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Hybrid electric vehicles (HEVs) are considered the most practical option for reducing emissions and fuel consumption of conventionally powered vehicles. Energy management strategies (EMSs) are the core technology of HEVs because of decreasing the cost of the system and limiting its negative effects. Equivalent consumption minimization strategy (ECMS) can achieve instantaneous optimal control and has attracted attention in recent years. In this study, an adaptive equivalent consumption minimization strategy (A-ECMS) based on driving cycle recognition is constructed for a parallel HEV. First, select the standard driving cycle and analyze its characteristic parameters. And then training learning vector quantization (LVQ) neural network-based driving cycle recognizer to achieve an average of 98% accuracy. At last, the optimal equivalent factor (EF) is selected for ECMS by recognizing the current driving cycle. It is jointly simulated and analyzed by AVL CRUISE and MATLAB/Simulink software under NEDC and CHTC-LT driving cycle. The results show that compared with the logic-based EMS, in the NEDC driving cycle the 100 km fuel consumption of A-ECMS decreases by 3.8%, and the battery state of charge (SOC) increases by 1.1%. In the CHTC-LT driving cycle, fuel economy improves by 3.6%, proving the superiority of the A-ECMS.

To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.

With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.