International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
Smart Electric Vehicle Management Using a Software Engineering Approach Dr. Addagatla Nagaraju1, *, Akkela Krishnaveni2 , K. Venkatesh3, Ayesha3, B. Prakash4, CH. Anil4 1Lecturer in EEE, Government Polytechnic for Women, Siddipet 2Lecturer in EEE, SRRS Government polytechnic, Sircilla
3Lecturer in CSE, Government Polytechnic for Women, Medek 4Lecturer in CSE, Government Polytechnic for Women, Siddipet
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Abstract - This paper presents a management system for
Energy conservation is a crucial aspect in the electric vehicle (EV) industry, largely due to the inherent limitations in battery capacity. To address this constraint, numerous studies have explored various strategies aimed at reducing energy consumption. These strategies often concentrate on limiting the use of certain vehicle features and functions, particularly those involving actuation and auxiliary components [6]. Efficient information exchange between sensors, actuators, and controllers within the vehicle is also of paramount importance. This communication must be governed by an appropriate protocol, such as the Message Queuing Telemetry Transport (MQTT), which ensures reliable and timely data transfer. In addition to selecting suitable communication protocols, dedicated algorithms are needed to schedule sensor readings and manage data processing effectively [7]. Software engineering models have been increasingly adopted in the development of such systems to enhance reliability, scalability, and maintainability. In electric vehicle systems, it is vital that management algorithms be designed using these models to meet operational requirements and ensure responsive actuation actions under varying conditions [8]–[10]. This paper proposes the use of software engineering models to design both data exchange and resource management algorithms in electric vehicles equipped with a Sensor Network (SN). The SN is integrated via a wired setup across various vehicle components connected to the controller. The data exchange algorithm coordinates the flow of sensor readings and actuation signals through the SN, while the resource management algorithm aims to minimize energy consumption and storage requirements during vehicle operation. This optimization is achieved by employing a software engineering model that dynamically selects among various driving modes, ranging from economical to highperformance settings. A deep learning approach, specifically trained on simulated SN data using MATLAB, is applied to
smart electric vehicles, developed using software engineering models and an integrated Sensor Network (SN). Two distinct software engineering frameworks are applied to design algorithms for both information exchange and resource management, ensuring the vehicle meets its performance requirements. For optimal resource management, a LeNet-5 deep learning model is utilized to select the most suitable driving mode from five available options, based on data generated from a simulated sensor network. The evaluation results indicate effective performance in both data communication and resource handling. A Message Queuing Telemetry Transport (MQTT) broker server is implemented to facilitate and monitor communication among sensors, actuators, and the vehicle's controller. Message transmission delay remains under one second even for 1,000 messages. Additionally, the proposed system demonstrates energy savings ranging from 1 to 8 kWh and a reduction in storage usage between 9 and 95 MB over 100 kilometers. Key Words: Electric Vehicle, Hardware, Software, System
1.INTRODUCTION The global shift from oil-based energy sources to renewable alternatives is compelling for automobile manufacturers to adopt electric vehicle (EV) technologies. However, electric cars face various challenges that must be addressed to support the industry's sustainable transition. Key among these challenges are efficient energy consumption and factors that directly influence vehicle performance. To ensure adaptability across diverse conditions, electric vehicles must incorporate intelligent management systems. Sensor Networks (SNs) play a critical role in enhancing the intelligence of EVs by enabling real-time monitoring of environmental conditions. These networks allow for dynamic interpretation of sensor data and facilitate appropriate system responses. Furthermore, the integration of Artificial Intelligence (AI) techniques—including machine learning and deep learning—with SNs has become increasingly prevalent, enabling the development of optimized, data-driven management solutions [1]–[5].
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