Fuel Consumption Optimization: Early Experiments

Suzdaleva, Evgenia; Nagy, Iván; Pavelková, Lenka

doi:10.3182/20120711-3-be-2027.00080

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…But these variables are not to be optimized, and in this section they are omitted for more transparent formulation of the problem. More detailed information about modeling and experiments with the mentioned variables included is available in [12].…”

Section: "Driver-vehicle" Closed Loopmentioning

confidence: 99%

Servo Problem within Fuel Consumption Optimization

Suzdaleva¹,

Nagy²,

Pavelková³

et al. 2012

Engineering and Applied Science

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The presented paper deals with a problem of fuel consumption optimization. Today's automotive industry solves this problem mainly via various conceptual approaches (hybrid and electric vehicles). However, it leads to high initial cost of a vehicle. This paper focuses on fuel economy for conventional vehicles. For this aim, recursive algorithms of adaptive optimal quadratic control under Bayesian methodology are used. A stochastic servo problem, including setpoint tracking, is a part of the considered adaptive control design. In this paper, fuel consumption and speed of a driven vehicle are the controlled variables, where the first one is to be optimized and the second one is pushed to track its set-point. This set-point is a recommended roaddependent speed. Experiments with real data measured on a driven vehicle are provided.

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Section: "Driver-vehicle" Closed Loopmentioning

confidence: 99%

Servo Problem within Fuel Consumption Optimization

Suzdaleva¹,

Nagy²,

Pavelková³

et al. 2012

Engineering and Applied Science

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“…The set X ? contains such internals X (6) for which the noise terms in (1) and (2) are within the multivariate box defined by (4) and (13), that is, for t , specified by (3),…”

Section: Probabilistic Representation Of the Lsu Modelmentioning

confidence: 99%

“…It also illustrates validity of the claims made with respect to KF-based estimators. The reader interested in the motivating practical application is referred to works [4][5][6]33]. A detailed study including the innovative features of the proposed estimator will be published in an independent paper.…”

Section: Illustrative Experimentsmentioning

confidence: 99%

“…These demands have arisen from practical needs of traffic control [4][5][6] where the reliable control and/or prediction requires information about the estimation quality. There, bounded variables are obviously involved such as a queue length at signalised intersection [4], a car position on the road [5], or a maximal permitted drive speed [6]. Strongly correlated errors occur whenever the adopted model is obtained by the linearisation [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…There, bounded variables are obviously involved such as a queue length at signalised intersection [4], a car position on the road [5], or a maximal permitted drive speed [6]. Strongly correlated errors occur whenever the adopted model is obtained by the linearisation [5,6]. To our best knowledge, no ready solutions meet the demands (a)-(c) as the further discussion indicates.…”

Section: Introductionmentioning

confidence: 99%

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State and parameter estimation of state‐space model with entry‐wise correlated uniform noise

Pavelková

Kárný

2013

Adaptive Control & Signal

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Joint parameter and state estimation is proposed for linear state-space model with uniform, entry-wise correlated, state and output noises (LSU model for short). The adopted Bayesian modelling and approximate estimation produce an estimator that (a) provides the maximum a posteriori estimate enriched by information on its precision, (b) respects correlated noise entries without demanding the user to tune noise covariances, and (c) respects bounded nature of real-life variables.L. PAVELKOVÁ AND M. KÁRNÝ KF, however, faces significant, extensively counteracted, troubles with respect to both (b) and (c) as seen in the following representative samples of existing solutions.As for requirements (a) and (b), KF and its extensions work well when the noise covariances are well chosen. Then, they also provide adequate information on the estimate precision. The covariances are predominantly taken as design parameters of KF as their estimation represents a highly nonlinear problem [8,9]. The number of covariance entries grow quadratically with the state and output dimensions, which soon makes their experimental tuning infeasible. The number of opted entries can be decreased by a nontrivial parsimonious parametrisation [10] or made linearly growing in a factorised version of the state-space model [11,12], but the sensitivity to covariances choice persists, and neither (a) nor (b) are met.The requirement (c) can be fulfilled by a projection of the estimates onto the constraint surface via quadratic programming [13]. Another way is proposed by the probability density function (pdf) truncation approach [14] where the pdf of the state estimate is computed by the standard KF and then truncated at the constraint edges. The constrained state estimate is equal to the mean of the truncated pdf. The induced high-computational demands limit the degree of meeting (c). More importantly, techniques employing a projection or truncation in conjunction with the system model having unbounded support and light tails respect the constraints during estimation but not during modelling. Consequently, the posterior pdf forming the outcome of the Bayesian treatment is a worse estimator than necessary. Indeed, this pdf is a product of prior pdf on parameters and initial state, and likelihood function as a product of pdfs describing the state-space model with observed data inserted. The system model with the constrained states has a complex state and parameter-dependent normalising factor entering the likelihood. This factor is neglected by the discussed techniques.The complexity of the support and form of the exact posterior pdf corresponding to the adequate modelling of boundedness limits feasibility of the Bayesian estimation. Nevertheless, it is desirable to address it as there is a broad range of problems in which explicit inclusion of constraints into the system model significantly improves the estimation quality.The correct modelling of state boundedness and elaboration of the corresponding joint estimator providing both point estimates and info...

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