Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

Abstract

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

Real-Time Implementation Comparison of Urban Eco-Driving Controls

“…The information which is available to CAVs comes from the advanced driver assistance system (ADAS) system of the CAV and from V2I communication where available. The data which are available [64].…”

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

“…Additionally, estimating IMU yaw misalignment by fusing information from automotive onboard sensors and an adaptive Kalman filter can enhance the accuracy of ML models in capturing vehicle dynamics [177]. IoT-based datasets [44,47,56,64,81,85,95,[106][107][108]110,117,122,125,126,128,134,136,138,140,144,149,155,172] 24…”

“…Highway [19][20][21][22]24,25,27,28,30,[32][33][34][35]38,44,45,49,[53][54][55]57,60,64,66,69,70,73,84,86,[88][89][90][91][93][94][95][96][97][98][99]101,102,105,107,108,112,113,[115][116][117][118][119]121,…”

Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand

“…Petkevicius et al (2021) proposed deep-learning models that are built from electric vehicle tracking data and for predicting EV energy use [35]. Few researchers worked on velocity predictions of electric vehicles using machine learning algorithms, and thereby carried out effective energy management [36][37][38][39][40][41][42].…”