Advanced Research on Energy Management Strategy Optimization for Extended-Range Electric Vehicles

In the context of the global automotive industry’s vigorous transformation towards new energy, extended-range electric vehicles (EREVs) have emerged as a crucial transitional product, effectively addressing the range anxiety of pure electric vehicles while maintaining environmental friendliness. However, the performance of EREVs largely depends on the efficiency of their energy management strategies. Recently, a groundbreaking study on optimizing energy management strategies for EREVs has attracted widespread attention in the industry. This research, conducted by Gaojunpeng from Kunming University of Science and Technology, delves into the core issues of energy management in EREVs, providing valuable insights for the further development of the industry.

The Rising Significance of EREVs in the New Energy Landscape

With the escalating global energy crisis and increasingly stringent environmental regulations, the automotive industry is undergoing an unprecedented shift towards electrification. Traditional fuel-powered vehicles, once dominant, are gradually giving way to new energy vehicles due to their heavy reliance on fossil fuels and significant carbon emissions. Electric vehicles, with their advantages of energy efficiency, economy, and environmental friendliness, have become the focus of development in the global automotive sector .

Yet, pure electric vehicles still face challenges such as limited driving range, inconvenient charging, and severe battery aging, which restrict their widespread adoption. In this scenario, EREVs have emerged as a viable solution. By integrating an internal combustion engine as a range extender (APU) alongside an electric motor and battery pack, EREVs can operate in electric mode for short distances and switch to the range extender to charge the battery or directly drive the vehicle when the battery level is low, thereby significantly increasing the driving range.

Against this backdrop, the optimization of energy management strategies for EREVs has become a hot topic in academic and industrial research. A search on CNKI for “energy management strategies for extended-range electric vehicles” reveals a growing trend in related studies, reflecting the industry’s increasing focus on this field.

Overview of Current Research on Energy Management Strategies

Over the years, numerous scholars and research institutions have dedicated themselves to exploring energy management strategies for EREVs, yielding a series of important research results.

Xuyu Zhe, Wang Wei, Wang Rujia, and others applied Matlab/Simulink to model the proposed control strategies and conducted co-simulation analysis on the vehicle model built in the Cruise environment. This research laid the foundation for the simulation and verification of energy management strategies.

Wang Chao, Cao Li, and colleagues, in accordance with the relevant requirements for the Charge Sustaining Mode (CSM) under the GB 18352.6-2016 regulation, carried out research on control strategies for Extended-Range Electric Vehicles (EREVs) based on energy management.They built simulation calculation models and control models for EREVs using AVL CRUSE simulation software and Simulink modeling software. Both simulation and test results demonstrated that the range extender under this control strategy can meet power demands, providing a practical reference for engineering applications.

Feng Renhua, Sun Wangbing, Zhao Zhichao utilized GT-SUITE software to establish a simulation analysis model for extended-range hybrid vehicles, which further improved the fuel economy of the entire vehicle. This highlighted the importance of software modeling in optimizing vehicle performance.

Chen Yong, Wei Changyin, Li Xiaoyu addressed the issue that the design of fuzzy energy management strategies relies heavily on expert experience and is difficult to adapt to complex working conditions. They proposed a fuzzy energy management strategy for EREVs based on neural network working condition identification. This strategy enhanced the adaptability of the energy management system to varying driving conditions.

In order to improve the fuel economy of EREVs under complex working conditions, Baishujie, Wei Changyin, and Chen Yong proposed a rule-based energy management strategy using genetic algorithm-optimized backpropagation neural network (GA-BP) for working condition identification. Aiming at the problems of slow convergence speed and poor generalization ability of the BP algorithm, they adopted genetic algorithms to optimize the BP neural network, significantly improving the performance of the energy management strategy.

These studies have laid a solid theoretical and practical foundation for the development of energy management strategies for EREVs, while also pointing out the direction for further optimization.

Case Study: Energy Management Strategy Optimization for a 600kg EREV

Gaojunpeng’s research focused on a specific case of an EREV with a total mass of 600kg, conducting in-depth exploration into energy management strategy optimization.

Vehicle Parameter Selection

In EREVs, the voltage level of the battery is a critical parameter that affects the overall performance of the vehicle. There is an inverse relationship between voltage level and current loss: the lower the voltage level, the higher the battery current loss. For example, if the power battery efficiency is low, the current-carrying capacity of the wires will decrease accordingly. However, a higher voltage level does not necessarily mean better overall performance. Excessively high voltage levels may compromise safety, and achieving high voltage requires increasing the number of series-connected cells, which to some extent affects the uniformity of the power battery.

In response to this, the research referred to the regulations on power supply voltage levels in GB/T18488.1-2006. Considering that the engine and generator in EREVs are coaxially coupled, the selection of battery voltage level should meet the requirements of the motor’s operating voltage changes, promoting consistency between the power battery and motor voltage levels in EREVs. After comprehensive consideration, the study determined that the power battery of the EREV should have a rated voltage Ub=600V and ultimately selected a ternary lithium battery.

Development of Energy Management Strategies

The research proposed two energy management strategies: the single-point thermostat strategy and the multi-point control strategy based on power demand, and conducted a comparative analysis of their performance.

Single-Point Thermostat Strategy

The single-point thermostat control strategy determines the on/off state of the APU based on the state of charge (SOC) of the power battery. One of its significant advantages is that the APU output power remains constant. The specific start and stop logic is designed to ensure the battery operates within a reasonable SOC range, preventing over-discharge or over-charge.

