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摘要: 由于插电式混合动力汽车电池可以通过电网获取比较廉价的电量，传统的控制策略只考虑充分利用电池电量，但忽略了过度使用电池，会加快动力电池容量的衰退。因此，如何权衡充分利用电池电量与抑制电池容量衰退是新的研究重点。基于电池的半经验衰退模型，引入电池利用程度因子，建立权衡电池容量衰退的能量管理策略。通过Pareto非劣目标域选取合适的权重因子，将多目标优化问题转化为单目标问题，采用动态规划算法获得权重系数全局最优解，通过权衡不同权重下的油耗和电池容量衰退程度选择最优权重系数。在燃油消耗相当的情况下，当权重系数为0.9时，可有效抑制电池寿命的衰减速度。最后，通过在线等效油耗最小策略仿真与在同一权重下的动态规划解进行比较来验证其有效性。Abstract: As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult. In contrast, new energy vehicles are environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV) is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions. The optimization of the energy management strategy (EMS) mainly focuses on reducing fuel consumption and improving the economy. However, the durability of the power battery also needs attention, as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs. Because PHEV batteries can obtain relatively cheap power through the grid, the traditional control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus. Based on the semiempirical decay model of the battery, the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation with the dynamic programming solution under the same weight.
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Key words:
- battery aging /
- energy management strategy /
- fuel consumption /
- weight coefficient /
- dynamic programming
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图 1 电池循环次数与放电深度的关系
Figure 1. Relationship between the number of battery cycles and the depth of discharge
图 4 不同充电截止电压下的电池容量衰退
Figure 4. Battery capacity degradation at different charge cutoff voltages
图 5 电池SOC、温度和严重程度系数关系图
Figure 5. Chart of battery SOC, temperature, and severity coefficient
图 6 不同温度下的严重程度系数关系图。（a）15 ℃；（b）30 ℃；（c）45 ℃；（d）60 ℃
Figure 6. Relationship diagram of severity coefficients at different temperatures：(a) 15 ℃；(b) 30 ℃；(c) 45 ℃；(d) 60 ℃
图 7 考虑电池容量衰退的权衡控制策略DP求解流程图
Figure 7. Solution flow chart of the trade-off control strategy DP considering battery capacity decline
图 8 7个US06工况下不同权重时的DP解
Figure 8. DP solutions of seven US06 operating conditions with different weights
图 9 不同权重下的SOC曲线。（a）车速示意图；（b）SOC变化曲线
Figure 9. SOC curves under different weights: (a) speed diagram; (b) SOC change curve
图 10 不同权重下的仿真结果。（a）充放电倍率；（b）严重程度系数
Figure 10. Simulation results under different weights: (a) charge and discharge ratio; (b) severity coefficient
图 11 不同权重下的仿真结果图。（a）电机转矩；（b）发动机转矩
Figure 11. Simulation results under different weights: (a) motor torque; (b) engine torque
图 14 不同方法下的仿真结果对比图。（a）发动机转矩；（b）电机转矩对比
Figure 14. Comparison diagram of simulation results under different methods: (a) engine torque; (b) motor torque comparison
图 13 不同方法下的仿真结果对比图。（a）充放电倍率；（b）严重程度系数
Figure 13. Comparison figure of simulation results under different methods: (a) charge and discharge ratio; (b) severity coefficient
表 1 7个US06工况下的仿真结果
Table 1. Simulation results of 7 US06 operating conditions
$ \alpha $ Fuel consumption/L Effective ampere-hour flux/(A·h) 1 3.941 257.6 0.9 3.983 137.9 0.8 4.046 76.3 0.7 4.102 46.9 0.6 4.158 30.1 0.5 4.200 21 0.4 4.256 13.3 0.3 4.326 7.7 0.2 4.410 2.8 0.1 4.452 1.4 
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表 2 不同方法下的仿真结果对比
Table 2. Comparison of simulation results under different methods
Control strategy Fuel consumption/L Effective ampere-hour flux/（A·h） The final SOC DP 3.983 137.9 0.3032 ECMS 4.016 143.2 0.3010
下载: 导出CSV
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