This paper examines the development of digital dashboards (clusters) in electric vehicles (EVs) aimed at providing drivers with clear, real-time, and essential information. As EV adoption continues to rise due to government incentives and growing environmental awareness, the demand for an intuitive, user-friendly, and efficient dashboard has become increasingly important. An EV’s digital cluster displays crucial data, such as speed, battery level, estimated driving range, and energy consumption. Additionally, it incorporates features like navigation, warning alerts, and entertainment systems, ensuring drivers stay informed without being overwhelmed. Given the complexity of EV functionalities, including regenerative breaking and energy-efficient driving modes, the dashboard must present this information in a simplified and easily comprehensible manner. Moreover, this study provides a detailed, step-by-step approach to designing and implementing EV indicators using the QT simulator, a widely recognized tool for creating human-machine interfaces (HMI) in automotive applications. It offers valuable insights into the technical aspects of developing, displaying, and refining various dashboard components to enhance efficiency and user convenience. The research primarily focuses on developing an EV dashboard that is both functional and user centric. It explores various display configurations, customization options, and adaptability to driver preferences. Furthermore, it examines the integration of a newsfeed within the dashboard and elaborates on the process of designing EV indicators using the QT simulator, outlining the key steps involved in their creation and presentation.

Keywords: Electric vehicle, cluster design, human machine interface (HMI), QT simulator,
machine learning

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1. Bedollo LC, Alves AD, Sakai F. Technologies and trends for instrument clusters. SAE Technical Paper; 2004 Nov 16.
2. Vora G, Gundewar P. Survey on designing of electric vehicle instrument cluster. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) 2018 Jun 14 (pp. 765–771). IEEE.
3. Dhaliwal A, Nagaraj SC, Ali S. Hardware-in-the-loop simulation for hybrid electric vehicles–an overview, lessons learned and solutions implemented.
4. Bock C. Model-driven HMI development: Can meta-CASE tools do the job? In 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07). 2007 Jan 3. pp. 287b–287b. IEEE.
5. Amditis A, Kubmann H, Polychronopoulos A, Engstrom J, Andreone L. System architecture for integrated adaptive HMI solutions. In 2006 IEEE Intelligent Vehicles Symposium. 2006 Jun 13. pp. 388–393. IEEE.
6. Perisoara LA, Sacaleanu DL, Vasile A. Instrument clusters for monitoring electric vehicles. In 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). 2017 Oct 26. pp. 379–382. IEEE.
7. Mahdavian A, Shojaei A, Mccormick S, Papandreou T, Eluru N, Oloufa AA. Drivers and barriers to implementation of connected, automated, shared, and electric vehicles: An agenda for future research. IEEE Access. 2021 Feb 1;9:22195–213.
8. Li C, Zhang S, Ling W, Zhao L, Pan Y. Enhancing user experience in electric vehicle charging applications (EVCA): A comprehensive analysis in the chinese context. J Knowl Econ. 2024 Feb 28:1–36.
9. Sumith GS, Bhatt R, Shrinivasan L, Bagri Y, Shrinivas SV. Digital dashboard for electric vehicles. In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). 2022 Oct 8. pp. 413–418. IEEE.
10. Drogeanu NM, Perişoară LA, Văduva JA. Web interface for IoT vehicle monitoring system. In 2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging (SIITME). 2022 Oct 26. pp. 185–190. IEEE.

This paper examines the development of digital dashboards (clusters) in electric vehicles (EVs) aimed at providing drivers with clear, real-time, and essential information. As EV adoption continues to rise due to government incentives and growing environmental awareness, the demand for an intuitive, user-friendly, and efficient dashboard has become increasingly important. An EV’s digital cluster displays crucial data, such as speed, battery level, estimated driving range, and energy consumption. Additionally, it incorporates features like navigation, warning alerts, and entertainment systems, ensuring drivers stay informed without being overwhelmed. Given the complexity of EV functionalities, including regenerative breaking and energy-efficient driving modes, the dashboard must present this information in a simplified and easily comprehensible manner. Moreover, this study provides a detailed, step-by-step approach to designing and implementing EV indicators using the QT simulator, a widely recognized tool for creating human-machine interfaces (HMI) in automotive applications. It offers valuable insights into the technical aspects of developing, displaying, and refining various dashboard components to enhance efficiency and user convenience. The research primarily focuses on developing an EV dashboard that is both functional and user centric. It explores various display configurations, customization options, and adaptability to driver preferences. Furthermore, it examines the integration of a newsfeed within the dashboard and elaborates on the process of designing EV indicators using the QT simulator, outlining the key steps involved in their creation and presentation.

Keywords: Electric vehicle, cluster design, human machine interface (HMI), QT simulator,
machine learning

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Bedollo LC, Alves AD, Sakai F. Technologies and trends for instrument clusters. SAE Technical Paper; 2004 Nov 16.
2. Vora G, Gundewar P. Survey on designing of electric vehicle instrument cluster. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) 2018 Jun 14 (pp. 765–771). IEEE.
3. Dhaliwal A, Nagaraj SC, Ali S. Hardware-in-the-loop simulation for hybrid electric vehicles–an overview, lessons learned and solutions implemented.
4. Bock C. Model-driven HMI development: Can meta-CASE tools do the job? In 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07). 2007 Jan 3. pp. 287b–287b. IEEE.
5. Amditis A, Kubmann H, Polychronopoulos A, Engstrom J, Andreone L. System architecture for integrated adaptive HMI solutions. In 2006 IEEE Intelligent Vehicles Symposium. 2006 Jun 13. pp. 388–393. IEEE.
6. Perisoara LA, Sacaleanu DL, Vasile A. Instrument clusters for monitoring electric vehicles. In 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). 2017 Oct 26. pp. 379–382. IEEE.
7. Mahdavian A, Shojaei A, Mccormick S, Papandreou T, Eluru N, Oloufa AA. Drivers and barriers to implementation of connected, automated, shared, and electric vehicles: An agenda for future research. IEEE Access. 2021 Feb 1;9:22195–213.
8. Li C, Zhang S, Ling W, Zhao L, Pan Y. Enhancing user experience in electric vehicle charging applications (EVCA): A comprehensive analysis in the chinese context. J Knowl Econ. 2024 Feb 28:1–36.
9. Sumith GS, Bhatt R, Shrinivasan L, Bagri Y, Shrinivas SV. Digital dashboard for electric vehicles. In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). 2022 Oct 8. pp. 413–418. IEEE.
10. Drogeanu NM, Perişoară LA, Văduva JA. Web interface for IoT vehicle monitoring system. In 2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging (SIITME). 2022 Oct 26. pp. 185–190. IEEE.