Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control

Moura, Scott J.; Stein, Jeffrey L.; Fathy, Hosam K.

doi:10.1109/tcst.2012.2189773

Cited by 200 publications

(126 citation statements)

References 40 publications

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“…Finally, (16) is a side reaction overpotential constraint. It models when unwanted side reactions occur, such as lithium plating [14], [15] when U s = 0V [12], and can also model accelerated growth of the solid/electrolyte interphase film formation [16], [17] when U s = 0.4V [17], [18].…”

Section: B Constraintsmentioning

confidence: 99%

“…The output variables y = [c ± s , c e , T, η s ] T must exist in set Y, characterized by inequalities (13)- (16). The goal is to find the maximum value of β which maintains the state in O…”

Section: Modified Reference Governor (Mrg) Designs a Nonlinear mentioning

confidence: 99%

“…where F, G are vectors that incorporate the constraints (13)- (16) and depend on x(t) and I r (t) as follows…”

Section: B Linear Mrg Designmentioning

confidence: 99%

“…This distribution elucidates how the MRG allows for greater charge power (negative power) than the VO controller. Table II presents the mean power (discharge and charge) benefit percentage results from using the MRG over the VO controller for the US06x3 drive cycle as well as five other automotive drive cycles (UDDSx2, SC04x4, LA92x2, DC1, DC2) from [16]. In the most aggressive drive cycle (US06x3) the MRG achieves 11.03% and 150.61% more discharge and charge power, respectively, over the VO controller in the 1.4I case.…”

Section: A Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Enhanced Performance of Li-Ion Batteries via Modified Reference Governors and Electrochemical Models

Pérez

Shahmohammadhamedani

Moura

2015

IEEE/ASME Trans. Mechatron.

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Abstract-This paper examines reference governor (RG) methods for satisfying state constraints in Li-ion batteries. Mathematically, these constraints are formulated from a first principles electrochemical model. Consequently, the constraints explicitly model specific degradation mechanisms, such as lithium plating, lithium depletion, and overheating. This contrasts with the present paradigm of limiting measured voltage, current, and/or temperature. The critical challenges, however, are that (i) the electrochemical states evolve according to a system of nonlinear partial differential equations, and (ii) the states are not physically measurable. Assuming available state and parameter estimates, this paper develops RGs for electrochemical battery models. The results demonstrate how electrochemical model state information can be utilized to ensure safe operation, while simultaneously enhancing energy capacity, power, and charge speeds in Li-ion batteries.

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Section: B Constraintsmentioning

confidence: 99%

Section: Modified Reference Governor (Mrg) Designs a Nonlinear mentioning

confidence: 99%

“…where F, G are vectors that incorporate the constraints (13)- (16) and depend on x(t) and I r (t) as follows…”

Section: B Linear Mrg Designmentioning

confidence: 99%

Section: A Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Performance of Li-Ion Batteries via Modified Reference Governors and Electrochemical Models

Pérez

Shahmohammadhamedani

Moura

2015

IEEE/ASME Trans. Mechatron.

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“…Based on these models, the optimization control strategies with the consideration of battery health are developed. The several representative methods are as follows: the charge pattern optimization for energy cost and battery longevity by Bashash et al [20] and the energy management with consideration of battery health on stochastic drive cycles by Moura et al [21]. A weighted ampere-hour throughput model based on the severity factor is built by Onori et al [22] to estimate the battery life, which is used in PHEV.…”

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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Stochastic battery energy storage scheduling considering cell degradation and distributed energy resources

Tang

Sun

et al. 2019

Int Trans Electr Energ Syst

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Summary The penetration of distributed energy resources (DERs) greatly raises the risk of distribution network operation such as peak shaving and voltage stability. Battery energy storage (BES) has been widely accepted as the most potential application to cope with the challenge of high penetration of DERs. To cope with the uncertainties and variability of DERs, a stochastic BES scheduling model and its algorithm are proposed. The overall economy is achieved by fully considering the cell degradation, DERs, electricity purchasing, and active power losses. The rainflow algorithm‐based cycle counting method is incorporated in the BES scheduling model to capture the cell degradation, greatly extending the expected BES lifetime and achieving a better economy. DER power scenarios are generated to consider the uncertainties and correlations based on the Copula theory. To solve the BES scheduling model, we propose a Lagrangian relaxation‐based algorithm, which has a significantly reduced complexity with respect to the existing techniques. For this reason, the proposed algorithm enables much more scenarios incorporated in the BES scheduling model and better captures the DER uncertainties and correlations. Finally, numerical studies for the day‐ahead BES scheduling in the IEEE 123‐node test feeder are presented to demonstrate the merits of the proposed method. Results show that the actual BES life expectancy of the proposed model has increased to 4.89 times compared with the traditional ones. The problems caused by DERs are greatly alleviated by fully capturing the uncertainties and correlations with the proposed method.

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Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control

Cited by 200 publications

References 40 publications

Enhanced Performance of Li-Ion Batteries via Modified Reference Governors and Electrochemical Models

Enhanced Performance of Li-Ion Batteries via Modified Reference Governors and Electrochemical Models

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

Stochastic battery energy storage scheduling considering cell degradation and distributed energy resources

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