Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed. 

pdf/probabilistic-machine-learning-for-battery-health-5cxb6l0qnb.pdf

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Workflow scheduling in cloud environment – Challenges, tools, limitations & methodologies: A review

Lithium-ion batteries (LIBs) Remaining Useful Life (RUL) prediction is vital for Battery Management Systems (BMS). It is crucial for providing the optimum performance and longevity of batteries used in different industries. In this study, we propose an innovative approach that combines machine learning techniques and hybrid modeling strategies to enhance the accuracy and robustness of battery analysis. By leveraging the power of k-Nearest Neighbors (kNN), Random Forest (RF), and XGBoost algorithms, our proposed model effectively captures complex relationships and patterns in battery data. We meticulously curate a comprehensive dataset comprising essential battery parameters: capacity, voltage, cycle, and temperature. Through rigorous experimentation and evaluation, our proposed method outperforms existing approaches in predicting RUL and capacity fade. The insights gained from our analysis offer valuable guidance for optimizing battery performance, enabling informed maintenance planning, and improving energy storage system efficiency. 

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. 

Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative Study

Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research. 

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

The remaining useful life (RUL) degradation under driving conditions is complex. The features from incremental capacity-differential voltage (IC-DV) curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL. This paper proposes a light gradient boosting machine (LightGBM) based framework with electro- chemical theory to achieve RUL prediction under driving conditions. The degradation modes are identified as loss of conductivity, loss of active material and loss of lithium ion, and the EIS is refined as ohmic resistance, charge transfer resistance, solid electrolyte interphase film resistance, and Warburg resistance. The LightGBM model is improved by the adaptive robust loss function to achieve multi-loss functions adaptive adjustment for different cases and limit the effect of measurement noise on gradients. Based on the perspective of multitask learning, the Pearson correlation is analyzed to design the sharing principle, which ensures full use of features and reduce experimental costs. The proposed framework is validated by the database under four vibration cases (static, X-axis, Y-axis, and Z-axis). Experimental results demonstrate that the proposed framework is capable of providing accurate and steady RUL prediction under driving conditions even with measurement noise. The proposed framework could guide periodic maintenance and stable operation to avoid risks. 

A LightGBM Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions

Battery energy storage system (BESS) is of great significance to ensure underground engineering (UE) microgrid to have reliable power supply. Distributed energy management is one of the solutions that can enhance the microgrid reliability by efficiently scheduling the distributed appliances (such as diesel generator, BESS) to accommodate various scenarios. A distributed energy management model is proposed, which can help in reliable power supply by prolonging the lifetime of BESS and reducing the load loss. Considering the different energy consumption needs and dispatching capabilities of three regions of the UE microgrid, the proposed energy management model distributed dispatches the three regions in UE microgrid. In addition, considering the relationship between the depth of discharge and lifetime, the proposed energy management model also contains the BESS lifetime extension model constructed with dynamic charge-discharge rate and dynamic bidirectional AC/DC converter efficiency. Based on the deterministic optimisation method, the optimal solution of the proposed energy management model is obtained. The effectiveness of the proposed energy management model is validated under six scenarios (that is grid connected mode, off grid mode, partial interconnection mode, interconnection mode, and independent modes with and without power exchange). The simulation results demonstrate that compared with the conventional model, the proposed model can reduce the operation cost by 6.46% and the load loss rate by 0.747%, which helps to improve the reliability of UE microgrid. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/esi2.12093

Distributed energy management for underground engineering microgrid with reliable power supply

In order to solve the problems of hard adaptability and poor dynamic response of traditional state of charge (SoC) estimation methods, data-driven methods are adopted. In order to reduce the variance and deviation in the estimation process and make full use of the respective merits of bagging and boosting, a data-driven local cascade ensemble learning method is proposed to battery SoC estimation. Firstly, to achieve data-driven SoC estimation, the relationship between open circuit voltage (OCV) and SoC is analyzed. The effect of OCV hysteresis on SoC is discussed. Then, for the traditional SoC estimation methods difficult to consider hysteresis effect, a data-driven ensemble learning model is established to estimate SoC using relationship between OCV and SoC. Meanwhile, the form of local cascade based on random and extreme gradient boosting is adopted to minimize variance and deviation of estimation. Finally, the effectiveness and adaptability of the proposed method is verified by dataset from the built experimental bench under different external factors (temperature effect and mechanical stress). The results demonstrate that the proposed method can accurately estimate SoC under different external factors.

A Local Cascade Ensemble Learning Method for Lithium Ion Battery SOC Estimation under Multi External Factors Considering OCV Hysteresis

Test case prioritization is a method to prioritize test cases to improve the testing efficiency of service-oriented workflow applications. Existing prioritization methods prioritize test cases in different application environments according to different metrics (for example, statement coverage, and path coverage). Web services are orchestrated by service-oriented workflow applications to provide different functions, especially in cloud-based mobile systems. As a result, those applications need more precise scheduling to run test cases that can detect faults earlier. Unfortunately, most regression test case prioritization studies in service-oriented workflow applications neglect the use of activity dependency, which is an important factor that affects test case prioritization. By analyzing the dependences between activities, the modification effects of activities on the modified version of service-oriented workflow applications are calculated. On this basis, this paper proposes a new prioritization method for regression test cases. Experimental results show that our method is more effective than the traditional coverage-based technique in testing case priority.

/pdf/test-case-prioritization-for-service-oriented-workflow-4vule3clio.pdf

Test Case Prioritization for Service-Oriented Workflow Applications: A Perspective of Modification Impact Analysis

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Papers

A LightGBM Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions

Distributed energy management for underground engineering microgrid with reliable power supply

A Local Cascade Ensemble Learning Method for Lithium Ion Battery SOC Estimation under Multi External Factors Considering OCV Hysteresis

Test Case Prioritization for Service-Oriented Workflow Applications: A Perspective of Modification Impact Analysis