Lightweight and explainable deep learning model for EV battery voltage prediction
Date
2024
Authors
Shahriar, Saleh Mohammed
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Abstract
Electric vehicles (EVs) play an important role in reducing the greenhouse gas emissions by providing an environment-friendly alternative to the fossil-fuel-based means of transportation. EVs are typically powered by Li-ion battery packs supported by a Battery Management System (BMS). The latter is tasked with monitoring and keeping the battery voltage, current, and temperature within safe operating limits, as well as estimating and improving the battery performance-related parameters, such as the battery state-of-charge and lifespan. In this thesis, we aim to extend the BMS capabilities by enabling battery voltage predictions under a given load profile (i.e., discharge/charge current varying over time). Such predictions are useful for proactive (as opposed to reactive) load management, as they allow a BMS to forecast the battery voltage behaviour under various anticipated load conditions.
Using a data-driven deep learning (DL) approach, we propose a novel model that generates battery voltage estimates given the battery current, temperature, and consumed charge over time. It has a V-shaped architecture that features two wings to enhance the model explainability. The first wing predicts the steady-state opencircuit voltage (OCV) component, based on the consumed battery charge information, while the second wing predicts the transient voltage component, based on the battery current and temperature information. The total number of the model parameters is under 2.6K.
A well-known experimental dataset was used in this study for training, validation, and testing purposes. This dataset contains measurements taken on a Li-ion battery subjected to various EV driving cycles interleaved with charging cycles. The mean absolute percentage error (between predicted and measured battery voltage values) was under 1%, demonstrating the accuracy of the proposed model. Given that a battery must operate within certain maximum and minimum voltage limits, early and accurate voltage estimation has the potential to extend the battery lifetime by enabling proactive optimizations of the battery discharge-charge cycles. An extended battery life implies that a battery-powered EV can remain operational for a longer duration of time, which in turn can facilitate a wider adoption of EVs as an environmentally-friendly transportation alternative.
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Keywords
Electric vehicles, Explainable AI, Deep learning, Voltage prediction