As the global focus on carbon neutrality intensifies, the development of electric vehicles has accelerated, positioning them as a key player in the automotive market. In China, the electric vehicle (China EV) industry has seen rapid growth, driven by government policies and technological advancements. A critical aspect of enhancing the performance and range of electric vehicles is the efficient management of energy storage systems. The hybrid energy storage system (HESS), which combines lithium-ion batteries and supercapacitors, offers a promising solution by leveraging high energy density and high power density, respectively. This paper explores an energy management strategy based on optimized vehicle speed using fuzzy control to improve the efficiency of HESS in electric vehicles. By addressing issues such as excessive charge and discharge, this strategy aims to extend battery life and optimize energy utilization, contributing to the sustainable development of China EV and the broader electric vehicle sector.
The hybrid energy storage system in electric vehicles typically consists of lithium-ion batteries, bidirectional DC-DC converters, and supercapacitors. This configuration, known as a semi-active topology, allows for efficient power distribution and voltage stabilization. The lithium-ion battery provides high energy density for long-range driving, while the supercapacitor offers high power density for handling peak loads, such as during acceleration or regenerative braking. The DC-DC converter plays a crucial role in matching the voltage levels between these components, ensuring minimal energy loss and enhanced system reliability. For instance, the bidirectional DC-DC converter can step up or step down voltages as needed, facilitating seamless energy flow. The supercapacitor is often modeled using a standard RC circuit, where the equivalent capacitance and resistance are connected in series. This model helps in analyzing the dynamic behavior of the supercapacitor under varying load conditions. The relationship between the supercapacitor’s terminal voltage, current, and internal parameters can be expressed using Kirchhoff’s laws, as shown in the following equation:
$$ U_{sc} = U_c – i_c R_{sc} $$
where \( U_{sc} \) is the terminal voltage of the supercapacitor, \( i_c \) is the current through the supercapacitor, \( U_c \) is the voltage across the equivalent capacitance, and \( R_{sc} \) is the equivalent resistance. This equation is fundamental for designing control strategies that regulate energy flow in HESS for electric vehicles.
The energy control characteristics of HESS in electric vehicles are complex due to the nonlinear and time-varying nature of vehicle dynamics. Factors such as road conditions, driving patterns, and load demands make it challenging to develop precise mathematical models. Fuzzy control emerges as a suitable approach because it does not require an exact model and can handle uncertainties effectively. Fuzzy control systems use linguistic variables and rules derived from expert knowledge to make decisions, making them robust against parameter variations and disturbances. In the context of China EV, where urban and highway driving conditions vary widely, fuzzy control can adapt to changing power requirements by adjusting the power distribution between the battery and supercapacitor. The structure of a fuzzy controller includes fuzzification, a knowledge base (comprising rules and databases), fuzzy inference, and defuzzification. For example, inputs such as vehicle speed and power demand are fuzzified into terms like “high” or “low,” and rules like “if speed is high and power demand is large, then reduce motor speed” are applied to generate control outputs. This flexibility allows the fuzzy controller to optimize HESS performance in real-time, enhancing the overall efficiency of electric vehicles.
To further refine the energy management strategy, this paper proposes a fuzzy control approach based on optimized vehicle speed. The core idea is to use the real-time vehicle speed to estimate a reference value for the supercapacitor’s state of charge (SOC) and compare it with the actual SOC. This comparison enables dynamic adjustment of the power分配系数 (allocation coefficient) between the battery and supercapacitor, reducing large current fluctuations in the battery and improving energy recovery during braking. The theoretical reference SOC for the supercapacitor, denoted as \( z_{ck} \), is derived from the vehicle’s kinetic energy and supercapacitor parameters. The relationship is given by:
$$ z_{ck} = 1 – \left( \frac{V}{V_{\text{max}}} \right)^2 $$
where \( V \) is the current vehicle speed and \( V_{\text{max}} \) is the maximum speed. The error between the reference and actual SOC, \( \Delta z = z_{ck} – z_{sj} \), is used to modify the power allocation coefficient \( L \) in the fuzzy controller. In driving mode, if \( \Delta z > 0 \), the battery’s discharge power is increased to conserve supercapacitor energy; if \( \Delta z < 0 \), the supercapacitor’s power share is raised to prevent battery over-discharge. In braking mode, the opposite adjustments are made to optimize energy recuperation. The final power allocations are computed as:
$$ P_c = \begin{cases}
L \cdot P_Q + L_1 \cdot (\Delta z \cdot P_Q) & \text{if } \Delta z > 0 \text{ in driving mode} \\
L \cdot P_Q + L_2 \cdot (\Delta z \cdot P_Q) & \text{if } \Delta z < 0 \text{ in driving mode} \\
L \cdot P_Q + L_3 \cdot (\Delta z \cdot P_Q) & \text{if } \Delta z > 0 \text{ in braking mode} \\
L \cdot P_Q + L_4 \cdot (\Delta z \cdot P_Q) & \text{if } \Delta z < 0 \text{ in braking mode}
\end{cases} $$
where \( P_c \) is the supercapacitor power, \( P_Q \) is the vehicle power demand, and \( L_1 \) to \( L_4 \) are correction coefficients optimized through simulation. This strategy ensures that the HESS operates efficiently across various driving scenarios, which is crucial for the diverse conditions faced by electric vehicles in China.
