ITS based Predictive Intelligent Battery Management System for plug-in Hybrid and Electric vehicles

Abdul-Hak, Mohamad; Al-Holou, Nizar

doi:10.1109/vppc.2009.5289858

Cited by 14 publications

(5 citation statements)

References 14 publications

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“…Therefore, a two-scale DP is proposed to solve the mentioned problems (Gong et al, 2007;Gong et al 2008;Gong et al, 2009). Furthermore, the driving cycle prediction can be done by the new geographical systems like GPS, GIS, and traffic information system (Zhang et al, 2010;Abdul-Hak et al, 2009;Gong et al, 2008;Gong et al, 2007). (Moura et al, 2011) uses a stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle by means of Markov chains.…”

Section: Introductionmentioning

confidence: 99%

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

Fidalis¹,

S.Jincy.²,

Pushpa³

2019

IJRTER

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An online power management strategy (PMS) for plug-in hybrid electric vehicles is presented in this paper. This PMS with the capability to reduce both the fuel consumption and the emission, has simple mathematics and excludes a priori knowledge of the driving cycle. The only required information is the driving duration that can be estimated by the driver or by the vehicle information systems, so the proposed method can be easily implemented. Furthermore, an adaptive form of this PMS is presented and its performance is compared with other strategies. Using the online adaptive PMS method, the incoming driving cycle condition is predicted by the vehicle past conditions. In this paper, the engine fuel characteristics are linearized to several zones. At any instant, one of these zones is selected for the engine operation. In each zone, an optimal cost function is minimized for the fuel consumption and the emission reduction. Moreover, different cost functions are defined and used on various engines. Finally, the proposed PMS is simulated in the ADVISOR environment and compared with conventional method.

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Section: Introductionmentioning

confidence: 99%

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

Fidalis¹,

S.Jincy.²,

Pushpa³

2019

IJRTER

View full text Add to dashboard Cite

show abstract

“…Lin et al have found near‐optimal rules for a parallel HEV EMS by using DP, and improved the fuel economy and emissions performance of the vehicle. A two‐scale DP method is used for optimal EMS design in , where the power demand is calculated according to the prior knowledge from the trip, and the best battery depletion profile is derived using a simple model of the battery.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Route‐Based Energy Management Systems for Phevs

Taghavipour¹,

Vajedi²,

Azad³

et al. 2015

Asian Journal of Control

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Plug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.

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“…With the development of modern wireless communication technology and ground communications equipment, people take more and more attention on the driver assist driving system now. Because the driver assist driving system can make effective "communication" between the vehicle controller and driver, thus achieve to the economic-driving (Eco-driving) [1][2][3][4][5][6][7]. In 2009, Sindhura Mandava et al proposed an arterial velocity planning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Arterial Velocity Planning Based on Traffic Light Conditions for Driver Assist Driving System

Wen

Zhang

Tian

et al. 2014

AMM

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With the development of modern wireless communication technology, the driver assist driving system gets more and more attention from people, because it could receives many real-time information, such as traffic lights information, road information and so on. The arterial velocity planning algorithm based on traffic lights information makes vehicle more economically and efficiently, because it could help driver decrease the acceleration/ deceleration pattern and idling time. This paper creates 3 arterial velocity planning algorithms by analyzing the vehicle driving process, and the acceleration/deceleration parameters and driving time of these three algorithms are optimized. When the vehicle is driving, the arterial velocity planning algorithm gives the dynamic speed advice to the driver through driver assist driving system, ensures that the vehicle will cross the traffic light without coming to a stop. The results show that the fuel consumption savings for vehicles with arterial velocity planning is above 11% on a relatively smooth arterial road.

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ITS based Predictive Intelligent Battery Management System for plug-in Hybrid and Electric vehicles

Cited by 14 publications

References 14 publications

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

Fuel Consumption and Emission Reduction Power Management Strategy for Hybrid Electric Vehicles

A Comparative Analysis of Route‐Based Energy Management Systems for Phevs

Arterial Velocity Planning Based on Traffic Light Conditions for Driver Assist Driving System

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