Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle

Bubna, Piyush; Brunner, Doug; Advani, Suresh G.; Prasad, Ajay K.

doi:10.1016/j.jpowsour.2010.04.008

Cited by 54 publications

(22 citation statements)

References 10 publications

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“…Thus, a max-normalization method can be used to normalize the mean-square value, effective value, mean value, and median of U, as shown in Equ. (3). For U/I, its mean value and its dispersion coefficient apparently change.…”

Section: B Feature Extractionmentioning

confidence: 96%

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Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Piao¹,

Li²,

Lu³

et al. 2016

Journal of Power Electronics

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A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

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Section: B Feature Extractionmentioning

confidence: 96%

“…Lifetime is a key performance index with regard to battery technology [3], [4]. At present, the development of power battery technologies is in a stagnant stage.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Piao¹,

Li²,

Lu³

et al. 2016

Journal of Power Electronics

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show abstract

“…Hybrid electric vehicles (HEVs), which combine the merits of conventional vehicles and pure electric vehicles, have significant improvements in fuel consumption and emissions. Nowadays, HEVs have achieved great market success, and hydraulic hybrids [1,2], fuel cell-battery hybrids [3,4], battery-supercapacitor hybrids [5], etc., are still the focus of active research.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Hydraulic/Electric Synergy Bus

et al. 2017

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Abstract:In recent years, increasing concerns regarding environmental pollution and requirements for lower fossil fuel consumption have increased interest in alternative hybrid powertrains. As a result, this paper presents a novel hydraulic/electric synergy powertrain with multiple working modes. The three energy sources (i.e., engine, battery, and hydraulic accumulator) in this configuration are regulated by a dual planetary gear set to achieve optimal performances. This paper selects the component sizes of a hybrid electric vehicle (HEV), a hydraulic hybrid vehicle (HHV), and a hydraulic/electric synergy vehicle (HESV), based on the dynamic performance of a target vehicle (TV). In addition, this paper develops the forward simulation models of the four aforementioned vehicles in the MATLAB/Simulink/Driveline platform, in which the fuel economy simulations are carried out in relation to the Chinese urban bus cycle. The simulation results show that the fuel consumption of the three hybrid vehicles is similar, but much better than, that of the TV. Finally, based on the operating cost calculations over a five-year working period, the lowest cost ranges of the three hybrid vehicles are determined, which provides a method for choosing the optimal hybrid scheme.

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“…To adapt different driving cycles, researchers have proposed a model predictive control (MPC) for HEV which is a closed-loop optimal control strategy [14][15][16][17][18][19]. To obtain the current control action, the optimal control problem in the finite domain is solved at each sampling instant.…”

Section: Introductionmentioning

confidence: 99%

An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning

Liu

et al. 2015

Energies

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Abstract:In order to realize the online learning of a hybrid electric vehicle (HEV) control strategy, a fuzzy Q-learning (FQL) method is proposed in this paper. FQL control strategies consists of two parts: The optimal action-value function Q*(x,u) estimator network (QEN) and the fuzzy parameters tuning (FPT). A back propagation (BP) neural network is applied to estimate Q*(x,u) as QEN. For the fuzzy controller, we choose a Sugeno-type fuzzy inference system (FIS) and the parameters of the FIS are tuned online based on Q*(x,u). The action exploration modifier (AEM) is introduced to guarantee all actions are tried. The main advantage of a FQL control strategy is that it does not rely on prior information related to future driving conditions and can self-tune the parameters of the fuzzy controller online. The FQL control strategy has been applied to a HEV and simulation tests have been done. Simulation results indicate that the parameters of the fuzzy controller are tuned online and that a FQL control strategy achieves good performance in fuel economy.

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Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle

Cited by 54 publications

References 10 publications

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Research on a Novel Hydraulic/Electric Synergy Bus

An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning

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