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This doctoral dissertation examines the mode and efficacy of environmental and climate policy in China’s automotive sector. The ascent of China’s automobile market to the largest worldwide has detrimental effects on the country’s energy security situation, worsens environmental pollution, and increases greenhouse gas emissions. Environmental and climate policy measures to ameliorate these repercussions are the most apt tools available to the Chinese government. The objective of this dissertation is to identify the dominant mode of environmental and climate policies in China’s automotive industry and to assess the efficacy of select policy instruments. It does so by asking whether a uniform national approach to policy instrument adoption can be discerned that reflects China’s institutional and administrative history or whether modal exceptions exist. Secondly, if modal differences exist, to what extent do different instruments confirm the current understanding of the advantages and pitfalls of individual policy instrument types? And finally, how do Chinese instruments compare to those in other ambits in terms of policy mode and instrument efficacy? The literature on policy instruments holds that, due to their alleged efficiency advantages, incentive-based instruments dominate the political agenda of industrialised countries and international organisations (environmental consensus). This favouring of flexible instruments in academic and political circles contrasts with an evident lack of incentive-based instruments in practice and an observed lack of efficiency of some of those instruments actually implemented. Moreover, the policy mode adopted in developing countries and emerging markets has not yet received sufficient academic attention despite significant differences in institutional design, enforcement capacities, resources, and development paths that may imply reason for modal deviation. Applying a blend of qualitative and quantitative social sciences research methods, I add the case of China to the comparative literature and show that command-and-control regulation indeed forms the backbone of environmental and climate policy in China’s automotive industry. At the same time, modal differences exist between national regulation and local/ municipal incentive-based policy as well as in the electric vehicle sector, which shows a trend towards more incentive-based instruments and flexibility mechanisms in conventional regulation. Compared to other ambits, China has established a relatively flexible policy regime, at least for the case of vehicle efficiency standards. For the time being, incentive-based instruments remain comparatively ineffective and flexibility mechanisms in conventional regulation have an erosive effect on instrument stringency.

Today, hybrid electric propulsion technology provides a promising and practical solution for improving vehicle performance, increasing energy efficiency, and reducing harmful emissions, due to the additional flexibility that the technology has provided in the optimal power control and energy management, which are the keys to its success. In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for the Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for development of the next generation HEVs and PHEVs. / Graduate / laindeece@gmail.com

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.