A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation

Li, Jie; Adewuyi, Kasim; Lotfi, Nima; Landers, Robert G.; Park, Jong‐Hyun

doi:10.1016/j.apenergy.2018.01.011

Cited by 430 publications

(154 citation statements)

References 46 publications

Supporting

Mentioning

154

Contrasting

Order By: Relevance

“…3 Battery management system (BMS) is necessary for monitoring and protecting the battery. 7 In order to assess the amount of energy stored in cells, the SOC is defined as the available capacity of a cell as a percentage of its total capacity. The SOC reflects the remaining energy of a battery at the current cycle, and the SOH represents battery degradation level.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

et al. 2019

View full text Add to dashboard Cite

Summary The state of charge and state of health estimations are two of the most crucial functions of a battery management system, which are the quantified evaluation of driving mileage and remaining useful life of electric vehicles. This paper investigates a novel data‐driven–enabled battery states estimation method by combining recurrent neural network modeling and particle‐filtering–based errors redress. First, a recurrent neural network with long‐short time memory is employed to learn the long‐term nonlinear relation between batteries states and measurable signals of lithium‐ion batteries, such as current, voltage, and temperature. Second, to denoise the estimation errors of the neural network model, particle filtering is employed to smooth the state of charge estimation results. Third, the terminal voltage difference of battery is highly related to the internal resistance of the battery, which is thus taken as a new input to track the internal resistance of the battery. The performance of the proposed method is verified by multiple comparisons with conventional techniques under randomized loading profiles and different temperatures.

show abstract

Section: Introductionmentioning

confidence: 99%

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

et al. 2019

View full text Add to dashboard Cite

show abstract

“…3 However, accurate battery SOH estimation is challenged by the intricate aging mechanism inside a battery and uncertain external loading conditions. Li et al 9 developed an advanced single particle (SP) model containing degradation physics to estimate SOH over a broad temperature range. 5,6 The adaptive approach aims to connect the online measured battery characteristic signals with the SOH using adaptive filters and battery models.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the heavy computational burden of electrochemical full-order models, Bartlett et al 8 presented a reduced-order EM then realized the SOC and SOH estimation using the dual-nonlinear observer. Li et al 9 developed an advanced single particle (SP) model containing degradation physics to estimate SOH over a broad temperature range. To simplify the conservative pseudo-two-dimensional (P2D) model, Xiong et al 10 solved the mathematical equations with finite analysis method.…”

Section: Introductionmentioning

confidence: 99%

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

View full text Add to dashboard Cite

Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

show abstract

“…2,3 The precision and complexity of ECMs are significant for SOC and SOH prediction in BMS. 4 The most commonly used battery modeling techniques can be grouped into two main categories 5 : physical-based electrochemical models 6,7 and semi-empirical equivalent circuit models (ECMs). [8][9][10] For physical-based Li-ion battery models, poor knowledge about the parameters of internal battery chemistry and high computational load often make it unpractical for engineering applications.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

et al. 2019

View full text Add to dashboard Cite

Summary Battery modeling plays an important role in remaining range prediction and battery management system development. An accurate and realistic battery model is essential to design an efficient electric storage system. The goal of this paper is to investigate the performance of different circuit topologies for diffusion process in the equivalent circuit models (ECMs). The theory of diffusion process approximation by using resistive‐capacitor (RC) networks is explained in frequency domain. The terminal voltage predictive capabilities of the ECMs are compared and validated with test data. The numerical simulation results show that model prediction accuracy and computation burdens increase along with the number of RC pairs. The ECM with three RC networks is the best choice in terms of the balance between accuracy and complexity for ternary lithium batteries. In addition, a novel method of combining unscented Kalman filter (UKF) algorithm with initial state of charge (SOC) acceleration convergence for SOC estimation is proposed. The results of urban dynamometer driving schedule (UDDS) show that ECM with three RC networks has the best comprehensive performance on calculation cost and SOC estimation accuracy.

show abstract

A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation

Cited by 430 publications

References 46 publications

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Contact Info

Product

Resources

About