Affiliation:
1. Loughborough University
2. Jaguar Land Rover
Abstract
<div class="section abstract"><div class="htmlview paragraph">Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach. The solution proposed intends to use a combination of the states determined in other sensors and the control inputs made into the system to predict the state of the sensor to be virtualised, with the aim of an average accuracy of 95% and a worst-case accuracy of 80%. Also discussed are the potential methods of nonlinear system identification that can be used to achieve these goals, concluding through a literature review that a Neural Network solution is the most probable method to produce an accurate result. Based on this an analysis is performed of the challenges of neural network development, from collecting and processing data, to actually training the neural network and evaluating the performance outcome. Establishing that depending on the quality and quantity of data collection a range of methods that could be implemented.</div></div>
Reference72 articles.
1. Shelly , T.J. , Weibel , J.A. , Ziviani , D. , and Groll , E.A. Comparative Analysis of Battery Electric Vehicle Thermal Management Systems Under Long-Range Drive Cycles Applied Thermal Engineering 198 2021 117506
2. Shelly T. , Weibel J.A. , Ziviani D. , and Groll E.A. A Dynamic Co-Simulation Framework for the Analysis of Battery Electric Vehicle Thermal Management Systems InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITHERM 2022 1 8
3. Sanguesa , J.A. , Torres-Sanz , V. , Garrido , P. , Martinez , F.J. et al. A Review on Electric Vehicles: Technologies and Challenges Smart Cities 4 1 2021 372 404
4. Schmitt , J. , Bönig , J. , Borggräfe , T. , Beitinger , G. et al. Predictive Model-Based Quality Inspection Using Machine Learning and Edge Cloud Computing Advanced Engineering Informatics 45 2020 101101
5. Huang , Y. , Cheng , Y. , Bapna , A. , Firat , O. et al. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism Advances in Neural Information Processing Systems 32 2019 1 11