function of LTC3305 by a NUCLEO F767ZI microcontroller, so that the balancing process, the battery voltage, the drawn current to or from the auxiliary battery, and the surrounding temperature can be fully monitored. The prototype of a microcontroller-based lead-acid battery balancing system for electrical vehicle application has been fabricated successfully in this work. The batteries voltage monitoring, the auxiliary battery drawn current monitoring, the overcurrent and overheat protection system of this device has also successfully built. Based on the experimental results, the largest voltage imbalance is between battery 1 and battery 2 with a voltage imbalance of 180 mV. This value is still higher than the target of voltage imbalance that must be lower than 12.5 mV. The balancing process for the timer mode operation is faster 1.5 times compared to the continuous mode operation. However, there were no overcurrent or overtemperature occurred during the balancing process for both timer mode and continuous mode operation. Furthermore, refinement of this device prototype is required in the future to improve the performance significantly.

Study of Several Types of Lithium-polymer Batteries With 3s Battery Management System

Lithium batteries have been identified as one of the most promising energy conversion and storage devices because of their high energy density, safety, and long cycling life. Lithium-polymer batteries have been widely used in various applications ranging from electric vehicles to mobile devices. The purpose of this study was to determine the best type of lithium-polymer and VRLA batteries in the review of the balance of battery life timeout comparison for a predetermined load. Each battery has a different actual balance and theoretical comparison value. The best balance value is close to 1. The best balance comparison after the experiment was a LiPo battery type with a balance value of 0.77 R158F076A7 BMS 3s, then VRLA with a balance of 0.67, and the smallest balance is a LiPo GSE 18650 battery with a balance of 0.25. For both types of batteries with the same input parameters provided, the terminal voltage, current, and characteristics output of Lithium-polymer Li-Po GSE 18650. Batteries were found to be better than a lead-acid with a timeout of use that is 51.64 minutes.

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.

Battery Management System in Electric Vehicles

Here this document provides the data about the batteries of electric vehicles. It consists of numerous data about various energy storage methods in EVs and how it is different from energy storage of IC-engine vehicles. How electric vehicles will take over ICEngine vehicles due to advancement in battery technology and the shrink in its prices. Various types of batteries are listed in the document with their specifications. Possible future battery technology which will have more or same energy density than current gasoline fuels and also with the significant reduction in battery weights; which will make EVs cheaper than current condition. Some examples are listed showing current battery capacities of various EVs models. Some battery parameters are shown in the document with introduction to BMS (Battery Management System). Then a brief introduction about the charging of these EV batteries and its types displaying variations in charging time in different types of EVs according to their charger type and manufacturers. How DC charging is more time saving method than AC and how smart charging will help to grid in case of peak or grid failure conditions.