Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles

Li, Weihan; Cui, Han; Nemeth, Thomas; Jansen, Jonathan; Ünlübayir, Cem; Wei, Zhongbao; Zhang, Lei; Wang, Zhenpo; Ruan, Jiageng; Dai, Haifeng; Wei, Xuezhe; Sauer, Dirk Uwe

doi:10.1016/j.est.2021.102355

Cited by 102 publications

(37 citation statements)

References 34 publications

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“…Based on deep reinforcement learning, a hybrid battery system comprising a high-power and high-energy battery pack was described. In order to minimize energy loss and raise both the electrical and thermal safety of the system, an energy management strategy was generated based on the electrical and thermal characterization of the battery cells [51]. For electric vehicles, this article offered a novel resource allocation scheme based on deep reinforcement learning that did not work at the level of complex vehicle dynamics.…”

Section: Other Methodsmentioning

confidence: 99%

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

2022

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Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

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Section: Other Methodsmentioning

confidence: 99%

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

2022

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“…These examples from the fields catalysis and solar cells are barely illustrative. Machine learning (and the same methods as mentioned above) is also used to help design battery materials [23,24,67,68] and for other energy technologies as indicated above, but ML is in demand not just at the material or device level but also at the system level [69][70][71][72]. In an energy mix containing solar farms, wind farms, and other intermittent technologies alongside more established generation methods such as nuclear or natural gas powered stations, one needs to constantly balance supply and demand.…”

Section: Examples Of Input-output Mappings Used In ML For Energy Tech...mentioning

confidence: 99%

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

Ihara¹,

Manzhos²

2022

Preprint

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Machine learning (ML) has found increasing use in research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we will focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with sub-dimensional functions by combining the high-dimensional model representation ansatz with machine learning methods like neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy.

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“…Another rule-based strategy is presented in [522], where the control rules are abstracted from the optimization results with dynamic programming and the learningbased method shows its effectiveness. A more advanced approach present Li et al [523], which combines reinforcement learning with deep learning. Thereby, the energy management strategy not only aims to minimize the energy loss (as in [520,521]), but also to increase the electrical and thermal safety level of the HBSS.…”

Section: Operationmentioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles

Cited by 102 publications

References 34 publications

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

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