Energy Management System For Electric Vehicles Research Articles

Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

Transportation is currently advancing towards Electric Vehicle (EV) technology. This paper presents a brief and systematic analysis of the real-time issues obtained in Electric Vehicles (EVs) due to the various ranges of energy storage devices. In general, EV energy management system is integrated with power electronic circuits for effective power conversion and reliable operation. Some issues are addressed while using the batteries in EV systems such as charging time, efficiency of battery, and raw materials. Not only battery issues but also real-time non-technical issues and operational issues are also discussed in this paper. During energy conversion with power electronic circuits, the system attains extreme temperature levels which in turn reduces the performance of the system. To maintain an optimum temperature level, it is important to study the temperature effect of the system at the most prior levels. Due to the adaption of power electronic components, some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, to mitigate the EMI effect, the low pass filter-based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply systems in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behaviour of BCS the proposed RNN controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.

Microcomputer Energy Saving Control System for Electric Vehicles Under Artificial Intelligence

Faced with the problem of dwindling oil resources and increasingly strict automobile exhaust emission standards, the advantages of pure electric vehicles in energy saving and emission reduction have become more and more prominent compared with traditional fuel vehicles. However, in view of the current development scale of the pure electric vehicle market, there are still many deficiencies in the aspects of power density, cycle service life and replacement cost. This paper mainly studies the microcomputer energy saving control system of electric vehicle under the background of artificial intelligence. In this paper, the STM32F103 series chip is used as the main control chip of the battery energy management system. Then according to the requirements of the subject of each module circuit design, forming a complete battery energy management system circuit design. In this paper, the designed electric vehicle energy management system has been tested. The test results show that the electric vehicle battery energy management system has perfect function, sTable operation, and can realize the energy management of electric vehicle battery pack. It can accurately predict the state of charge of electric vehicle lithium battery, and effectively improve the battery performance of electric vehicle lithium battery, and achieve the effect of energy saving.

Implementation of hybrid electric vehicle energy management system for two input power sources

Abstract Aiming at reducing both size and losses of energy saving system and also integrating the system’s output current and voltage, this paper proposed a new energy management strategy using novel controller in application of hybrid electric vehicles (HEVs). The proposed controller, called hybrid sliding mode controller (HSMC), significantly improves the transient response and disturbance rejection of the two input bidirectional converters (TIBCs), while preserving the closed loop stability. The combination of the SMC and invasive weed optimization (IWO), realizes a fast transient response over a wide transient load changes and input voltage disturbances. The development of TIBC modelling based on state space averaging technique has been discussed. Practical and simulation results are reported to validate the theoretical predictions and to confirm the superior performance of the proposed nonlinear controller when it is compared with a traditional SMC. Finally the performance of manufactured EV which equipped with proposed controller was investigated through practical test in case of the stochastic EV driving cycle.

Software Defined Machine-to-Machine Communication for Smart Energy Management

The successful realization of smart energy management relies on ubiquitous and reliable information exchange among millions of sensors and actuators deployed in the field with little or no human intervention. This motivates us to propose a unified communication framework for smart energy management by exploring the integration of software-defined networking with machine-to-machine communication. In this article, first we provide a comprehensive review of the state-of-the-art contributions from the perspective of software defined networking and machineto- machine integration. Second, the overall design of the proposed software-defined machine-to-machine (SD-M2M) framework is presented, with an emphasis on its technical contributions to cost reduction, fine granularity resource allocation, and end-to-end quality of service guarantee. Then a case study is conducted for an electric vehicle energy management system to validate the proposed SD-M2M framework. Finally, we identify several open issues and present key research opportunities.

Energy Management System Optimal Strategy for Pure Electric Vehicle Based on the Dynamic Programming

This thesis puts forward one optimal adaptive fuzzy control method based on the pure electric vehicle energy management system of the fuzzy control which has been founded already. By adding an optimizing researching model based on the conventional fuzzy control strategy, the thesis can pick up the valuable control rules based on the dynamic programming theory and also can adjust the parameter of the fuzzy controller automatically according to the system operating. These can make the sum of the energy loss reduce to the min. The experiment points out that this method makes the vehicle possess good economic performance in the same driving cycle.

Fuzzy Control Based Study on Energy Management System of Electric Vehicle with Dual-Energy Storage System

Regarding to the electric vehicle (EV) with dual-energy storage system (DESS) composed of batteries and ultra-capacitors, study on the structure and drive modes of DESS, after a detailed analysis of energy storage system based on power, resistance and constraints in driving, establish a mathematical model of energy management system of EV with DESS, and an energy management based on the fuzzy control strategy is designed. Finally, a simulation of EV with DESS by using ADVISOR simulation platform is studied, whose results show that the EV with DESS based on fuzzy control strategy can be more effective in distributing power between energy storage systems, and the dynamic performance as well as economic efficiency are also improved

A Battery Management System using Nonlinear Model Predictive Control for a Hybrid Electric Vehicle

This present paper introduces a battery management system using nonlinear model predictive control for a hybrid electric vehicle. This paper adds two new contributions to this field. First, the apparent relationship between the battery power and the future road load is addressed in the cost function of the fuel economy optimal control problem with a simplified hybrid electric vehicle energy management system model. Second, it examines quantitatively the effects of operating the engine along the best efficiency line of the engine with a continuously variable transmission using a commercially available hybrid electric vehicle energy management electronic control unit simulator. Effectiveness of the proposed algorithm is validated by the JSAE-SICE benchmark problem II simulator.

Advanced digital control for a switched capacitor and interleaved switched capacitor hybrid electric vehicle energy management system

This paper presents the analysis and novel controller design for a two-quadrant hybrid switched capacitor (SC) converter and interleaved two-quadrant SC bidirectional DC/DC converter for a hybrid electric vehicle dual energy storage system. The designed novel control strategy enables simpler dynamics compared to a standard buck converter with input filter, good regulation capability, low EMI, lower source current ripple, ease of control, and continuous input current waveform in both buck as well as boost modes of operation.