A novel hybrid data-driven method based on uncertainty quantification to predict the remaining useful life of lithium battery

Li, Hao; Fu, Lijun; Zhang, Yan

doi:10.1016/j.est.2022.104984

Cited by 18 publications

(3 citation statements)

References 42 publications

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“…Li et al (2022) [23] employed a hybrid data-driven approach to analyze battery capacity. Initially, raw capacity data were processed using ensemble empirical mode decomposition and Hurst exponent-based methods to extract two local fluctuation components and one long-term memory feature component.…”

Section: Related Workmentioning

confidence: 99%

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

Yalçın,

Herdem

2024

Energies

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This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems.

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Section: Related Workmentioning

confidence: 99%

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

Yalçın,

Herdem

2024

Energies

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“…Mechanistic modeling is mainly based on detailed analysis and accurate characterization of the degradation process by physical methods [6], and combining mathematical equations and corresponding physical knowledge of the failure and degradation process [7], which is reasonable and accurate when the physical knowledge of degradation is relevant enough [8]. However, since real electrochemical processes are not observable, much less accessible through empirical knowledge, and the physical degradation models developed in the laboratory are very different from the real operating environment, it is difficult and low-precision to adopt a purely mechanistic approach for RUL prediction [9,10].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Keshun,

Guangqi,

Yingkui

2023

Meas. Sci. Technol.

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Due to the complex changes in physicochemical properties of lithium-ion batteries during the process from degradation to failure, it is difficult for methods based on physical or data-driven models to fully characterize this nonlinear process, and existing methods that hybridize physical and data-driven models suffer from ambiguous hybridization, which results in the vast majority of existing methods for predicting the RUL of lithium-ion batteries suffering from a lack of accuracy and robustness. In this study, a novel hybrid approach based on empirical modeling and data-driven techniques is proposed for predicting the remaining useful life (RUL) of lithium-ion batteries. To better capture its complexity, stochasticity, and state transition, and improve the modeling accuracy and RUL prediction precision, Gamma stochasticity and state-space modeling are used to empirically model the complex Li-ion battery degradation process. Moreover, the expectation maximization (EM) method of particle filtering (PF) was used to estimate the hidden parameters of the empirical model, and the estimated parameters were corrected using an optimized support vector regression (SVR) method to enhance the generalization performance and robustness of the data-driven model. The results show that the gamma state-space model is effective in capturing the inherent stochastic properties of the battery degradation and the proposed hybrid method outperforms the existing prediction methods in RUL prediction. The experiments show that the Sparrow Search Algorithm (SSA) optimized SVR is considered to be the most effective correction method for the estimated parameters, while the new EM-PF-SSA-SVR hybrid method provides better performance for state assessment and RUL prediction of lithium-ion batteries. It is indicated that the proposed EM-PF-SSA-SVR method with Gamma stochastic process has hybrid validity and superior performance with equal performance and less parameter computation relative to the existing state-of-the-art deep learning RUL prediction methods.

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“…The approach generates a high-dimensional mapping model by learning the mapping relationship between the input and the output variables, allowing for future predictions to be obtained based on the next input. Data-driven methods have a natural advantage in solving highly nonlinear problems that are influenced by many uncontrollable factors [22], such as building energy systems [23].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

2023

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The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.

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A novel hybrid data-driven method based on uncertainty quantification to predict the remaining useful life of lithium battery

Cited by 18 publications

References 42 publications

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

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