A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.

Electrode Materials for Supercapacitors in Hybrid Electric Vehicles: Challenges and Current Progress

For hybrid electric vehicles, supercapacitors are an attractive technology which, when used in conjunction with the batteries as a hybrid system, could solve the shortcomings of the battery. Supercapacitors would allow hybrid electric vehicles to achieve high efficiency and better power control. Supercapacitors possess very good power density. Besides this, their charge-discharge cycling stability and comparatively reasonable cost make them an incredible energy-storing device. The manufacturing strategy and the major parts like electrodes, current collector, binder, separator, and electrolyte define the performance of a supercapacitor. Among these, electrode materials play an important role when it comes to the performance of supercapacitors. They resolve the charge storage in the device and thus decide the capacitance. Porous carbon, conductive polymers, metal hydroxide, and metal oxides, which are some of the usual materials used for the electrodes in the supercapacitors, have some limits when it comes to energy density and stability. Major research in supercapacitors has focused on the design of stable, highly efficient electrodes with low cost. In this review, the most recent electrode materials used in supercapacitors are discussed. The challenges, current progress, and future development of supercapacitors are discussed as well. This study clearly shows that the performance of supercapacitors has increased considerably over the years and this has made them a promising alternative in the energy sector.

Assessing the impact of Transportation on Climate Change

India's North-East Region has greater demand for road and personalized modes of transport powered by fossil fuels. And due to emissions, there has been evidence of climate change. It has been found that diesel cars cause greater emissions (per kilometer travelled) as compared to petrol cars; therefore, the use of diesel should be discouraged. The chapter suggests that the emissions in case of public transport passenger-km are lesser than other modes of transport. However, in the North-East Region, there is negligible share of public transport due to poor infrastructure and service facilities. Therefore, improvements should be made with regard to public transport system so that considerable number of passengers shifts to public transport modes. Further, it is inferred that use of alternate vehicle or fuel technologies like hybrid electric vehicles, biofuel, biodiesel, hydrogen fuel need to be initiated to mitigate the climate change.

Impact of Electric Vehicle Charging Infrastructure on the Distribution Network Power Quality

This chapter discusses the available charging systems for electric vehicles (EV) which include battery electric vehicles (BEV) and plugged hybrid electric vehicles (PHEV). These architectures are categorized as common DC bus charging (CDCB) station and common AC bus charging (CACB) station. CACB charging stations are generally used as slow chargers or semi-fast chargers (on-board chargers). CDCB charging stations are used as fast chargers (off-board chargers). These chargers are vital to popularize the electric vehicles (EVs) as a green alternative to the internal combustion engine (ICE) vehicles. Further, this chapter covers the power quality problems related to the grid-connected fast charging stations (FCS), AC-DC converter, control strategies for converters, proposed system of architectures, methodology, system results with comparisons, and finally, a conclusion.