Analysis of Fuel Economy and Battery Life depending on the Types of HEV using Dynamic Programming

Increasing demands of eco-friendly vehicles, various types of hybrid electric vehicle (HEV) have been researched and released. Recently, some research has interest in not only the efficiency of the vehicle but also the durability of battery because the life of battery has influence on the cost of maintenance, stability and performance of the vehicle. In this study, backward simulation based on dynamic programming depending on the type of HEV which is consists of engine and battery or engine, battery and ultra-capacitor was conducted. The developed backward simulation algorithm can calculate the optimal fuel economy according to the driving cycle and other vehicle and components conditions. For the analysis of battery life, a battery capacity fade model was applied to the result of backward simulation. Battery life was estimated with an assumption that the vehicle drives repeatedly to follow the result of backward simulation derived to find the optimal fuel economy. From the simulation results, it is shown that HEV with ultra-capacitor has better fuel economy though it is almost similar with HEV without ultra-capacitor. However, the battery life of HEV with ultra-capacitor was estimated better because of the difference of battery power usage. Consequently, applying the ultra-capacitor to the typical parallel HEV has no large advantage in terms of fuel economy but has significant benefit in terms of battery life

Vehicle Level Control Analysis for Voltec Powertrain

The next generation of the Volt vehicle with the new “Voltec” extended-range propulsion system was introduced into the market in 2016. The second-generation Volt’s powertrain architecture provides five modes of operation, including two electric vehicle operations and three extended-range operations. Vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The study first focused on assessing the improvement of the new Voltec system by comparing the system efficiency with the previous system. Second, control behavior and performance were analyzed under normal ambient temperature to understand the supervisory control strategy on the Voltec system based on the test data. The analysis focused on the engine on/off strategy, powertrain operation mode, energy management, and engine operating conditions. Third, test data from the control analysis were used to summarize the vehicle control logic

On-Track Demonstration of Automated Eco-Driving Control for an Electric Vehicle

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase). The target vehicle is a Chevrolet Bolt, an electric vehicle, which is outfitted with a drive-by-wire (DBW) system that allows external APP/BPP to command the speed of the vehicle, while the operator remains in charge of the steering wheel. The DBW is connected to a rapid prototyping unit by dSpace. This unit includes: (1) real-time software that gathers all digital and analog sensors, as well as signals from the CAN bus; (2) a simple digital twin representation of the track; and (3) automated driving controls. The digital twin representation includes virtual stop signs, speed limits, and traffic lights. The digital twin can broadcast information about current and future road environment (e.g. SPaT) based on the actual position of the vehicle on the track, and correlate that to a position in the digital twin. The automated driving controls include eco-driving controls and an additional safety-focused control layer. The experiments include five road scenarios, and three control calibrations, and each combination is repeated three times. The road scenarios are all within 3.7 km in length, corresponding to one full loop around an oval track at the American Center for Mobility in Michigan, and feature various combinations of stop signs, traffic signals, and speed limits. The control calibrations correspond to a human-driver-like baseline, non-connected automated driving, and automated driving with V2I connectivity. Test-to-test variability is within 2%, thanks to careful thermal conditioning of the vehicle prior to tests. Functionality is verified and demonstrated: no excessive jerk and no violations of traffic laws occur. Energy savings of up to 7% are demonstrated in the no-connectivity case, and up to 22% in the V2I connectivity case. These tests demonstrate the real-world energy-saving potential of automated eco-driving controls