燃料电池汽车车速与能量联合优化

Co-optimization of Vehicle Speed and Energy for Fuel Cell Vehicles

随着物联网、云计算、大数据等新技术的快速发展,从交通行为和路网系统的角度进行车辆能量优化迎来了新的契机。针对燃料电池汽车通过多个信号灯的场景,提出一种基于静态氢耗图的车速与能量的联合优化方法。结合燃料电池汽车的动力系统结构,利用动态规划(Dynamic programming,DP)对车辆的耗氢量进行离线计算,得到车辆在不同速度和加速度下的静态氢耗图;基于静态氢耗图和交通信号灯配时信息,借助DP算法生成最优车速轨迹;采用交替方向乘子法(Alternating direction method of multipliers,ADMM)来解决能量管理问题,并通过循环检测策略约束燃料电池系统功率变化率。仿真结果表明,车辆最优速度轨迹和能量管理的联合优化计算具备实时性潜力。该方法的优化结果与DP离线计算的最优节能性能仅差1%左右,并且比基于ADMM的能量管理策略下的改进智能驾驶员模型(Intelligent driving model,IDM)的节氢量高17%以上。

With the rapid development of new technologies such as the internet of things,cloud computing,and big data,vehicle energy optimization from the perspective of traffic behavior and road network systems has ushered in new opportunities.Aiming at the scene of fuel cell vehicles passing through multiple signal lights,a co-optimization method of vehicle speed and energy based on a static hydrogen consumption map is proposed.Combined with the powertrain structure of the fuel cell vehicle,the hydrogen consumption of the vehicle is calculated offline by dynamic programming(DP),and the static hydrogen consumption map of the vehicle at different speeds and accelerations is obtained.Based on the static hydrogen consumption map and traffic signal timing information,an optimal vehicle speed trajectory is generated with the help of the DP algorithm.The alternating direction method of multipliers(ADMM)is used to solve the energy management problem,and a cyclic constraint inspection strategy is used to constrain the fuel cell system power change rate.The simulation results show that the co-optimization calculation of the vehicle's optimal speed trajectory and energy management has real-time potential.The optimization result of this method is only about 1%difference from the optimal energy-saving performance of DP offline calculation,and it is more than 17%higher than the hydrogen saving of the improved intelligent driving model(IDM)under the energy management strategy based on ADMM.