A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter

Lai, Xin; Huang, Yunfeng; Han, Xiao; Gu, Huanghui; Zheng, Yuejiu

doi:10.1016/j.est.2021.103269

Cited by 51 publications

(22 citation statements)

References 37 publications

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“…The working conditions of lithium-ion batteries are complex, and the sensor is often affected by uncontrollable factors such as external temperature and noise, resulting in accumulated errors in the integration process. Lastly, the method has a high dependence on the initial energy value of the battery [57]. Nevertheless, the aging of the battery leads to energy attenuation, which reduces the reference energy, resulting in a decrease in the prediction accuracy of SOE [58].…”

Section: Power Integration Methodsmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

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Section: Power Integration Methodsmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

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“…The values of the DWA are negative since the remaining energy decreases with decreasing SoC , showing that more energy per SoC can generally be drawn from a cell for higher SoC s. Therefore, although the SoC metric is commonly used for residual energy estimation, it cannot reflect the energy that can be drawn from a battery cell accurately [7] . Another challenge that additionally occurs for residual usable energy estimation is that it is influenced by future operating factors such as temperature and current rate [4,8] . Figure 1(b) shows the influence of operating factors by illustrating that the total usable energy increases for lower current rates and higher temperatures which is not reflected in the traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy

Quade

Jöst

Sauer

et al. 2023

Batteries & Supercaps

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An accurate estimation of the residual energy, i. e., State of Energy (SoE), for lithium‐ion batteries is crucial for battery diagnostics since it relates to the remaining driving range of battery electric vehicles. Unlike the State of Charge, which solely reflects the charge, the SoE can feasibly estimate residual energy. The existing literature predominantly focuses on showcasing diverse methods with a gap in conducting in‐depth analysis and comparison of the SoE. The scope of this work is to provide a comprehensive understanding of the SoE by discussing the feasibility and applicability of various definitions and estimation approaches from the literature. For the first time, we classify existing SoE definitions, considering the differences between the inherent stored and usable energy. In the absence of a unified definition in the literature, we propose two physically feasible definitions. Based on the proposed definitions, we critically analyze the practical challenges of SoE estimation. Additionally, to examine the accuracy of the most suitable SoE definition, we conduct an experimental evaluation using a commercial NCA/C+Si cell. Compared to the State of stored Energy, we demonstrate that the State of Charge underestimates the residual energy by more than 3 %, highlighting the importance of adequate SoE estimation.

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“…In this case, many scholars handle nonlinear systems using the adaptive algorithm-based approach to solve accumulated errors and device defects. Furthermore, efficient SOE estimators are built to obtain promising results, such as Kalman filtering [ 12 , 13 ], adaptive unscented Kalman filtering [ 14 ], extended Kalman filtering, and particle filtering [ 15 ]. Given the battery management system’s limited storage and computing performance, many complex algorithms and models in the battery management system (BMS) are challenging to calculate.…”

Section: Introductionmentioning

confidence: 99%

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

Mei

Karimi

Chen

et al. 2022

Sensors

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The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles’ (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

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A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter

Cited by 51 publications

References 37 publications

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

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