Remaining discharge energy estimation for lithium-ion batteries using pattern recognition and power prediction

Hatherall, Ollie; Niri, Mona Faraji; Barai, Anup; Li, Yang; Marco, James

doi:10.1016/j.est.2023.107091

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Due to the complexity and limitations of the model-based or purely experimental methods, data-driven SoC estimation has been attempted in various studies in recent decades. A wide range of models such as NNs [123], GPRs [124], SVRs [125], and Markov models [126][127][128] are developed for this purpose. However, unlike the fair number of research studies that utilise the explainability and interpretability techniques for the SoH or RUL prediction of batteries as listed in the previous section, the state of charge or energy estimation problems have been less focused on the XML.…”

Section: State Of Charge and Energy Estimationmentioning

confidence: 99%

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

Faraji Niri,

Aslansefat,

Haghi

et al. 2023

Energies

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Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community.

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Section: State Of Charge and Energy Estimationmentioning

confidence: 99%

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

Faraji Niri,

Aslansefat,

Haghi

et al. 2023

Energies

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“…The Markov chain is widely used in natural science and engineering technology, and is effective in state prediction. Therefore, many researchers use the Markov algorithm to conduct research into the prediction of working conditions [33,34]. In the actual driving process of a vehicle, it is usually impossible to achieve predetermined energy saving and emission reduction effects.…”

Section: Introductionmentioning

confidence: 99%

Equivalent Consumption Minimization Strategy of Hybrid Electric Vehicle Integrated with Driving Cycle Prediction Method

Yao

Zheng

et al. 2023

Machines

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Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved.

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Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating

Lai,

Weng,

Yang

et al. 2023

J Solid State Electrochem

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Remaining discharge energy estimation for lithium-ion batteries using pattern recognition and power prediction

Cited by 5 publications

References 35 publications

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

Equivalent Consumption Minimization Strategy of Hybrid Electric Vehicle Integrated with Driving Cycle Prediction Method

Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating

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