Novel estimation solution on lithium‐ion battery state of charge with current‐free detection algorithm

Abstract

Lithium-ion battery as an efficient, sustainable, and clean energy for electric vehicles (EVs) and smart devices becomes more popular with the worldwide demand for reduction of greenhouse gas emission. In all kinds of applications, an accurate real-time estimation for state of charge (SOC) of battery is necessary. Some conventional methods usually need to sample both battery currents and voltages. This article presents a novel SOC estimation algorithm without current detection. This algorithm just acquires the port voltages of cell to calculate the open-circuit voltage (OCV) which is related to SOC. By extracting a large number of battery voltages based on a step response, some important parameters that can track battery working process are determined. In order to verify the algorithm feasibility and accuracy, it has been tested on a commercial common field-programmable gate array (FPGA) in different application conditions. The algorithm accuracy is mainly limited by model accuracy and sampling sensor accuracy. The maximum error between ideal SOC and calculated SOC by this algorithm is within 4%, and the mean error is about 0.99%. So, this high-feasibility, accredited accuracy, easy integration, and low-cost solution has bright potential in smarter future.

A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm

“…Then, the noise data is processed by the unbiased time-varying estimator by doing high-quality studies together with its large scale demands, according to which the weighting strategy of the data is investigated by using the indexed weighting methods. The innovation and forgetting operators can be initialized accordingly with the long-term pattern and particular treatment to obtain the high accuracy estimation results, the calculation process of which is shown in Equation (21).…”

“…The simple calculation ignores the battery internal variation that is involved in different battery types and performs the charged state prediction. It is realized through a simple correspondence model between the external battery parameters, such as the Ampere-hour integration, open circuit voltage and impedance modeling methods [21][22][23][24][25]. The advantage of this typical estimation algorithm is that it does not require the complicated modeling treatment of the battery to be adapted to different charged state levels, making it simple and easy application at any time [26].…”

Pure hardware design and implementation on FPGA of an EKF based accelerated SoC estimator for a lithium‐ion battery in electric vehicles

“…Nowadays, Electric Vehicles (EV) are evolving exponentially and are becoming an important research area thanks to their efficiency in reducing environmental pollution. Energy modules such as batteries and supercapacitors are key elements in EV since they are mandatory to power electric machines and lighting systems [1,2]. Among the existing technologies, Li-Ion batteries of Nickel-Manganese-Cobalt (NMC) are suitable for EV applications because they offer high energy and power densities, which has allowed them to become a reference technology [3].…”

Improved voltage transfer method for lithium battery string management chip

“…Recently, the lithium-breed batteries gradually replace other types of batteries due to their advantages of higher voltage level, long service life, nontoxic and no pollution, and are widespread exist in a variety of hand-held electronics [1][2][3][4][5]. As discussed in [3,[6][7][8][9][10][11][12][13][14][15], lithium batteries can be damaged by many conditions, such as excessively high or low voltages, and in some cases the results can be catastrophic.…”