Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

Analysis of a Modified Equivalent Circuit Model for Lithium-Ion Battery Modules in CubeSats

Failure of the electrical power system (EPS) to meet mission requirements is a common problem in nano-size satellites commonly referred to as CubeSats. The motivation for this research stems from the desire to prevent EPS failure through a process of testing and space qualification of components. Utilizing models to predict the behavior of an EPS before it is designed, built, and tested for space can provide critical insight in areas of limitation in performance and survivability. Modeling an entire EPS system is challenging because it requires extensive knowledge of all components and their behavior. This research focuses specifically on the storage component of the EPS often referred to as secondary batteries. The secondary batteries, such as Li-Ion battery cells, are modeled to predict the performance of the storage component in the space environment. Experimental test data is collected under a simulated space environment through the use of a Thermal Vacuum Chamber (TVAC). Data collected from battery testing in the space environment is used to validate a modified Thevenin Equivalent Circuit model. The experimental test data and battery model are compared and evaluated resulting in a promising model that can reasonably predict performance of a battery pack in a two-series two-parallel configuration

Equivalent Circuit Model Generation for Batteries Using Non-ideal Test Data

Modeling is a key component in the development of battery products. While there are multiple levels of complexity which may be achieved in model development, equivalent circuit modeling is able to quickly produce reliable and accurate predictions for battery behavior. While the use of equivalent circuit models has been described in great detail for lithium ion batteries, it is also desirable to use this methodology regardless of chemistry, specifically with respect to lead-acid technology. When developing battery models for predicting battery behavior in a vehicle, the testing methods meant to mimic vehicle applications often cause non-ideal data for model generation. Specifically, periods of constant voltage charging can limit the model’s capabilities and accuracy. This is due to the imposed voltage limit required for constant voltage charging which is not an inherent battery behavior. By thoroughly examining equivalent circuit models of increasing complexity, it is shown that lead-acid and lithium ion batteries behave similarly so that minimal impact is had on model development. Additionally, three methods are considered for modifying the fitting process so that test data which contains voltage limits may still be considered useful for model development

Battery models for battery powered applications: A comparative study

Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters' identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer's datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed

Unified model of lithium-ion battery and electrochemical storage system

Nowadays, energy storage systems are of paramount importance in sectors such as renewable energy production and sustainable mobility because of the energy crisis and climate change issues. Although there are various types of energy storage systems, electrochemical devices such as electric double layer capacitors (EDLCs), lithium-ion capacitors (LiCs), and lithium-ion batteries (LiBs) are the most common because of their high efficiency and flexibility. In particular, LiBs are broadly employed in many applications and preferred in the mobility sector, where there is a need for high energy and high power. To ensure good operating conditions for a battery and limit its degradation, it is important to have a precise model of the device. The literature contains numerous equivalent circuit models capable of predicting the electrical behavior of an LiB in the time or frequency domain. In most of them, the battery impedance is in series with a voltage source modeling the open circuit voltage of the battery for simulation in the time domain. This study demonstrated that an extension of a model composed exclusively of passive elements from the literature for EDLCs and LiCs would also be suitable for LiBs, resulting in a unified model for these types of electrochemical storage systems. This model uses the finite space Warburg impedance, which, in addition to the diffusion process of lithium\lithium ions in the electrodes\electrolyte, makes it possible to consider the main capacitance of the battery. Finally, experimental tests were performed to validate the proposed model

Battery State-of-Charge Estimation with Extended Kalman-Filter using Third-Order Thevenin Model

Lithium-ion battery has become the mainstream energy storage element of the electric vehicle. One of the challenges in electric vehicle development is the state-of-charge estimation of battery. Accurate estimation of state-of-charge is vital to indicate the remaining capacity of the battery and it will eventually maximize the battery performance and ensures the safe operation of the battery. This paper studied on the application of extended Kalman-filter and third order Thevenin equivalent circuit model in state-of-charge estimation of lithium ferro phosphate battery. Random test and pulse discharge test are conducted to obtain the accurate battery model. The simulation and experimental results are compared to validate the proposed state-of-charge estimation method

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

Electric cars have undergone many developments in the current digital era. This is to avoid the use of increasingly scarce fuel. Recent studies on electric cars show that battery estimation is an interesting topic to be implemented directly. The battery estimation strategy is carried out by the Battery Management System (BMS). BMS is an indispensable part of electric vehicles or hybrid vehicles to ensure optimal and reliable operation of regulating, monitoring, and protecting batteries. A reliable BMS can extend battery life by setting voltage, temperature, and charging and discharging current limits. The main estimation strategy used by BMS is battery fault, SOH, and battery life. Battery State of Health (SOH) is part of the information provided by the BMS to avoid battery damage and failure. SOC is the proportion of battery capacity SOH is a measure of battery health. This study aims to develop a method for estimating SOH simultaneously using Coulomb Counting and Open Circuit Voltage (OCV) algorithms. The battery is modeled to obtain battery parameters and components of internal resistance, capacitance polarization and OCV voltage source. Several tests were implemented in this research by applying the constant current (CC)-charge CC-discharge test. The state-space system is then formed to apply the Coulomb Counting and OCV algorithms so that SOH can be estimated simultaneously. The OCV-SOC function is obtained in the form of a tenth order polynomial and the battery model parameters say that these parameters change with the health of the battery. The results of the model validation are able to accurately model the battery with an average relative error of 0.027%. Coulomb Counting resulted in an accurate SOH estimation with an error of 3.4%