Jibin Yang

The coupled effects of power distribution and stack temperature strongly influence hydrogen economy, durability, and operating stability in proton exchange membrane fuel cell (PEMFC) vehicles. This study proposes an integrated energy–thermal management strategy based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm for a PEMFC hybrid bus. The energy management agent regulates PEMFC power using vehicle demand, battery state of charge, and stack temperature, while the thermal management agent controls coolant and air mass flow rates using temperature errors and commanded PEMFC power. Under the unseen CHTC-C cycle, MADDPG reduces equivalent hydrogen consumption by 0.49% and 1.79% compared with SAC and DDPG, respectively, while remaining 2.92% above the offline dynamic programming benchmark. Under an independent real-world bus cycle, MADDPG reduces the maximum stack outlet temperature deviation by 98.56% and 98.07%relative to SAC and MPC, respectively. Additional tests under ambient temperature and aging variations show bounded thermal responses without retraining, and HIL experiments confirm real-time execution at a 1 s control period. Overall, the proposed strategy improves energy economy, thermal regulation, adaptability, and real-time applicability through coordinated power–temperature information exchange.

Accurately estimating the battery State of Health (SOH) is crucial for the safe and reliable operation of electric vehicles. Based on the actual operating data of electric buses, this article proposes a battery SOH estimation method that can be applied to multiple operating conditions and indicators. Specifically, the complex operating conditions are simplified into charging and driving conditions through data preprocessing. Under charging conditions, combined with Coulomb counting and incremental capacity analysis methods, a battery SOH estimation model of capacity indicators based on the Bayesian optimization bidirectional gated recursive unit model (BO-BiGRU) is established. Under driving conditions, the adaptive forgetting factor recursive least squares method considering the influence of current is used to identify the battery internal resistance feature. In addition, two separate battery SOH estimation models are established: one for internal resistance indicators based on BO-BiGRU and another for power indicators derived from the actual operational data feature. Finally, a joint battery SOH estimation method considering temperature and different operating conditions is proposed based on the SOH estimation results of the three battery indicators. The verification results show that the average error of the battery SOH estimation method proposed in this article is less than 2%, which has better accuracy for actual vehicles.

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.