With the increasing deployment of fuel cell systems in heavy-duty applications, high operating cost remains a critical challenge, with performance inconsistency in multi-stack architectures being a major contributing factor. This study presents a novel bi-level energy management strategy tailored for heavy-duty multi-stack fuel cell power systems. The upper-layer framework employs a model predictive control-based multi-objective optimization to minimize operating cost, while the lower-layer framework optimizes power allocation across fuel cell stacks to mitigate performance deviations and enhance consistency. The simulation results demonstrate that, across an 1800s driving cycle, the proposed Cost-Consistency-oriented energy management strategy achieves an 11.25% reduction (1.75μV) in voltage degradation for aged stack compared to healthy stack. Furthermore, compared to two benchmark methods, the proposed strategy reduces power fluctuations in aged stacks by 20.4% and 5.5% respectively, while achieving operating cost reductions of 1.3% and 20.2%. These results validate the bi-level architecture's capability in simultaneously optimizing economic feasibility and performance consistency for multi-stack fuel cell systems, which provides a scalable solution for heavy-duty applications.

Under complex driving scenarios, power-allocating mismatches among energy sources within hybrid propulsion system of fuel cell heavy trucks could lead to energy waste and lifetime degradation of vehicle. In order to improve vehicular economy and durability, a model predictive control-based energy management strategy with a novel computation-friendly frequency-decoupling optimization solver is proposed. The solver can quickly approximate the optimization problem, which helps to simplify the solution time for non-linear problems. Within the proposed solver, the ‘Haar’ wavelet transform is introduced to decouple the power demand change rate series at three wavelet levels. The sensitivity factor is then adjusted in real time by driving state recognition and used to calculate the output power of the fuel cell system to solve the energy management optimization problem based on model predictive control. The proposed strategy is verified by the Processor-in-the-Loop testing based on a RTU-BOX platform. Testing results indicated that under MPC-based framework, the proposed multi-mode frequency decoupling method showed a similar performance in power allocation versus a quadratic programming solver but saves up to 58.95% of computation time. Moreover, the proposed strategy outperforms two benchmark strategies in terms of total operating cost by 22.86% on average, indicating its effectiveness in terms of efficiency and durability enhancement.

Highly accurate speed prediction technology is of great significance for online implementation of energy management strategy (EMS). Due to the complex and variable driving conditions, the accuracy of conventional speed prediction method is yet to be improved by driving pattern adaption. This paper proposes a vehicle speed prediction method based on long- and short-term memory neural network (LSTM) with driving pattern recognition and integrates it in energy management framework based on model predictive control (MPC). First, similar samples belonging to the same driving pattern are selected offline to train a more efficient and targeted LSTM. Then an online speed prediction algorithm based on driving pattern recognition is proposed. The results show that root mean square error (RMSE) of the whole driving cycle is reduced by 39% compared to conventional LSTM. Meanwhile, fuel economy and fuel cell system (FCS) durability are improved, which proves the effectiveness of the proposed method. And the real-time applicability of the proposed predictive EMS is verified.

As the different driving modes have a great influence on the performance of fuel cell hybrid electric heavy trucks, it's vital to study a driving mode-consensus energy management strategy. In order to improve the economy and durability of the system and further enhance the real-time performance of energy management strategy (EMS) in different driving conditions, in this paper, a multi-dimension fuzzy control energy management strategy based on a model predictive control framework (MPC) was raised. In the offline phase, the proposed strategy selects the appropriate parameters for each mode by an evaluation function which give full consideration to fuel cell hydrogen consumption, battery consumption, and degradation of fuel cells and batteries. Meanwhile, in the online phase, the proposed strategy dynamically matches the weighting coefficients of objective functions and optimal Markov transfer probability matrix (TPM) by multi-dimensional fuzzy optimization of parameters to better adapting to changing driving patterns overtime. The simulation results demonstrate that compared to traditional MPC-EMS proposed strategy can maintains a more stable battery SoC and reduce the total consumption function by 8.75%, hydrogen consumption by 22.65%, batteries degradation functions by 6.56% and the fuel cells degradation functions by 47.22% under driving cycle1. Moreover, the robustness of the proposed method is verified by testing it under two different driving cycles. The proposed method under CWTVC still shows better performance. Therefore, the proposed strategy can effectively improve the economy and durability of fuel cell hybrid electric heavy trucks system.