REAL-TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research

Effectiveness of smoking cessation therapies: a systematic review and meta-analysis

BACKGROUND: Smoking remains the leading preventable cause of premature deaths. Several pharmacological interventions now exist to aid smokers in cessation. These include Nicotine Replacement Therapy [NRT], bupropion, and varenicline. We aimed to assess their relative efficacy in smoking cessation by conducting a systematic review and meta-analysis. METHODS: We searched 10 electronic medical databases (inception to Sept. 2006) and bibliographies of published reviews. We selected randomized controlled trials [RCTs] evaluating interventions for smoking cessation at 1 year, through chemical confirmation. Our primary endpoint was smoking cessation at 1 year. Secondary endpoints included short-term smoking cessation (~3 months) and adverse events. We conducted random-effects meta-analysis and meta-regression. We compared treatment effects across interventions using head-to-head trials and when these did not exist, we calculated indirect comparisons. RESULTS: We identified 70 trials of NRT versus control at 1 year, Odds Ratio [OR] 1.71, 95% Confidence Interval [CI], 1.55–1.88, P =< 0.0001). This was consistent when examining all placebo-controlled trials (49 RCTs, OR 1.78, 95% CI, 1.60–1.99), NRT gum (OR 1.60, 95% CI, 1.37–1.86) or patch (OR 1.63, 95% CI, 1.41–1.89). NRT also reduced smoking at 3 months (OR 1.98, 95% CI, 1.77–2.21). Bupropion trials were superior to controls at 1 year (12 RCTs, OR1.56, 95% CI, 1.10–2.21, P = 0.01) and at 3 months (OR 2.13, 95% CI, 1.72–2.64). Two RCTs evaluated the superiority of bupropion versus NRT at 1 year (OR 1.14, 95% CI, 0.20–6.42). Varenicline was superior to placebo at 1 year (4 RCTs, OR 2.96, 95% CI, 2.12–4.12, P =< 0.0001) and also at approximately 3 months (OR 3.75, 95% CI, 2.65–5.30). Three RCTs evaluated the effectiveness of varenicline versus bupropion at 1 year (OR 1.58, 95% CI, 1.22–2.05) and at approximately 3 months (OR 1.61, 95% CI, 1.16–2.21). Using indirect comparisons, varenicline was superior to NRT when compared to placebo controls (OR 1.66, 95% CI 1.17–2.36, P = 0.004) or to all controls at 1 year (OR 1.73, 95% CI 1.22–2.45, P = 0.001). This was also the case for 3-month data. Adverse events were not systematically different across studies. CONCLUSION: NRT, bupropion and varenicline all provide therapeutic effects in assisting with smoking cessation. Direct and indirect comparisons identify a hierarchy of effectiveness

Psychosocial interventions for supporting women to stop smoking in pregnancy

Background: Tobacco smoking remains one of the few preventable factors associated with complications in pregnancy, and has serious long-term implications for women and babies. Smoking in pregnancy is decreasing in high-income countries, but is strongly associated with poverty and is increasing in low- to middle-income countries. Objectives: To assess the effects of smoking cessation interventions during pregnancy on smoking behaviour and perinatal health outcomes. Search methods: In this sixth update, we searched the Cochrane Pregnancy and Childbirth Group's Trials Register (13 November 2015), checked reference lists of retrieved studies and contacted trial authors. Selection criteria: Randomised controlled trials, cluster-randomised trials, and quasi-randomised controlled trials of psychosocial smoking cessation interventions during pregnancy. Data collection and analysis: Two review authors independently assessed trials for inclusion and trial quality, and extracted data. Direct comparisons were conducted in RevMan, with meta-regression conducted in STATA 14. Main results: The overall quality of evidence was moderate to high, with reductions in confidence due to imprecision and heterogeneity for some outcomes. One hundred and two trials with 120 intervention arms (studies) were included, with 88 trials (involving over 28,000 women) providing data on smoking abstinence in late pregnancy. Interventions were categorised as counselling, health education, feedback, incentives, social support, exercise and dissemination. In separate comparisons, there is high-quality evidence that counselling increased smoking cessation in late pregnancy compared with usual care (30 studies; average risk ratio (RR) 1.44, 95% confidence interval (CI) 1.19 to 1.73) and less intensive interventions (18 studies; average RR 1.25, 95% CI 1.07 to 1.47). There was uncertainty whether counselling increased the chance of smoking cessation when provided as one component of a broader maternal health intervention or comparing one type of counselling with another. In studies comparing counselling and usual care (largest comparison), it was unclear whether interventions prevented smoking relapse among women who had stopped smoking spontaneously in early pregnancy. However, a clear effect was seen in smoking abstinence at zero to five months postpartum (11 studies; average RR 1.59, 95% CI 1.26 to 2.01) and 12 to 17 months (two studies, average RR 2.20, 95% CI 1.23 to 3.96), with a borderline effect at six to 11 months (six studies; average RR 1.33, 95% CI 1.00 to 1.77). In other comparisons, the effect was unclear for most secondary outcomes, but sample sizes were small. Evidence suggests a borderline effect of health education compared with usual care (five studies; average RR 1.59, 95% CI 0.99 to 2.55), but the quality was downgraded to moderate as the effect was unclear when compared with less intensive interventions (four studies; average RR 1.20, 95% CI 0.85 to 1.70), alternative interventions (one study; RR 1.88, 95% CI 0.19 to 18.60), or when smoking cessation health education was provided as one component of a broader maternal health intervention. There was evidence feedback increased smoking cessation when compared with usual care and provided in conjunction with other strategies, such as counselling (average RR 4.39, 95% CI 1.89 to 10.21), but the confidence in the quality of evidence was downgraded to moderate as this was based on only two studies and the effect was uncertain when feedback was compared to less intensive interventions (three studies; average RR 1.29, 95% CI 0.75 to 2.20). High-quality evidence suggests incentive-based interventions are effective when compared with an alternative (non-contingent incentive) intervention (four studies; RR 2.36, 95% CI 1.36 to 4.09). However pooled effects were not calculable for comparisons with usual care or less intensive interventions (substantial heterogeneity, I2 = 93%). High-quality evidence suggests the effect is unclear in social support interventions provided by peers (six studies; average RR 1.42, 95% CI 0.98 to 2.07), in a single trial of support provided by partners, or when social support for smoking cessation was provided as part of a broader intervention to improve maternal health. The effect was unclear in single interventions of exercise compared to usual care (RR 1.20, 95% CI 0.72 to 2.01) and dissemination of counselling (RR 1.63, 95% CI 0.62 to 4.32). Importantly, high-quality evidence from pooled results demonstrated that women who received psychosocial interventions had a 17% reduction in infants born with low birthweight, a significantly higher mean birthweight (mean difference (MD) 55.60 g, 95% CI 29.82 to 81.38 g higher) and a 22% reduction in neonatal intensive care admissions. However the difference in preterm births and stillbirths was unclear. There did not appear to be adverse psychological effects from the interventions. The intensity of support women received in both the intervention and comparison groups has increased over time, with higher-intensity interventions more likely to have higher-intensity comparisons, potentially explaining why no clear differences were seen with increasing intervention intensity in meta-regression analyses. Among meta-regression analyses: studies classified as having 'unclear' implementation and unequal baseline characteristics were less effective than other studies. There was no clear difference between trials implemented by researchers (efficacy studies), and those implemented by routine pregnancy staff (effectiveness studies), however there was uncertainty in the effectiveness of counselling in four dissemination trials where the focus on the intervention was at an organisational level. The pooled effects were similar in interventions provided for women classified as having predominantly low socio-economic status, compared to other women. The effect was significant in interventions among women from ethnic minority groups; however not among indigenous women. There were similar effect sizes in trials with biochemically validated smoking abstinence and those with self-reported abstinence. It was unclear whether incorporating use of self-help manuals or telephone support increased the effectiveness of interventions. Authors' conclusions: Psychosocial interventions to support women to stop smoking in pregnancy can increase the proportion of women who stop smoking in late pregnancy and the proportion of infants born low birthweight. Counselling, feedback and incentives appear to be effective, however the characteristics and context of the interventions should be carefully considered. The effect of health education and social support is less clear. New trials have been published during the preparation of this review and will be included in the next update

