State of health (SOH) estimation of lithium-ion batteries is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous aging mechanisms and sensitivity to the ...applied conditions of lithium-ion batteries, the recognition of aging mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and aging mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional-order model is constructed, and the parameter identification approach is proposed, and a comparison between integer-order model and fractional-order model has been done from the prospect of predicting accuracy. Then, based on the identified open-circuit voltage, the battery aging mechanism can be analyzed by the means of an incremental capacity analysis method. Moreover, the normalized incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation, and the estimation error of the remaining capacity can be reduced within 3.1%.
Incremental capacity analysis is a popular tool for the evaluation of state-of-health in battery management. In digital systems, the incremental capacity is generally approximated with the ratio of ...the capacity difference to voltage difference (ΔQ∕ΔV), which unavoidably amplifies measurement noises. To enhance its resilience against noises and improve the estimation accuracy, a two-dimensional filter is designed by employing historical information from both time and batch (cycle) directions inspired by batch-wise repetitiveness of the incremental capacity trajectories. Specifically, in the batch direction, a Luenberger observer is utilised to provide a batch-to-batch smoothing at the beginning of each charging cycle, while in the time direction, a bias-corrected Gaussian moving average filter is applied to smooth the incremental capacity value with respect to the voltage at every sampling time. Experimental results show that the root-mean-square-error of the proposed filter is 50% lower than the benchmark algorithms, and the noise sensitivity is significantly reduced by 93%. When using incremental capacity peaks extracted from the proposed filter for state-of-health modelling, the width of the 99% confidence interval would be narrowed by 45%. Moreover, the model-free nature of the proposed method enables its application to different batteries, paving a reliable way for effective battery health assessment.
•The incremental capacity values are modelled as a function of voltage.•Bias-corrected Gaussian-moving-average filter is developed for real-time filtering.•Symmetric windows could be used for filtering without violating the causality.•Batch-wise Luenberger observer is developed for curve smoothing in each cycle.
In this study, a method is proposed to analyse the capacity improvement ability of using virtual coupling technology to the sharing-corridor metro lines for solving the problem of capacity ...bottleneck. A mixed integer linear programming model is developed to optimise the train timetable of sharing-corridor lines under the assumption of using the virtual coupling technology, considering the constraints related to train timetabling, virtual coupling technology, and passenger assignment, with the objective to improve the service quality. The model adopts the departure times, arrival times, and whether each train service is planned as virtually coupled, with its adjacent front one or the following one, as decision variables. Furthermore, a method is proposed to evaluate the capacity utilisation referring to UIC 406 and considering the feature of virtual coupling technology. The experiments on Shanghai Metro Line 3 and Line 4 are implemented to verify the effectiveness on transport capacity and passenger service quality of the proposed method. The results show that using virtual coupling technology can launch eight more train services during morning peak hours and at most reduce passengers’ waiting time by 73.8 s.
•A passenger-oriented timetable optimisation method is proposed for sharing-corridor lines.•Passengers’ heterogeneous vehicle selection behaviours are considered.•A transport capacity evaluation method considering the features of virtual coupling is proposed.•Numerical experiments based on real-life data of Shanghai Metro Lines is performed.
An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries is proposed and validated in this work. It describes the open circuit voltage as a function of the ...state-of-charge by a polynomial of high degree, with a lumped thermal model to account for the effect of temperature. When applied for practical use, the model requires a prior learning from the initial charging or discharging data for the sake of parameter identification, using e.g. a nonlinear least squares method, but it is undemanding to implement. The study shows that the model is able to estimate the state-of-health of a LiFePO4 cell cycled under conditions where the temperature has fluctuated significantly with a relative error less than 0.45% at most. A short part of a constant current profile is enough for state-of-health estimation, and the effect of size and location of voltage window on the model's accuracy is also studied. In particular, the reason of accuracy change with different voltage windows is explained by incremental capacity analysis. Additionally, the versatility and flexibility of the model to different chemistries and cell designs are demonstrated.
•An open circuit voltage-based model is proposed to estimate the SOH of LIBs.•The accuracy of the model is validated by experimental results of an LFP cell.•The effect of size and location of voltage window on the model accuracy is studied.•ICA is used to explain the model accuracy change with different voltage window.•The versatility of the model to different chemistries is demonstrated.
Accurate state-of-health (SOH) estimation for lithium-ion batteries is of great significance for future intelligent battery management systems. This study proposes a novel method combining ...voltage-capacity (VC)-model-based incremental capacity analysis (ICA) with support vector regression (SVR) for battery SOH estimation. For accurate and efficient capture of IC curves, 18 VC models are first compared, and then, suitable models are selected for two types of batteries with different chemistries, enabling multitype health features to be obtained by parameterizing the VC models. After correlation analysis of these extracted health features with the reference battery capacity, the SVR algorithm is adopted to construct SOH estimation models. Finally, four aging datasets are employed for validation of the proposed method. The experimental results show that the SVR models achieve high accuracy in SOH estimation, i.e., the respective mean absolute errors (MAEs) and root mean square errors (RMSEs) of all batteries are limited to within 1.1%. Moreover, the method is robust against different initial aging statuses and cycle conditions of the batteries: after migration and fine-tuning, both the MAEs and RMSEs can be confined to within 2.3% by utilizing the established SVR models.
•Different voltage-capacity (VC) models are thoroughly compared.•Multitype health features are obtained by parameterizing the VC models.•Support vector regression algorithm is employed for state-of-health estimation.•Four battery aging datasets are utilized for validation of the proposed method.
