Risk-Aware Energy Management for Drive Mode Control in Plug-in Hybrid Electric Vehicles

Watanabe, Ryunosuke; Ibuki, Tatsuya; Sakayanagi, Yoshihiro; Funada, Riku; Sampei, Mitsuji

doi:10.1109/access.2022.3206091

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…The entropic value‐at‐risk (EVaR) (Ahmadi‐Javid, 2012) is a widely used risk measure owing to its analytical tractability and close relationship with other existing risk measures. Applications of EVaR include economics (Chen et al, 2019), mechanical engineering (Wasserburger et al, 2020; Watanabe et al, 2022), and portfolio optimization (Yu & Sun, 2021).…”

Section: Introductionmentioning

confidence: 99%

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Yoshioka,

Yoshioka

2023

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The modeling and identification of time series data with a long memory are important in various fields. The streamflow discharge is one such example that can be reasonably described as an aggregated stochastic process of randomized affine processes where the probability measure, we call it reversion measure, for the randomization is not directly observable. Accurate identification of the reversion measure is critical because of its omnipresence in the aggregated stochastic process. However, the modeling accuracy is commonly limited by the available real‐world data. We resolve this issue by proposing the novel upper and lower bounds of a statistic of interest subject to ambiguity of the reversion measure. Here, we use the Tsallis value‐at‐risk (TsVaR) as a convex risk functional to generalize the widely used entropic value‐at‐risk (EVaR) as a sharp statistical indicator. We demonstrate that the EVaR cannot be used for evaluating key statistics, such as mean and variance, of the streamflow discharge due to the blowup of some exponential integrand. We theoretically show that the TsVaR can avoid this issue because it requires only the existence of some polynomial moment, not exponential moment. As a demonstration, we apply the semi‐implicit gradient descent method to calculate the TsVaR and corresponding Radon–Nikodym derivative for time series data of actual streamflow discharges in mountainous river environments.

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Section: Introductionmentioning

confidence: 99%

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Yoshioka,

Yoshioka

2023

Environmetrics

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Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption

et al. 2023

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The article reviews the existing methods of increasing the energy efficiency of electric transport by analyzing and studying the methods of increasing the energy storage resource. It is grouped according to methods, approaches, and solutions. The most effective methods and ways of their implementation are identified. General methods of increasing energy efficiency, methods of increasing recuperation during braking, methods of energy-efficient energy consumption, the use of energy-saving technologies, and improving the energy efficiency of the traction drive are considered. The purpose of this work is to identify the main operating factors on the basis of a critical review of existing methods for assessing the technical condition of batteries and experimental results on the degradation of lithium-ion batteries. Using the great experience of the research group in the field of modeling, diagnostics, and forecasting of life of electric cars, as well as their intellectual management, the new theoretical and practical methods of integrated assessment of the parameters of the traction battery and state of charge, which are operated in the heavy forced regenerative regimes of electric traction, are created and proposed. A great role is played by the construction of the transport model. The development is based on physical laws that passengers and vehicle owners are unaware of. For each model there is a different area of application, and what is suitable for one object may not be suitable for another. The overview shows that there is no one-size-fits-all way to improve energy efficiency. It is necessary to make a choice among several proposed models after a thorough feasibility study.

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Risk-Aware Energy Management for Drive Mode Control in Plug-in Hybrid Electric Vehicles

Cited by 2 publications

References 47 publications

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption

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