Risk-averse real driving emissions optimization considering stochastic influences

Wasserburger, Alexander; Hametner, Christoph; Didcock, Nico

doi:10.1080/0305215x.2019.1569646

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…An interesting topic for future research is the extension of the method to larger calibration problems, where the entire operating range of the engine consisting of more than one OP is considered and optimized simultaneously. This adds additional uncertain parameters such as route, traffic, and driving style to the probabilistic problem [17]. A major challenge for solving this kind of task is to create global engine models that are sensitive with regard to the design parameters and are able to capture engine dynamics at the same time.…”

Section: Discussionmentioning

confidence: 99%

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A stochastic design optimization methodology to reduce emission spread in combustion engines

Mourat

Eckstein

Koch

2020

Automot. Engine Technol.

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This paper introduces a method for efficiently solving stochastic optimization problems in the field of engine calibration. The main objective is to make more conscious decisions during the base engine calibration process by considering the system uncertainty due to component tolerances and thus enabling more robust design, low emissions, and avoiding expensive recalibration steps that generate costs and possibly postpone the start of production. The main idea behind the approach is to optimize the design parameters of the engine control unit (ECU) that are subject to uncertainty by considering the resulting output uncertainty. The premise is that a model of the system under study exists, which can be evaluated cheaply, and the system tolerance is known. Furthermore, it is essential that the stochastic optimization problem can be formulated such that the objective function and the constraint functions can be expressed using proper metrics such as the value at risk (VaR). The main idea is to derive analytically closed formulations for the VaR, which are cheap to evaluate and thus reduce the computational effort of evaluating the objective and constraints. The VaR is therefore learned as a function of the input parameters of the initial model using a supervised learning algorithm. For this work, we employ Gaussian process regression models. To illustrate the benefits of the approach, it is applied to a representative engine calibration problem. The results show a significant improvement in emissions compared to the deterministic setting, where the optimization problem is constructed using safety coefficients. We also show that the computation time is comparable to the deterministic setting and is orders of magnitude less than solving the problem using the Monte-Carlo or quasi-Monte-Carlo method.

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Section: Discussionmentioning

confidence: 99%

“…Stochastic methodology has already been applied in powertrain development. [16] and [17] performed stochastic optimization aiming to quantify and minimize uncertainty resulting from different driving cycles. In [18] chance-constrained optimization was performed to improve thermal efficiency.…”

Section: Introductionmentioning

confidence: 99%

A stochastic design optimization methodology to reduce emission spread in combustion engines

Mourat

Eckstein

Koch

2020

Automot. Engine Technol.

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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

Environmetrics

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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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“…For arbitrarily complex types of uncertainties typical robust control is challenging. Therefore, in this article a stochastic optimisation similar to the robust engine calibration approach presented in [25] is suggested. Suitable controller parameters and the spacing policy are determined by stochastic, risk-averse optimisation which adequately considers the possible disturbances like various transmission time delays while minimizing energy consumption and collision risk.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

2022

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Cooperative adaptive cruise control systems have the potential to improve fuel efficiency and safety. However, due to the large amount of uncertainties, which are encountered in platooning applications, typical controller calibrations are often not reliable. Therefore, in order to ensure a satisfying performance in the presence of various information topologies and relevant uncertainties such as errors in data transmission or extreme manoeuvres of the lead vehicle, a risk-averse stochastic optimisation approach for controller calibration is suggested and demonstrated for a pre-existing control scheme. Realistic vehicle dynamics simulation experiments with a prescribed set of uncertainties, such as transmission delays and different vehicle parameters, are performed. The results show that the collision probability and energy consumption are reduced by the risk-averse calibration of the controller and its spacing policy compared with classical calibration which assumes perfect communication.

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Risk-averse real driving emissions optimization considering stochastic influences

Cited by 12 publications

References 27 publications

A stochastic design optimization methodology to reduce emission spread in combustion engines

A stochastic design optimization methodology to reduce emission spread in combustion engines

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

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

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