The issues of global warming and depletion of fossil fuels have paved opportunities to electric vehicle (EV). Moreover, the rapid development of power electronics technologies has even realized high energy-efficient vehicles. EV could be the alternative to decrease the global green house gases emission as the energy consumption in the world transportation is high. However, EV faces huge challenges in battery cost since one-third of the EV cost lies on battery. This paper reviews state-of-the-art of the energy sources, storage devices, power converters, low-level control energy management strategies and high supervisor control algorithms used in EV. The comparison on advantages and disadvantages of vehicle technology is highlighted. In addition, the standards and patterns of drive cycles for EV are also outlined. The advancement of power electronics and power processors has enabled sophisticated controls (low-level and high supervisory algorithms) to be implemented in EV to achieve optimum performance as well as the realization of fast-charging stations. The rapid growth of EV has led to the integration of alternative resources to the utility grid and hence smart grid control plays an important role in managing the demand. The awareness of environmental issue and fuel crisis has brought up the sales of EV worldwide.

A review of energy sources and energy management system in electric vehicles

Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Control of hybrid electric vehicles

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

In the near future, a significant increase in electric power consumption in vehicles is expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of electric power will be used. Inspired by the research on energy management for hybrid electric vehicles (HEVs), this paper presents an extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments. For this purpose, both off-line optimization methods using knowledge of the driving pattern and on-line implementable ones are developed and tested in a simulation environment. Results show a reduction in fuel use of 2%, even without a prediction of the driving cycle being used. Simultaneously, even larger reductions of the emissions are obtained. The strategies can also be applied to a mild HEV with an integrated starter alternator (ISA), without modifications, or to other types of HEVs with slight changes in the formulation.

http://www.mate.tue.nl/mate/pdfs/5064.pdf

Energy management strategies for vehicular electric power systems

Control strategies for hybrid powertrains are algorithms that choose the power split between the engine and motor of a hybrid vehicle in order to minimize the fuel consumption and/or emissions. The goal of this paper is to propose an efficient tool to evaluate minimal fuel consumption that is achievable in simulation. Several approaches have been proposed, using heuristics (Delprat et al. 1999) or dynamic programming (Brahma et al., 2000; Rimaux et al., 1999). One drawback of these approaches is the huge amount of time required to obtain solutions. The approach described here is based on optimal control theory (Lewis & Syrmos, 1995) and avoids this drawback. Moreover, it can be easily applied to a large family of parallel arrangements.

Control of a parallel hybrid powertrain: optimal control

This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the driving situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the driving condition using a limited duration of driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

This paper represents the second part of a two-part paper on development of an intelligent energy management agent (IEMA) for parallel hybrid vehicles. In this part, energy management strategies for the torque distribution and charge sustenance tasks are established and implemented. Driving situation awareness-based fuzzy rule bases are developed to make intelligent decisions on the power split function. A charge sustenance strategy is developed in parallel to maintain adequate reserves of energy in the storage device for supporting an extended range of driving. Simulation study is conducted for the proposed IEMA and performance results are analyzed to evaluate its viability as a possible solution to and an extendable framework for energy management for parallel hybrid electric vehicles.

Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results

An energy management control system for a parallel hybrid electric vehicle is presented. The proposed system incorporates an optimization scheme to assess the amount of engine torque for generating propulsive power (torque distribution task) as well as a charge sustaining scheme to ensure that battery's state of charge is maintained at sufficiently high level. In order to accomplish the torque distribution task, torque distribution control problem is formulated as a multi-objective nonlinear optimization problem, and recast and solved as a single objective linear optimization problem. Furthermore, a new vehicle-mode-based state-of-charge compensator is developed to accomplish the charge sustaining task. Computer simulation work is carried out to evaluate the proposed energy management system. Finally, through sensitivity analysis, robustness of the solution to changes in the parameters of the objective functions is investigated.

An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT

A fuzzy torque distribution controller for energy management (and emission control) of a parallel hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy and emissions.

Fuzzy torque distribution control for a parallel hybrid vehicle

This paper proposes a "traffic situation awareness" driven intelligent agent for energy management of parallel hybrid vehicles A coordinating device that governs energy flow in the powertrain is proposed based on the idea that driving environment (traffic situation) as well as the vehicle's mode of operation and the style of driver behavior directly affect fuel usage and pollutant emissions For the realization of driving situation awareness, identification processes for roadway type is performed by extracting the driving information from the (past) driving data Expert knowledge that characterizes the relationship between the driving situation and fuel consumption and emissions is implemented in the fuzzy torque distributor that performs intelligent decisionmaking for the torque distribution task Charge sustenance operation is performed in the State-of-Charge (SOC) compensator to keep the level of the state of charge within prescribed levels The mission of the energy management system, so called Intelligent Energy Management Agent (IEMA), is to enable the vehicle to be driven in an economically and environmentally friendly way while satisfying the driver's performance demand Simulation work is carried out for the validation of proposed IEMA, and the results reveal its viability for energy management of a parallel hybrid vehicle

Jong-Seob Won

Papers

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results

An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT

Fuzzy torque distribution control for a parallel hybrid vehicle

Integrated drive cycle analysis for fuzzy logic based energy management in hybrid vehicles