Real-time energy management for diesel heavy duty hybrid electric vehicles

In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start-stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications

Decoupling control of electrified turbocharged diesel engines

Engine electrification is a critical technology in the promotion of engine fuel efficiency, among which the electrified turbocharger is regarded as a promising solution for its advantages in engine downsizing and exhaust gas energy recovery. By installing electrical devices on the turbocharger, the excess energy can be captured, stored, and re-used. The control of the energy flows in an electrified turbocharged diesel engine (ETDE) is still in its infancy. Developing a promising multi-input multi-output (MIMO) control strategy is essential in exploring the maximum benefits of electrified turbocharger. In this paper, the dynamics in an ETDE, especially the couplings among multiple loops in the air path are analyzed. Based on the analysis, a model-based MIMO decoupling control framework is designed to regulate the air path dynamics. The proposed control strategy can achieve fast and accurate tracking on selected control variables and is successfully validated on a physical model in simulations

Advanced methodology for the optimal sizing of the energy storage system in a hybrid electric refuse collector vehicle using real routes

This paper presents a new methodology for optimal sizing of the energy storage system ( ESS ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( RCV ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( Li – Po ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.Postprint (published version

Real-time optimal energy management of electrified engines

© 2016 The electrification of engine components offers significant opportunities for fuel economy improvements, including the use of an electrified turbocharger for engine downsizing and exhaust gas energy recovery. By installing an electrical device on the turbocharger, the excess energy in the air system can be captured, stored, and re-used. This new configuration requires a new control structure to manage the air path dynamics. The selection of optimal setpoints for each operating point is crucial for achieving the full fuel economy benefits. In this paper, a control-oriented model for an electrified turbocharged diesel engine is analysed. Based on this model, a structured approach for selecting control variables is proposed. A model-based multi-input multi-output decoupling controller is designed as the low level controller to track the desired values and to manage internal coupling. An equivalent consumption minimization strategy is employed as the supervisory level controller for real-time energy management. The supervisory level controller and low level controller work together in a cascade which addresses both fuel economy optimization and battery state-of-charge maintenance. The proposed control strategy has been successfully validated on a detailed physical simulation model

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163948/1/els2bf00154.pd

Decoupling control of electrified turbocharged diesel engines

Engine electrification is a critical technology in the promotion of engine fuel efficiency, among which the electrified turbocharger is regarded as a promising solution for its advantages in engine downsizing and exhaust gas energy recovery. By installing electrical devices on the turbocharger, the excess energy can be captured, stored, and re-used. The control of the energy flows in an electrified turbocharged diesel engine (ETDE) is still in its infancy. Developing a promising multi-input multi-output (MIMO) control strategy is essential in exploring the maximum benefits of electrified turbocharger. In this paper, the dynamics in an ETDE, especially the couplings among multiple loops in the air path are analyzed. Based on the analysis, a model-based MIMO decoupling control framework is designed to regulate the air path dynamics. The proposed control strategy can achieve fast and accurate tracking on selected control variables and is successfully validated on a physical model in simulations

Real-time energy management of the electric turbocharger based on explicit model predictive control

The electric turbocharger is a promising solution for engine downsizing. It provides great potential for vehicle fuel efficiency improvement. The electric turbocharger makes engines run as hybrid systems so critical challenges are raised in energy management and control. This paper proposes a real-time energy management strategy based on updating and tracking of the optimal exhaust pressure setpoint. Starting from the engine characterisation, the impacts of the electric turbocharger on engine response and exhaust emissions are analysed. A multivariable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The two-level controller works in a highly efficient way to fulfill the optimal energy management. This strategy has been validated in physical simulations and experimental testing. Excellent tracking performance and sustainable energy management demonstrate the effectiveness of the proposed method

Holistic Thermal Energy Modelling for Full Hybrid Electric Vehicles (HEVs)

Full hybrid electric vehicles are usually defined by their capability to drive in a fully electric mode, offering the advantage that they do not produce any emissions at the point of use. This is particularly important in built up areas, where localized emissions in the form of NOx and particulate matter may worsen health issues such as respiratory disease. However, high degrees of electrification also mean that waste heat from the internal combustion engine is often not available for heating the cabin and for maintaining the temperature of the powertrain and emissions control system. If not managed properly, this can result in increased fuel consumption, exhaust emissions, and reduced electric-only range at moderately high or low ambient temperatures negating many of the benefits of the electrification. This paper describes the development of a holistic, modular vehicle model designed for development of an integrated thermal energy management strategy. The developed model utilizes advanced simulation techniques, such as co-simulation, to incorporate a high-fidelity 1D thermo-fluid model, a multi-phase HVAC model, and a multi-zone cabin model within an existing longitudinal powertrain simulation environment. It is shown that the final model is useful of detailed analysis of thermal pathways including energy losses due to powertrain warm-up at various ambient temperatures and after periods of parked time. This enables identification of sources of energy loss and inefficiency over a wide range of environmental conditions. </div

Modelling and Co-simulation of hybrid vehicles: A thermal management perspective

Thermal management plays a vital role in the modern vehicle design and delivery. It enables the thermal analysis and optimisation of energy distribution to improve performance, increase efficiency and reduce emissions. Due to the complexity of the overall vehicle system, it is necessary to use a combination of simulation tools. Therefore, the co-simulation is at the centre of the design and analysis of electric, hybrid vehicles. For a holistic vehicle simulation to be realized, the simulation environment must support many physical domains. In this paper, a wide variety of system designs for modelling vehicle thermal performance are reviewed, providing an overview of necessary considerations for developing a cost-effective tool to evaluate fuel consumption and emissions across dynamic drive-cycles and under a range of weather conditions. The virtual models reviewed in this paper provide tools for component-level, system-level and control design, analysis, and optimisation. This paper concerns the latest techniques for an overall vehicle model development and software integration of multi-domain subsystems from a thermal management view and discusses the challenges presented for future studies

Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model