Simulation experiments were conducted using MATLAB/Simulink to validate the proposed strategy under different driving cycles, including urban, highway, and combined conditions. The parameters for the HESS components, such as battery capacity and supercapacitor ratings, are summarized in Table 1. The simulation compared the optimized speed-based fuzzy control with an existing approach based on the Aquila Optimizer (AO) algorithm, focusing on battery SOC trends and energy efficiency. Results showed that the proposed strategy maintained a higher battery SOC over time, indicating better energy conservation. For instance, in a combined driving cycle, the battery SOC decreased by only 5% with the optimized strategy, compared to 8% with the AO-based method. This demonstrates the effectiveness of the speed-based fuzzy control in reducing energy waste and enhancing the longevity of HESS components in electric vehicles.
| Component | Parameter | Value |
|---|---|---|
| Lithium-ion Battery | Capacity | 50 Ah |
| Nominal Voltage | 360 V | |
| Supercapacitor | Capacitance | 1000 F |
| Equivalent Resistance | 0.1 Ω | |
| DC-DC Converter | Efficiency | 95% |
| Voltage Range | 200-400 V |
The fuzzy control rules and membership functions were designed based on vehicle speed and power demand inputs. For example, the input variables “speed” and “power” were divided into fuzzy sets like “low,” “medium,” and “high,” and output variable “allocation coefficient” was defined similarly. A sample of the fuzzy rules is provided in Table 2, which guides the power distribution in real-time. These rules were implemented in the simulation to handle the dynamic power requirements of electric vehicles, ensuring that the HESS responds optimally to changes in driving conditions.
| Speed | Power Demand | Allocation Coefficient |
|---|---|---|
| Low | Low | Increase Supercapacitor Share |
| Low | High | Increase Battery Share |
| High | Low | Maintain Balance |
| High | High | Decrease Supercapacitor Share |
In addition to the fuzzy control, the energy dynamics of the HESS were modeled using differential equations to simulate the SOC changes. For the battery, the SOC is governed by:
$$ \frac{dSOC_b}{dt} = -\frac{I_b}{Q_b} $$
where \( SOC_b \) is the battery SOC, \( I_b \) is the battery current, and \( Q_b \) is the battery capacity. Similarly, for the supercapacitor, the energy balance involves:
$$ E_{sc} = \frac{1}{2} C_{sc} U_c^2 $$
where \( E_{sc} \) is the energy stored in the supercapacitor and \( C_{sc} \) is its capacitance. These equations were integrated into the simulation to provide a comprehensive analysis of HESS performance. The results, as shown in Figure 5 of the original text, indicated that the optimized strategy consistently outperformed the AO-based method across all driving cycles, with slower SOC depletion and improved energy recovery. This is particularly relevant for China EV applications, where frequent stop-and-go traffic in cities demands efficient energy management.
Furthermore, the correction coefficients \( L_1 \) to \( L_4 \) were optimized through iterative simulations to achieve the best performance. The optimal values were found to be \( L_1 = -0.05 \), \( L_2 = 0.05 \), \( L_3 = 0.1 \), and \( L_4 = -0.1 \), which minimized energy losses and prevented over-charging or over-discharging. A comparison of the energy efficiency under different strategies is presented in Table 3, highlighting the superiority of the speed-based fuzzy control. For instance, in urban driving conditions, the proposed strategy achieved an energy efficiency of 92%, compared to 85% with the AO-based method. This underscores the potential of this approach to enhance the sustainability of electric vehicles, especially in the context of China’s growing EV market.
| Driving Cycle | Optimized Speed-Based Fuzzy Control Efficiency (%) | AO-Based Control Efficiency (%) |
|---|---|---|
| Urban | 92 | 85 |
| Highway | 88 | 82 |
| Combined | 90 | 84 |
In conclusion, the hybrid energy storage system is a vital component for electric vehicles, and its energy management strategy significantly impacts vehicle performance and range. The proposed fuzzy control strategy based on optimized vehicle speed offers a robust solution for managing power distribution between batteries and supercapacitors. By leveraging real-time speed data and fuzzy logic, this approach reduces battery stress and improves energy efficiency, which is essential for the advancement of China EV and the global electric vehicle industry. Future work could focus on integrating machine learning techniques to further adapt the control parameters to individual driving behaviors, enhancing personalization and efficiency. As electric vehicles continue to evolve, such innovations will play a key role in achieving carbon neutrality goals and promoting sustainable transportation.
The development of HESS and its control strategies is not only technical but also economic, as it can reduce the total cost of ownership for electric vehicles by extending battery life. In China, where government incentives and infrastructure development are boosting EV adoption, efficient energy management systems can accelerate market penetration. Moreover, the principles discussed here can be applied to other types of electric vehicles, such as buses and trucks, contributing to a broader reduction in carbon emissions. Through continuous research and simulation-based optimization, the energy management strategies for HESS will become more intelligent and reliable, paving the way for a greener future in transportation.