An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications

He, Haibo; Wang, Yunlong; Han, Ruoyan; Han, Mo; Bai, Yang; Liu, Qingwu

doi:10.1016/j.energy.2021.120273

Cited by 89 publications

(22 citation statements)

References 19 publications

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“…This improves the speed prediction and can handle any sudden traffic varioations. 150 Li et al proposed a DRL based EMS for a hybrid electric bus incorporating the terrain information.…”

Section: Integrated Emsmentioning

confidence: 99%

A review on hybrid source energy management strategies for electric vehicle

Kashyap

Castelino

2021

Int J Energy Res

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Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro-electronic interfaces, and energy management strategies (EMS). Significant issues of battery-powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated-based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC-DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. K E Y W O R D Sbattery, electric vehicle (EV), energy management strategies (EMSs), hybrid power source systems (HPSS), supercapacitor (SC)

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“…This improves the speed prediction and can handle any sudden traffic varioations. 150 Li et al proposed a DRL based EMS for a hybrid electric bus incorporating the terrain information.…”

Section: Integrated Emsmentioning

confidence: 99%

A review on hybrid source energy management strategies for electric vehicle

Kashyap

Castelino

2021

Int J Energy Res

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“…In the last several decades, within the exploitation of Artificial Intelligence (AI), ML applied to ADAS systems has started to be more and more common. Successful applications have been reported for vehicle velocity prediction [31,32], lane detection [33], ACC [34], ECO-ACC [35], lane changing detection [36] and EMS with V2x connectivity [37]. This widespread diffusion is mainly due to the algorithms' ability to properly predict and identify a wide range of behaviours.…”

Section: Predictionmentioning

confidence: 99%

“…The scenario influence on fuel economy performance for MPC-based controller was also considered by He et al [37]. The performance of frameworks that incorporated traffic information in Traffic Vehicle Dynamics (TVD) model environment were evaluated for different scenarios and the results showed a reduction in fuel consumption of roughly 13% as compared to the rule-based strategy.…”

Section: Tests For Performance Evaluationmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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“…On the other hand, the ECMS procedure is causal and results in a fast computation but optimum only at a given time instance and, hence, suboptimum in a global context. As a compromise that extracts the best features from these two approaches, the model predictive control (MPC) performs optimization over a moving finite horizon [32][33][34]. The MPC strategy may not be as fast as ECMS or as optimum as DP, but it is causal, fast enough and optimum over a practical range of a prediction horizon.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Ezemobi

Yakhshilikova

Ruzimov

et al. 2022

WEVJ

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The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

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An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications

Cited by 89 publications

References 19 publications

A review on hybrid source energy management strategies for electric vehicle

A review on hybrid source energy management strategies for electric vehicle

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

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