Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles

Ebbesen, Soren; Elbert, Philipp; Guzzella, Lino

doi:10.1109/tvt.2012.2203836

Cited by 241 publications

(94 citation statements)

References 26 publications

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“…Five rules were written for the controller (16,17,18,19,20). It manipulates the on/off state of the ICE in function of the required energy by RCV and the state of charge of the energy storage system.…”

Section: Results Analysismentioning

confidence: 99%

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Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-This paper presents a new methodology to estimate the fuel economy in a hybrid electric refuse collector vehicle (RCV). This methodology is based on determinating the fuel consumption in a conventional RCV using real routes, through a quasi-static model that incorporates a mathematical modeling of each component of the powertrain, including the longitudinal dynamic of the conventional RCV. Moreover, a classification of different operational modes of a conventional RCV using real routes is proposed to determinate the required energy that must be provided by the hybrid electric counterpart. The optimal sizing of the energy storage system for the hybrid electric powertrain is presented in order to estimate the fuel consumption by using a real route. The error in the estimation of fuel consumption in a conventional RCV is less than 1.5% in the worst case scenario. The energy storage system sizing allows a 8.62% reduction in the hybrid electric refuse collector vehicle's fuel consumption, by considering 10% of hybridization.

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Section: Results Analysismentioning

confidence: 99%

“…The transmission is an important component of the powertrain, due to the transformation that is performed from torque and angular speed of the ICE to a determined torque (5) and the angular speed (6), both defined by the gear ratio (γ) [18], [19].…”

Section: Quasi-static Model Of a Refuse Collection Vehiclementioning

confidence: 99%

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…The commonly-used health models of lithium-ion batteries can be classified into two categories, electrochemical models [21] and empirical models [17]. Electrochemical models that provide detailed performance of the battery are helpful to understand the principle of battery aging, but the computational loads of these models are too heavy because of the intrinsic complexity requiring chemical and physical knowledge [26], such as active materials and electrolytes, etc.…”

Section: Battery Modelmentioning

confidence: 99%

“…Additionally, in order to reduce the total daily consumption of PHEVs, a new convex programming is proposed by Hu et al [16] to optimize charging, on-road power management and battery degradation mitigation. In [17] the state of battery health (SOH) is also taken as a state variable in the control problem, and two separate controllers, SOC controller and SOH controller, are designed to maintain SOC and SOH in the desired states. A hybrid energy storage system with an ultracapacitor is usually considered to prolong battery life.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

et al. 2017

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This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.Keywords: plug-in hybrid electric vehicle (PHEV); battery health; energy management strategy; stochastic dynamic programming (SDP); particle swarm optimization (PSO)

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“…In addition, a great deal of attention has been paid towards the influence of charging patterns on battery life for plug-in EVs [21,22]. In [23], the proposed strategy not only seeks to minimize fuel consumption while maintaining the SOC of batteries within reasonable bounds but also to minimize wear of the batteries by penalizing the instantaneous battery usage.…”

Section: Introductionmentioning

confidence: 99%

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

et al. 2015

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Abstract:The increasing number of electric vehicles (EVs) connected to existing distribution networks as time-variant loads cause significant distortions in line current and voltage. A novel EV's intelligent integrated station (IIS) making full use of retired batteries is introduced in this paper to offer a potential solution for accommodating the charging demand of EVs. It proposes the concept of generalized energy in IIS, based on the energy/power flow between IIS and EVs, and between IIS and the power grid, to systematically evaluate the energy capacity of IIS. In order to derive a unique and satisfactory operation mode, information from both the grid (in terms of load level) and IIS (in terms of its energy capacity and EVs battery charging/exchanging requests) is merged. Then, based on the generalized energy of different systems, a novel charging/discharging control strategy is presented and whereby the operating status of the grid and energy capacity of IIS are monitored to make reasonable operation plans for IIS. Simulation results suggest that the proposed IIS offers peak load shifting when EV battery charging/exchanging requests are satisfied compared to existing charging stations. OPEN ACCESSEnergies 2015, 8 4189

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Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles

Cited by 241 publications

References 26 publications

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

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