Precisely battery state of health estimation and remaining useful lifetime prediction are crucial factors in ensuring the reliability and safety for system operation. This paper thus focuses on the ...short-term battery state of health estimation and long-term battery remaining useful lifetime prediction. A novel hybrid method by fusion of partial incremental capacity and Gaussian process regression is proposed and dual Gaussian process regression models are employed to forecast battery health conditions. First, the initial incremental capacity curves are filtered by using the advanced signal process technology. Second, the important health feature variables are extracted from partial incremental capacity curves using correlation analysis method. Third, the Gaussian process regression is applied to model the short-term battery SOH estimation using the feature variables. Forth, an autoregressive long-term battery remaining useful lifetime model is established using the results of battery SOH values and previous output. The predictive capability and effectiveness of two models are demonstrated by four battery datasets under different cycling test conditions. Otherwise, the robustness of the two models is verified using four datasets with different health levels. The experimental results show that the proposed method can provide accurate battery state of health estimation and remaining useful lifetime.
•Dual GPR-based models are proposed to establish battery degradation models.•The health indexes are extracted from partial IC curves as model input features.•Correlational coefficient analysis method is applied to extract feature variables.•An autoregressive RUL model is developed using the capacity vs. cycle number.•Four batteries with different initial health levels are used to verify robustness.
All-vanadium redox flow battery (VRFB) is a promising large-scale and long-term energy storage technology. However, the actual efficiency of the battery is much lower than the theoretical efficiency, ...primarily because of the self-discharge reaction caused by vanadium ion crossover, hydrogen and oxygen evolution side reactions, vanadium metal precipitation and other operational factors. Based on the above analysis, this paper proposes effective strategies and methods for improving the efficiency of the VRFB. The experimental results indicate that the voltage efficiency and system efficiency increased by 1.86% and 0.48%, respectively, when constant flow rate and variable current density charge/discharge methods are used. Meanwhile, when variable flow rate and current density charge/discharge methods are employed, the energy efficiency and system efficiency increased by 9.07% and 8.34%, respectively, resulting in significant improvement in energy storage capacity. The experiment has verified that this method can improve the efficiency and performance of VRFB, and can promote the development of VRFB.
Accurate battery aging prediction is essential for ensuring efficient, reliable, and safe operation of battery systems in electric vehicle application. This article presents a novel battery aging ...assessment method based on the incremental capacity analysis (ICA) and radial basis function neural network (RBFNN) model. The RBFNN model is used to depict the relationship between battery aging level and its influencing factors based on real-world operation datasets of electric city transit buses. The ICA method together with the Gaussian window (GW) filter method is used to derive the peak values of IC curves which are utilized to represent battery aging levels, and the support vector regression (SVR) method is used in several scenarios for data preprocessing. The considered influencing factors include accumulated mileage of vehicles and initial charging state-of-charge (SOC), average charging temperature, average charging current, and average operating temperature of battery systems. The datasets collected from real-world electric city buses are used for RBFNN model training, validation, and test. The results show that an average prediction error of 4.00% is reached, and the derived model has a confidential interval of 92% with the prediction accuracy of 90%. This work provides insights for battery aging prediction based on massive real-time operation data.
The reliability and safety of battery operations necessitate an efficient battery management system (BMS) with accurate battery state of charge (SOC) and capacity estimation techniques. This paper ...investigates the incremental capacity analysis (ICA) and differential voltage analysis (DVA) methods for onboard battery SOC and capacity estimation. Since the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed battery resistance and polarization during battery aging processes, the SOC based ICA/DVA methods are proposed to address this problem as so to accurately identify features of interest on incremental capacity (IC) and differential voltage (DV) curves for applications. Three feature points (FPs) that are potential to be easily identified by battery management systems are extracted from the SOC based IC/DV curves, and then the relations between FPs and cell SOCs/capacities are quantified and applied for battery SOC and capacity estimation. The robustness of the proposed approach against various aging levels and erroneous cumulative capacities is evaluated. Promising results with the maximum absolute error of 1.0% and the relative error of 2.0% can be achieved for battery SOC and capacity estimation, respectively.
•ICA and DVA methods are developed for onboard battery SOC and capacity estimation.•The SOC based IC/DV curves can reflect the relation between IC/DV values and SOCs.•The relations between feature points and SOCs/capacities are quantified.•The proposed method can perform well even with biased cumulative capacities.
•A capacity model based on charging process is proposed to estimate SOH.•The accuracy of the model is validated on commercial lithium ion batteries.•The universality of the model is verified on ...different batteries and statuses.
The incremental capacity (IC) analysis method is widely used to analyze the aging origins and state of health (SOH) of lithium ion batteries. This paper analyzes the technical difficulties during the application of the IC analysis method at first. A universal capacity model based on charging curve is then proposed, which not only inherits the advantages of IC analysis method but also avoids the tedious data preprocessing procedure, to estimate SOH of lithium ion batteries. The feasibility and accuracy of the model are demonstrated. To verify the accuracy and flexibility of the proposed capacity model, it is applied on different types of lithium ion batteries including LiFePO4,LiNi1/3Co1/3Mn1/3O2, and Li4/3Ti5/3O4. Furthermore, the proposed capacity model is applied on the aged cells to validate the model accuracy during the whole life span of lithium ion batteries. The results show that the model error is less than 4% of the nominal capacity for each case.
