Smart charging of electric vehicles is a promising concept for solving the current challenges faced by connecting mobility and electricity within the context of the ongoing sustainable energy transition. It allows cost savings for the expansion and operation of the power grid and a more efficient use of renewable energies. However, wide implementation of smart charging requires further work on technical and regulatory issues and further development of standards, especially an end-to-end consistency of the control signals. A fully automated process, as well as customisable services and flexible tariffs, would also facilitate wider market penetration. The novelty of this paper is the consensus of German pilot projects funded within the German programme “Elektro-Mobil” on the communication channel between all stakeholders for the use cases of smart charging based on market price incentives. Within this consensus, the projects have illustrated how specific standards can facilitate the communication between smart charging stakeholders, become a reality in the pilot projects and should be applied to further use cases in the low-voltage network. This consensus results in a white paper. On this basis, the adjustment of the standards can be made to ensure the consistency of the control signals from the beginning of the control process up to the end. In an advanced Edition, solutions for the prioritisation and orchestration of the different control signals could be designed.

Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist-rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection). To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run-to-failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear-out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.

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Increasing requirements for reliability of modern powertrainscan be achieved by predictive maintenance and reliabilitybasedcontrol based on lifetime prediction. This contributionpresents lifetime prediction for a dry clutch, being an essentialcomponent of automated manual transmissions. Modelbaseddevelopment of lifetime prediction requires knowledgeof dry clutch wear, which was identified in previous experiments.The derived wear model allows estimation of characteristicwear-dependent values, like friction lining materiallosses and friction coefficient changes. Based on these estimatedvalues the presented lifetime prediction was developedby fusing these estimated values into a health index (HI) describingthe systems healthiness. Furthermore, the remaininguseful lifetime (RUL) becomes predictable from observationsof health index trend using an exponential weightedmoving average. Eventually, the presented lifetime predictionwas implemented and tested on real-time operating hardwaresimilar to common transmission control units. In order tocontrol the system lifetime in normal operation, target trendsfor health index and predicted remaining useful lifetime weredefined. Based on trend deviations, a fuzzy-logic based controlstrategy was realized, which sets the optimization targetfor a reliability-based control. Thus, the optimization targetcan be varied between comfort-optimized or wear-optimizedclutch engagements. Finally, an outline of reliability-basedcontrol concepts is given.

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