Habiballah Rahimi-Eichi

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells

Real-time estimation of the state of charge (SOC) of the battery is a crucial need in the growing fields of plug-in hybrid electric vehicles and smart grid applications. The accuracy of the estimation algorithm directly depends on the accuracy of the model used to describe the characteristics of the battery. Considering a resistance-capacitance (RC)-equivalent circuit to model the battery dynamics, we use a piecewise linear approximation with varying coefficients to describe the inherently nonlinear relationship between the open-circuit voltage (VOC) and the SOC of the battery. Several experimental test results on lithium (Li)-polymer batteries show that not only do the VOC-SOC relationship coefficients vary with the SOC and charging/discharging rates but also the RC parameters vary with them as well. The moving window least squares parameter-identification technique was validated by both data obtained from a simulated battery model and experimental data. The necessity of updating the parameters is evaluated using observers with updating and nonupdating parameters. Finally, the SOC coestimation method is compared with the existing well-known SOC estimation approaches in terms of performance and accuracy of estimation.

Adaptive parameter identification and State-of-Charge estimation of lithium-ion batteries

Estimation of the State of Charge (SOC) is a fundamental need for the battery, which is the most important energy storage in Electric Vehicles (EVs) and the Smart Grid. Regarding those applications, the SOC estimation algorithm is expected to be accurate and easy to implement. In this paper, after considering a resistor-capacitor (RC) circuit-equivalent model for the battery, the nonlinear relationship between the Open Circuit Voltage (VOC) and the SOC is described in a lookup table obtained from experimental tests. Assuming piecewise linearity for the VOC-SOC curve in small time steps, a parameter identification technique is applied to the real current and voltage data to estimate and update the parameters of the battery at each step. Subsequently, a reduced-order linear observer is designed for this continuously updating model to estimate the SOC as one of the states of the battery system. In designing the observer, a mixture of Coulomb counting and VOC algorithm is combined with the adaptive parameter-updating approach and increases the accuracy to less than 5% error. This paper also investigates the correlation between the SOC estimation error and the observability criterion for the battery model, which is directly related to the slope of the VOC- SOC curve.

Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation

Finding an accurate and easily to implement model of batteries is an essential step in properly estimating the state of charge (SOC) of the battery in real-time. In this paper, an equivalent circuit based battery model with nonlinear relationship between the open circuit voltage (VOC) and the SOC is projected into several piece-wise linear functions. Moving window Least Squares (LS) parameter identification technique is then utilized to estimate and update the parameters of the battery model in each sampling time. The continuously updated parameters are fed to a linear observer to estimate the SOC of the battery. The effectiveness of the proposed modeling and estimation approach are verified experimentally on Lithium Polymer batteries.

Parameter identification of Li-Po batteries in electric vehicles: A comparative study

An effective management of the onboard energy storage system is a key point for the development of electric vehicles. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as state-of-charge and state-of-health. Its estimation demands an accurate model to represent the static and dynamic behaviour of the battery. Developing such a model requires the online identification of the battery parameters. This paper aims at comparing the performance of two popular system identification techniques, i.e., the Extended Kalman Filter and the classic Least Squares method. A significant contribution of this work is the definition of a benchmark which is representative of the real use of the battery in an electric vehicle. Simulation results show the peculiarities of both methods and their effectiveness.

Big-data framework for electric vehicle range estimation

Range anxiety is a major contributor in low penetration of electric vehicles into the transportation market. Although several methods have been developed to estimate the remaining charge of the battery, the remaining driving range is a parameter that is related to different standard, historical, and real-time data. Most of the existing range estimation approaches are established on an overly simplified model that relies on a limited collection of data. However, the sensitivity and reliability of the range estimation algorithm changes under different environmental and operating conditions; and it is necessary to have a structure that is able to consider all data related to the range estimation. In this paper, we propose a big-data based range estimation framework that is able to collect different data with various structures from numerous resources; organize and analyze the data, and incorporate them in the range estimation algorithm. MATLAB/SIMULINK code is demonstrated to read real-time and historical data from different web databases and calculate the remaining driving range.

Adaptive online battery parameters/SOC/capacity co-estimation

Total capacity is one of the most important parameters to characterize the performance and application of a battery. Although the nominal capacity is provided by the manufacturer, the actual capacity is subject to change with cycling effect, temperature and even storage ageing of the battery. Following our previous publications in which we developed an online adaptive parameters/state of charge (SOC) co-estimation algorithm to identify the parameters of the dynamic model of the battery and accordingly design an observer to estimate the SOC. In this paper, first we show that the parameters identification and SOC estimation results are not dependent on the correct approximation of the capacity. Afterwards, using the estimated SOC, we design another observer to estimate the actual capacity of the battery.

Sensitivity analysis of lithium-ion battery model to battery parameters

Different models have been proposed so far to represent the dynamic characteristics of batteries. These models contain a number of parameters and each of them represents an internal characteristic of the battery. Since the battery is an entity that works based on many electrochemical reactions, the battery parameters are subject to change due to different conditions of state of charge (SOC), C-rate, temperature and ageing. Referring to our previous work on online identification of the battery parameters, the change in the parameters even during one charging cycle is an experimental fact at least for many lithium-ion batteries. In this paper, the terminal voltage is used as the output to investigate the effect of changes in the parameters on the battery model. Therefore, we analyze the sensitivity of the model to the parameters and validate the analysis by comparing it with the simulation results. Since the output of the model is one of the main components in estimation of the state of charge (SOC), the sensitivity analysis determines the need to update each of the battery parameters in the SOC estimation structure.

E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle

Electric vehicles (EVs) are a promising solution to reduce the transportation dependency on oil, as well as the environmental concerns. Realization of E-transportation relies on providing electrical energy to the EVs in an effective way. Energy storage system (ESS) technologies, including batteries and ultra-capacitors, have been significantly improved in terms of stored energy and power. Beside technology advancements, a battery management system is necessary to enhance safety, reliability and efficiency of the battery. Moreover, charging infrastructure is crucial to transfer electrical energy from the grid to the EV in an effective and reliable way. Every aspect of E-transportation is permeated by the presence of an intelligent hardware platform, which is embedded in the vehicle components, provided with the proper interfaces to address the communication, control and sensing needs. This embedded system controls the power electronics devices, negotiates with the partners in multi-agent scenarios, and performs fundamental tasks such as power flow control and battery management. The aim of this paper is to give an overview of the open challenges in E-transportation and to show the fundamental role played by embedded systems. The conclusion is that transportation electrification cannot fully be realized without the inclusion of the recent advancements in embedded systems.

Incorporating big data analysis in speed profile classification for range estimation

Incorporation of data from multiple resources and various structures is necessary for accurate estimation of the driving range for electric vehicles. In addition to the parameters of the vehicle model, states of the battery, weather information, and road grade, the driving behavior of the driver in different regions is a critical factor in predicting the speed/acceleration profile of the vehicle. Following our previously proposed big data analysis framework for range estimation, in this paper we implement and compare different techniques for speed profile generation. Moreover we add the big data analysis classification results to especially improve the performance of the Markov Chain approach. The quantitative results show the significant influence of considering the big data analysis results on range estimation.