This strategy is relatively simple in design and easy to implement, making it suitable for relatively stable driving conditions. However, its fixed output power may not adapt well to complex and variable driving scenarios, potentially leading to inefficient energy use.

Multi-Point Control Strategy Based on Power Demand

To address the limitations of the single-point thermostat strategy, the research proposed a multi-point control strategy based on power demand. This strategy mainly determines the APU operating point based on the SOC of the EREV’s power battery and the required power P_req of the EREV.

The logic control rules of the multi-point control strategy are as follows:

When P_req < Plow:

If SOC < SOC_low, the APU is on, and Pe=Plow.
If SOC_low ≤ SOC < SOC_high, the APU is in hold mode, and Pe=Plow or Pe=0.
If SOC > SOC_high, the APU is off.

When Plow < P_req < Phigh:

If SOC < SOC_low, the APU is on, and Pe=Phigh.
If SOC_low ≤ SOC < SOC_high, the APU is in hold mode, and Pe=Plow/Phigh or Pe=0.
If SOC > SOC_high, the APU is off.

When P_req > Phigh:

If SOC < SOC_low, the APU is on, and Pe=Phigh.
If SOC_low ≤ SOC < SOC_high, the APU is in hold mode, and Pe=Phigh or Pe=0.
If SOC > SOC_high, the APU is off.

When P_req < 0, the APU is off, and Pe=0.

Under the multi-point control strategy, the APU output power switches according to P_req. Therefore, the number of operating points should be less than two in the setting process. The APU operating points can be controlled through two operating points: cs_min_pwr and cs_max_pwr. If the APU required power is relatively large, it operates at the cs_max_pwr power point; if the required power is small, it switches to the cs_min_pwr operating point.

In the specific design, the research comprehensively considered the APU’s optimal operating curve and analyzed the feasibility of engine speed switching, setting cs_min_pwr to 48.5kW and cs_max_pwr to 60kW. cs_max_pwr is set as the power point, and cs_min_pwr is kept consistent with the thermostat operating point to achieve the optimal operating state.

This strategy enables the APU to adjust its output power according to actual demand, which is expected to improve energy utilization efficiency under complex working conditions.

Comparative Analysis of Different Energy Management Strategies

To evaluate the performance of the two energy management strategies, the research conducted a comparative analysis of their energy flow and energy consumption.

Energy Flow Comparison

The energy flow of the single-point thermostat strategy and the multi-point control strategy shows different characteristics. Under the single-point thermostat strategy, the energy distribution is relatively fixed due to the constant output power of the APU. In contrast, the multi-point control strategy adjusts the APU output power according to demand, resulting in a more flexible energy flow, which can better match the varying energy needs during vehicle operation.

Energy Consumption Comparison

Energy consumption is a key indicator to measure the performance of energy management strategies. The research results show that the energy consumption of the multi-point control strategy for EREVs is higher than that of the single-point thermostat strategy. In terms of power battery energy consumption, the single-point thermostat strategy is higher than the multi-point control strategy, with the multi-point control strategy reducing battery consumption by 6.14%.

In terms of energy consumed by the engine, the multi-point control strategy is higher than the single-point thermostat strategy, which is consistent with the distribution law of EREV energy consumption. This indicates that the difference in engine energy consumption under different energy management strategies has a relatively significant impact on total energy consumption. From the perspective of engine energy consumption, the single-point thermostat strategy has obvious advantages over the multi-point control strategy, with the multi-point control strategy increasing average engine consumption by 3.76 units.

In terms of generator energy consumption, the multi-point control strategy is higher than the single-point thermostat strategy, which is consistent with the law of vehicle energy consumption distribution. At the same time, the engine consumption of the single-point thermostat strategy is relatively lower. However, in terms of the power battery charging coefficient and fuel consumption, the single-point thermostat strategy is higher than the multi-point control strategy, which means that the service life of the power battery is optimized under the multi-point control strategy.

Conclusion and Future Outlook

The research on optimizing energy management strategies for EREVs holds significant importance for the development of the new energy vehicle industry. Gaojunpeng’s study, through in-depth analysis of specific cases, compares the advantages and disadvantages of the single-point thermostat strategy and the multi-point control strategy, providing practical references for the application of energy management strategies in EREVs.

The single-point thermostat strategy has the advantage of lower engine energy consumption, making it suitable for relatively stable driving environments. The multi-point control strategy, on the other hand, excels in reducing battery consumption and optimizing battery life, showing better adaptability in complex working conditions. Therefore, in actual vehicle design and application, appropriate energy management strategies should be selected according to specific usage scenarios.

Looking ahead, with the continuous advancement of technology, energy management strategies for EREVs will move towards more intelligent and adaptive directions. The integration of technologies such as artificial intelligence, big data, and Internet of Things will enable real-time adjustment of energy management strategies based on driving conditions, driver habits, and road information, further improving the energy efficiency and performance of EREVs.

This research not only enriches the theoretical system of energy management for EREVs but also provides valuable guidance for the industrial application of EREVs. It is believed that with the continuous deepening of related research, EREVs will play a more important role in the global new energy vehicle market, contributing to the realization of global carbon neutrality goals.

Author: Gaojunpeng, Kunming University of Science and Technology