Comprehensive Control Strategy of Fuel Consumption and Emissions Incorporating the Catalyst Temperature for PHEVs Based on DRL

Guangli Zhou, Fei Huang, Wenbing Liu, Chunling Zhao, Yangkai Xiang and Hanbing Wei ()
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Guangli Zhou: China Road and Bridge Engineering Co., Ltd., Beijing 100022, China
Fei Huang: China Road and Bridge Engineering Co., Ltd., Beijing 100022, China
Wenbing Liu: China Road and Bridge Engineering Co., Ltd., Beijing 100022, China
Chunling Zhao: School of Mechatronic &Vehicle Engineering, Jiaotong University, Chongqing 400074, China
Yangkai Xiang: School of Mechatronic &Vehicle Engineering, Jiaotong University, Chongqing 400074, China
Hanbing Wei: School of Mechatronic &Vehicle Engineering, Jiaotong University, Chongqing 400074, China

Energies, 2022, vol. 15, issue 20, 1-18

Abstract: PHEVs (plug-in hybrid electric vehicles) equipped with diesel engines have multiple model transitions in the driving cycle for their particular structure. The high frequency of start–stop of a diesel engine will increase fuel consumption and reduce the catalytic efficiency of SCR (Selective Catalyst Reduction) catalysts, which will increase cold start emissions. For comprehensive optimization of fuel consumption and emissions, an optimal control strategy of PHEVs that originated from the PER-TD3 algorithm based on DRL (deep reinforcement learning) is proposed in this paper. The priority of samples is assigned with greater sampling weight for high learning efficiency. Experimental results are compared with those of the DP (dynamic programming)-based strategy in HIL (hardware in loop) equipment. The engine fuel consumption and NO X emissions were 2.477 L/100 km and 0.2008 g/km, nearly 94.1% and 90.1% of those of the DP-based control strategy. By contrast, the fuel consumption and NOx of DDPG (Deep Deterministic Policy Gradient)-based and TD3(Twin Delayed Deep Deterministic Policy Gradient) -based control strategy were 2.557, 0.2078, 2.509, and 0.2023, respectively. By comparative results, we can see that the comprehensive control strategy of PHEVs based on the PER-TD3 algorithm we proposed can achieve better performance with comparison to TD3-based and DDPG-based, which is the state-of-the-art strategy in DRL. The HIL-based experimental results prove the effectiveness and real-time potential of the proposed control strategy.

Keywords: PHEVs; deep reinforcement learning; control strategy; PER-TD3 algorithm; catalyst temperature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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