Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.

Drive cycle analysis of the performance of hybrid electric vehicles

 

Power-cycle-library-based control strategy for plug-in hybrid electric vehicles

It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.

Look-ahead intelligent energy management of a parallel hybrid electric vehicle

Improving fuel efficiency in vehicles can reduce the energy consumption concerns associated with operating the vehicles. This paper presents a model for a parallel hybrid electric vehicle. In the model, the flow of energy starts from wheels and spreads toward engine and electric motor. A fuzzy logic based control strategy is implemented for the vehicle. The controller manages the energy flow from the engine and the electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines the vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. It then determines the best values for continuous variable transmission ratio, speed, and torque. A slope window method is formed that takes into account the look-ahead slope information, and determines the best vehicle speed. The developed model and control strategy are simulated using real highway data relating to Nowra-Bateman Bay in Australia, and SAE Highway Fuel Economy Driving Schedule. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces the fuel consumption of the vehicle.

Direct method for optimal power management in hybrid electric vehicles

Hybrid electric vehicles are powered by an electric system and an internal combustion engine. The components of a hybrid electric vehicle need to be coordinated in an optimal manner to deliver the desired performance. This paper presents an approach based on direct method for optimal power management in hybrid electric vehicles with inequality constraints. The approach consists of reducing the optimal control problem to a set of algebraic equations by approximating the state variable which is the energy of electric storage, and the control variable which is the power of fuel consumption. This approximation uses orthogonal functions with unknown coefficients. In addition, the inequality constraints are converted to equal constraints. The advantage of the developed method is that its computational complexity is less than that of dynamic and non-linear programming approaches. Also, to use dynamic or non-linear programming, the problem should be discretized resulting in the loss of optimization accuracy. The propsed method, on the other hand, does not require the discretization of the problem producing more accurate results. An example is solved to demonstrate the accuracy of the proposed approach. The results of Haar wavelets, and Chebyshev and Legendre polynomials are presented and discussed. © 2011 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Intelligent control and look-ahead energy management of hybrid electric vehicles

A review of the state of knowledge in the field of control and energy management in HEVs is carried out. The key innovation of the project is the development of a model of a PHEV using the real road data with an intelligent look-ahead online controller. Another novelty of this work is the method of route planning. It combines the information of vehicle sensors such as accelerometer and speedometer with the data of a GPS to create a road grade map for use within the look-ahead energy management strategy in the vehicle. For the PHEV, an adaptive cruise controller is modelled and an optimisation method is applied to obtain the best speed profile during a trajectory. Finally, the nonlinear model of the vehicle is applied with the sliding mode controller. The effect of using this controller is compared with the universal cruise controller. The stability of the system is studied and proved.

Integrated thermal and energy management of plug-in hybrid electric vehicles

In this research, the significant influence of engine and cabin thermal management on the fuel efficiency and emissions of plug-in hybrid electric vehicles is investigated. A practical solution to implement an optimal energy management strategy of plug-in hybrid electric vehicles which considers the temperature noise factor is introduced.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

A study on plug-in hybrid electic vehicles

Plug-in hybrid electric vehicle (PHEV), which is a hybrid vehicle whose batteries can be recharged by plugging into an electric power source, is creating many interests due to its significant potential to improve fuel efficiency and reduce pollution. PHEVs would be the next generation of vehicles that are expected to replace conventional hybrid electric vehicles. This paper presents a study on PHEV. It gives a review of different drivetrain architectures associated with PHEVs. In addition, different control strategies that could bring about realization of advantages of PHEV capabilities are discussed and compared.

Backward modelling and look-ahead fuzzy energy management controller for a parallel hybrid vehicle

This paper focuses on a parallel hybrid electric vehicle. It first develops a model for the vehicle using the backward-looking approach where the flow of energy starts from wheels and spreads towards engine and electric motor. Next, a fuzzy logic-based strategy is developed to control the operation of the vehicle. The objectives of the controller include managing the energy flow from engine and electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines current vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. Then, it determines the best values for continuous variable transmission ratio, speed, and torque. A slope window scheme is also developed to take into account the look-ahead slope information and determine the best vehicle speed for better fuel economy. The developed model and control strategy are simulated. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces fuel consumption.