Alexander Franciscus Anita Serrarens

Design of CVT-Based Hybrid Passenger Cars

In this paper, the hybridization of a small passenger car equipped with a continuously variable transmission (CVT) is investigated. Designing a hybrid drive train is a multiobjective design problem. The main design objectives are fuel consumption, emissions, and performance. However, it is difficult to find a global optimal integral design solution due to the interdependence of design choices (parameters) regarding the drive-train topology, component sizes, component technologies, and control strategy, as well as the unknown sensitivity of the design objectives to the design parameters. In this paper, a parametric optimization procedure is presented to solve the design problem, where the main design objective is fuel consumption. The effects of parameter variation on fuel consumption have been investigated. Furthermore, a reduced hybrid drive-train model is introduced, with which the effects of design parameter variation is very quickly studied with an average error of less than 1.6%.

Predictive gear shift control for a parallel Hybrid Electric Vehicle

In this paper, Model Predictive Control (MPC) framework is exploited to synthesize a predictive controller for a parallel Hybrid Electric Vehicle (HEV) equipped with an Automated Manual Transmission. The algorithm also controls the gear shift command, together with the power split between the engine and electric machine and the engine on-off state using the route information ahead. A non-predictive controller based on a combination of Dynamic Programming (DP) and Pontryagins Minimum Principle (PMP) is described and taken as a benchmark control solution for optimizing the gear shift problem of the parallel HEV in terms of computational efficiency. This so-called DP-PMP control approach is then utilized in the MPC framework to realize the predictive controller for a gear shift problem in a receding horizon mode. Simulation results show that the non-predictive controller improves the fuel economy up to 35.9% and 43.5% on NEDC and FTP75 respectively when compared with a conventional vehicle. Even with a short horizon, fuel saving of the predictive controller is very close to that of the non-predictive controller with a relative difference of 0.3%. Moreover, the predictive controller can be seen as a suitable realtime implementable control candidate with a fast computation property.

Rule-Based Equivalent Fuel Consumption Minimization Strategies for Hybrid Vehicles

Abstract The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one main design parameter and requires no tuning of many threshold control values and parameters. This design parameter represents the maximum propulsion power of the secondary power source (i.e., electric machine/battery) during pure electric driving. The RB-ECMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB-ECMS proposed in this paper requires significantly less computation time with the similar result as DP (within ±1% accuracy).

Hybrid component specification optimisation for a medium-duty hybrid electric truck

This paper presents a modelling and simulation approach for determining the optimal degree-of-hybridisation for the drive train (engine, electric machine size) and the energy storage system (battery, ultra capacitor) for a medium-duty truck. The results show that the degree-of-hybridisation of known medium-duty hybrid electric trucks is close to the optimal degree-of-hybridisation using the methods as described in this paper. Furthermore, it is found that the Li-ion battery is from an energy and power density as well as cost point of view the most preferable energy storage system.

Improvement of fuel economy in Power-Shift Automated Manual Transmission through shift strategy optimization - an experimental study

In this paper, an optimal gear shifting control strategy based on Dynamic Programming (DP) for a vehicle equipped with a Power-Shift Automated Manual Transmission (PS-AMT) is proposed in order to explore the potential fuel savings. Simulation results on the city part of the New European Drive Cycle (NEDC), called ECE cycle, reveal that the relative fuel economy improvement can be reached up to 15.4% by applying DP shifting strategy compared to the case of applying a prescribed gear shift schedule. A forward facing dynamic power train model and control system are designed and developed for the prototype PS-AMT vehicle in order to validate the system modeling and shifting algorithm implementation. The test results of the prototype vehicle on the roller bench show that 11.2% improvement of fuel economy is achieved. It can be concluded that significant potential fuel savings can be obtained by optimal gear shift control and the proposed design method is consistent.

RULE-BASED ENERGY MANAGEMENT STRATEGIES FOR HYBRID VEHICLE DRIVETRAINS: A FUNDAMENTAL APPROACH IN REDUCING COMPUTATION TIME

Abstract The hybrid vehicle control problem at the highest level is termed the Energy Management Strategy (EMS). This paper presents a new, and simple Rule-Based (RB) EMS, whereby maximum power level of the electric machine during pure electric driving is the control design variable. This maximum power level determines the overall power -, and efficiency specifications of the electric machine. A RB EMS consist of a selection of driving modes. The RB EMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB method proposed in this paper requires 3000 times less computation time with the same accuracy (±1%) as DP. The RB strategy in this paper is a semi-empirical EMS with which the generic component specifications for the secondary power source (battery, power electronics, electric machine), primary power source (engine) and transmission technology can be obtained. Provided these generic specifications, a technology designer can quickly specify the hybrid technologies. In this way, control, optimization and component design are merged in a single framework.

Analyzing the performance index for a Hybrid Electric Vehicle

The definition of a performance index for the optimization design and optimal control problem of a Hybrid Electric Vehicle is not often considered and analyzed explicitly. In literature, there is no study about proposing a method of building or evaluating whether a performance index is appropriate. In this paper a method of objectively analyzing the performance index for the optimal control problem of a parallel Hybrid Electric Vehicle is introduced. The correlations and interdependencies among the objectives of the performance index are addressed by using the Singular Value Decomposition method. It is found that a simplified performance index consisting of fuel consumption and comfort can be obtained without sacrificing the vehicle performance compared to the case with the original one including fuel consumption, comfort and driveability.