Hari Om Bansal

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

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.

Energy management strategy for hybrid electric vehicles using genetic algorithm

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 engines 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.

Energy Management Strategy Implementation for Hybrid Electric Vehicles Using Genetic Algorithm Tuned Pontryagin’s Minimum Principle Controller

To reduce apace extraction of natural resources, to plummet the toxic emissions, and to increase the fuel economy for road transportation, hybrid vehicles are found to be promising. Hybrid vehicles use batteries and engine to propel the vehicle which minimizes dependence on liquid fuels. Battery is an important component of hybrid vehicles and is mainly characterized by its state of charge level. Here a modified state of charge estimation algorithm is applied, which includes not only coulomb counting but also open circuit voltage, weighting factor, and correction factor to track the run time state of charge efficiently. Further, presence of battery and engine together needs a prevailing power split scheme for their efficient utilization. In this paper, a fuel efficient energy management strategy for power-split hybrid electric vehicle using modified state of charge estimation method is developed. Here, the optimal values of various governing parameters are firstly computed with genetic algorithm and then fed to Pontryagin’s minimum principle to decide the threshold power at which engine is turned on. This process makes the proposed method robust and provides better chance to improve the fuel efficiency. Engine efficient operating region is identified to operate vehicle in efficient regions and reduce fuel consumption.

Thermoelectric modeling and online SOC estimation of Li-ion battery for Plug-In Hybrid Electric Vehicles

The increasing oil price, energy demand, and environmental concern are leading to a global switch towards Plug-In Hybrid Electric Vehicles (PHEVs). In a PHEV, Li-ion battery is considered as the primary propelling source. Therefore, an accurate battery model is required to predict the I-V characteristic and dynamic behavior of a battery. This paper presents a highly effective thermoelectric model of Li-ion battery developed in Simulink. An algorithm is proposed for estimation of state of charge (SOC) and open circuit voltage (OCV) adaptively to notify the exact SOC level for better utilization of battery power and optimal vehicle performance. Thermal behavior of Li-ion battery is investigated for wide temperature range and its effect on resistance, capacity, and OCV is recorded. The minimum SOC level to which battery can get depleted is calculated using gradient method. The proposed simulation results are analyzed with those of earlier models and found to be better.

Fuel efficiency optimization of input-split hybrid electric vehicle using DIRECT algorithm

For cleaner and greener future, Hybrid vehicle has been accepted as best practical applications for transportation. The presence of two power sources, i.e. engine and battery in hybrid electric vehicles makes it necessary to intelligently split the power for lesser fuel consumption. An intelligent power management strategy is developed to fulfil on road power demand with good fuel economy. This article uses DIRECT method to control toggling between the engine and battery to reduce the overall liquid fuel consumption. The battery charge is utilized effectively without deteriorating its health. The control strategy is based on the optimization of vital parameters such as state of charge in the battery, engine idle speed, engine on duration and power demand. Numerous simulations are executed on the advanced vehicle simulator (ADVISOR) to authenticate the feasibility of the proposed controller.

Adaptive artificial neural network based control strategy for shunt active power filter

Shunt active power filter (SAPF) is used to mitigate the current harmonics and to improve the power factor. In this paper, Adaptive linear-neuron (ADALINE) based phase lock loop (PLL) controlling scheme is presented for SAPF. ADALINE networks estimate the fundamental supply frequency. This scheme detects the phase information of the supply voltage and also used for parallel computing as it provides faster convergence. This algorithm is trained by least-mean squares (LMS) rule which offers low computational burden on the system. In this work, ADALINE is tuned using particle swarm optimization (PSO) technique to improve the dynamic performance of the system. The results obtained are compared with conventional PLL control technique and are found to be significantly better. The performance of the proposed ADALINE based control algorithm is validated using MATLAB/Simulink.

SCADA and its applications to renewable energy systems integration

The demand of electricity is growing up day by day. The present generation is trying to find new modes of electricity generation and their proper distribution. To enhance the operation of a power system we need to ensure perfect coordination between its various components. SCADA is going to play an integral role in the above task. It is a computer system whose role is to gather and analyze a real time data. In this paper we shall elucidate upon the basics of SCADA, its components and their functioning. The problems concerned with the renewable energy integration will be highlighted. We will illustrate upon the importance of SCADA communication networks in renewable energy integration. We shall also look upon into the various challenges concerned with the SCADA communication networks.

Electronic aid for elder and sick people IR-remote controlled switch board for changing state of electrical appliances

This paper propose a wireless switch board for easy-accessibility in an hospital-like environment for controlling various electrical appliances like light, fan, etc. without interference with similar wireless switches in adjoining room. The data signal is communicated to receiver wirelessly by IR Remote Controller. The receiver consists of IR Receiver, TSOP1838, to read data signal operating at 38 kHz and a microcontroller to process data and communicate trigger signal to change the state of switch.

Green transportation: need, technology and challenges

Internal combustion (IC) engine based vehicles are the backbone of the modern transport sector. These vehicles use fossil fuels as a source of energy to propel it and emit toxic gases. These noxious gases harm the environment and causes human health problems. Hasty usage of fossil fuels results in rapid depletion of these resources and price inflation. These concerns encourage the modern society to discover alternatives for sustainable future transportation. Various fuel efficient technologies, like hybrid vehicles, are essentially the solution to fulfil the world’s need of greener environment. This paper discusses about various aspects like sources of pollution, decreasing level of fossil fuel, dependency on oil energy and need of green vehicles. It suggests to adopt hybrid vehicles, tells the challenges in accepting them as part of the transportation system and their remedies also. The status of hybrid vehicles on the roads worldwide and initiatives taken by different governments are discussed in lucent manner. The paper deliberately describes governments’ schemes to focus on securing its energy resources, trim down reliance on fossil fuels and to promote hybrid vehicles on roads in the pollution-free world.

Solar powered mini steam power plant: a simulation approach

The solar powered steam power plant is a mini power plant that is meant to generate power at the location of the consumer hence playing a pivotal role in distributed power generation. This plant will also have a huge impact on the environment as it utilises the sun’s energy, making it eco-friendly and reducing its impact on the environment by reducing the amount of fossil fuel consumption. In this paper, an effort has been made to use the sun energy to meet the energy needs by proposing a hybrid power plant. The simulation is implemented in Labview.