An availability approach to thermal energy recovery in vehicles

Availability is a well-established and widely recognized way of describing the work-producing potential of energy systems. A first-law analysis is helpful in setting the energy context and ensuring that energy flows balance, but it is a second-law analysis based on availability that places an upper bound on the potential work output. In this analysis a new approach to thermal management intended for vehicle propulsion is examined and developed. Starting with a simple analysis of the chemical energy flow, a realistic heat exchange performance is introduced to establish a practical architecture. Within this framework, the availability analysis shows that effective thermal efficiencies of between 25 and 30 per cent are feasible. With a spark ignition engine operating at a high load condition, and the thermal recovery system at an operating pressure of 100 bar, the maximum efficiency possible with a steady flow work-producing device is 37 per cent (with fully reversible thermodynamic processes). In a water-based thermal recovery system, work could only reasonably be produced with heat transfer from a reservoir at the saturation temperature corresponding to the operating pressure. At 100 bar the maximum efficiency would be 33 per cent. In a different mode of operation, where heat is transferred incrementally to a thermal accumulator and work produced as required, the efficiency is 32 per cent at only 20 bar operating pressure. These efficiency values apply to work production to supplement a combustion engine at any operating condition. An analysis of a reciprocating expander as the work-producing device shows substantial flexibility in operation. Control of system operating pressure is shown to be of value in that periodic adjustments enhance the availability content of the thermal reservoir. The operating pressure of a fluid power system is related to the temperature of operation, and therefore the heat transfer processes. Choice of too high a pressure leads to reduced heat transfer, and ultimately a reduction in work output. There is an optimum condition that can be selected at design time and maintained during the running of the system

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

Evaluating the performance improvement of different pneumatic hybrid boost systems and their ability to reduce turbo-lag

The objective of the work reported in this paper was to identify how turbocharger response time ("turbo-lag") is best managed using pneumatic hybrid technology. Initially methods to improve response time have been analysed and compared. Then the evaluation of the performance improvement is conducted using two techniques: engine brake torque response and vehicle acceleration, using the engine simulation code, GT-SUITE [1]. Three pneumatic hybrid boost systems have been considered: Intake Boost System (I), Intake Port Boost System (IP) and Exhaust Boost System (E). The three systems respectively integrated in a six-cylinder 7.25 l heavy-duty diesel engine for a city bus application have been modelled. When the engine load is increased from no load to full load at 1600 rpm, the development of brake torque has been compared and analysed. The findings show that all three systems significantly reduce the engine response time, with System I giving the fastest engine response. The vehicle performance has been also considered. Systems I and IP have been integrated respectively into the bus model giving two different configurations. The acceleration capability of the two types of vehicle has been simulated. Both Systems I and IP significantly reduce the vehicle acceleration time by substantially reducing turbo-lag

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

Unified backwards facing and forwards facing simulation of a hybrid electric vehicle using MATLAB Simscape

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs. The model represents a parallel hybrid vehicle, which is being developed in the TC48 project. The project goal is to produce a prototype of a plug-in parallel hybrid system which is integrated into existing front wheel drive powertrains with modest additional engineering, cost, volume, and mass requirements. This paper explains the motivation for the project, and presents examples of the simulations which were used to guide the design. The vehicle simulation models used to evaluate the layout options are described and discussed. Sensitivity analyses are presented which informed the design decisions. A novel use of the Simscape component of MATLAB/Simulink which allows the same model structure to be used for both forwards and backwards simulations is demonstrated. This method has the possibility for more general application, and a toolbox is being developed which assists the generation of mathematical models of this type

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

Scheduling and control co-design of networked induction motor control systems

This paper investigates the co-design of remote speed control and network scheduling for motion coordination of multiple induction motors through a shared communication network. An integrated feedback scheduling algorithm is designed to allocate the optimal sampling period and priority to each control loop to optimize the global performance of a networked control system (NCS), while satisfying the constraints of stability and schedulability. The rational gain of the network speed controllers is calculated using the Lyapunov theorem and online tuned by fuzzy logic to guarantee the robustness against complicated variations on the communication network. Furthermore, a state predictor is designed to compensate the time delay occurred in data transmission from the sensor to the controller, as a part of the networked controller. Simulation results are given to illustrate the effectiveness of the proposed control-and-scheduling co-design approach

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

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

Integrated feedback scheduling and control co-design for motion coordination of networked induction motor systems

This paper investigates the codesign of remote speed control and network scheduling for motion coordination of multiple induction motors through a shared communication network. An integrated feedback scheduling algorithm is designed to allocate the optimal sampling period and priority to each control loop to optimize the global performance of a networked control system (NCS), while satisfying the constraints of stability and schedulability. A speed synchronization method is incorporated into the scheduling algorithm to improve the speed synchronization performance of multiple induction motors. The rational gain of the network speed controllers is calculated using the Lyapunov theorem and tuned online by fuzzy logic to guarantee the robustness against complicated variations on the communication network. Furthermore, a state predictor is designed to compensate the time delay which occurred in data transmission from the sensor to the controller, as a part of the networked controller. Simulation results support the effectiveness of the proposed control-and-scheduling codesign approach