@inproceedings{jin_wang_hu_2016, title={A Fuzzy logic based power management strategy for hybrid energy storage system in hybrid electric vehicles considering battery degradation}, booktitle={2016 IEEE Transportation Electrification Conference and Expo (ITEC)}, author={Jin, F. N. and Wang, M. Q. and Hu, C. J.}, year={2016} }
 @inproceedings{hu_gao_alex_2015, title={Power management strategy of hybrid electric vehicles based on particle swarm optimization}, DOI={10.1109/itec.2015.7165795}, abstractNote={The Power management strategy of HEV using global optimization techniques can achieve optimum control solution. However, the “a priori” nature of the trip information and heavy computational cost prohibit it from being utilized in real world application. In this paper, a power management strategy using particle swarm optimization (PSO) is proposed. The aim is to achieve real time implementation and sub-optimal control solution without requiring the “a priori” knowledge of the driving cycle. Using pricewise linearization, at each time step, normalized comprehensive energy loss for each power split scenario is obtained and normalized over the traveling distance. The power split strategy that minimizes the normalized comprehensive energy loss is considered optimal. However, searching for the optimal power split is mathematically challenging and time consuming. To address the real time implementation, PSO algorithm is employed as the global minima searching tool. Simulation study on a series-parallel configuration passenger vehicle has been performed. In addition, Dynamic Programming (DP) technique has also been implemented in the simulation for the comparison purpose. The simulation results demonstrated that the proposed control strategy is able to achieve comparable fuel economy with global optimization while feasible for real time implementation.}, booktitle={2015 IEEE Transportation Electrification Conference and Expo (ITEC)}, author={Hu, C. J. and Gao, Y. M. and Alex, Q. H.}, year={2015} }
 @inproceedings{hu_huang_gao_2013, title={Comprehensive lost minimization strategy for parallel plug-in hybrid electric vehicles}, DOI={10.1109/itec.2013.6573501}, abstractNote={Plug-in hybrid electric vehicles (PHEVs) have two energy inputs, the petroleum fuel and electric energy from the utility grid. Due to the different operation costs, the energy management has significant effects on the fuel economy. In this paper, an energy management strategy, stemmed from the AER-focused and blended strategy, is developed. It features the “smart” utilization of the stored electric energy and meanwhile provides intensive electric range. A traffic pattern identification algorithm is proposed. Using the historic and current traffic data, the algorithm can identify the highway and urban traffic pattern, which provides the guidance of the “smart” utilization. In addition, an innovative real time control algorithm for charge sustenance is proposed. It computs the comprehensive energy loss of the hybrid drive train. The operation points with the minimum comprehensive energy loss is found to be the optimized engine and motor operation points. Simulation results show that significant improvement of fuel efficiency can be achieved.}, booktitle={2013 IEEE Transportation Electrification Conference and Expo (ITEC)}, author={Hu, C. J. and Huang, A. Q. and Gao, Y. M.}, year={2013} }