An electric vehicle’s battery life model under car-following model

Yang, Shichun; Li, M.; Tang, Tie-Qiao; Lin, Yu

doi:10.1016/j.measurement.2013.07.035

Cited by 17 publications

(5 citation statements)

References 18 publications

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“…Among the several varieties of energy storage technologies, it is recognized that Li-ion battery systems enable a number of high-value opportunities given their benefits in portability and superior efficiency (see Figure 1 for comparisons 1 ).…”

Section: Introductionmentioning

confidence: 99%

“…That said, no sound explication has been provided in the open literature to help on the best choice of the studied physical quantity. For example, some previous works were based on the state of capacity, [10][11][12][13][14] the electro-motive force (EMF), 15 the electricity consumption, 1 analyzing the voltage and temperature profiles, 7 and so on.…”

Section: Introductionmentioning

confidence: 99%

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Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

Ali

Azizi

Saïdi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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State of health condition monitoring of Li-ion batteries is an important issue for safe and reliably operation of battery-powered products. Consequently, it remains a challenging subject for industrial and academic studies. In this article, an incremental support vector regression is proposed for battery state of health lifetime estimation. In order to improve the battery state of health forecasting accuracy, the quantum-behaved particle swarm optimization is proposed to define reliably the incremental support vector regression parameters. The validation of the proposed method was done based on the NASA battery data set, and it demonstrates that it yields good performance in remaining useful life estimation of Li-ion batteries. This case study shows that compared with the linear, polynomial regression methods, and compared to previous works, the proposed method can obtain more accurate state of health prediction results. Even for state of health prediction starting from the cycle near capacity regeneration, the proposed model can still accurately estimate the global degradation trend. Furthermore, the proposed quantum-behaved particle swarm optimization–incremental support vector regression combination has greater robustness when the training data contain noise and measurement outliers. This allows satisfactory prediction performances without pre-processing the data manually.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

Ali

Azizi

Saïdi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Lamonde [1] proposed the criteria called "activity oriented ergonomics" for coordination at a distance design. In [2,3], Tang et al and Yang et al used the empirical data to calibrate the speed-headway function and proposed a car-following model to investigate the effects of real-time road condition on each vehicle's speed, acceleration, headway, fuel consumption, CO, HC, and NO X under uniform flow. The numerical results showed that under the smooth driving behavior the electric vehicle's battery life can be prolonged.…”

Section: Introductionmentioning

confidence: 99%

A New Calibration Method for the Real-Time Calculation of Dynamic Safety Following Distance under Railway Moving Block System

Pan

Luo

Zhao

et al. 2018

Mathematical Problems in Engineering

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Only the actual following distance that is a little greater than the optimum safety following distance at any time can make the following train move in safety and efficiency. For this purpose, a new calibration method is studied for the real-time calculation of the dynamically optimum safety following distance. To cope with the complex situations of train following operation, the mathematic model of train deceleration operation based on the hyperbolic function with a variable acceleration control strategy is established to simulate the speed-changing behavior of high-speed train steered by the well-experienced driver. Using the evaluation of train behavior adjustment quality and the numerical analysis theory, we build the fitting function of the optimum absolute safety following distance changing with the following train's velocity for the real-time calibration of safe following distance under absolute braking mode. And then, we discussed the real-time calculation of the optimum safety following distance under relative braking mode (i.e., the relative safety following distance). The study results will help a high-speed train to evaluate and optimize its own following behavior according to the current operation states of train following system, the actual following distance, and the absolute or relative safety following distance. The actual following distance is rationally controlled by the scientific adjustment of the following train's behavior so that train following movement can be always safe, efficient, and smooth (comfortable).

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“…Energy saving is another hot area of study in vehicle following control. Tang et al [18,19] and Yang et al [20,21] established the energy consumption models of car-following systems. Simulation and experiments have shown that under the smooth driving behavior the electric vehicle's battery lifetime can be prolonged and energy consumption will increase with traffic interruption, accelerating or decelerating suddenly.…”

Section: Introductionmentioning

confidence: 99%

Establishment, maintenance, and re-establishment of the safe and efficient steady-following state

Pan¹,

Zheng²

2015

Chinese Phys. B

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We present an integrated mathematical model of vehicle-following control for the establishment, maintenance, and re-establishment of the previous or new safe and efficient steady-following state. The hyperbolic functions are introduced to establish the corresponding mathematical models, which can describe the behavioral adjustment of the following vehicle steered by a well-experienced driver under complex vehicle following situations. According to the proposed mathematical models, the control laws of the following vehicle adjusting its own behavior can be calculated for its moving in safety, efficiency, and smoothness (comfort). Simulation results show that the safe and efficient steady-following state can be well established, maintained, and re-established by its own smooth (comfortable) behavioral adjustment with the synchronous control of the following vehicle’s velocity, acceleration, and the actual following distance.

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An electric vehicle’s battery life model under car-following model

Cited by 17 publications

References 18 publications

Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

A New Calibration Method for the Real-Time Calculation of Dynamic Safety Following Distance under Railway Moving Block System

Establishment, maintenance, and re-establishment of the safe and efficient steady-following state

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