Beschreibung
Due to the future CO2 targets for vehicles, electrification of powertrains and operation strategies for electrified powertrains have drawn more attention. This work presents a predictive multi-objective operation strategy for hybrid electric vehicles (HEVs), which simultaneously minimizes the fuel consumption and the cycle aging of traction batteries by using the predictive information. In this work, the benefits of different operation strategies are demonstrated in a full HEV with P2-configuration. For the cycle aging of a lithium-ion battery, an empirical model is built up with Gaussian processes based on measurement data. Two different optimization algorithms, "Deterministic Dynamic Programming" and extended "Multi-Objective Equivalent Consumption Minimization Strategy", are carried out with a priori knowledge of cycle information, to obtain the Pareto front between fuel consumption and battery cycle aging. A meaningful weighting factor for battery cycle aging is chosen based on the Pareto front. In order to achieve the maximal potential of the multi-objective operation strategy for in-vehicle optimization, a predictive operation strategy (pMO-ECMS) is further developed based on acausal MO-ECMS. Different methods are considered to incorporate predictive information into the operation strategies. This pMOECMS has been implemented in an experimental vehicle. It optimizes the torque split between Internal Combustion Engine and Electric Drive realtime. The measurements on a realistic driving cycle show that the developed multiobjective operation strategy can reduce the battery cycle aging significantly and the prediction makes sense to reduce the fuel consumption in real driving conditions. In the end, this proposed strategy shows high robustness and wide application.
