A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Tens of thousands of plastics produced by people have caused incalculable harm to the environment. At the same time, the consumption of energy is becoming more and more serious, and the use of foss...

Waste Plastic Triboelectric Nanogenerators Using Recycled Plastic Bags for Power Generation

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

/pdf/a-method-of-state-of-charge-estimation-for-ev-power-lithium-3e8vmbcvv6.pdf

A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

State-of-charge (SOC) estimation is an important aspect for modern battery management systems. Extended Kalman filter (EKF) has been extensively used in battery SOC estimation. However, EKF cannot obtain accurate estimation results when the model parameters have strong uncertainty or/and the accurate initial value of noise covariance matrix is unknown. To overcome these defects, the parameters of Lithium-ion battery model on the basis of the second-order resistor–capacitor (RC) equivalent model are identified, and then an improved adaptive EKF (IAEKF) of SOC estimation method for Lithium-ion battery pack is proposed for enhancing estimation accurate and robustness. In IAEKF, the statistical characteristics of measurement noise is adaptively corrected using a forgetting factor, namely, Sage–Husa EKF (SHEKF), and the error covariance matrix is adaptively corrected in accordance with the innovation, in which the calculation of the actual innovation covariance matrix adopts the variable sliding window length. Results of numerical simulation and experiment show that the proposed SOC estimation method can accurately estimate SOC under complex driven condition and has strong robustness to the uncertainty of model parameters and the initial value of the noise covariance matrix.

State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles

State of Charge Estimation for Lithium-Ion Batteries Based on Adaptive Dual Kalman Filter

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.

https://www.mdpi.com/2079-9292/8/9/1012/pdf

State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

Spatio-Temporal Distribution, Composition and Influencing Factors of Economic Losses from Storm Surge Disasters: An Empirical Study from China (2007-2016)

Lithium-ion battery heat generation characteristics during aging are crucial for the creation of thermal management solutions. The heat generation characteristics of 21700 (NCA) cylindrical lithium-ion batteries during aging were investigated using the mathematical model that was created in this study to couple electrochemical mechanisms, heat transfer, and aging loss. These findings indicate that, at the same operating current, the heat generation power of the cell increased significantly with battery aging. This increase was primarily due to the energy loss caused by the growth of the solid–electrolyte interface (SEI) and a reduction in the negative porosity and other physical characteristics of the SEI, such as its ionic conductivity and molar volume, which also had an impact on the heat generation power. By investigating the variations in battery heat generation in different aging modes, the electrochemical mechanisms underlying the effects of aging on battery heat generation can be comprehended in depth.

/pdf/simulation-study-on-heat-generation-characteristics-of-2ch3d1np.pdf

Yidan Xu

Papers

State of Charge Estimation for Lithium-Ion Batteries Based on Adaptive Dual Kalman Filter

State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

Spatio-Temporal Distribution, Composition and Influencing Factors of Economic Losses from Storm Surge Disasters: An Empirical Study from China (2007-2016)

Simulation Study on Heat Generation Characteristics of Lithium-Ion Battery Aging Process