The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Faced with environmental issues caused by fossil fuel burning in the industrial and transportation sectors, innovations towards cleaner solutions to replace the ever diminishing fossil fuels have been the focus of not only researchers but governments all around the world. The hybrid electric vehicle (HEV) technology is the result of the desire to have vehicles with a better fuel economy and lower tailpipe emissions to meet the requirements of environmental policies as well as to absorb the impact of rising fuel prices. The objectives are met by combining a conventional internal combustion engine (ICE) with one or more electric motors powered by a battery pack that can be charged using an on-board generator and the regenerative braking technology to power the transmission. The challenge is to develop an efficient energy management strategy (EMS) to satisfy the objectives while not having a reduced vehicle performance. In this paper, EMSs that are proposed and developed in the recent years are revisited and reviewed. Additionally, the Plug-in HEV is discussed in a new perspective from the EMS point of view. The through-the-road (TtR) HEV with in-wheel motors (IWM) is a fairly new concept in the HEV design that features less complicated configuration with reduced hardware requirements and lower cost. Recent research findings are evaluated throughout this paper leading to a hypothetical TtR HEV materialization. A thorough discussion is made encompassing the advantages and disadvantages of the concept, its performance compared to conventional HEVs and the way forward.

A review on hybrid electric vehicles architecture and energy management strategies

Model predictive control power management strategies for HEVs: A review

Carbon impression and the growing reliance on fossil fuels are two unique concerns for world emission regulatory agencies. These issues have placed electric vehicles (EVs) powered by lithium-ion batteries (LIBs) on the forefront as alternative vehicles. The LIB has noticeable features, including high energy and power density, compared with other accessible electrochemical energy storage systems. However, LIB is exceedingly nonlinear and dynamic; therefore, it generally requires an accurate online state-of-charge (SOC) estimation algorithm for real-time applications. Accurate battery modelling is an essential and primary requirement of online SOC estimation to simulate the dynamics. In this paper, different modelling methods suitable for online SOC estimation are discussed, and four groups of available online SOC estimation approaches are reviewed. After the general survey, the study explores the available Kalman filter (KF) family algorithms suitable for model-based online SOC estimation. The mathematical process and limitations of different KF family algorithms are analysed in depth and discussed. Moreover, challenging steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application. The on-going research is propelled by KF-based online SOC estimation approaches distinctly emphasised through reviewing various studies for future research progression.

Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

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A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

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A comprehensive review on hybrid electric vehicles: architectures and components

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

Shunt active power filter: Current status of control techniques and its integration to renewable energy sources

Energy management strategies significantly influence the fuel efficiency of hybrid electric vehicles. They play a crucial role in splitting the power between two sources, namely, engine and the battery. Power split between these two intelligently will enhance the fuel economy and regulates the power flow. Power split between engine and motor depends on state of charge (SOC) of battery, power required at the wheels, and engine's operating range. Various parameters of power train are considered to control the toggling between engine and battery. To achieve parameter optimization, genetic algorithm is practised to realize the optimal performance. A modified SOC estimation algorithm is employed with different battery models to analyze the vehicle performance. The battery models with internal resistance only and combinations of 1RC and 2RC are used. Parameter optimization over different battery models with modified SOC estimation algorithm is performed in different situations and a comparative study is elaborated.

Hari Om Bansal

Papers

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

A comprehensive review on hybrid electric vehicles: architectures and components

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

Shunt active power filter: Current status of control techniques and its integration to renewable energy sources

Energy management strategy for hybrid electric vehicles using genetic algorithm