Power-cycle-library-based control strategy for plug-in hybrid electric vehicles

It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.

Altered movement patterns but not muscle recruitment in moderately trained triathletes during running after cycling

Previous studies have shown that cycling can directly influence neuromuscular control during subsequent running in some highly trained triathletes, despite these triathletes' years of practice of the cycle-run transition. The aim of this study was to determine whether cycling has the same direct influence on neuromuscular control during running in moderately trained triathletes. Fifteen moderately trained triathletes participated. Kinematics of the pelvis and lower limbs and recruitment of 11 leg and thigh muscles were compared between a control run (no prior exercise) and a 30 min run that was preceded by a 15 min cycle (transition run). Muscle recruitment was different between control and transition runs in only one of 15 triathletes (<7%). Changes in joint position (mean difference of 3°) were evident in five triathletes, which persisted beyond 5 min of running in one triathlete. Our findings suggest that some moderately trained triathletes have difficulty reproducing their pre-cycling movement patterns for running initially after cycling, but cycling appears to have little influence on running muscle recruitment in moderately trained triathletes.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.

A medium-temperature solar thermal power system and its efficiency optimisation

This paper firstly expounds that the reheat-regenerative Rankine power cycle is a suitable cycle for the parabolic trough collector, a popular kind of collector in the power industry. In a thermal power cycle, the higher the temperature at which heat is supplied, the higher the efficiency of the cycle. On the other hand, for a given kind of collector at the same exiting temperature, the higher the temperature of the fluid entering the collector, the lower the efficiency of the collector. With the same exiting temperature of the solar field and the same temperature differences at the hottest end of the superheater/reheater and at the pinch points in the heat exchangers (e.g., the boiler) in the cycle, the efficiencies of the system are subject to the temperature of the fluid entering the collector or the saturation temperature at the boiler. This paper also investigates the optimal thermal and exergetic efficiencies for the combined system of the power cycle and collector. To make most advantage of the collector, the exiting fluid is supposed to be at the maximum temperature the collector can harvest. Hence, the thermal and exergetic efficiencies of the system are related to the saturation temperature at the boiler here.

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.

Reliability of performance in repeated sprint cycling tests

We have estimated the reliability of performance in a commonly employed exercise test consisting of repeated sprints on a cycle ergometer. Eight recreationally active young men completed a practice trial and three more trials at 3- to 6-day intervals. Each trial consisted of two bouts of 30-s maximal-effort cycling on an electromagnetically braked cycle ergometer; the bouts were separated by 4 min of rest. The typical (standard) errors of measurement for peak and mean power between trials 2 to 4 were 2.5 and 1.7% respectively for the first bout and 1.9 and 1.8% for the second bout. These errors are substantially less than those in previous reliability studies of single 30-s sprint tests, probably because of differences in quality of ergometer. The typical errors for the difference between bouts (i.e., fatigue) for peak power and mean power were 3.0 and 2.5%, respectively. Typical errors for the average of the two bouts were 1.6 and 1.2% for peak and mean power respectively, which are small enough to give adequate precision for moderate treatment effects in studies with modest sample sizes.

The influence of pacing during 6-minute supra-maximal cycle ergometer performance

The aim of this study was to evaluate the influence of pacing on performance, oxygen uptake (V̇O₂), oxygen deficit and blood lactate accumulation during a 6-minute cycle ergometer test. Six recreational cyclists completed three 6-minute cycling tests using fast-start, even-pacing and slow-fast pacing conditions. Cycle ergometer performance was measured as the mean power output produced for each cycling test. Energy system contribution during each cycling trial was estimated using a modified accumulated oxygen deficit (AOD) method. Blood lactate concentration was analysed from blood sampled using a catheter in a forearm vein prior to exercise, at 2 minutes, 4 minutes and 6 minutes during exercise, and at 2 minutes, 5 minutes and 10 minutes post-exercise. There was no significant difference between the pacing conditions for mean power output (P=0.09), peak V̇O₂ (P=0.92), total V̇O₂ (P=0.76), AOD (P=0.91), the time-course of V̇O₂ (P=0.22) or blood lactate accumulation (P=0.07). There was, however, a significant difference between the three pacing conditions in the oxygen deficit measured over time (P=0.02). These changes in the time-course of oxygen deficit during cycling trials did not, however, significantly affect the mean power output produced by each pacing condition.

Effect of posture on high-intensity constant-load cycling performance in men and women

The time sustained during a graded cycle exercise is ~10% longer in an upright compared with a supine posture. However, during constant-load cycling this effect is unknown. Therefore, we tested the postural effect on the performance of high-intensity constant-load cycling. Twenty-two active subjects (11 men, 11 women) performed two graded tests (one upright, one supine), and of those 22, 10 subjects (5 men, 5 women) performed three high-intensity constant-load tests (one upright, two supine). To test the postural effect on performance at the same absolute intensity, during the upright and one of the supine constant-load tests subjects cycled at 80% of the peak power output achieved during the upright graded test. To test the postural effect on performance at the same relative intensities, during the second supine test subjects cycled at 80% of the peak power output achieved during the supine graded test. Exercise time on the graded and absolute intensity constant-load tests for all subjects was greater (P<0.05) in the upright compared with supine posture (17.9±3.5 vs. 16.1±3.1 min for graded; 13.2±8.7 vs. 5.2±1.9 min for constant-load). This postural effect at the same absolute intensity was larger in men (19.4±8.5 upright vs. 6.6±1.6 supine, P<0.001) than women (7.1±2 upright vs. 3.9±1.4 supine, P>0.05) and it was correlated (P<0.05) with both the difference in VO₂ between positions during the first minute of exercise (r=0.67) and the height of the subjects (r=0.72). In conclusion, there is a very large postural effect on performance during constant-load cycling exercise and this effect is significantly larger in men than women.

