基于改进PPO算法的混合动力汽车能量管理策略

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中图分类号：U469.72 文献标识码：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

汽车保有量的迅速增长，不仅给国家能源安全带来巨大的挑战，环境污染问题也日益凸显[1]，而混合动力汽车（hybrid electric vehicle，HEV）被认为是减排的重要角色。（剩余12888字）