Adaptive Energy Management System Based on a Real-Time Model Predictive Control With Nonuniform Sampling Time for Multiple Energy Storage Electric Vehicle

Gomozov, Oleg; Trovão, João P.; Kestelyn, Xavier; Dubois, Maxime R.

doi:10.1109/tvt.2016.2638912

Cited by 103 publications

(48 citation statements)

References 36 publications

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“…Moreover, pattern/route recognition is also applied to update cost equivalent factors of PMP and ECMS [59,65]. Adaptation of the computing rate is widely applied to MPC [72] and adaptive/stochastic DP [34,73]. More focus on some methods of Region-II is in the sequel.…”

Section: Integration To Power Management Methodsmentioning

confidence: 99%

“…However, another concept is to adapt the sampling time of MPC according to the prediction horizon i.e., small sampling time for short-time prediction and inversely. This decoupling concept offers better control of dynamical parameters and longer horizon prediction [72].…”

Section: Insight Into Optimal Real-time Methodsmentioning

confidence: 99%

“…Moreover, MPC can perform at a non uniform sampling time where small samples allow better stability, continuity, and smoothness of power references and large samples (relaxation) is applied for long term planning of the energy sources [71,72].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Section: Integration To Power Management Methodsmentioning

confidence: 99%

Section: Insight Into Optimal Real-time Methodsmentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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“…These specific situations are very demanding for battery-based powertrain systems. 2 Moreover, the battery has a relatively low power density compared with other energy storage devices. Therefore, it needs a large number of cells to meet the power required from a traction motor, increasing the battery size and the vehicle mass.…”

Section: Introductionmentioning

confidence: 99%

Energy storage and control optimization for an electric vehicle

et al. 2018

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Summary Two big issues involving electric vehicles are energy supply and power management control. To deal with the energy supply problem, this paper proposes the application of a hybrid energy source system, composed of battery pack and ultracapacitor bank. The power management control between the energy supplies was defined by a fuzzy logic with inference rules optimized through genetic algorithm. The genetic algorithm optimizes lower and upper limits of membership functions aiming to reduce the hybrid energy source system total mass while maximizing the electric vehicle drive range and performance. Through the Pareto frontier, we found the best trade‐off solution.

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“…Lithium-ion battery storage system plays a vital role in electric vehicle (EV) applications [1][2][3][4][5]. Portable lithium batteries are commonly used for their high energy density and low cost.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

Gao

Chin

Toh

et al. 2017

Journal of Advanced Transportation

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This paper presents an integrated state-of-charge (SOC) estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4) battery power system. The strong tracking cubature extended Kalman filter (STCEKF) gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

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Adaptive Energy Management System Based on a Real-Time Model Predictive Control With Nonuniform Sampling Time for Multiple Energy Storage Electric Vehicle

Cited by 103 publications

References 36 publications

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Energy storage and control optimization for an electric vehicle

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

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