Battery degradation-aware energy management strategy with driving pattern severity factor feedback correction algorithm

The battery degradation and equivalent hydrogen consumption are of great significance in vehicle performance improvement for fuel cell hybrid electric vehicle. However, few energy management strategies proposed by previous researchers can optimize the objectives at the same time. To overcome this drawback, a battery degradation-aware energy management strategy with a driving pattern severity factor feedback correction algorithm is developed to achieve the optimal trade-off between power battery degradation and vehicle economy improvement. The proposed strategy adjusts the equivalent factor according trip distance and correct the battery power to directly address the battery degradation and vehicle economy problem. First, a cost function using weighting factor to trade off the battery degradation and vehicle economy is formulated. The severity factor based on the battery aging model with effective ampere-hour throughput is used to measure the degree of battery degradation. Then, the genetic algorithm back propagation neural network for driving pattern recognition is developed. The trip distance adaptive equivalent consumption minimization strategy is introduced to correct the equivalent factor according to the driving pattern information and the battery degradation feedback correction strategy of the severity factor integrated with driving patterns recognition is constructed. Finally, a comparison analysis study and hardware-in-the-loop experiment are conducted to validate the effectiveness of the proposed strategy. Experimental results under Urban Dynamometer Driving Schedule and Extra Urban Driving Cycle combined with the equivalent consumption minimum strategy indicate that the battery degradation feedback correction algorithm can significantly reduce the battery degradation degree while sacrificing hydrogen consumption optimization to a small extent.