Systematic control on energy recovery of electrified turbocharged diesel engines

© 2015 IEEE.Recovering energy from exhaust gas is seen as the promising solution to save fuel consumption of diesel engines, where the key issue in maximizing fuel economy benefits is the management of energy flows in the optimal way. This paper proposes a systematic control strategy on both energy management and air path regulation of an electrified turbocharged diesel engine (ETDE). The Energy management and air path regulation is formulated as a multi-variable online optimization problem with constraints. The equivalent consumption minimization strategy (ECMS) is employed as the supervisory level controller, to calculate the optimal energy flow distribution. An explicit model predictive controller (EMPC) is developed as the low level controller to implement the optimal energy flow distribution. The two controllers work together as cascaded modules in real-time, while simulation results based on a physical model show the superior performance over the conventional distributed single-input single-output (SISO) control method

Finding the LQR weights to ensure the associated Riccati equations admit a common solution

This paper addresses the problem of finding the linear quadratic regulator (LQR) weights such that the associated discrete-time algebraic Riccati equations admit a common optimal stabilising solution. Solving such a problem is key to designing LQR controllers to stabilise discrete-time switched linear systems under arbitrary switching, or stabilise polytopic systems (e.g., Takagi-Sugeno fuzzy systems and linear parameter varying systems) in the entire operating region. To ensure problem tractability and reduce the searching space, this paper proposes an efficient framework of finding only the state weights based on the given input weights. Linear matrix inequality conditions are derived to conveniently check feasibility of the problem. An iterative algorithm with quadratic convergence and low computational complexity is developed to solve the problem. Efficacy of the proposed method is illustrated through numerical simulations of systems with various sizes

Real-time optimal energy management of heavy duty hybrid electric vehicles

The performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for improving fuel economy as well as reducing exhaust emissions. The heavy duty HEVs engaged in cycles characterized by start-stop configuration has attracted widely interests, especially in off-road applications. In this paper, a fuzzy equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for heavy duty HEVs. The online optimization problem is formulated as minimizing a cost function, in terms of weighted fuel power and electrical power. A fuzzy rule-based approach is applied on the weight tuning within the cost function, with respect to the variations of the battery state-of-charge (SOC) and elapsed time. Comparing with traditional real-time supervisory control strategies, the proposed F-ECMS is more robust to the test environments with rapid dynamics. The proposed method is validated via simulation under two transient test cycles, with the fuel economy benefits of 4.43% and 6.44%, respectively. The F-ECMS shows better performance than the telemetry ECMS (T-ECMS), in terms of the sustainability of battery SOC

A review of intelligent road preview methods for energy management of hybrid vehicles

Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV)

Real time energy management of electrically turbocharged engines based on model learning

Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery. The capability of generating power strongly depends on the engine operating point. One big challenge is that the calibration of generating power capability is time-consuming in experiments. This paper proposes a neuro-fuzzy approach to model the engine. Based on the virtual engine model, the capability of generating power at arbitrary engine operating point can be obtained fast and accurately, which is applicable to implement in real time

Real-time modelling and parallel optimisation of a gasoline direct injection engine

With the increasing complexity of engines and number of control parameters, optimal engine parameter sets need to be searched in the high dimensionality. Traditional calibration methods are too complicated, expensive and timeconsuming. The model-based optimisation is of critical importance for engine fuel efficiency improvement and exhaust emissions reduction. The optimisation highly depends on the model accuracy. In this paper, a multi-layer modelling method is proposed, which can be used to generate the engine model at arbitrary operating points in real time with high accuracy. An enhanced heuristic-algorithm-based optimiser is combined with the real-time modelling method to perform a parallel optimisation. The proposed modelling and optimisation strategy can achieve the minimal fuel consumption fast and accurately. This strategy has been successfully verified using experimental data sets

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

Hierarchical modeling and speed control of networked induction motor systems

This paper proposes a hierarchical modeling method and a fuzzy speed control strategy for nonlinear networked induction motor systems subject to network induced time delay and packets dropout. The networked induction motor control system consists of a networked speed controller and a local controller. Fuzzy gain scheduling is applied on the networked speed controller to guarantee the robustness against complicated variations on the communication network. The state predictor is to compensate the time delay occurred in data transmission in the feedback channel. In stability analysis, the upper allowed limits of the time delay and packets dropout are calculated using the Lyapunov-Krasovskii theorem, respectively. Simulation and experimental results are given to illustrate the effectiveness of the proposed approach

Optimising the energy efficiency and transient response of diesel engines through an electric turbocharger

The electric turbocharger provides great potential for vehicle fuel efficiency improvement, exhaust emissions reduction and transient response acceleration. It makes the engine runs as a hybrid system so critical challenges are raised in energy management and control. This paper proposes a realtime energy management strategy for the electric turbocharger. A multi-variable 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 controllers work in a highly efficient way to achieve the optimal energy management. This strategy has been validated in simulations and experiments. Excellent tracking performance and high robustness demonstrate the effectiveness of the proposed method

Safe and robust data-driven cooperative control policy for mixed vehicle platoons

This article considers mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). The uncertainties and randomness in human driving behaviors highly affect the platoon safety and stability. However, most existing control strategies are either for platoons of pure AVs, or for special formations of mixed platoons with known HV models. This article addresses the control of mixed platoons with more general formations and unknown HV models. An innovative data-driven policy learning strategy is proposed to design the controllers for AVs based on vehicle-to-vehicle (V2V) communications. The policy learning strategy is embedded with the constraints of control input, inter-vehicular distance error and V2V communication topology. The strategy establishes a safe and robustly stable mixed platoon using prescribed communication topologies. The design efficacy is verified through simulations of a mixed platoon with different communication topologies and leader velocity profiles