A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium-Ion Cells

2015 ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Sliding Mode ◽  
Direct Method ◽  
Lithium Ion ◽  
Battery Management ◽  
Li Ion Battery ◽  
Modeling Framework ◽  
Battery Cell ◽  
Safety And Reliability ◽  
Li Ion ◽  
Sliding Mode Observers

Improvement of the safety and reliability of the Lithium-ion (Li-ion) battery operation is one of the key tasks for advanced Battery Management Systems (BMSs). It is critical for BMSs to be able to diagnose battery electrochemical faults that can potentially lead to catastrophic failures. In this paper, an observer-based fault diagnosis scheme is presented that can detect, isolate and estimate some internal electrochemical faults. The scheme uses a reduced-order electrochemical-thermal model for a Li-ion battery cell. The paper first presents a modeling framework where the electrochemical faults are modeled as parametric faults. Then, multiple sliding mode observers are incorporated in the diagnostic scheme. The design and selection of the observer gains as well as the convergence of the observers are verified theoretically via Lyapunov’s direct method. Finally, the performance of the observer-based diagnostic scheme is illustrated via simulation studies.

scholarly journals Battery Management System Performance Enhancement using Single Sliding Mode Based Charge Equalization Controller

2021 ◽  
Vol 2128 (1) ◽  
pp. 012026
Author(s):  
Ali Ashraf ◽  
Abdul-Azim Sobaih ◽  
Essam Nabil
Keyword(s):  
Performance Enhancement ◽  
Sliding Mode ◽  
Lithium Ion ◽  
Small Scale ◽  
Sliding Mode Controller ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Algorithm Efficiency ◽  
Li Ion

Abstract In this paper, the study aims to evolve a precision model of a Battery charge equalization controller (BCEC) that manage each cell of a lithium-ion (Li-ion) battery, monitoring a nd balancing by charging a nd discharging a series-connected Li-ion battery with several cells (n) in electric vehicle (E.V.) applications. An intelligent Sliding mode controller is evolved to activate bidirectional cell switches and regulate a chopper circuit’s direct current (DC-DC flyback converter circuit) with PWM generation. To implement a small-scale BCEC, the model consists of individual models of an E.V., cells of Li-ion battery, a fly-back converter, and a single sliding mode controller (SSMC) for charging and discharging are integrated with n series-connected cells of Li-ion-battery. The BCEC output shows that the cells are operated within a safe range, and the difference in the state of charge (SOC) is maintained to be 0.1%. Comparing the developed BCEC with the existing controllers, with the performance, control algorithm, efficiency, power loss, size, and cost.

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

scholarly journals A New All-Solid-State Lithium-Ion Battery Working without a Separator in an Electrolyte

2018 ◽  
Author(s):  
Seonggyu Cho ◽  
Shinho Kim ◽  
Wonho Kim ◽  
Seok Kim ◽  
Sungsook Ahn
Keyword(s):  
Solid State ◽  
Ionic Conductivity ◽  
Solid Electrolyte ◽  
System Development ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Metal Anode ◽  
Li Ion

Considering the safety issues of Li ion batteries, all-solid-state polymer electrolyte has been one of the promising solutions. In this point, achieving a Li ion conductivity in the solid state electrolytes comparable to liquid electrolytes (>1 mS/cm) is particularly challenging. Employment of polyethylene oxide (PEO) solid electrolyte has not been not enough in this point due to high crystallinity. In this study, hybrid solid electrolyte (HSE) systems are designed with Li1.3Al0.3Ti0.7(PO4)3(LATP), PEO and Lithium hexafluorophosphate (LiPF6) or Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Hybrid solid cathode (HSC) is also designed using LATP, PEO and lithium cobalt oxide (LiCoO2, LCO)—lithium manganese oxide (LiMn2O4, LMO). The designed HSE system displays 3.0 × 10−4 S/cm (55 ℃) and 1.8 × 10−3 S/cm (23 ℃) with an electrochemical stability as of 6.0 V without any separation layer introduction. Li metal (anode)/HSE/HSC cell in this study displays initial charge capacity as of 123.4/102.7 mAh/g (55 ℃) and 73/57 mAh/g (25 °C). To these systems, Succinonitrile (SN) has been incorporated as a plasticizer for practical secondary Li ion battery system development to enhance ionic conductivity. The incorporated SN effectively increases the ionic conductivity without any leakage and short-circuits even under broken cell condition. The developed system also overcomes the typical disadvantages of internal resistance induced by Ti ion reduction. In this study, optimized ionic conductivity and low internal resistance inside the Li ion battery cell have been obtained, which suggests a new possibility in the secondary Li ion battery development.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

scholarly journals Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Manh-Kien Tran ◽  
Andre DaCosta ◽  
Anosh Mevawalla ◽  
Satyam Panchal ◽  
Michael Fowler
Keyword(s):  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Li Ion ◽  
And Control

Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.

scholarly journals A Novel Battery Management System for Series Parallel Connected Li-Ion Battery Pack for Electric Vehicle Application.

