Enhanced Fuzzy Control Strategy Boosts EV Regenerative Braking Efficiency
In the rapidly evolving landscape of electric mobility, one of the most critical challenges remains the optimization of energy efficiency to extend vehicle range. As global demand for sustainable transportation grows, researchers are intensively exploring ways to maximize energy recovery during braking—a process known as regenerative braking. A recent breakthrough in this domain comes from a team of engineers at Yunnan Minzu University, who have developed an innovative control strategy that significantly improves the energy recovery performance of pure electric vehicles (PEVs). By integrating advanced computational intelligence with vehicle dynamics, the research introduces a refined fuzzy logic control system optimized through a modified whale optimization algorithm, setting a new benchmark for efficiency in EV energy management.
The study, published in the peer-reviewed journal Modern Manufacturing Engineering, presents a comprehensive approach to enhancing regenerative braking by dynamically adjusting the distribution of braking force between the front electric motor and the mechanical braking system. The core innovation lies in the intelligent modulation of the regenerative braking ratio coefficient, denoted as K, which determines how much of the required braking torque is supplied by the electric motor versus the hydraulic brakes. This coefficient is no longer fixed or rule-based but is instead dynamically adjusted in real time based on multiple driving conditions, including vehicle speed, braking intensity, and battery state of charge (SOC).
Lead researcher Li Huaxin, along with colleagues Chen Fangfang, Xu Tianqi, Cheng Sanbang from Yunnan Minzu University, and Mao Yisheng from Huadian Chongqing New Energy Co., Ltd., designed a fuzzy control system that takes these three parameters as inputs. The system evaluates driving scenarios and determines the optimal level of regenerative braking effort without compromising safety or driver comfort. Unlike conventional strategies that apply static thresholds or simple logic rules, this new method adapts to varying road conditions and driver behavior, ensuring maximum energy capture during deceleration phases.
One of the key limitations of traditional fuzzy control systems is their reliance on expert-defined membership functions—mathematical representations that define how input variables are interpreted within the fuzzy logic framework. These functions are often tuned manually, leading to suboptimal performance due to human bias or incomplete modeling of complex vehicle dynamics. To overcome this, the team employed an improved version of the whale optimization algorithm (WOA), a nature-inspired metaheuristic technique that mimics the hunting behavior of humpback whales. The standard WOA, while effective, can sometimes converge prematurely to local optima, resulting in less-than-ideal solutions. The researchers addressed this issue by introducing an adaptive inertia weight mechanism, which dynamically adjusts the search behavior of the algorithm throughout the optimization process.
This adaptive weighting allows the algorithm to maintain a balance between exploration—searching new areas of the solution space—and exploitation—refining known good solutions. As a result, the optimization process avoids getting trapped in local optima and achieves faster convergence toward globally optimal membership function parameters. The enhanced algorithm was used to fine-tune the shape and positioning of the membership functions for SOC, speed, and braking intensity, ultimately leading to a more responsive and efficient control system.
The optimization process focused on maximizing the proportion of braking force contributed by the front axle motor, which is particularly advantageous in front-wheel-drive electric vehicles—a common configuration in today’s EV market. By increasing the contribution of the electric motor during braking, more kinetic energy is converted back into electrical energy and stored in the battery, thereby improving overall energy efficiency. However, this must be done within strict safety constraints defined by international regulations such as the ECE R13 braking standards and the theoretical I-curve, which describes the ideal distribution of braking forces between the front and rear axles to prevent wheel lockup and maintain vehicle stability.
To ensure compliance with these safety requirements, the control strategy divides the braking process into four distinct phases based on braking intensity (Z). When braking is light (Z ≤ 0.19), the system relies solely on the front axle for deceleration, allowing full utilization of regenerative braking. As braking intensity increases (0.19 < Z ≤ 0.51), the rear axle begins to contribute mechanically to meet regulatory minimums, while the front motor continues to provide the majority of the braking torque. In the moderate to high braking range (0.51 < Z ≤ 0.70), the system prioritizes safety by increasing mechanical braking at the rear, but still allows significant regenerative contribution from the front. Only when braking exceeds 0.70—indicating emergency stopping conditions—does the system disengage regenerative braking entirely, relying on conventional friction brakes to ensure maximum stopping power and stability.
This multi-stage approach strikes a delicate balance between energy recovery and safety, ensuring that the vehicle remains compliant with global standards while maximizing efficiency under normal driving conditions. The researchers validated their strategy using MATLAB/Simulink simulations under the New European Driving Cycle (NEDC), a standardized test cycle used to evaluate vehicle performance and emissions. The results were striking: compared to traditional regenerative braking strategies, the optimized fuzzy control system increased energy recovery by 44.44%. When compared to the same fuzzy control system before optimization, the improvement was an additional 39.98%, demonstrating the substantial impact of the improved whale algorithm.
