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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (11): 2206-2214    DOI: 10.3785/j.issn.1008-973X.2019.11.019
Energy Engineering     
Battery-health conscious energy management optimization in plug-in hybrid electric vehicles
Xiao-hua ZENG(),Xing-qi WANG,Da-feng SONG*(),Nan-nan YANG,Zhen-wei WANG
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
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Abstract  

Considering the effect of battery life on the full-life-cycle cost of plug-in hybrid electric vehicle, a multi-objective optimization method of managing the input/output battery power was proposed, with the minimization of vehicle fuel consumption and battery’s health degradation as the optimization objective. The weight coefficient was introduced to transform the multi-objective optimization problem into a single targeted problem, then the dynamic programming (DP) algorithm was employed to achieve global optimization and the optimal weight coefficient was selected according to optimization results. A neural network controller based on optimization results of the optimal weight coefficient was trained and applied to control strategy, in order to solve the problem that the DP algorithm is slow in computation speed and needs working conditions in advance. Simulation results demonstrate that compared with the single-objective optimization of fuel consumption, the multi-objective optimization method can reduce the degradation rate of battery life by 13.5% while increasing the fuel consumption by only 0.5%. The proposed method lessens the degradation rate of battery life effectively with little increase in fuel consumption. The neural network-based control strategy can overcome the shortcomings of the DP algorithm and achieve the similar optimization effect with the DP algorithm, thus it has a good application prospects.

Key words： vehicle engineering      vehicle energy management optimization      dynamic programming      battery life      neural network     
Received: 05 September 2018      Published: 21 November 2019
CLC:  U 461  
Corresponding Authors: Da-feng SONG     E-mail: zeng.xiaohua@126.com;songdf@126.com
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Xiao-hua ZENG
Xing-qi WANG
Da-feng SONG
Nan-nan YANG
Zhen-wei WANG
Cite this article:

Xiao-hua ZENG,Xing-qi WANG,Da-feng SONG,Nan-nan YANG,Zhen-wei WANG. Battery-health conscious energy management optimization in plug-in hybrid electric vehicles. Journal of ZheJiang University (Engineering Science), 2019, 53(11): 2206-2214.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.11.019     OR     http://www.zjujournals.com/eng/Y2019/V53/I11/2206


考虑电池寿命的插电式混合动力汽车能量管理优化

考虑电池寿命对插电式混合动力汽车全寿命周期成本的影响，以综合燃油消耗和电池寿命衰减最小为目标开展电池充放电功率的多目标优化研究. 引入权重系数将多目标优化问题转化为单目标优化问题，采用动态规划（DP）算法求解实现全局最优，并根据优化结果选择最优权重系数. 为了解决动态规划算法运算速度慢、须预知工况的缺陷，以最优权重系数的优化结果训练神经网络控制器并将其应用于控制策略中. 仿真结果表明，与以油耗为单一目标的优化相比，多目标优化可使电池寿命衰减减少13.5%，而燃油消耗仅增加0.5%，在保证燃油经济性的同时有效减少电池寿命的衰减程度；基于神经网络的控制策略有效克服了动态规划算法的缺点并能达到与其相近的运算效果，具有较好的应用前景.


关键词： 车辆工程,  整车能量管理优化,  动态规划,  电池寿命,  神经网络 
Fig.1 Plug-in planetary hybrid electric system
动力源 参数 数值
发动机 最大功率/kW 80
最高转速/（r·min?1） 6 000
电机MG1 额定转速/（r·min?1） 6 000
额定扭矩/（N·m） 36
额定功率/kW 23
电机MG2 额定转速/（r·min?1） 3 000
额定扭矩/（N·m） 127
额定功率/kW 40
三元锂电池 额定电压/V 346
电池能量/（kW·h） 15
Tab.1 Main configuration parameters of vehicle
Fig.2 Engine universal characteristic curves
Fig.3 Engine optimal operation line
Fig.4 Battery equivalent internal resistance model
Fig.5 Global optimization results of different trip distances
Fig.6 DP solving process of power source working points
μ CE/L QH/(A·h) μ CE/L QH/(A·h)
0 3.76 196.9 0.25 4.14 139.0
0.05 3.77 189.2 0.30 4.44 120.0
0.10 3.78 174.3 0.50 6.73 23.1
0.15 3.81 170.2 0.70 7.12 13.1
0.20 3.98 154.2 1.00 7.18 11.0
Tab.2 Simulation results in 9×NEDC working condition
Fig.7 Global optimization results of different weight coefficients
Fig.8 Battery SOC profile in 9×NEDC working condition
Fig.9 Battery charge-discharge rate in 9×NEDC working condition
Fig.10 Comparison of vehicle power distribution under two weight coefficients with high SOC
Fig.11 Comparison of vehicle power distribution under two weight coefficients with low SOC
行驶里程 μ CE/L CH/(A·h)
8×NEDC 0 3.14 181.7
0.1 3.23 160.4
9×NEDC 0 3.76 196.9
0.1 3.78 174.3
10×NEDC 0 4.30 207.0
0.1 4.39 179.8
Tab.3 Optimization results of different trip distances
Fig.12 Battery SOC profile of different trip distances
Fig.13 Vehicle transmission system structure based on neural network energy management control
Fig.14 Prediction results of SOC change rate based on neural network
Fig.15 Simulation results in 8×NEDC working condition based on NN control strategy
行驶距离 模型 终态SOC CE/L QH/(A·h) t/s
8×NEDC DP 0.317 1 3.23 160.4 43 949
NN 0.313 5 3.26 162.5 1 527
10×NEDC DP 0.312 4 4.39 179.8 53 288
NN 0.311 2 4.43 181.3 1 822
Tab.4 Comparison of simulation results between DP and NN control strategies
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