To apply the actual fuel cell hybrid system, improve the efficiency of fuel cells, and reduce the operating cost, the model predictive control (MPC)-based energy management strategy (EMS) combining self-trending prediction and the subset-searching algorithm is proposed in this article. Compared with the traditional MPC, the proposed EMS reduces the error of speed prediction and simplifies the process of solving the optimal control trajectory, which increases the potential for practical engineering applications. Moreover, the proposed EMS considers the degradation of the power sources, which would lead to low operating costs from a long-term scale perspective. Finally, this article carries out a hardware-in-the-loop experiment to verify the feasibility and superiority of the proposed EMS. The results show that compared with the optimal benchmark, the operating cost is only increased by 7.70%, and the fuel cells operate in the high-efficiency range. Otherwise, compared with the traditional Markov chain and dynamic programming, the root-mean-square error and single time of each rolling optimization, respectively, reduce 27.79% and 64.9% at least. In addition, this article tests the adaptability of the proposed EMS on other track sections. The results show that in any track section, the proposed EMS can maintain the state of charge (SOC) and reduce the operating cost.

Model Prediction Control-Based Energy Management Combining Self-Trending Prediction and Subset-Searching Algorithm for Hydrogen Electric Multiple Unit Train

Electrification of powertrain systems offers numerous advantages in the global trend in vehicular applications. A wide range of energy sources and zero-emission propulsion in the tank to wheel significantly add to electric vehicles’ (EV) attractiveness. This paper presents analyses of the energy balance between micro-photovoltaic (PV) installation and small electric vehicle in real conditions. It is based on monitoring PV panel’s energy production and car electricity consumption. The methodology included energy data from real household PV installation (the most common renewable energy source in Poland), electric vehicle energy consumption during real driving conditions, and drivetrain operating parameters, all collected over a period of one year by indirect measuring. A correlation between energy produced by the micro-PV installation and small electric car energy consumption was described. In the Winter, small electric car energy consumption amounted to 14.9 kWh per 100 km and was 14% greater than summer, based on test requirements of real driving conditions. The 4.48 kW PV installation located in Poznan produced 4101 kWh energy in 258 days. The calculation indicated 1406 kWh energy was available for EV charging after household electricity consumption subtraction. The zero-emission daily distance analysis was done by the simplified method.

https://www.mdpi.com/1996-1073/14/4/1085/pdf

Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization

To apply the actual fuel cell hybrid system, improve the efficiency of fuel cells, and reduce the operating cost, the model predictive control (MPC)-based energy management strategy (EMS) combining self-trending prediction and the subset-searching algorithm is proposed in this article. Compared with the traditional MPC, the proposed EMS reduces the error of speed prediction and simplifies the process of solving the optimal control trajectory, which increases the potential for practical engineering applications. Moreover, the proposed EMS considers the degradation of the power sources, which would lead to low operating costs from a long-term scale perspective. Finally, this article carries out a hardware-in-the-loop experiment to verify the feasibility and superiority of the proposed EMS. The results show that compared with the optimal benchmark, the operating cost is only increased by 7.70%, and the fuel cells operate in the high-efficiency range. Otherwise, compared with the traditional Markov chain and dynamic programming, the root-mean-square error and single time of each rolling optimization, respectively, reduce 27.79% and 64.9% at least. In addition, this article tests the adaptability of the proposed EMS on other track sections. The results show that in any track section, the proposed EMS can maintain the state of charge (SOC) and reduce the operating cost. 

A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction

The growing number of electric vehicles in recent years is observable in almost all countries. The country’s energy transition should accompany this rise in electromobility if it is currently generated from non-renewable sources. Only electric vehicles powered by renewable energy sources can be considered zero-emission. Therefore, it is essential to conduct interdisciplinary research on the feasibility of combining energy recovery/generation structures and testing the energy consumption of electric vehicles under real driving conditions. This work presents a comprehensive approach for evaluating the energy consumption of a modern public building–electric vehicle system within a specific location. The original methodology developed includes surveys that demonstrate the required mobility range to be provided to occupants of the building under consideration. In the next step, an energy balance was performed for a novel near-zero energy building equipped with a 199.8 kWp photovoltaic installation, the energy from which can be used to charge an electric vehicle. The analysis considered the variation in vehicle energy consumption by season (winter/summer), the actual charging profile of the vehicle, and the parking periods required to achieve the target range for the user.

Capabilities of Nearly Zero Energy Building (nZEB) Electricity Generation to Charge Electric Vehicle (EV) Operating in Real Driving Conditions (RDC)

A novel energy consumption prediction model with combination of road information and driving style of BEVs

Battery electric vehicle (BEV) has been thought as a key factor in decreasing global greenhouse gas emissions and energy conservation. Therefore, recent developments in BEVs have heightened the nee...

Yuanbin Yu

Papers

A novel energy consumption prediction model with combination of road information and driving style of BEVs

Integrated multistep Markov-based velocity predictor of energy consumption prediction model for battery electric vehicles