The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route.

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

Gear ratio optimization and shift control of 2-speed I-AMT in electric vehicle

https://opus.lib.uts.edu.au/bitstream/10453/122645/4/OCC-112847_AM.pdf

A comparative study energy consumption and costs of battery electric vehicle transmissions

Electric and hybrid electric vehicles (EV/HEV) are promising solutions for fossil fuel conservation and pollution reduction for a safe environment and sustainable transportation. The design of these energy-efficient powertrains requires optimization of components, systems, and controls. Controls entail battery management, fuel consumption, driver performance demand emissions, and management strategy. The hardware optimization entails powertrain architecture, transmission type, power electronic converters, and energy storage systems. In this overview, all these factors are addressed and reviewed. Major challenges and future technologies for EV/HEV are also discussed. Published suggestions and recommendations are surveyed and evaluated in this review. The outcomes of detailed studies are presented in tabular form to compare the strengths and weaknesses of various methods. Furthermore, issues in the current research are discussed, and suggestions toward further advancement of the technology are offered. This article analyzes current research and suggests challenges and scope of future research in EV/HEV and can serve as a reference for those working in this field.

State of the Art and Trends in Electric and Hybrid Electric Vehicles

Driveline modeling and estimation of individual clutch torque during gear shifts for dual clutch transmission

Dynamic and kinematic model of a dual clutch transmission

Analysis and simulation of a torque assist automated manual transmission

In the last decade, Hybrid Electric Vehicles (HEVs) have spread worldwide due to their capability to reduce fuel consumption. Several studies focused on the optimisation of the energy management system of hybrid vehicles are available in literature, whilst there are few articles dealing with the drivability and the dynamics of these new powertrain systems. In this paper a ‘Through-the-Road-Parallel HEV’ is analysed. This architecture is composed of an internal combustion engine mounted on the front axle and an electric motor powering the rear one. These two powertrains are not directly connected to each other, as the parallel configuration is implemented through the road-tyre force interaction. The main purpose of this paper is the drivability analysis of this layout of HEVs, using linearised mathematical models in both time (i.e. vehicle response during tip-in tests) and frequency domain (i.e. frequency response functions), considering the effect of the engaged gear ratio. The differences from a traditional Front-Wheel-Drive (FWD) configuration are subsequently highlighted. Furthermore, the authors compare different linearised dynamic models, with an increasing number of degrees of freedom, in order to assess which model represents the best compromise between complexity and quality of the results. Finally, a sensitivity analysis of the influence of the torque distribution between the front (thermal) and rear (electric) axles on vehicle drivability is carried out and presented in detail.

/pdf/drivability-analysis-of-through-the-road-parallel-hybrid-2sdx1fy59n.pdf

Drivability analysis of through-the-road-parallel hybrid vehicles

With the advent of electric vehicles with multiple motors, the steady-state and transient cornering responses can be designed based on high-level reference targets, and implemented through the continuous torque control of the individual wheels, i.e., torque-vectoring or direct yaw moment control. The literature includes several papers describing the application of the sliding mode control theory to torque-vectoring. However, the experimental implementations of sliding mode controllers on real vehicle prototypes are very limited at the moment. More importantly, to the knowledge of the authors, there is lack of experimental assessments of the performance benefits of direct yaw moment control based on sliding modes, with respect to other controllers, such as the proportional integral derivative controllers or linear quadratic regulators currently used for stability control in production vehicles. This paper aims to reduce this gap by presenting an integral sliding mode controller for concurrent yaw rate and sideslip control. A new driving mode, the Enhanced Sport mode, is proposed, inducing sustained high values of sideslip angle, which can be safely limited to a specified threshold. The system is experimentally assessed on a four-wheel-drive electric vehicle along a wide range of maneuvers. The performance of the integral sliding mode controller is compared with that of a linear quadratic regulator during step steer tests. The results show that the integral sliding mode controller brings a significant enhancement of the tracking performance and yaw damping with respect to the more conventional linear quadratic regulator based on an augmented single-track vehicle model formulation.

/pdf/on-the-experimental-analysis-of-integral-sliding-modes-for-2k670ujjk4.pdf

Enrico Galvagno

Papers

Dynamic and kinematic model of a dual clutch transmission

Analysis and simulation of a torque assist automated manual transmission

Drivability analysis of through-the-road-parallel hybrid vehicles

On the experimental analysis of integral sliding modes for yaw rate and sideslip control of an electric vehicle with multiple motors

A model for a flywheel automatic assisted manual transmission