Hybrid transmissions for the Optimisation of the efficiency of Internal Combustion Engines

Concli, Franco; Nezzi, Chiara

doi:10.2495/tdi-v4-n4-321-329

Cited by 1 publication

(3 citation statements)

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“…It is based on an investigation of the fuel consumption of a representative powertrain in different driving cycles by means of innovative system architectures. 6 Compared with the energy management strategy (EMS) research, there is less research and literature on hybrid vehicle coordinated control. The internal energy distribution of hybrid electric systems, which are rarely involved in conventional vehicles, has become the most urgent problem to be solved.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the iterative process, the TPM under the current driving condition is constantly changing. To generate a TPM that is closer to the actual situation and prevent excessive fluctuation, the TPM can be rewritten as shown in (6). This formula is used in the online part of the EMS.…”

Section: Energy Management Control Strategy Tpm and Kl Divergencementioning

confidence: 99%

“…It is based on an investigation of the fuel consumption of a representative powertrain in different driving cycles by means of innovative system architectures. 6…”

Section: Introductionmentioning

confidence: 99%

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A real-time energy management strategy for off-road hybrid electric vehicles based on the expected SARSA

Han

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the power performance and fuel economy and reduce the online computation time of hybrid electric vehicles (HEVs) in off-road driving environments, a real-time energy management strategy based on the expected state-action-reward-state’-action’ (SARSA) algorithm is proposed. This strategy achieves the control effect through coordinated control of the engine generator set (EGS) and the energy distribution between the battery and the EGS. The driving environment is expressed as a transition probability matrix (TPM) of the electric power demand, and the optimal control problem of energy management is solved offline by the expected SARSA algorithm to obtain the control laws. Then, the Kullback-Leibler (KL) divergence rate is used as the evaluation index of the switching control strategy, and these control laws are used online to realize real-time control. Control strategies based on traditional reinforcement learning (RL), stochastic dynamic programming (SDP), and dynamic programming (DP) are considered benchmark strategies to verify the effectiveness of the proposed energy management strategy based on the expected SARSA. The fuel economy is improved by a maximum of 13.2%, and the elapsed time is reduced by more than 99.8% compared with the values achieved with the benchmark algorithms. These results show that the proposed strategy has flexible adaptability to complex and changeable off-road conditions without prior knowledge of the whole driving cycle.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Energy Management Control Strategy Tpm and Kl Divergencementioning

confidence: 99%