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摘要: 由于插電式混合動力汽車電池可以通過電網獲取比較廉價的電量，傳統的控制策略只考慮充分利用電池電量，但忽略了過度使用電池，會加快動力電池容量的衰退。因此，如何權衡充分利用電池電量與抑制電池容量衰退是新的研究重點。基于電池的半經驗衰退模型，引入電池利用程度因子，建立權衡電池容量衰退的能量管理策略。通過Pareto非劣目標域選取合適的權重因子，將多目標優化問題轉化為單目標問題，采用動態規劃算法獲得權重系數全局最優解，通過權衡不同權重下的油耗和電池容量衰退程度選擇最優權重系數。在燃油消耗相當的情況下，當權重系數為0.9時，可有效抑制電池壽命的衰減速度。最后，通過在線等效油耗最小策略仿真與在同一權重下的動態規劃解進行比較來驗證其有效性。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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圖 1 電池循環次數與放電深度的關系
Figure 1. Relationship between the number of battery cycles and the depth of discharge
圖 4 不同充電截止電壓下的電池容量衰退
Figure 4. Battery capacity degradation at different charge cutoff voltages
圖 5 電池SOC、溫度和嚴重程度系數關系圖
Figure 5. Chart of battery SOC, temperature, and severity coefficient
圖 6 不同溫度下的嚴重程度系數關系圖。（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 ℃
圖 7 考慮電池容量衰退的權衡控制策略DP求解流程圖
Figure 7. Solution flow chart of the trade-off control strategy DP considering battery capacity decline
圖 8 7個US06工況下不同權重時的DP解
Figure 8. DP solutions of seven US06 operating conditions with different weights
圖 9 不同權重下的SOC曲線。（a）車速示意圖；（b）SOC變化曲線
Figure 9. SOC curves under different weights: (a) speed diagram; (b) SOC change curve
圖 10 不同權重下的仿真結果。（a）充放電倍率；（b）嚴重程度系數
Figure 10. Simulation results under different weights: (a) charge and discharge ratio; (b) severity coefficient
圖 11 不同權重下的仿真結果圖。（a）電機轉矩；（b）發動機轉矩
Figure 11. Simulation results under different weights: (a) motor torque; (b) engine torque
圖 14 不同方法下的仿真結果對比圖。（a）發動機轉矩；（b）電機轉矩對比
Figure 14. Comparison diagram of simulation results under different methods: (a) engine torque; (b) motor torque comparison
圖 13 不同方法下的仿真結果對比圖。（a）充放電倍率；（b）嚴重程度系數
Figure 13. Comparison figure of simulation results under different methods: (a) charge and discharge ratio; (b) severity coefficient
表 1 7個US06工況下的仿真結果
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 
下載: 導出CSV
表 2 不同方法下的仿真結果對比
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
下載: 導出CSV
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