Analysis of $$\hbox {CO}_2$$ reduction potentials and component load collectives of 48 V-hybrids under real-driving conditions

Förster, Daniel; Decker, Lukas; Doppelbauer, Martin; Gauterin, Frank

doi:10.1007/s41104-021-00076-3

Cited by 4 publications

(11 citation statements)

References 26 publications

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“…In the case of nested sizing and control schemes, the component sizing is optimized in an outer loop, whereas the control optimization is conducted in an inner loop. The nested optimization method leads to a higher number of total function evaluations but is able to find the global optimum [12,19]. A comparison of different nested optimization procedure frameworks is given in [5,20].…”

Section: Methods For Modelling Electric Drive Systemsmentioning

confidence: 99%

“…If the variation of the parameters is conducted on system level, the most common method is the deformation of a predefined loss map, as used in [19,20,35,[37][38][39]. This base characteristic can be the result of a preliminary calculation or a measurement.…”

Section: System Levelmentioning

confidence: 99%

“…Such a variation is similar to the variation of the machine's length and number of turns when using the scaling laws previously described, even though much faster. Several simplifications are met to scale the EDS by deforming the loss map [19]. These simplifications lead to a restricted applicable variation range, and the influence of such a variation on the power electronics losses cannot be considered.…”

Section: System Levelmentioning

confidence: 99%

“…The objective function includes a consumption simulation with a nested optimization approach for the component sizing and the operating strategy. An equivalent consumption minimization strategy (ECMS) is used as it was implemented and described in [19]. The assumed exemplary D-segment vehicle has a 1.5 L gasoline engine, and its main parameters are presented in Table 3.…”

Section: Powertrain System Optimizationmentioning

confidence: 99%

“…In the first step, the ANN predicting the CO 2 emissions is validated. To do so, the predicted valuesẑ are compared to the calculated values z by applying the statistics Q ISI and r 2 in Equations ( 18) and (19). This time, the variable z denotes the CO 2 emission obtained by the consumption simulation, andẑ the emissions obtained with the ANN model.…”

Section: Hev System Optimizationmentioning

confidence: 99%

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Physics-Based and Data-Enhanced Model for Electric Drive Sizing during System Design of Electrified Powertrains

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In multi-drive electrified powertrains, the control strategy strongly influences the component load collectives. Due to this interdependency, the component sizing becomes a difficult task. This paper comprehensively analyses different electric drive system sizing methods for multi-drive systems in the literature. Based on this analysis, a new data-enhanced sizing approach is proposed. While the characteristic is depicted with a physics-based polynomial model, a data-enhanced limiting function ensures the parameter variation stays within a physically feasible range. Its beneficial value is demonstrated by applying the new model to a powertrain system optimization. The new approach enables a detailed investigation of the correlations between the characteristic of electric drive systems and the overall vehicle energy consumption for varying topologies. The application results demonstrate the accuracy and benefit of the proposed model.

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Section: Methods For Modelling Electric Drive Systemsmentioning

confidence: 99%

Section: System Levelmentioning

confidence: 99%

Section: System Levelmentioning

confidence: 99%

Section: Powertrain System Optimizationmentioning

confidence: 99%

Section: Hev System Optimizationmentioning

confidence: 99%

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Physics-Based and Data-Enhanced Model for Electric Drive Sizing during System Design of Electrified Powertrains

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A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

2022

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In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended.

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Optimal Control of Electrified Powertrains in Offline and Online Application Concerning Dimensioning of Li-Ion Batteries

2022

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Various energy management systems (driving strategies) have been developed to improve the efficiency of electrified vehicle drives. These include strategies from the field of offline optimization to determine the theoretical optimum for a given system, as well as online strategies designed for an on-board application in the vehicle. In this paper, investigations are performed on an SUV electrified by a 48 V hybrid system in P14 topology regarding both offline and online strategies. To calculate the global optimum, the performance of Dynamic Programming (DP) compared to an Equivalent Consumption Minimization Strategy (ECMS) with an iteratively determined equivalence factor is shown. Furthermore, with regard to online energy management strategies (EMS), it is presented how a predictive Online ECMS achieves additional fuel savings compared to a robust, non-predictive implementation. The simulation-based vehicle development allows detailed investigations regarding interactions between battery requirements and EMS. In this context, it is shown how various battery capacities are exploited by the discussed EMS.

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Analysis of $$\hbox {CO}_2$$ reduction potentials and component load collectives of 48 V-hybrids under real-driving conditions

Cited by 4 publications

References 26 publications

Physics-Based and Data-Enhanced Model for Electric Drive Sizing during System Design of Electrified Powertrains

Physics-Based and Data-Enhanced Model for Electric Drive Sizing during System Design of Electrified Powertrains

A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

Optimal Control of Electrified Powertrains in Offline and Online Application Concerning Dimensioning of Li-Ion Batteries

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