Dual Fuzzy Control Enhances Battery Protection in Hybrid Electric Vehicles
The evolution of electric mobility continues to accelerate, driven by innovations in energy storage and power management technologies. Among the most critical components shaping the future of electric vehicles (EVs) is the energy management system (EMS), which governs how power is distributed between different energy sources. A recent study conducted by researchers at Xi’an University of Architecture and Technology introduces a novel dual fuzzy control strategy designed to significantly improve the performance and longevity of lithium-ion batteries in hybrid storage electric vehicles (HSEVs). This advancement not only addresses longstanding challenges in battery stress and efficiency but also sets a new benchmark for intelligent power distribution in next-generation EVs.
As urbanization and environmental concerns intensify, the demand for cleaner, more efficient transportation solutions has never been higher. Electric vehicles have emerged as a key player in reducing greenhouse gas emissions and dependence on fossil fuels. However, despite their growing popularity, EVs still face technical hurdles—particularly in energy storage systems. Lithium-ion batteries, while offering high energy density and long cycle life, are susceptible to degradation when subjected to high current loads or frequent charge-discharge cycles. These conditions, common during acceleration and regenerative braking, can lead to thermal stress, capacity fade, and reduced lifespan.
To mitigate these issues, researchers have increasingly turned to hybrid energy storage systems (HESS), which combine lithium-ion batteries with supercapacitors. Supercapacitors excel in delivering and absorbing high bursts of power, making them ideal for handling transient loads such as rapid acceleration or braking. By offloading these high-power demands from the battery, supercapacitors help preserve battery health and extend its operational life. However, the effectiveness of this approach hinges on the sophistication of the energy management strategy employed to coordinate the two energy sources.
Traditional energy management strategies fall into two broad categories: rule-based and optimization-based. Rule-based methods are known for their simplicity, real-time responsiveness, and robustness, making them suitable for onboard implementation. However, they often lack adaptability to diverse driving conditions and may not optimize energy usage efficiently across varying power demands. On the other hand, optimization-based strategies such as dynamic programming or model predictive control offer superior performance in terms of fuel economy or energy efficiency but require extensive computational resources and prior knowledge of driving cycles, limiting their feasibility for real-time applications.
In response to these limitations, a growing body of research has focused on hybrid control approaches that blend the strengths of different methodologies. One such approach gaining traction is fuzzy logic control, which mimics human decision-making processes by handling imprecise inputs and generating smooth, adaptive outputs. Fuzzy logic has been successfully applied in various automotive control systems due to its ability to manage nonlinearities and uncertainties inherent in real-world driving.
Building on this foundation, the research team led by Yang Lei, Bai Zhifeng, Wang Juan, and Huang Lin from the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology has developed a dual fuzzy control energy management strategy specifically tailored for HSEVs. Their work, published in Mechanical Science and Technology, presents a refined control architecture that enhances battery protection while maintaining system efficiency and responsiveness.
The core innovation of this strategy lies in its layered, dual-controller design. Rather than applying a single fuzzy logic system across all operating conditions, the researchers segmented the vehicle’s power demand into three distinct levels: low, medium, and high. This segmentation allows for more precise control tuning based on the characteristics of each power range. In low-power scenarios—such as steady cruising—the battery primarily supplies energy, while the supercapacitor remains in a standby or charging state to preserve its charge for future high-demand events. When the vehicle enters medium or high-power modes—such as during acceleration or hill climbing—the respective fuzzy controllers activate to determine the optimal power split between the battery and supercapacitor.
This hierarchical approach addresses a key limitation of conventional fuzzy control systems: the trade-off between rule complexity and control accuracy. A single fuzzy controller managing the entire power spectrum would require an extensive rule base, increasing computational load and reducing interpretability. By dividing the control task into two specialized fuzzy modules—one for medium power and another for high power—the researchers effectively reduce system complexity while improving responsiveness and precision.
The first fuzzy controller, designated for medium power conditions (defined as 10% to 40% of the motor’s rated power), focuses on smoothing out minor current fluctuations in the battery. It takes into account both the state of charge (SOC) of the lithium battery and the supercapacitor, along with the instantaneous power demand, to calculate an appropriate power contribution from the supercapacitor. This helps prevent localized current spikes that could otherwise degrade the battery over time.
The second controller, activated under high power demand (above 40% of rated power), is optimized for peak current suppression. During rapid acceleration or aggressive driving, the battery is most vulnerable to high current draw, which can lead to overheating and accelerated aging. The high-power fuzzy controller dynamically adjusts the supercapacitor’s output to absorb the majority of the transient load, thereby shielding the battery from excessive stress. The control rules are designed to prioritize supercapacitor utilization when its SOC is sufficient, ensuring that the battery operates within a safer, more stable current range.
To further enhance the system’s adaptability, the researchers integrated a driver intention recognition module. This component analyzes the accelerator pedal position and its rate of change to infer the driver’s immediate power demand. For instance, a rapid increase in pedal input suggests an aggressive acceleration intent, prompting the system to preemptively engage the supercapacitor. Conversely, a gradual pedal application indicates a more conservative driving style, allowing the system to maintain a balanced power distribution. This predictive capability enables the energy management system to respond proactively rather than reactively, improving both efficiency and driving experience.
