Self-optimization energy management considering stochastic influences for a hybrid energy storage of an electric road vehicle

Electric and hybrid-electric vehicles place high demands for peak power, energy content and efficiency on the energy storage. By hybridization of the storage, adding double layer capacitors, the battery can be relieved from the stress of peak power and even downsized to meet only energy demands instead of power demands. Thus, the storage weight and losses can be significantly reduced. An energy management to distribute the power to both storages can be mathematically optimized applying Stochastic Dynamic Programming (SDP), considering stochastic influences of the driving process. To handle different conditions and driving cycles, we propose self-optimization control strategies involving multi-objective optimization. These strategies are able to autonomously adapt their behavior and relevance of objectives, offering an optimal and secure operation in different situations.