2019 ◽  
Vol 8 (4) ◽  
pp. 496-499
Keyword(s):  
Management System ◽  
Lithium Ion ◽  
Green Energy ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Efficient Operation ◽  
In Series ◽  
Li Ion

The green energy evolution initiated the use of electric and hybrid electric vehicles at present on roads. These vehicles extensively use different types of batteries and among them lithium ion batteries are prominent. The Li-ion battery pack constitutes number of Li-ion battery cells connected in series and parallel configuration. This battery bank needs a suitable battery management system for its efficient operation. This paper presents a novel battery management system to monitor and control the battery current, voltage, state of charge and most importantly the cell temperature. The detail BMS scheme for Li-ion battery pack is presented and simulation is carried out to validate its performance with a driving cycle of electric car.

scholarly journals Lithium-ion battery models: a comparative study and a model-based powerline communication

2017 ◽  
Vol 15 ◽  
pp. 83-91 ◽  
Author(s):  
Fida Saidani ◽  
Franz X. Hutter ◽  
Rares-George Scurtu ◽  
Wolfgang Braunwarth ◽  
Joachim N. Burghartz
Keyword(s):  
Real Time ◽  
Equivalent Circuit ◽  
Lithium Ion ◽  
Complex Model ◽  
Physical Models ◽  
Chemical System ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Powerline Communication ◽  
Li Ion

Abstract. In this work, various Lithium-ion (Li-ion) battery models are evaluated according to their accuracy, complexity and physical interpretability. An initial classification into physical, empirical and abstract models is introduced. Also known as white, black and grey boxes, respectively, the nature and characteristics of these model types are compared. Since the Li-ion battery cell is a thermo-electro-chemical system, the models are either in the thermal or in the electrochemical state-space. Physical models attempt to capture key features of the physical process inside the cell. Empirical models describe the system with empirical parameters offering poor analytical, whereas abstract models provide an alternative representation. In addition, a model selection guideline is proposed based on applications and design requirements. A complex model with a detailed analytical insight is of use for battery designers but impractical for real-time applications and in situ diagnosis. In automotive applications, an abstract model reproducing the battery behavior in an equivalent but more practical form, mainly as an equivalent circuit diagram, is recommended for the purpose of battery management. As a general rule, a trade-off should be reached between the high fidelity and the computational feasibility. Especially if the model is embedded in a real-time monitoring unit such as a microprocessor or a FPGA, the calculation time and memory requirements rise dramatically with a higher number of parameters. Moreover, examples of equivalent circuit models of Lithium-ion batteries are covered. Equivalent circuit topologies are introduced and compared according to the previously introduced criteria. An experimental sequence to model a 20 Ah cell is presented and the results are used for the purposes of powerline communication.

Physics-Based State of Health Estimation of Lithium-Ion Battery Using Sequential Experimental Design

2018 ◽  
Author(s):  
Yu Hui Lui ◽  
Meng Li ◽  
Mohammadkazem Sadoughi ◽  
Chao Hu ◽  
Shan Hu
Keyword(s):  
Process Model ◽  
Design Space ◽  
Sequential Sampling ◽  
Lithium Ion ◽  
State Of Health ◽  
Li Ion Battery ◽  
Simulation Experiments ◽  
Battery Cell ◽  
Sampling Process ◽  
Li Ion

State of health (SOH) estimation is a critical yet challenging task due to the complex degradation process of lithium-ion (Li-ion) battery. This paper proposes to combine physics-based modeling of Li-ion battery and sequential design of simulation experiments to build an accurate SOH estimator in a computationally efficient manner. A novel sequential backward optimization process is adopted to build a multivariate Gaussian process model that quantifies three degradation modes in a Li-ion battery cell: loss of lithium inventory and losses of active materials in the positive and negative electrodes. The sequential process for the design of simulation experiments is realized via the use of an acquisition function, the maximization of which gives rise to a new sample point in the design space for the next experiment. The acquisition function achieves an optimal balance between exploration of new regions in the design space with high prediction uncertainty and exploitation of challenging regions with high response nonlinearity. The preliminary results from COMSOL Multiphysics degradation scenario simulations show that the SOH estimator designed with the sequential sampling process can provide faster error decay in degradation estimation when compared to that without the sequential sampling process.

scholarly journals Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Electrochemical-Thermal Modeling of Li-Ion Battery Packs

2014 ◽  
Author(s):  
Guodong Fan ◽  
Ke Pan ◽  
Alexander Bartlett ◽  
Marcello Canova ◽  
Giorgio Rizzoni
Keyword(s):  
Cell Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management ◽  
Li Ion Battery ◽  
Thermal Boundary Conditions ◽  
Battery Packs ◽  
Li Ion ◽  
Electrical Connections ◽  
The Impact

Lithium-ion batteries for automotive applications are subject to aging with usage and environmental conditions, leading to the reduction of the performance, reliability and life span of the battery pack. To this extent, the ability of simulating the dynamic behavior of a battery pack using high-fidelity electrochemical and thermal models could provide very useful information for the design of Battery Management Systems (BMS). For instance such models could be used to predict the impact of cell-to-cell variations in the electrical and thermal properties on the overall performance of the pack, as well as on the propagation of degradation from one cell to another. This paper presents a method for fast simulation of an integrated electrochemical-thermal battery pack model based on first-principles. First, a coupled electrochemical and thermal model is developed for a single cell, based upon the data of a composite LiNi1/3Mn1/3Co1/3O2 – LiMn2O4 (LMO-NMC) Li-ion battery, and validated on experimental data. Then, the cell model is extended to a reconfigurable and parametric model of a complete battery pack. The proposed modeling approach is completely general and applicable to characterize any pack topology, varying electrical connections and thermal boundary conditions. Finally, simulation results are shown to illustrate the effects of parameter variability on the pack performance.

Sign in / Sign up

Export Citation Format

Share Document