The entire control strategy was implemented and validated using a co-simulation framework combining AVL CRUISE and MATLAB/Simulink. AVL CRUISE, a widely used vehicle system simulation platform, provided a high-fidelity model of the vehicle dynamics, powertrain, and energy storage components. Meanwhile, MATLAB/Simulink was employed to develop and test the control algorithms, which were then compiled into a dynamic link library (DLL) and integrated back into the CRUISE environment for closed-loop simulation.
The test vehicle was a front-wheel-drive electric car with a total curb weight of 1,650 kg and a maximum speed requirement of 160 km/h. The powertrain featured a permanent magnet synchronous motor with a peak power output of 110 kW, chosen for its high efficiency and torque performance. The hybrid energy storage system consisted of a lithium-ion battery pack rated at 336 V and a supercapacitor module operating at 400 V. The battery pack was composed of 105 cells in series and 9 in parallel, providing a total capacity suitable for a 300-km driving range under standard conditions. The supercapacitor, with a total capacitance of 3,000 F and a voltage range between 40% and 100% of its maximum, was designed to handle short-term, high-power transients.
Simulations were conducted under two standardized driving cycles: the New European Driving Cycle (NEDC) and the Federal Test Procedure 75 (FTP-75). These cycles represent a mix of urban and highway driving conditions, allowing for a comprehensive evaluation of the control strategy under varying traffic patterns and speed profiles. Each simulation ran for three consecutive cycles to ensure statistical reliability and to observe long-term system behavior.
The results demonstrated a marked improvement in battery current regulation compared to both conventional rule-based and single fuzzy control strategies. Under the NEDC cycle, the maximum peak current drawn from the lithium battery was reduced to 67.8 A with the dual fuzzy control strategy, down from 130.5 A in a single-source battery system and 63.6 A in a rule-based hybrid system. While the rule-based approach achieved a slightly lower peak current, the dual fuzzy strategy showed superior performance in minimizing current fluctuations across the entire driving cycle, resulting in a smoother and more stable battery discharge profile.
In the FTP-75 cycle, which includes more dynamic acceleration and deceleration events, the dual fuzzy control system achieved the lowest peak current at 59.4 A, outperforming both the rule-based (63.3 A) and single fuzzy (62.6 A) approaches. This indicates that the dual fuzzy controller is particularly effective in managing high-frequency power transients, where the ability to rapidly deploy supercapacitor power is crucial.
Beyond peak current reduction, the study also evaluated the overall energy efficiency of the system. At the end of the NEDC cycle, the battery’s state of charge was 87.19% under the dual fuzzy control strategy, slightly higher than the 87.15% achieved with rule-based control and significantly better than the 86.88% with single fuzzy control. This suggests that the proposed strategy not only protects the battery but also improves energy utilization, likely due to more efficient power routing and reduced internal losses.
Another key advantage of the dual fuzzy approach is its real-time feasibility. Unlike optimization-based methods that require extensive computation, fuzzy logic controllers operate with minimal processing overhead, making them suitable for implementation in embedded vehicle control units. The modular design further enhances scalability, as individual controllers can be fine-tuned or replaced without affecting the entire system.
The implications of this research extend beyond academic interest. As automakers strive to extend battery life and reduce warranty costs, advanced energy management strategies like the one proposed by Yang and colleagues offer a practical path forward. By integrating intelligent control with hybrid energy storage, manufacturers can deliver vehicles that are not only more efficient but also more durable and reliable.
Moreover, the success of this strategy underscores the importance of system-level thinking in EV design. Rather than focusing solely on improving individual components—such as increasing battery energy density or reducing motor losses—engineers must consider how these components interact within the broader vehicle architecture. Intelligent control systems act as the “brain” of the vehicle, orchestrating the flow of energy to maximize performance and longevity.
Looking ahead, the research team suggests several directions for future work. One possibility is the integration of predictive driving data, such as GPS-based route information or traffic flow forecasts, to further refine the control strategy. For example, knowing that a steep hill lies ahead could prompt the system to pre-charge the supercapacitor, ensuring it is ready to assist during the climb. Another avenue is the application of machine learning techniques to automatically tune the fuzzy rules based on real-world driving data, enabling continuous improvement over time.
In conclusion, the dual fuzzy control energy management strategy developed by Yang Lei, Bai Zhifeng, Wang Juan, and Huang Lin represents a significant step forward in the quest for smarter, more sustainable electric vehicles. By combining power-level segmentation, adaptive fuzzy logic, and driver intention recognition, the system achieves a delicate balance between performance, efficiency, and battery protection. As the automotive industry continues its transition toward electrification, innovations like this will play a pivotal role in shaping the vehicles of tomorrow.
Dual Fuzzy Control Enhances Battery Protection in Hybrid Electric Vehicles
Yang Lei, Bai Zhifeng, Wang Juan, Huang Lin, Xi’an University of Architecture and Technology, Mechanical Science and Technology, DOI: 10.13433/j.cnki.1003-8728.20220238