Real-time energy management based on ECMS with stochastic optimized adaptive equivalence factor for HEVs

Jiao, Xiaohong; Yang, Li; Xu, Fuguo; Yuan, Jing

doi:10.1080/23311916.2018.1540027

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…Here, using a genetic algorithm, an initial equivalent factor map was constructed and a fuzzy controller was used to correct the equivalent factor in an adaptive manner, while the reference SOC trajectory was determined using simplified DP. In [17], the stochastic optimization approach was used to estimate the equivalent factor in an average manner. Infinite-horizon stochastic DP was used to find an offline map of the equivalent factor, which can be implemented online as an ECMS according to the actual driving circumstances.…”

Section: B Literature Review : Dp and Machine Learning Based Approachesmentioning

confidence: 99%

“…While these DP-based approaches have the advantage of being able to use a global optimal solution, a disadvantage is that it is difficult to use these approaches for real-time vehicle controllers owing to the large computational load of DP, and the prediction of future driving information remains challenging. Even in the stochastic optimization approaches reported in [17], the transition probability should reflect the current driving cycle; thus, these approaches also have limitations as the DP method.…”

Section: B Literature Review : Dp and Machine Learning Based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

Lee

Cha

2021

IEEE Access

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Section: B Literature Review : Dp and Machine Learning Based Approachesmentioning

confidence: 99%

Section: B Literature Review : Dp and Machine Learning Based Approachesmentioning

confidence: 99%

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

Lee

Cha

2021

IEEE Access

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“…The crucial component of an electric scooter is the electric motor selection which at the end decides the amount of torque at the wheel that meets the requirement of tractive effort at various speeds [37][38][39]. Total vehicle weight is calculated including two passengers (driver and pillion), a hub motor, and a lithium-ion battery.…”

Section: Hub Motor Selectionmentioning

confidence: 99%

Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler

Kalyankar-Narwade

Kumar

Patil

2021

WEVJ

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Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, and energy optimization. Two power sources need to be synchronized skillfully for optimum energy utilization. A prominent variant of HEV is that it easily converts conventional scooters into parallel hybrids by “Through-the-Road (TTR)” architecture. This paper focuses on three switching control strategies of HEVs based on the state of charge, fuzzy logic, and neural network. Further, to optimize energy usage, all these control strategies are compared. Energy management control for the TTR model is developed with vehicle parameters in the Simulink environment and simulated using the “World Harmonized Motorcycle Test Cycle” (WMTC) drive cycle. The multivariable input model is presented with a fuzzy rule-based hybrid switching control. A similar system is also modeled with a neural network-based decision control and the observations are tabulated for the fuel economy and energy management. Simulation results show that the neural network-based optimization results in minimal energy consumption among all three hybrid operations.

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“…After the multi-objective problem is transformed into a single-objective problem, this article defines the fitness function, which simplifies the manufacturing cost of the whole vehicle power system to the cost of the engine and the motor. The following Equation (5) is obtained [11][12][13], cos t(X) = 849 + 12.236P emax + 10.888P mmax (5) In the formula, P emax and P mmax are the peak powers of the engine and the motor, respectively. Genetic algorithms are then used to optimize energy management strategies and power system parameters:…”

Section: Energy Management Strategy and Optimization Of Power System mentioning

confidence: 99%

“…For the instantaneous optimization of energy management strategy, Jiao et al proposed an adaptive equivalent fuel consumption minimum strategy (A-ECMS), which obtains the equivalent factor under current driving conditions based on the equivalent factor map in energy distribution. The fuel consumption is minimized throughout the driving route, and the battery state of charge (SOC) is kept within a reasonable range [5]. On the other hand, Zhang et al proposed an energy management strategy based on the minimum equivalent fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost

et al. 2020

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For the oil-electric-hydraulic hybrid power system, a logic threshold energy management strategy based on the optimal working curve is proposed, and the optimal working curve in each mode is determined. A genetic algorithm is used to determine the optimal parameters. For driving conditions, a real-time energy management strategy based on the lowest instantaneous energy cost is proposed. For braking conditions and subject to the European Commission for Europe (ECE) regulations, a braking force distribution strategy based on hydraulic pumps/motors and supplemented by motors is proposed. A global optimization energy management strategy is used to evaluate the strategy. Simulation results show that the strategy can achieve the expected control target and save about 32.14% compared with the fuel consumption cost of the original model 100 km 8 L. Under the New European Driving Cycle (NEDC) working conditions, the energy-saving effect of this strategy is close to that of the global optimization energy management strategy and has obvious cost advantages. The system design and control strategy are validated.

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Real-time energy management based on ECMS with stochastic optimized adaptive equivalence factor for HEVs

Cited by 16 publications

References 27 publications

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler

Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost

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