运用改进鲸鱼算法优化纯电动汽车再生制动模糊控制策略  

Optimization of pure electric vehicle regenerative braking fuzzy control strategy using improved whale algorithm

作　　者：李华鑫 陈芳芳[1,2] 徐天奇[1] 程三榜[1] 毛一胜 LI Huaxin;CHEN Fangfang;XU Tianqi;CHENG Sanbang;MAO Yisheng(School of Electrical and Information Technology,Yunnan Minzu University,Kunming 650504,China;Yunnan Provincial Key Laboratory of Unmanned Autonomous Systems,Yunnan Minzu University,Kunming 650504,China;Huadian Chongqing New Energy Co.,Ltd.,Chongqing 404600,China)

机构地区：[1]云南民族大学电气信息工程学院,昆明650504 [2]云南民族大学云南省无人自主系统重点实验室,昆明650504 [3]华电重庆新能源有限公司,重庆404600

出　　处：《现代制造工程》2024年第11期96-102,130,共8页Modern Manufacturing Engineering

基　　金：国家自然科学基金项目(61761049)。

摘　　要：为了提高纯电动汽车再生制动能量回收效率,同时保证车辆制动效果,提出了运用改进鲸鱼算法优化纯电动汽车再生制动模糊控制策略。引入电池荷电状态(State of Charge,SOC)、车速和制动强度作为模糊控制输入,以再生制动比例系数K作为输出,利用改进鲸鱼算法优化控制参数,从而提高前轴电机制动力占比。同时,改进鲸鱼算法的自适应权重避免了算法迭代过程中陷入局部最优。通过仿真分析验证了在NEDC工况下,优化后的模糊控制策略相比优化前和传统控制策略在提高能量回收效果的同时,也满足了制动的有效性。To enhance the regenerative braking energy recovery rate of pure electric vehicles while ensuring effective braking performance,a refined approach was proposed to optimize the fuzzy control strategy using an improved whale algorithm.Introducing battery State of Charge(SOC),vehicle speed,and braking intensity as fuzzy control inputs,with regenerative braking proportion coefficient K as the output,the control parameters were optimized using an improved whale algorithm,thereby enhancing the proportion of braking power provided by the front axle motor.Concurrently,refining the adaptive weights of the whale algorithm prevents the algorithm from converging to local optima during the iterative process.Through simulation analysis,the enhanced fuzzy control strategy proposed has been validated to not only improve energy recovery compared to both the pre-optimized and traditional control strategies but also ensure braking effectiveness under NEDC conditions.

关 键 词：纯电动汽车 再生制动 模糊控制 改进鲸鱼算法 

分 类 号：U469.72[机械工程—车辆工程]