The high efficiency of modern fully electrified powertrains helps to reduce the traction energy demand of vehicles to a minimum. Yet, providing this energy from fossil fuel power plants in the public grid negates this efficiency effect. A possible solution is to recharge traction batteries with renewable energy provided by local sources. The availability of renewable energy is non-deterministic. To enable a significant renewable contribution ratio, charging strategies need to be predictive. This paper shows a model-based development approach for such complex charging strategies. Possible strategies, a test-bed based on Modelica, SimulationX, the Green Building Library and FMI coupling to energy management and controller software are shown. The results are demonstrated in hardware with a development charging station and an electric scooter. Using a model based approach in development of charging strategies helps to speed-up the engineering process and to reduce the cost. A major improvement is the fast test of even partially finished algorithms and software. Much faster than real-time simulation allows to test multiple scenarios and identify typical situations as well as to investigate problematic stress situations. This is especially helpful since battery charging or weather changes are rather slow in real-time. This also helped to cope with uncertainties in energy prediction and user behavior. Still successful charging strategies need a change of mindset. The user needs to connect the charging station when possible, not when the tank is empty.
Simulation and evaluation of (eVehicle) charging strategies based on renewable energy availability
2012
9 Seiten, 7 Bilder, 9 Quellen
Aufsatz (Konferenz)
Englisch
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