Change in running kinematics after cycling are related to alterations in running economy in triathletes

Emerging evidence suggests that cycling may influence neuromuscular control during subsequent running but the relationship between altered neuromuscular control and run performance in triathletes is not well understood. The aim of this study was to determine if a 45 min high-intensity cycle influences lower limb movement and muscle recruitment during running and whether changes in limb movement or muscle recruitment are associated with changes in running economy (RE) after cycling. RE, muscle activity (surface electromyography) and limb movement (sagittal plane kinematics) were compared between a control run (no preceding cycle) and a run performed after a 45 min high-intensity cycle in 15 moderately trained triathletes. Muscle recruitment and kinematics during running after cycling were altered in 7 of 15 (46%) triathletes. Changes in kinematics at the knee and ankle were significantly associated with the change in VO₂ after cycling (p < 0.05). The change in ankle angle at foot contact alone explained 67.1% of the variance in VO₂. These findings suggest that cycling does influence limb movement and muscle recruitment in some triathletes and that changes in kinematics, especially at the ankle, are closely related to alterations in running economy after cycling.

Textural fractography of fatigue failures under variable cycle loading

A new concept of counting time at fatigue processes is proposed, aimed to reach fractographic compatibility in cases of different loading sequences. Values of cycle effectivity are summarized to give the new reference time. The improvement is shown in application - textural fractography of three specimens loaded by constant cycle, constant cycle with periodic overloading, and a random block, respectively. In contrast to the conventional crack growth rate, the reference crack growth rate is related to common morphologic features of all fracture surfaces.

The effect of environmental conditions on performance in timed cycling events

Air temperature, pressure and humidity are environmental factors that affect air density and therefore the relationship between a cyclist’s power output and their velocity. These environmental factors are changeable and are routinely quite different at elite cycling competitions conducted around the world, which means that they have a variable effect on performance in timed events. The present work describes a method of calculating the effect of these environmental factors on timed cycling events and illustrates the magnitude and significance of these effects in a case study. Formulas are provided to allow the calculation of the effect of environmental conditions on performance in a time trial cycling event. The effect of environmental factors on time trial performance can be in the order of 1.5%, which is significant given that the margins between ranked performances is often less than this. Environmental factors may enhance or hinder performance depending upon the conditions and the comparison conditions. To permit the fair comparison of performances conducted in different environmental conditions, it is recommended that performance times are corrected to the time that would be achieved in standard environmental conditions, such as 20 oC, 760 mmHg (1013.25 hPa) and 50% RH.

Modeling intermittent cycling performance in hypoxia using the critical power concept

PURPOSE: This study investigated the efficacy of an intermittent critical power model, termed the "work-balance" (W'BAL) model, during high-intensity exercise in hypoxia. METHODS: Eleven trained, male cyclists (mean ± SD; age 27 ± 6.6 yr, V[Combining Dot Above]O2peak 4.79 ± 0.56 L.min) completed a maximal ramp test and a 3 min "all-out" test to determine critical power (CP) and work performed above CP (W'). On another day an intermittent exercise test to task failure was performed. All procedures were performed in normoxia (NORM) and hypoxia (HYPO; FiO2 ≈ 0.155) in a single-blind, randomized and counter-balanced experimental design. The W'BAL model was used to calculate the minimum W' (W'BALmin) achieved during the intermittent test. W'BALmin in HYPO was also calculated using CP + W' derived in NORM (N+H). RESULTS: In HYPO there was an 18% decrease in V[Combining Dot Above]O2peak (4.79 ± 0.56 vs 3.93 ± 0.47 L.min ; P<0.001) and a 9% decrease in CP (347 ± 45 vs 316 ± 46 W; P<0.001). No significant change for W' occurred (13.4 ± 3.9 vs 13.7 ± 4.9 kJ; P=0.69; NORM vs HYPO). The change in V[Combining Dot Above]O2peak was significantly correlated with the change in CP (r = 0.72; P=0.01). There was no difference between NORM and HYPO for W'BALmin (1.1 ± 0.9 kJ vs 1.2 ± 0.6 kJ). The N+H analysis grossly overestimated W'BALmin (7.8 ± 3.4 kJ) compared with HYPO (P<0.001). CONCLUSION: The W'BAL model produced similar results in hypoxia and normoxia, but only when model parameters were determined under the same environmental conditions as the performance task. Application of the W'BAL model at altitude requires a modification of the model, or that CP and W' are measured at altitude.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.