Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle

A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration. The model adaptability to different road conditions and tire wear levels has been verified through a sensitivity analysis and model testing on real-world data proves the robustness and accuracy of the proposed solution achieving a root mean square error (RMSE) of 0.18 deg and a maximum absolute error of 1.52 deg on the test dataset. The proposed model can be considered as a reliable and cheap potential solution for the real-time on-board side-slip angle estimation in serial cars

Reducing the computational cost for artificial intelligence-based battery state-of-health estimation in charging events

Powertrain electrification is bound to pave the way for the decarbonization process and pollutant emission reduction of the automotive sector, and strong attention should hence be devoted to the electrical energy storage system. Within such a framework, the lithium-ion battery plays a key role in the energy scenario, and the reduction of lifetime due to the cell degradation during its usage is bound to be a topical challenge. The aim of this work is to estimate the state of health (SOH) of lithium-ion battery cells with satisfactory accuracy and low computational cost. This would allow the battery management system (BMS) to guarantee optimal operation and extended cell lifetime. Artificial intelligence (AI) algorithms proved to be a promising data-driven modelling technique for the cell SOH prediction due to their great suitability and low computational demand. An accurate on-board SOH estimation is achieved through the identification of an optimal SOC window within the cell charging process. Several Bi-LSTM networks have been trained through a random-search algorithm exploiting constant current constant voltage (CCCV) test protocol data. Different analyses have been performed and evaluated as a trade-off between prediction performance (in terms of RMSE and customized accuracy) and computational burden (in terms of memory usage and elapsing time). Results reveal that the battery state of health can be predicted by a single-layer Bi-LSTM network with an error of 0.4% while just monitoring 40% of the entire charging process related to 60–100% SOC window, corresponding to the constant-voltage (CV) phase. Finally, results show that the amount of memory used for data logging and processing time has been cut by a factor of approximately 2.3

Modeling and On-Road Testing of an Electric Two-Wheeler towards Range Prediction and BMS Integration

The automotive sector is currently shifting its focus from traditional fossil fuels to electrification. The deployment of a Battery Management System (BMS) unit is the key point to oversee the battery state of the electric vehicle (EV) to ensure safety and performances. The development and assessment of electric vehicle models in turn lays the groundwork of the BMS design as it provides a quick and cheap solution to test battery optimal control logics in a Software-in-the-Loop environment. Despite the various contribution to the literature in battery and vehicle modeling, electric scooters are mostly disregarded together with a reliable estimation of their performance and electric range. The present paper hence aims at filling the gap of knowledge through the development of a numerical model for considering a two-wheeler. The latter model relies on the conservation energy based-longitudinal dynamic approach and is coupled to a Li-Ion Battery second-order RC equivalent circuit model for the electric range prediction. More specifically, the presented work assesses the performance and electric range of a two-wheeler pure electric scooter in a real-world driving cycle. The e-powertrain system embeds an Electrical Energy Storage System (EESS) Li-Ion Battery pack. On-road tests were initially conducted to retrieve the main model parameters and to perform its validation. A global battery-to-wheels efficiency was also calibrated to account for the percentual amount of available net power for the vehicle onset. The model proved to properly match the experimental data in terms of total distance traveled over a validation driving mission