Battery SOC analysis further confirmed the effectiveness of the strategy. Over the course of the NEDC simulation, the battery’s state of charge decreased from 0.80 to 0.6917 under the optimized control, representing the smallest drop among all tested strategies. This indicates not only higher energy recovery but also reduced strain on the battery, potentially extending its lifespan. The total energy recovered reached 389 kJ, significantly outperforming both the traditional strategy (192.4 kJ) and the unoptimized fuzzy control (277.9 kJ).
Beyond numerical improvements, the study highlights the importance of intelligent algorithms in next-generation vehicle control systems. As electric vehicles become more sophisticated, the role of artificial intelligence and machine learning in managing energy flow, thermal systems, and driver assistance functions will continue to grow. This research exemplifies how bio-inspired optimization techniques can be effectively applied to real-world engineering problems, offering practical solutions that enhance both performance and sustainability.
The implications of this work extend beyond academic interest. For automakers, adopting such optimized control strategies could lead to tangible improvements in vehicle range without requiring larger batteries—a costly and resource-intensive solution. For consumers, it means longer driving distances on a single charge and lower operating costs. For the environment, it translates to reduced energy consumption and lower carbon emissions over the vehicle’s lifecycle.
Moreover, the methodology presented in this paper is not limited to front-wheel-drive vehicles. With appropriate modifications, it could be adapted for all-wheel-drive or rear-wheel-drive configurations, broadening its applicability across different vehicle segments. The modular nature of the fuzzy control system also makes it compatible with various battery chemistries and motor types, enhancing its versatility in the diverse and rapidly evolving EV market.
Another notable aspect of the research is its focus on real-time feasibility. The computational load of the optimized fuzzy controller remains within acceptable limits for implementation in embedded vehicle control units (VCUs). This ensures that the benefits of the strategy can be realized in production vehicles without requiring prohibitively expensive hardware upgrades. The use of a fixed number of membership functions and a well-defined rule base contributes to the system’s robustness and predictability, essential qualities for safety-critical automotive applications.
From a broader perspective, this study aligns with global trends toward smarter, more efficient transportation systems. As cities invest in smart infrastructure and connected vehicle technologies, the ability of EVs to intelligently manage energy will become increasingly important. Vehicles equipped with advanced regenerative braking systems like the one described here will be better positioned to integrate with grid-based energy management platforms, participate in vehicle-to-grid (V2G) programs, and contribute to overall energy resilience.
The success of this project also underscores the value of interdisciplinary collaboration. Combining expertise in electrical engineering, control theory, and computational optimization enabled the research team to tackle a complex problem from multiple angles. The involvement of both academic and industry partners—Yunnan Minzu University and Huadian Chongqing New Energy Co., Ltd.—ensures that the research remains grounded in practical engineering challenges while pushing the boundaries of theoretical innovation.
Looking ahead, the team suggests several avenues for future work. One direction involves extending the control strategy to include predictive elements, such as using navigation data or traffic signal information to anticipate upcoming stops and optimize regenerative braking accordingly. Another possibility is the integration of machine learning techniques to allow the system to adapt to individual driver habits over time, further personalizing energy recovery performance.
Additionally, real-world testing on physical prototypes would provide valuable validation beyond simulation environments. Field trials could assess the system’s performance under diverse weather conditions, road surfaces, and driving patterns, offering insights into long-term durability and user acceptance. Such testing would also help identify any unforeseen interactions between the optimized control system and other vehicle subsystems, such as stability control or adaptive cruise control.
In conclusion, the research conducted by Li Huaxin and his team represents a significant step forward in the quest for more efficient electric vehicles. By refining fuzzy control through an improved whale optimization algorithm, they have demonstrated a practical and effective method for boosting regenerative braking performance. Their work not only advances the state of the art in EV energy management but also provides a blueprint for how intelligent algorithms can be leveraged to solve complex engineering challenges in sustainable transportation.
As the automotive industry continues its transition toward electrification, innovations like this will play a crucial role in shaping the future of mobility. By making electric vehicles more efficient, reliable, and user-friendly, such advancements help accelerate the adoption of clean transportation technologies worldwide. The study serves as a compelling example of how academic research, when combined with practical engineering insight, can deliver meaningful solutions to some of today’s most pressing technological and environmental challenges.
Li Huaxin, Chen Fangfang, Xu Tianqi, Cheng Sanbang, Mao Yisheng, Yunnan Minzu University, Huadian Chongqing New Energy Co., Ltd., Modern Manufacturing Engineering, DOI: 10.16731/j.cnki.1671-3133.2024.11.013