Ala A. Hussein

A Review of Charging Algorithms for Nickel and Lithium Battery Chargers

Battery-charging algorithms can be used for either single- or multiple-battery chemistries. In general, single-chemistry chargers have the advantages of simplicity and reliability. On the other hand, multichemistry chargers, or “universal battery chargers,” provide a practical option for multichemistry battery systems, particularly for portable appliances, but they have some limitations. This paper presents a review of some charging algorithms for major batteries, i.e., nickel-cadmium, nickel-metal-hydride, and lithium-ion batteries for single- and multiple-chemistry chargers. A comparison between these algorithms in terms of their charging schemes and charge termination techniques is included. In addition, some trends of recent chargers development are presented.

An overview of generic battery models

Battery performance prediction is crucial in many applications. A good prediction mechanism of the battery performance has many advantages. For example, it increases the battery lifetime by preventing over (dis)charging the battery, it allows utilizing the entire capacity of the battery, and it offers an access to the user to know the amount of energy in the battery pack. In this paper, some battery models are derived and tested on a commercial Lithium battery cell. The results show the capabilities of these models under different tests.

Distributed Battery Micro-Storage Systems Design and Operation in a Deregulated Electricity Market

This paper covers some design and operation aspects of distributed battery micro-storage systems in a deregulated electricity market system. In this paper, the term “micro” refers to the size of the energy storage (ES) system compared to the grid generation, with a capacity from few kilowatt-hours and up. Generally, ES enhances the performance of renewable distributed generators (DGs) and increases the efficiency of the entire power system. Energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. In this paper, different design aspects of distributed micro-storage systems are covered such as system architecture, system sizing, power stage design, battery management system (BMS), economic aspects and operation in a deregulated electricity market with and without renewable DGs.

Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks

In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve the state-of-charge (SOC) estimation accuracy over the lifespan of the battery, which leads to more reliable battery operation and prolonged lifetime. In addition, the proposed technique allows accurate prediction of the battery remaining service time. Two identical 3.6-V/16.5-Ah Li-ion battery cells were repeatedly cycled with constant current and dynamic stress test current profiles at room temperature, and their discharge capacities were recorded. The proposed technique shows that very accurate SOC estimation results can be obtained provided enough training data are used to train the ANN models. Model derivation and experimental verification are presented in this paper.

State-of-charge estimation for a single Lithium battery cell using Extended Kalman Filter

An accurate state-of-charge (SOC) estimation is desired in most battery systems. It increases the reliability of the system and extends the lifetime of the battery. This paper proposes an Extended Kalman Filter (EKF) algorithm to estimate the SOC of a Lithium battery cell. To implement the SOC algorithm, an improved Lithium battery cell model is used. The results of the model and EKF algorithm show the effectiveness and ease of implementation of the proposed technique.

Capacity fade estimation in electric vehicles Li-ion batteries using artificial neural networks

Battery performance degrades as the battery ages. For example, the battery capacity fades away after repeatedly cycling the battery. The degradation rate itself depends on many factors such as the depth-of-discharge (DOD), (dis)charge power, temperature, etc. In this paper, the application of artificial neural network (ANN) in estimating lithium-ion (Li-ion) battery capacity fade in electric vehicles (EVs) is investigated. The focus in this paper is on evaluating the performance of ANN-based techniques in estimating the battery capacity fade in order to: reliably estimate the battery state-of-charge (SOC) using the standard coulomb counting method through the battery life, and accurately predict the battery remaining life. Model derivation and experimental verification are presented in this paper.

Experimental modeling and analysis of lithium-ion battery temperature dependence

Battery performance is strongly dependent on the ambient temperature. For example, at moderate temperatures, the battery performance is optimal, whereas at extreme temperatures, the battery performance is not optimized and sometimes unexpected. In order to predict the battery behavior, a model that involves the batterys underlying dynamics is usually used. The majority of dynamic battery models are derived at only one single temperature (room temperature), which can easily lead to failure in predicting the battery performance when the temperature varies. Therefore, adding some temperature dependence to those models can make the battery management system more reliable, safer, and moreover, prolong the battery lifetime. In this paper, a 3.6V/1100mAh lithium-ion (Li-ion) battery cell is tested at temperature between -30°C and +50°C and its main parameters are measured. The measured parameters include the discharge capacity, the charge and discharge resistance, and the open-circuit voltage, which comprise the main parameters of equivalent electric-circuit based models. Experimental testing results and observations are presented in this paper.

Enhancement in Li-Ion Battery Cell State-of-Charge Estimation Under Uncertain Model Statistics

Accurate battery state-of-charge (SOC) estimation in real time is desired in many applications. Among other methods, the extended Kalman filter (EKF) allows for high-accuracy real-time tracking of the SOC. However, an accurate SOC model is needed to guarantee convergence. Additionally, knowledge of the statistics of the process noise and the measurement noise is needed for high-accuracy SOC estimation. In this paper, two methods, namely, the multiple-model EKF (MM-EKF) and the autocovariance least squares technique, are proposed for estimating the SOC of lithium-ion (Li-ion) battery cells. The first method has the advantage of minimizing the EKF algorithms dependence on the correct assumptions of the measurements noise statistics, thus, minimizing the impact of model mismatch. The MM-EKF assumes a number of hypotheses for the unknown measurement noise covariance. An EKF is assigned for each assumed measurement noise covariance. The SOC estimate is then obtained by probabilistically summing up the estimates of the hypothesized EKFs. On the other hand, the second method assumes that the measurement noise is unknown and determines its value from the statistics of the EKF. Given an initial and possibly wrong assumption of the measurement noise covariance, the method accounts for possible correlation in the measurement innovations. The estimated measurement noise covariance is subsequently used to obtain an optimal SOC estimate. The proposed methods are evaluated and compared with the conventional EKF method on experimental test data obtained from a 3.6-V Li-ion battery cell.

Design considerations for distributed micro-storage systems in residential applications

This paper presents some design considerations for distributed micro-storage systems in residential applications. In this paper, the term “micro-storage” refers to small residential energy storage units with a capacity of few kilowatt-hours. Generally, energy storage systems enhance the performance of distributed renewable generation systems and increase the efficiency of the entire power system. Energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. Different micro-storage system architectures as well as analysis of system sizing are discussed in the paper. In addition, different energy storage technologies, inverter design considerations, and smart grid integration issues will be also presented.

An efficient solar charging algorithm for different battery chemistries

This paper proposes a new, effective, robust and reliable solar battery charging algorithm for the widely used batteries; NiCd, NiMH, Lead-Acid and Lithium-Ion. The algorithm has the ability to charge the battery in the outdoor conditions, when the power is variable, and terminate charging when the battery is fully charged. The algorithm has two modes of operation; current mode and voltage mode. It can deal with the unexpected outdoor conditions, which may cause drops in the current, without falsely detecting the battery state of charge. A programmable power supply was programmed and the four battery types were charged to test the algorithm.