电动拖拉机混合电源负载自适应能量管理策略研究

Load-adaptive Energy Management Strategy for Electric Tractor Based on Hybrid Power Source

摘要:

针对纯电动拖拉机工况随机性大、动态负荷变化频繁导致能量利用率低等问题，提出了基于负载自适应的蓄电池/超级电容混合电源的纯电动拖拉机动力电源供能系统。通过分析纯电动拖拉机的作业工况特点以及功率需求，采用动态规划分析方法找出动力系统最优控制模式，并且从动态规划分析结果中提出分段式控制规则，建立了功率划分参数模型与负载统计量之间的函数关系。在此基础上提出了一种基于负载自适应规则的电动拖拉机能量管理策略，并对平稳路况组合负载周期和非平稳路况随机负载周期模式进行了验证实验。实验结果表明，与普通控制规则的能量管理策略相比，本文所提出的电动拖拉机负载自适应能量管理策略具有更强的电池保护和节能能力，能实现负载自适应模式下的最优能量管理，其中在不同工况下电池安培小时吞吐量及电源系统总能量损失分别降低10.5%~15.7%和8.7%~15.1%，表明所提出的电动拖拉机负载自适应能量管理策略能够以较低的计算成本实时在线实现最优的能量管理。

Abstract:

The large randomness of working conditions and frequent dynamic load changes leading to low energy utilization of pure electric tractor, in order to solve the problems, a power supply system of pure electric tractor based on load adaptive battery/super-capacitor hybrid power supply was proposed. Based on the analysis of operating conditions and power demand of pure electric tractor, the optimal control mode of power system was found by dynamic programming analysis method, and piecewise control rules were proposed from the dynamic programming analysis results, and the functional relationship between the power division parameter model and load statistics was established. Finally, an energy management strategy of electric tractor based on load adaptive rules was proposed, and the combined load cycle mode of stationary road conditions and random load cycle mode of non-stationary road conditions were verified by test. The results showed that compared with the energy management strategy of ordinary control rules, the load adaptive energy management strategy of electric tractor proposed had stronger battery protection and energy saving capabilities, and could realize the optimal energy management under the load adaptive mode. The battery ampere-hour throughput and the total energy loss of the power system were reduced by 10.5%~15.7% and 8.7%~15.1% respectively under different working conditions. The results showed that the proposed pure electric tractor load adaptability energy management strategy could achieve optimal energy management inreal time and online with low computational cost.