Advanced Deep Neural Network Framework for Performance Optimization and Safety Monitoring of Lithium-Ion Batteries in Vehicle Engineering

Abstract

Lithium-ion batteries are an essential part of electric vehicles (EVs), and their performance and safety monitoring are difficult because of a severely nonlinear and time-varying behavior under dynamic operating conditions. Traditional Battery Management Systems (BMS) can also be unable to give accurate estimates of such critical internal states as State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL) which can be represented as nonlinear functions of quantifiable inputs: SOC = f₁(V, I, T, t), SOH = f₂(Cₙ, Rₙ, n), RUL = f₃(D(t)), V is the voltage, I current, T is temperature, t is time, C n is capacity growth, R n is internal resistance, and D (t) is time variation. The study is aimed at creating an advanced deep neural network (DNN) model to maximize the performance of the battery and make it possible to detect the anomalies in real-time. There was a complete dataset gathered in different charge- discharge cycles at different environment conditions. Neural networks that were fully connected and networks that are hybrids between physics-based degradation dynamics and data-driven learning were trained to approximate nonlinear mappings: ŷ = F_θ(x), and where x ℝn is the inputs to the network, and 0 denotes the parameters of the network that are optimized by minimizing a loss function: L(θ) = (1/N) Σ (yᵢ − ŷᵢ)². An auto encoder design was adopted to detect anomalies, where the reconstruction error was used to reconstruct the input signals and detect deviations: ε = ‖x − x̂‖², at such an anomaly where ε is greater than τ (threshold). The findings showed that the hybrid DNN model was more predictive with the lowest root mean square error (RMSE) in SOC, SOH, and RUL estimation. The autoencoder was found to be very sensitive to identifying abnormal behavior of the battery and this allowed the approach to diagnose fault early. Concluding, the suggested deep learning model is an effective solution to the second generation of intelligent BMS since it offers fast and efficient state estimation, strong abnormal situation detection in-the-field, and, therefore, enhances the safety and efficiency of lithium-ion batteries used in EV operations.