Research on Operating Condition Identification and Energy Control Strategy of Pure Electric Vehicle
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摘要: 针对纯电动汽车能量管理策略优化主要面向单一工况，提出包含BP神经网络工况识别控制的能量管理策略。以锂电池-超级电容复合电源纯电动汽车为研究对象，引入工况识别的复合电源纯电动汽车能量管理策略对整车经济性和蓄电池工作状态的影响。在MATLAB/Simulink平台下修建整车模型并进行综合测试工况仿真，结果表明：包含工况识别的能量控制策略能够准确识别行驶工况，相较于优化前的能量管理策略，蓄电池能量消耗下降2.81%，总能量消耗下降1341 kJ，两种能量源之间的能量分配更加合理，蓄电池工作状态得到进一步优化，有效提高整车经济性。Abstract: Aiming at the optimization of energy management strategy for hybrid electric vehicle with pure power mainly for a single operating condition and lack of adaptive capacity in practical application, an energy management strategy including back propagation neural network operating condition identification control was proposed. Taking lithium battery-supercapacitor composite power pure electric vehicle as the research object, this paper studies the influence of energy management strategies of composite power pure electric vehicle with operating condition recognition on the vehicle economy and battery working state. Modified the vehicle model under MATLAB / Simulink platform and carried out comprehensive test condition simulation. The results show that the energy control strategy including condition identification can accurately identify the driving condition. Compared with the energy management strategy before optimization, the energy consumption of the battery decreases. 2.81%, the total energy consumption decreased by 1341 kJ, the energy distribution between the two energy sources was more reasonable, the battery working condition was further optimized, and the vehicle economy was effectively improved.
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表 1 变量语言值
变量 语言值 Preq 极小（TS）、小（S）、中（M）、大（B）、
极大（TB）SOCbat 低（L）、中（M）、高（H） SOCuc 低（L）、中（M）、高（H） Kuc 极低（TS）、低（S）、中（M）、高（B）、
极高（TB）
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表 2 Kuc输出变量控制规则
输入变量 Preq TS S M B TB SOCbat
(SOCuc L)L TS S S M M M TS TS S S S H TS TS TS S S SOCbat
(SOCuc M)L TS S M M B M TS S S M B H TS S S M M SOCbat
(SOCuc H)L S S M B TB M TS S M B TB H TS S M M B
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表 3 中心样本
类别 平均车速/
（km·h−1）怠速
占比/%平均加速度/
（m·s−2）所属
工况时间段/
s1 12.3162 0 0.9644 1015 211 ~ 298 2 17.0735 34.94 1.8873 ARB02 916 ~ 1239 3 12.2261 29.22 1.2342 UDDS 1313 ~1367
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表 5 仿真数据对比
名称 采用工况识别 未采用工况识别 总能量消耗/kJ 30313 31654 超级电容输出能量/kJ 1817 1058 蓄电池SOC 0.6069 0.5788 蓄电池输出平均功率/W 4672 5016 蓄电池SOC差值/% 2.81 总能耗差值/kJ 1341
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