Artificial Intelligence Based State of Charge Estimation of Electric Vehicle Battery

Nangrani, S.P.; Nangrani, Ishaan S.

doi:10.1007/978-981-16-6879-1_65

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…In Equation (19), if µ(i, j) ≥ 1, it indicates that the difference between the evaluations of one move and two moves of the same plane is larger than those of one move of two different planes. Therefore, the choice of planes at the same position will be different.…”

Section: Comparison Of One Move Ahead and Two Moves Aheadmentioning

confidence: 99%

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Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Han¹,

Zhou²

2022

Electronics

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Chinese Ludo, also known as Aeroplan Chess, has been a very popular board game for several decades. However, there is no mature algorithm existing for human–machine gambling. The major challenge is the high randomness of the dice rolls, where the algorithm must ensure that the machine is smarter than a human in order to guarantee that the owner of the game machines makes a profit. This paper presents a fast Chinese Ludo algorithm (named “Threat Matrix”) that we have recently developed. Unlike from most chess programs, which rely on high performance computing machines, the evaluation function in our program is only a linear sum of four factors. For fast and low-cost computation, we innovatively construct the concept of the threat matrix, by which we can easily obtain the threat between any two dice on any two positions. The threat matrix approach greatly reduces the required amount of calculations, enabling the program to run on a 32-bit 80 × 86 SCM with a 100 MHz CPU while supporting a recursive algorithms to search plies. Statistics compiled from matches against human game players show that our threat matrix has an average win rate of 92% with no time limit, 95% with a time limit of 10 s, and 98% with a time limit of 5 s. Furthermore, the threat matrix can reduce the computation cost by nearly 90% compared to real-time computing; memory consumption drops and is stable, which increases the evaluation speed by 58% compared to real-time computing.

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Section: Comparison Of One Move Ahead and Two Moves Aheadmentioning

confidence: 99%

“…David B. and his group focus on evolutionary algorithms, while Sebastian presents a rules learning method for general game playing. Thus, this great endeavour requires a combination of methods from a variety of subdisciplines [19,20].…”

Section: Introductionmentioning

confidence: 99%

Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Han¹,

Zhou²

2022

Electronics

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Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Ul Islam Khan,

Pathan,

Mominur Rahman

et al. 2024

IEEE Access

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Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-ϕ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-ϕ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

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Automatic Fault Location Technology of Electronic Circuit Based on Artificial Intelligence

Zheng

2024

2024 International Conference on Electrical Drives, Power Electronics &Amp;amp; Engineering (EDPEE)

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Artificial Intelligence Based State of Charge Estimation of Electric Vehicle Battery

Cited by 6 publications

References 25 publications

Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Automatic Fault Location Technology of Electronic Circuit Based on Artificial Intelligence

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