Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

Stetzel, Kirk D.; Aldrich, Lukas L.; Trimboli, M. Scott; Plett, Gregory L.

doi:10.1016/j.jpowsour.2014.11.135

Cited by 91 publications

(45 citation statements)

References 16 publications

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“…State estimation designs have also emerged for other electrochemical models that incorporate electrolyte dynamics. Examples include spectral methods with output error injection [25], residue grouping with Kalman filtering [26], semi-separable structures with an EKF [27], discretetime realization algorithms with an EKF [28], and composite electrodes with nonlinear filters [29].…”

Section: B Relevant Literaturementioning

confidence: 99%

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Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Moura

Argomedo

Klein

et al. 2017

IEEE Trans. Contr. Syst. Technol.

307

188

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Abstract-This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model with electrolyte (SPMe) that achieves higher predictive accuracy than the single particle model (SPM). Next, we propose an estimation scheme and prove estimation error system stability, assuming the total amount of lithium in the cell is known. The state estimation scheme exploits dynamical properties such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.

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Section: B Relevant Literaturementioning

confidence: 99%

“…(28) Next we derive each term on the right hand side of (28). Overpotentialη ± (t) is found by solving the Butler-Volmer equation (6) …”

Section: B Spme Model Derivationmentioning

confidence: 99%

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Moura

Argomedo

Klein

et al. 2017

IEEE Trans. Contr. Syst. Technol.

307

188

View full text Add to dashboard Cite

show abstract

“…Accumulated error of AH counting algorithm increases with time passing by; extended Kalman filter algorithm is implemented to eliminate accumulation error of AH counting algorithm. In order to keep computational complexity low, the extended Kalman filter algorithm operates for one battery cell after another and circles to the first battery cell after the last battery cell [10,11]. The correction process can be represented as follows.…”

Section: Algorithm Of the Proposed Strategy To Estimate Soc Of Multipmentioning

confidence: 99%

Study on a High-Accuracy Real-Time Algorithm to Estimate SOC of Multiple Battery Cells Simultaneously

Kan

Yin

et al. 2017

Journal of Control Science and Engineering

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In traditional battery equalization strategy, open-circuit voltage (OCV) of battery cells was used to judge the difference of SOC between them. However, OCV is not only determined by SOC but also influenced by internal resistance, polarization voltage, capacity, and other nonlinear factors. As a result, OCV is not an ideal indicator of SOC differences, especially in transient conditions. In order to control battery consistency accurately, it is best to use SOC directly as standard for battery consistency judgment and control. To achieve this, an algorithm that can estimate SOC of multiple battery cells simultaneously with low computational complexity and high accuracy is needed. Limited by computing speed of Battery Control Unit (BCU), existing SOC estimation method is hard to estimate SOC of each battery cell simultaneously with high accuracy. In this research, a new SOC estimation strategy was proposed to estimate SOC of multiple battery cells simultaneously for battery equalization control. Battery model is established based on experimental data, and a processor-in-the-loop test system was established to verify the actual performance of the proposed algorithm. Results of simulation and test indicate that the proposed algorithm can estimate SOC of multiple battery cells simultaneously and achieved good real-time performance and high accuracy.

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“…A number of electrochemical model parameters (for each of the fault conditions) were identified using a gradient free particle swarm optimization algorithm [17] on a reduced order model of lithium ion battery. An EKF based estimation method has been considered in [18], where internal electrochemical variables were estimated by using the reduced order model of a Li-Ion battery derived from a 1D physics based model.…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Anwar

Izadian

2017

Energies

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Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO 2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

show abstract

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

Cited by 91 publications

References 16 publications

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Study on a High-Accuracy Real-Time Algorithm to Estimate SOC of Multiple Battery Cells Simultaneously

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

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