基于改進(jìn)PPO算法的混合動(dòng)力汽車能量管理策略

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中圖分類號：U469.72 文獻(xiàn)標(biāo)識碼：A DOI：10.7535/hbkd.2025yx03001

Energy management strategy for hybrid electric vehicle based on improved PPO algorithm

MA Chao， SUN Tong，CAO Lei，YANG Kun， HU Wenjing （Collgeof TransportationandVehicle Engineering，Shandong Universityof Technology，Zibo，Shandong 2550oo，China

Abstract：Inorder toimprovetheeconomyof power-split hybrid electric vehicle（HEV），alongitudinal dynamics modelof the entire HEVvehicle was established，and an energy management strategy（EMS）basedonstrategy entropy optimization withanimproved proximalpolicyoptimization（PPO）algorithmwasproposed.Thealgorithmicframework wassimplified by employing an experiencepoling mechanism based on traditional PPO algorithm，andonlyone deep neural network was used forinteractivetrainingandupdating toreducethecomplexityofparametersynchronizationinthepolicynetwork.Inorderto efectively explore theenvironmentand learnmoreeficientstrategies，thestrategyentropywasadded tothelossfunctionto promotetheintellgencetostrikeabalancebetweenexplorationandutilizationandtoavoid prematureconvergenceofstrategies tolocaloptimal solutions.TheresultsshowthattheEMS basedontheimprovedPPOalgorithmwith single-policynetwork maintains thestateof charge（SOC）of the battry more efectivelythantheEMS basedonthedual-strategy network PPO under both UDDS and NEDC driving cycle. Additionally，the equivalent fuel consumption is reduced by 8.5% and 1.4% ， respectively，achieving energy-saving efectscomparable to the EMS basedon the dynamic programming （DP）algorithm. The proposed improvedPPOalgorithmcaneffectivelyenhance the fueleconomyof hybridvehiclesand provideareference for the design and development of EMS for hybrid vehicles.

Keywords：vehicleenginering；hybrid electric vehicle；energy managementstrategy；deepreinforcementlearning；proximal policy optimization

汽車保有量的迅速增長，不僅給國家能源安全帶來巨大的挑戰(zhàn)，環(huán)境污染問題也日益凸顯[1]，而混合動(dòng)力汽車（hybrid electric vehicle，HEV）被認(rèn)為是減排的重要角色。（剩余12888字）