MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

Yang, Xiaohu; Yang, Rong; Tan, Sheng-Lan; Yu, Xionghou; Fang, Liang

doi:10.1177/09544070211041074

Cited by 11 publications

(8 citation statements)

References 40 publications

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“…However, SGA only had one population per iteration for optimal individual selection, which was insufficient to assure loss and repeated selection. Through MPGA, the literature ( Yang et al, 2022 ) significantly enhances the algorithm’s speed. In addition, MPGA’s final iteration findings were consistent, indicating that MPGA’s target parameter search was more accurate and exhaustive.…”

Section: Discussionmentioning

confidence: 99%

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Inversion method of particle size distribution of milk fat based on improved MPGA

Ding

Zhou

et al. 2022

Front. Bioeng. Biotechnol.

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Milk fat’s particle size and distribution not only affect product quality, but also have great impacts on food safety in the economy and society. Based on total light scattering method, this paper has studied the inversion method of particle size distribution under dependent mode condition by combining multi-population genetic algorithm (MPGA) with Tikhonov smooth function. It has minimized the influence from light-absorb medium to improve the inversion accuracy. The approach introduces Tikhonov smooth function and apparent optical parameters to build an objective fitness function and weaken the ill condition of the particle size inversion equation. It also introduces multi-population genetic algorithm to solve the premature convergence of genetic algorithms. The results show that the relative error of the milk fat simulation solution with a nominal diameter is -3.52%, which meets the national standard of ±8% and better than the relative error of -5.01% of the standard genetic algorithm. Thus, the improved MPGA can reconstruct particle size distribution, with a good reliability and stability.

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Section: Discussionmentioning

confidence: 99%

“…After the objective function was constructed by Tikhonov ( Yang, 2016 ; Wang et al, 2019 ; Zhou et al, 20212021 ) regularization, MPGA ( Guo et al, 2020 ; Shi et al, 2021 ; Yang et al, 2022 ) was introduced to find the minimum value.…”

Section: Methodsmentioning

confidence: 99%

Inversion method of particle size distribution of milk fat based on improved MPGA

Ding

Zhou

et al. 2022

Front. Bioeng. Biotechnol.

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“…Besides, the gear ring (R1) of PG1 is connected to the planet carrier (C2) of PG2, and together they serve as the power output of the transmission. Ignoring the internal losses of the system, the coupling relationship of each component satisfies the following equation 35…”

Section: Hybrid Powertrain Modelingmentioning

confidence: 99%

Deep reinforcement learning-based energy management strategies for energy-efficient driving of hybrid electric buses

Wang

Yang

Huang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The fuel economy of hybrid electric vehicles is inextricably linked to the energy management strategy (EMS). In this study, a practicality-oriented learning-based EMS for a power-split hybrid electric bus (HEB) is presented, which combines the generative adversarial imitation learning (GAIL) and deep reinforcement learning (DRL). Considering the regular and fixed route of the HEB, optimal control samples that are not affected by the cost function can be obtained by the boundary-line dynamic programing (B-DP) method. On this basis, the samples are dynamically fitted using the GAIL method to inverse derive the reward function that can explain the B-DP control behavior. Concurrently, the proximal policy optimization DRL algorithm will regulate the energy distribution of the vehicle in real time and continuously optimize the energy management capability based on the reward feedback from GAIL. Finally, the feasibility and effectiveness of the proposed EMS is verified by simulation. The results show that the proposed strategy exhibits near-optimal control performance under both the China heavy-duty commercial vehicle cycle-bus and China city bus cycle.

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“…In [28], authors proposed a ECMS strategy where the equivalence factor is adapted to driving condition of the bus by means of neurofuzzy inference techniques. Similarly, in [29], the equivalence factor of the ECMS in a HEV bus is adapted by means of genetic algorithms. A model predictive control based on Pontryagin's minimum principle is applied in [30] to a hybrid electric bus, with a fuel consumption that exceed the theoretical optimum of 6% according to simulation results.…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Hybrid Electric Urban Bus by Off-Line Dynamic Programming Optimization and One-Step Look-Ahead Rollout

et al. 2022

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Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%.

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MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

Cited by 11 publications

References 40 publications

Inversion method of particle size distribution of milk fat based on improved MPGA

Inversion method of particle size distribution of milk fat based on improved MPGA

Deep reinforcement learning-based energy management strategies for energy-efficient driving of hybrid electric buses

Energy Management of Hybrid Electric Urban Bus by Off-Line Dynamic Programming Optimization and One-Step Look-Ahead Rollout

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