A Four-Dimensional First Principle Based Battery Degradation Model for State of Health (SOH) and State of Function (SOF) Estimation.
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State of health (SOH) estimation plays an important role in battery management system. This problem is addressed by introducing a second-order RC equivalent circuit model (ECM) and square root extended kalman filter (SREKF) in this work. According to the ECM, the ohmic resistance (R 0 ) which represents the SOH is molded as a state vector, and lithium battery SOH is obtained by using the internal relationship between ohm resistance and SOH. Then the state of the resulting nonlinear dynamic system is estimated in real time by the SREKF which can guarantee the symmetry and non-negative characteristics of the state variance matrix by the square root method recursively. The verification results show that the SREKF algorithm can estimate the SOH parameter more accurately and reduce the maximum error of the ohmic resistance by 6% under 1C-rate constant current test compared with the EKF algorithm.
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This paper proposed a co-estimation scheme of State of Charge (SOC), State of Health (SOH), and State of Function (SOF) for lithium-ion batteries. The Extended Kalman Filter is adopted to SOC estimation. Battery parameters are identified online by using Recursive Least Square Algorithm to further estimate battery SOH and SOF. The accuracy of the estimation is improved and the computation is reduced by making good use of the correlations among the states.
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This paper discusses the commonly used techniques to estimate the state of health (SOH) and state of function (SOF) of lithium ion batteries and their limitations. Factors affecting the health and SOF of the battery are discussed in this paper. The SOH of the battery is mainly represented by the capacity degradation and the increase in the internal resistance. The other indices that could represent the battery's health are also briefly discussed. The different techniques that are used to estimate the capacity and internal resistance of the battery are discussed along with their limitations. The concept of SOF and its relationship with SOC, SOH and temperature are discussed along with the commonly used techniques to estimate the SOF of the battery. This paper also discusses the limitations in the definition and estimation of the SOF.
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To avoid battery failure and keep the battery lifetime, a system needs control its use by considering two of several parameters of Battery Management System (BMS) such as State of Charge (SOH) and State of Health (SOH). The State of Charge in Battery Management System provides the percentage of battery capacity, while the State Of Health measures the battery health. The Thevenin battery model is used to describe polarization characteristic and dynamic behavior of the battery and estimared using KalmanFilter(KF). Parameters in the model were estimated using Recursive Least Square. As the results, KF is better then RLS to estimate SOH with a mean relative error as much as 5.26% while RLS has 7.08%.
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This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
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In order to accurately estimate the state of health (SOH) of a Li-ion battery, a reference performance test (RPT) needs to be conducted between several charging/discharging cycles for obtaining accurate data concerning the battery capacity and degradation. However, it is not practical to perform RPTs frequently because they are time-consuming and expensive; moreover, the Li-ion battery undergoes unnecessary degradation during test operations. Therefore, the RPTs should be performed as infrequently as possible. In this paper, a neural network-based SOH estimation scheme with reduced experimental data measured by the RPT is proposed for achieving economic efficiency and mitigating the dispensable degradation being caused by additional experiments. For the RPT-reduced experimental data, the continuous SOH estimation problem is formulated into a classification problem. The neural network learns how to estimate the SOH values using short time-series voltage and current data, labeled as the corresponding SOH values by the RPTs. Even in the SOH regions, where the data are not labeled as any given class and there is no prior knowledge on the corresponding SOH, the proposed SOH estimation scheme works well by performing regression with the class probability distribution.
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State-of-health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH estimation methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this article, we propose a novel SOH estimation method based on battery surface temperature. The differential temperature curves during constant charging are analyzed and found to be strongly related to SOH. Part of the differential temperature curves in a voltage range is adopted to establish a relationship with SOH using support vector regression. The influence of battery discrepancy, voltage range, and sampling step are systematically discussed and the best combination of voltage range and sampling step is determined using leave-one-out validation. The proposed method is then validated and compared with an incremental capacity analysis (ICA)-based SOH estimation method using the Oxford and NASA datasets, which were collected from different cells under different conditions, respectively. The results show that the proposed method is capable of estimating SOH with the root-mean-square error less than 3.62% and 2.49%, respectively. In addition, the proposed method can improve the overall SOH estimation accuracy and robustness by combining with the ICA-based method with little computational burden.
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A combined SOC (State Of Charge) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed. First, the SOC dependency of the nominal parameters of a first-order RC (resistor-capacitor) model is determined, and the performance degradation of the nominal model over the battery lifetime is quantified. Second, two Extended Kalman Filters with different time scales are used for combined SOC/SOH monitoring: the SOC is estimated in real-time, and the SOH (the capacity and internal ohmic resistance) is updated offline. The time scale of the SOH estimator is determined based on model accuracy deterioration. The SOC and SOH estimation results are demonstrated by using large amounts of testing data over the battery lifetime.
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Online Estimation and Error Analysis of both SOC and SOH of Lithium-ion Battery based on DEKF Method
The state-of-charge (SOC) and state-of-health(SOH) are two critical indexes in battery management system (BMS) for electric vehicles(EVs). To achieve accurate estimation of SOC and SOH, this paper establishes a battery equivalent circuit model and uses Forgetting Factor Recursive Least Squares (FFRLS) to realize online identification of model parameters. And based on the relationship between the ohmic internal resistance and the SOH, a joint estimator using Double extended Kalman filter(DEKF) algorithm is proposed for the estimation of both SOC and SOH. Then, an error model is established to analyze the influence of the battery OCV-SOC curve, battery capacity and battery parameters on the estimation of the SOC and SOH. The experiment results show that the maximum estimation error of SOC and SOH is 1.08% and 1.52% respectively, which have verified that accurate and robust SOC and SOH estimation results can be obtained by the proposed method. Besides, the OCV-SOC curve has the greatest influence on the estimation error of SOC and SOH among the three kinds of factors mentioned above.
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