mlsanityData to 14 Oct 2026

Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain

arXiv 2508.04874 ↗First posted 6 Aug 2025eess.SY cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

As hybrid electric vehicles (HEVs) gain traction in heavy-duty trucks, adaptive and efficient energy management is critical for reducing fuel consumption while maintaining battery charge for long operation times. We present a new reinforcement learning (RL) framework based on the Soft Actor-Critic (SAC) algorithm to optimize engine control in series HEVs. We reformulate the control task as a sequential decision-making problem and enhance SAC by incorporating Gated Recurrent Units (GRUs) and Decision Transformers (DTs) into both actor and critic networks to capture temporal dependencies and improve planning over time. To evaluate robustness and generalization, we train the models under diverse initial battery states, drive cycle durations, power demands, and input sequence lengths. Experiments show that the SAC agent with a DT-based actor and GRU-based critic was within 1.8% of Dynamic Programming (DP) in fuel savings on the Highway Fuel Economy Test (HFET) cycle, while the SAC agent with GRUs in both actor and critic networks, and FFN actor-critic agent were within 3.16% and 3.43%, respectively. On unseen drive cycles (US06 and Heavy Heavy-Duty Diesel Truck (HHDDT) cruise segment), generalized sequence-aware agents consistently outperformed feedforward network (FFN)-based agents, highlighting their adaptability and robustness in real-world settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 21 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

5 of 21 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  3. 2021Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPscited by 122
  4. 2025Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returnscited by 16
  5. 2020Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehiclecited by 3

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  2. 2026Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
  3. 2021A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
  4. 2026Driving Condition-Aware Multi-Agent Integrated Power and Thermal Management for Hybrid Electric Vehicles
  5. 2024Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approach
  6. 2022Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
  7. 2024Shared learning of powertrain control policies for vehicle fleets
  8. 2024Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning