规则控制与行驶工况相结合的现代有轨电车能量管理策略

规则控制具有鲁棒性强、灵活性高等显著优势，逐渐成为优化储能式现代有轨电车能量管理性能的经典方法，但也同时面临过于依赖专家经验与工况适应性差的问题。为此针对有轨电车用锂电池/超级电容混合储能系统，在传统模糊逻辑控制的输入中同时引入道路坡度和运行速度，提出一种动态功率分配新方法。依据行驶工况制定隶属度函数与论域，调整超级电容高功率密度响应时刻，优化列车动力性能；采用粒子群算法对模糊控制规则权重寻优，在保证列车功率需求的同时降低锂电池峰值电流，延长储能系统使用寿命；并将所提策略应用到北京现代有轨电车西郊线路数据的算例对比验证中。结果表明，融合行驶工况信息的模糊控制相较于传统模糊控制实现了在功率分配、锂电池和超级电容荷电状态偏移范围、锂电池运行应力及储能系统整体效率方面的多重最优，且再生制动能量回收率有显著改善；通过粒子群算法对规则权重寻优相较于传统固定权重方案使锂电池峰值电流降低31.02%，列车续驶里程提升了22.45%，且算法在迭代7次以内能够找到全局最优解，运算速度快，易于实现。

Energy Management Strategy of Modern Tram Based on the Combination of Rule Control and Driving Conditions

Rule based control has significant advantages of strong robustness and high flexibility. It has gradually become a classic method to optimize the energy management performance of energy storage modern tram, but it also faces the problem of relying too much on expert experience and poor adaptability to working conditions. Therefore, aiming at the lithium battery / super capacitor hybrid energy storage system for tram, a new dynamic power distribution method is proposed by introducing road slope and running speed into the input of traditional fuzzy logic control. The membership function and universe are formulated according to the driving conditions, and the response time of super capacitor high power density is adjusted to optimize the dynamic performance of the train; Particle swarm optimization algorithm is used to optimize the weight of fuzzy control rules, so as to reduce the peak current of lithium battery and prolong the service life of energy storage system while ensuring the power demand of train; The proposed strategy is applied to the comparison and verification of the data of the western suburb line of Beijing modern tram. The results show that compared with the traditional fuzzy control, the fuzzy control integrating driving condition information achieves multiple optimizations in power distribution, SOC(state of charge) offset range of lithium battery and super capacitor, operating stress of lithium battery and overall efficiency of energy storage system, and the regenerative braking energy recovery rate is significantly improved; Compared with the traditional fixed weight scheme, the peak current of lithium battery is reduced by 31.02%, the driving range of train is increased by 22.45%, and the algorithm can find the global optimal solution within 7 iterations. The operation speed is fast and easy to implement.