Analytical Solution to Equivalent Consumption Minimization Strategy for Series Hybrid Electric Vehicles

Shafikhani, Iman; Åslund, Jan

doi:10.1109/tvt.2021.3059205

Cited by 21 publications

(7 citation statements)

References 28 publications

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“…24 The accumulated fuel consumed in online mode for the fuzzy and IT2TSK Fig. 25 Tracking response of the speed profile from 0 to 200 s the time from 40 to 120 s (with two different cases) proves the effectiveness of the proposed algorithm, where the BE works with the total energy when the required torque exceeds the acceptable range. Moreover, the motor is prevented from operating because SOC < SOCmin, when the required torque becomes less than the BE torque, the engine operates in full energy to charge the battery.…”

Section: Special Case 1: the Required Torque Is More Than The Be And ...mentioning

confidence: 83%

“…Moreover, adjustment of the equivalent factor can guarantee that the electrical energy is gradually consumed throughout the trip and the SOC of battery meets the specified boundaries, as well. In [25], the equivalent coefficient can be determined based on the previous and expected driving conditions. Some important developments are addressed for HEVs depending on particle swarm optimization (PSO) for power management.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

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This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results.

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Section: Special Case 1: the Required Torque Is More Than The Be And ...mentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

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“…Based on this, the basic principle of the ECMS is to allocate part of the consumed energy to the battery so that the consumption or storage of this electrical energy is equivalent to the use or saving of a certain amount of fuel [110,111]. To obtain the control strategy that minimizes the instantaneous equivalent fuel consumption, a cost function for equivalent fuel consumption is defined, and the traversal of each instantaneous state quantity is solved [112].…”

Section: Equivalent Consumption Minimization Strategiesmentioning

confidence: 99%

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

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“…To be specific, as shown in (7), first the parameters of a multilayer perceptron (MLP) are optimized using the maximum likelihood estimation with mini-batch stochastic optimization using Adam [30]. After that the dynamic model is parameterized as 𝑀 (𝑠 , 𝑎 ), indicated in (8). By doing this, a local continuity can also be enabled which is beneficial for the following policy learning phase.…”

Section: B Data-driven Model-based Offline Rl Frameworkmentioning

confidence: 99%

“…Therefore, DP is often considered as a reference to compare with other strategies. Real-time optimization-based EMSs such as Equivalent Fuel Consumption Minimization Strategy (ECMS) [8] and Model Predictive Control Strategy (MPC) [9][10] do not rely on artificially set rules and can approximate the optimal solution under certain circumstances. Nevertheless, due to the current complex traffic conditions, the uncertainty of driver operation, and the computational complexity limitation of real controllers, the application of such EMSs still yields suboptimal behavior though they are being adopted by an increasing number of mass production HEVs recently.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Solution for Energy Management Strategy of Hybrid Electric Vehicles Based on Uncertainty-Aware Model-Based Offline Reinforcement Learning

Xiao

Zhang

et al. 2023

IEEE Trans. Ind. Inf.

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Energy management strategy (EMS) is the key technology to improving the fuel efficiency of hybrid electric vehicles (HEV). In recent years, the development of artificial intelligence has enabled tremendous advances by utilizing reinforcement learning (RL) for training and deploying deep neural network-based EMS. However, in contrast to the fields of deep learning such as computer vision and natural language processing which mainly rely on large-scale offline datasets, most RL-based policies must be trained online by trial-and-error with the initial performance being almost arbitrary. Such a paradigm is considered inefficient and unsafe for industrial automation and can only be used to tackle the EMS problems in the simulation world. Considering large historical interactive datasets are readily available in EMS domain, if a RL algorithm can be used to extract a policy purely offline from the prior collected dataset and improve upon data logging policy, the current issues including sample inefficiency, unsafe exploration, and simulation-to-real gap that prevent the widespread use of RL methods could be mitigated to a great extent. To this end, this paper presents a feasible algorithmic framework for model-based offline RL. Unlike vanilla RL approaches without any consideration against distributional shift, a data-driven dynamic model is built before the policy training using RL. After that, two techniques namely conservative MDP and state regularization are augmented which is proved to be effective against model over exploitation. A hardware-inthe-loop (HIL) test is used to evaluate the real-time performance and effectiveness of different strategies on a real controller, and it is found that the proposed naïve datadriven model-based offline RL algorithm can already solve the distributional shift problem inherent in vanilla RL to a large extent. Furthermore, by incorporating the guidance of uncertainty-awareness, a near optimal policy can be obtained by using only the dataset from a sub-optimal controller.

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Analytical Solution to Equivalent Consumption Minimization Strategy for Series Hybrid Electric Vehicles

Cited by 21 publications

References 28 publications

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

A Data-Driven Solution for Energy Management Strategy of Hybrid Electric Vehicles Based on Uncertainty-Aware Model-Based Offline Reinforcement Learning

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