Estimation of SOH for Battery Packs: A Real-Time Mixed Algorithm based on Coulomb Counting Method and Parameter-Varying Circuit Modeling

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

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Real-time adaptive control of active magnetic bearings using linear parameter varying models

Proceedings IEEE SoutheastCon 2002 (Cat. No.02CH37283) ◽

10.1109/secon.2002.995603 ◽

2003 ◽

Cited By ~ 4

Author(s):

N.S. Gibson ◽

G.D. Buckner

Keyword(s):

Adaptive Control ◽

Real Time ◽

Magnetic Bearings ◽

Active Magnetic Bearings ◽

Linear Parameter ◽

Linear Parameter Varying ◽

Parameter Varying

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Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

IEEE Control Systems Letters ◽

10.1109/lcsys.2021.3137711 ◽

2021 ◽

pp. 1-1

Author(s):

Chris van der Ploeg ◽

Emilia Silvas ◽

Nathan van de Wouw ◽

Peyman Mohajerin Esfahani

Keyword(s):

Real Time ◽

Discrete Time ◽

Fault Estimation ◽

Linear Parameter ◽

Linear Parameter Varying ◽

Linear Parameter Varying Systems ◽

Parameter Varying ◽

Time Linear

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Development of a Two-Dimensional Thermal Model for Li-Ion Battery Pack With Experimental Validation

Journal of Thermal Science and Engineering Applications ◽

10.1115/1.4043810 ◽

2019 ◽

Vol 12 (2) ◽

Author(s):

Haoting Wang ◽

Ning Liu ◽

Lin Ma

Keyword(s):

Real Time ◽

Large Scale ◽

Experimental Validation ◽

Computational Cost ◽

Battery Pack ◽

Two Dimensional ◽

Li Ion Battery ◽

New Approach ◽

Battery Packs ◽

Li Ion

Abstract This paper reports the development of a two-dimensional two states (2D2S) model for the analysis of thermal behaviors of Li-ion battery packs and its experimental validation. This development was motivated by the need to fill a niche in our current modeling capabilities: the need to analyze 2D temperature (T) distributions in large-scale battery packs in real time. Past models were predominately developed to either provide detailed T information with high computational cost or provide real-time analysis but only 1D lumped T information. However, the capability to model 2D T field in real time is desirable in many applications ranging from the optimal design of cooling strategies to onboard monitoring and control. Therefore, this work developed a new approach to provide this desired capability. The key innovations in our new approach involved modeling the whole battery pack as a complete thermal-fluid network and at the same time calculating only two states (surface and core T) for each cell. Modeling the whole pack as a complete network captured the interactions between cells and enabled the accurate resolution of the 2D T distribution. Limiting the calculation to only the surface and core T controlled the computational cost at a manageable level and rendered the model suitable for packs at large scale with many cells.

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Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Processes ◽

10.3390/pr8010002 ◽

2019 ◽

Vol 8 (1) ◽

pp. 2

Author(s):

Ma

Keyword(s):

Energy Storage ◽

Real Time ◽

Estimation Method ◽

State Of Charge ◽

Counting Method ◽

Inference System ◽

Battery Energy Storage ◽

Soc Estimation ◽

Estimation Scheme ◽

Battery Energy

In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery’s intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.

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