An Energy Management Strategy of Power-Split Hybrid Electric Vehicles Using Reinforcement Learning

Abstract

With the rapid development of science and technology, the automobile industry is also gradually expanding due to which energy security and ecological security are seriously threatened. This paper was aimed at studying the energy organization strategy of power-split hybrid electric vehicles based on a reinforcement learning algorithm. A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by using a planetary gear set to split and combine the power generated by electric machines and a combustion engine. This improves the fuel economy to some extent. However, to increase the fuel economy to a greater extent. This study primarily introduces the hybrid electric vehicle’s structure and presents a reinforcement learning-based management of energy approach for hybrid electric vehicles. It constructs the vehicle power model of HEV and Markov probability transfer model, then designs the energy control strategy based on reinforcement learning, and finally compares it with the energy control strategy based on PID (Proportional-Integral-Derivative). Using MATLAB/Simulink, the cycle conditions of NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure) are selected to carry out simulation experiments. The energy management technique suggested in this study, which is based on reinforcement learning, may efficiently enhance the usage rate of automotive gasoline. The replication results show that the fuel consumption per 100 km (kilometers) based on reinforcement learning management strategy is 4.6% and 2.7% lower than the PID management strategy under two working conditions. The economy of fuel of the hybrid electric vehicle is also effectively improved.

A Comprehensive Review on Smart Electromobility Charging Infrastructure

“…Additionally, ref. [161] investigated the use of reinforcement learning algorithms in the energy management strategy of powersplit hybrid EVs, highlighting the relevance of reinforcement learning in optimising energy utilisation in EVs. These reinforcement learning studies collectively illustrate the potential of reinforcement learning techniques to optimise EVs' performance, energy utilisation, and longevity.…”

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

“…SOC optimization or fuel reduction DDPG [5], DRL [40], DP [41], RL [42,43], DP, NN-based EMS [44], LTV-SMPC and PMP-stochastic MPC [45] Projected interior point method [3], LQP [45] DRL, rule-based, DDPG [40], Gaussian mixture model, SDP [41], QL, MPC [42], WF2SLOA [46], C/GMRES, BO [18], LQP, MPC, PMP [45] Hierarchical EMS [5], Hybrid EMS with torque split between the ICE and ESS [46], MPC EMS with non-linear losses [3] 16.34% of fuel savings [5], fuel economy improvement by 0.55% [40], LTV-SMPC and PMP-SMPC increase fuel economy by 8.79% and 14.42% respectively Prediction LSTM [5], Markov chain and LSTM [45] Power split with NN-based EMS [44] Speed [5,40,42] Prediction of mode and power split 2% higher compared to DP [44] Real-time power distribution MPC [5,42], C/GMRES [47] Polynomial fitting approx.…”

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

“…Value estimation [106] parallel PHEV τ engine , n gear discrete combined SARSA [107] FC PHEV P FC , weight of penalty on P bat discrete continuous Q-learning (table-based) [49][50][51]70,76,77,81,82] parallel HEV P x (x = EM or engine) discrete continuous [46,71,85,86,108,109] power-split HEV P bat [66] power-split HEV τ engine , ω engine [52,[56][57][58][59]67,69,78,92] series HEV P engine [84,94,95] battery-UC EV i bat…”