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Papers by Arun Kumar Kalakanti

ACM Computing Surveys, Jan 24, 2023

Systems, 2022

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important c... more Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need es...

IEEE Transactions on Intelligent Transportation Systems, 2021

With the advent of Electric Vehicles (EVs), issues connected to the electric vehicle charging sch... more With the advent of Electric Vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is NP\NPNP-hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest path problem; however, such formulations have not involved variables such as the charging rates, traffic congestion, scalability, and waiting time at a charging station (CS), that need to be considered in practical settings. Earlier research has also tended to focus on the strengths of particular evolutionary optimization algorithms like differential evolution (DE) or particle swarm optimization (PSO) over others or a traditional mathematical programming method, with only a limited study of hybrid approaches. In this paper, fast and slow charging options at a station have been considered in the EVCS problem for practical use. In previous studies, EVs have been considered to have fixed speeds; however, in order to mitigate CS congestion and thus waiting times at CSs dynamic speed control of EVs has been considered in this work. This work also investigates the scalability of EVCS solutions. A hybrid approach using PSO and the Firefly algorithm (FFA) with L\'evy flights search strategy has been designed and implemented to solve the EVCS. Also, different hybrid methods variants of PSO and FFA have been evaluated in this paper to find the best performing hybrid variant. Experimental results validate the effectiveness of our approach on both synthetic and the real-world transportation networks.

Systems, 2022

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important c... more Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.

Traditional pattern matching and classification algorithms in machine learning field build models... more Traditional pattern matching and classification algorithms in machine learning field build models by extracting patterns from whole data at once. It means once a model is learned about the data, the parameters of the model will never be updated and the model remains unchanged forever. However, in the industrial applications, a large volume of data is accumulated incrementally and the dynamics of the data may change over time. The traditional approach of learning the model using all the data at once may not be feasible because it may require a huge storage for data and take a very long time to build a model from a huge volume of data, and the one-time built model may not be able to learn the changed patterns automatically over time. To overcome this limitation of the traditional approach, we propose two novel algorithms of pattern matching and classification, based on Incremental Learning Support Vector Machine (ILSVM), which learn and update models as new data arrives. Our proposed ...

2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)

2015 IEEE International Conference on Big Data (Big Data), 2015

ACM Computing Surveys, Jan 24, 2023

Systems, 2022

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important c... more Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need es...

IEEE Transactions on Intelligent Transportation Systems, 2021

With the advent of Electric Vehicles (EVs), issues connected to the electric vehicle charging sch... more With the advent of Electric Vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is NP\NPNP-hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest path problem; however, such formulations have not involved variables such as the charging rates, traffic congestion, scalability, and waiting time at a charging station (CS), that need to be considered in practical settings. Earlier research has also tended to focus on the strengths of particular evolutionary optimization algorithms like differential evolution (DE) or particle swarm optimization (PSO) over others or a traditional mathematical programming method, with only a limited study of hybrid approaches. In this paper, fast and slow charging options at a station have been considered in the EVCS problem for practical use. In previous studies, EVs have been considered to have fixed speeds; however, in order to mitigate CS congestion and thus waiting times at CSs dynamic speed control of EVs has been considered in this work. This work also investigates the scalability of EVCS solutions. A hybrid approach using PSO and the Firefly algorithm (FFA) with L\'evy flights search strategy has been designed and implemented to solve the EVCS. Also, different hybrid methods variants of PSO and FFA have been evaluated in this paper to find the best performing hybrid variant. Experimental results validate the effectiveness of our approach on both synthetic and the real-world transportation networks.

Systems, 2022

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important c... more Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) the EV charging station placement (EVCSP) problem, and (ii) the CS need estimation problem for a region. In this work, different explainable solutions based on machine learning (ML) and simulation were investigated by incorporating quantitative and qualitative metrics. The solutions were compared with traditional approaches using a real CS area of Austin and a greenfield area of Bengaluru. For EVCSP, a different class of clustering solutions, i.e., mean-based, density-based, spectrum- or eigenvalues-based, and Gaussian distribution were evaluated. Different perspectives, such as the urban planner perspective, i.e., the clustering efficiency, and the EV owner perspective, i.e., an acceptable distance to the nearest CS, were considered. For the CS need estimation, ML solutions based on quadratic regression and simulations were evaluated. Using our CS planning methods urban planners can make better CS placement decisions and can estimate CS needs for the present and the future.

Traditional pattern matching and classification algorithms in machine learning field build models... more Traditional pattern matching and classification algorithms in machine learning field build models by extracting patterns from whole data at once. It means once a model is learned about the data, the parameters of the model will never be updated and the model remains unchanged forever. However, in the industrial applications, a large volume of data is accumulated incrementally and the dynamics of the data may change over time. The traditional approach of learning the model using all the data at once may not be feasible because it may require a huge storage for data and take a very long time to build a model from a huge volume of data, and the one-time built model may not be able to learn the changed patterns automatically over time. To overcome this limitation of the traditional approach, we propose two novel algorithms of pattern matching and classification, based on Incremental Learning Support Vector Machine (ILSVM), which learn and update models as new data arrives. Our proposed ...

2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)

2015 IEEE International Conference on Big Data (Big Data), 2015