Optimal Velocity and Power Split Control of Hybrid Electric Vehicles

An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space

Energieeffizientes Fahren 2014 (EFA2014) - 2. Projektphase Erhöhung der Reichweite von Elektrofahrzeugen: Abschlussbericht

In AP 1.4 wurde ein Verfahren zur Schaltzeitprognose verkehrsabhängiger Lichtsignalanlagen entwickelt, welches auf eine Vielzahl weiterer Lichtsignalanlagen anwendbar ist. Weiterhin wurden (AP.1.4.5) im Bereich der spurgenauen Ortung, die auf Basis von GPS ermittelten Positionen durch Fusion mit anderen Sensordaten, wie der axialen Beschleunigungen und den Drehraten um die Fahrzeughochachse sowie der Einbeziehung einer digitalen Karte (Digital Enhanced Map), diese hinsichtlich einer Spurdetektion weiterhin verbessert. Im Bereich der Datenübertragung (LSA-Fzg.) konnte die erste Teilstrecke von der Verkehrsmanagementzentrale zum Serviceprovider im Labor untersucht werden. In AP 2.1 wurde eine auf der optimalen Steuerung basierte Methode zum Energiemanagement von seriellen Hybriden entwickelt. Die optimale Ansteuerung von Motor-Start-Stopp, Gangwahl und Momentenaufteilung wird modellprädiktiv unter Beachtung des Kraftstoffverbrauchs und der Schademissionen berechnet. Nach Anpassung auf praktische Randbedingungen, lässt sich diese Methode in zukünftigen Hybridfahrzeugen als optimales Energiemanagement nutzen. Die in AP 3.1 entwickelte Softwareumgebung zur gekoppelten Fahrzeug und Verkehrssimulation wurde an Beispielszenarien getestet. Für ein Modell der Versuchsstrecke wurde umfangreiche Analysen des Ampelassistenzfunktion in komplexen Verkehrsszenarien durchgeführt. Für eine Variation verschiedener Parameter, wie Wirkreichweite, Verkehrsstärke, usw. konnten Aussagen über das Potential getroffen werden. In Zusammenarbeit mit AP 3.3 wurde ein Ampelassistenzsystem und die Ansteuerung des Active-Force-Feedback Pedals im Demonstrator implementiert. In AP 3.3 wurde ein Konzept zur Darstellung von LSA-Daten im Fahrzeug erarbeitet. Dieses wurde in einem Versuchsträger umgesetzt. Dazu wurde der Versuchsträger hardwareseitig ertüchtigt, und für die Untersuchung verschiedener Varianten der Darstellung eingesetzt.:I. Versionsübersicht 4 II. Kurze Darstellung 5 1. Aufgabenstellung 5 2. Voraussetzungen 6 3. Planung und Ablauf des Vorhabens 7 4. Wissenschaftlicher und technischer Stand 8 5. Bekannte Konstruktionen, Verfahren und Schutzrechte 9 6. verwendete Fachliteratur und Informations- und Dokumentationsdienste 9 7. Zusammenarbeit mit anderen Stellen 10 III. Eingehende Darstellung 11 1. Arbeitsinhalte und erzielte Ergebnisse 11 AP 1.4: Datenmanagement 11 AP 2.1: Range-Extender-Betriebsstrategien 40 AP 3.1: Fahrstrategie 63 AP 3.3: Mensch-Maschine-Interface 73 2. Nutzen der Ergebnisse 81 3. Fortschritt bei anderen Stellen 82 4. Veröffentlichungen und studentische Arbeiten 83 Vorträge 83 Publikationen 83 Studentische Arbeiten 84 IV. Literaturverzeichnis 8

Correction to: Macromolecular interactions in vitro, comparing classical and novel approaches

Correction to: European Biophysics Journal https://doi.org/10.1007/s00249-021-01517-5Peer reviewe

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

Optimal Velocity and Power Split Control of Hybrid Electric Vehicles

An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space

Optimal Velocity and Power Split Control of Hybrid Electric Vehicles

An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space

Assessing and Improving Protein Sample

International audienceOne essential prerequisite of any experiment involving a purified protein, such as interaction studies or structural and biophysical characterization, is to work with a "good-quality" sample in order to ensure reproducibility and reliability of the data. Here, we define a "good-quality" sample as a protein preparation that fulfills three criteria: (1) the preparation contains a protein that is pure and soluble and exhibits structural and functional integrity, (2) the protein must be structurally homogeneous, and (3) the preparation must be reproducible. To ensure effective quality control (QC) of all these parameters, we suggest to follow a simple workflow involving the use of gel electrophoresis, light scattering, and spectroscopic experiments. We describe the techniques used in every step of this workflow and provide easy-to-use standard protocols for each step

Computationally efficient velocity and power split control of hybrid electric vehicles

An assessment study is presented for online optimal control of hybrid electric vehicles (HEV), based on the Pontryagin\u27s Maximum Principle. A parallel HEV is used as an example, where states in the optimal control problem are the electric energy storage, kinetic energy and travel time. The solution quality and computational effort of the proposed method are assessed by comparing the results with a benchmark method that yields optimal performance. It is illustrated that the proposed method yields a good solution by solving the optimal control problem over 50 times faster than the benchmark method. Additionally, the novel approach brings up to 16 % decline in fuel consumption compared to a simple velocity trajectory which a human driver would choose

Optimal energy management and velocity control of hybrid electric vehicles

An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin\u27s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage, engine state and gear, kinetic energy, and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space