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a real-Time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in real-Time on a test vehicle equipped with a rapid prototyping embedded controller. Real-Time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a real-Time environment

Receding horizon control for mode selection and powertrain control of a multi-mode hybrid electric vehicle

Multi-mode Hybrid Electric Vehicles (HEVs) have shown the advantages over traditional single-mode HEVs. For multi-mode HEVs, selecting an appropriate vehicle mode and determining power- split are keys for the reduction of energy consumption. Scholars have used Dynamic Programming (DP) to determine mode selection and power-split offline due to the long computation time. This paper presents a DP-based Receding Horizon Control (RHC) method for mode selection and power- split. The DP-based RHC utilizes the Dynamic Programming algorithm to create an optimal solution within a moving prediction horizon, which has the potential for real-time application. The advantage of this DP-based receding horizon control is that it is able to handle discrete control problems. To optimize the mode selection and power-split, the experimentally determined mode shift fuel and electricity costs are incorporated in the algorithm. The simulations have been performed for DP and RHC with different sizes of prediction horizon. The control performance, fuel economy and computation time for different simulation scenarios are discussed and compared

Integrated Predictive Powertrain Control for a Multi-Mode Plug-in Hybrid Electric Vehicle

Due to the complexity of a multi-mode Plug-in Hybrid Electric Vehicle (PHEV) powertrain, the energy management strategy of said powertrain is a prime candidate for the application of optimal control methods. This paper presents a predictive control strategy for optimal mode selection and powertrain control for a multi-mode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This paper also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using Nonlinear Model Predictive Control (NMPC) to create a real-time Integrated Predictive Powertrain Controller (IPPC) responsible for all aspects of multi-mode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multi-mode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4-10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation

Leveraging connectivity and automation to improve propulsion system energy sufficiency

The use of connectivity and automation in mobility applications is rapidly increasing and being introduced into propulsion system controls to reduce energy consumption. With support from the US Department of Energy’s, ARPA-E agency and in partnership with General Motors, the Chevrolet Volt, generation II, is studied and tested for the benefits of Connected and Automated Vehicle (CAV) control applied to Vehicle Dynamics and Powertrain (VD&PT) to reduce energy consumption by 20% in real world driving scenarios. This investigation looks at application of model predictive control, energy utilization forecasting and external data regarding traffic and infrastructure to develop a mission profile for propulsion system and vehicle dynamics. Both a long and short prediction time horizon are created for propulsion system operation, determining blending of charge depleting and charge sustaining, with the objective of reducing the total energy utilized for the trip by upwards of 20%. The presentation/paper will present the VD&PT model predictive control methodology being developed as a supervisory controller and/or driver assistant. Measured data from a test fleet of generation 2 Chevrolet Volts will also be presented illustrating the benefits of CAV on a single vehicle on real world driving cycle. The experimental results cover a range of driving conditions, from rural to heavy urban; representing the potential reduction in energy consumption CAV control can provide a plugin hybrid electric propulsion system architecture

Optimal velocity prediction for fuel economy improvement of connected vehicles

© The Institution of Engineering and Technology 2018. With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie