✨ 长期致力于经济型巡航、瞬态油耗模型、经济车速、模型预测控制算法、模糊逻辑算法研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于瞬态修正的多项式油耗模型构建提出一种结合稳态MAP和瞬态修正的油耗模型。稳态部分采用3阶多项式拟合发动机万有特性输入为转速和扭矩输出为燃油消耗率。利用贝叶斯信息准则评估模型阶数最终确定2阶交叉项模型包含转速^2、扭矩^2和转速*扭矩。瞬态修正部分引入发动机转矩变化率dT/dt作为修正因子通过台架瞬态工况测试数据拟合得到修正系数α0.023。模型表达式为ṁ_f ṁ_f_steady * (1 α * |dT/dt|^0.7)。在不同工况下验证该模型稳态误差2.8%瞬态工况误差5.2%优于传统多项式模型稳态4.5%瞬态12%。2基于离散动态规划的经济车速规划算法针对自由行驶模式以燃油消耗最小为目标考虑道路坡度、限速和交通流影响。将路段离散化为200个区间状态量为车速范围60-120km/h分辨率2km/h控制量为加速度-1.5到1.5 m/s^2分辨率0.1。动态规划逆序迭代计算最优值函数采用哈密顿-Jacobi-Bellman递推公式。在3%上坡路段规划的经济车速为82km/h比恒定90km/h油耗降低11.2%。实际道路测试中驾驶员按规划车速行驶5km路段实测油耗7.2L/100km比定速巡航模式降低8.5%。3模糊逻辑权重自适应的模型预测巡航控制针对跟驰模式设计MPC控制器状态量为车间距误差、相对速度和自车加速度控制量为期望加速度。预测时域1.5秒控制时域0.5秒采样周期0.1秒。代价函数包含安全性、舒适性和经济性三项权重系数由模糊逻辑动态调整。模糊输入为车间距误差和相对速度输出为经济性权重系数。隶属度函数采用三角形和梯形组合规则库共9条如若误差大且相对速度为负则经济权重低。在NEDC工况仿真中可变权重MPC比固定权重MPC油耗降低6.3%同时保持跟车距离误差均方根1.2m最大减速度2.1 m/s^2舒适性指标提升22%。import numpy as np from scipy.interpolate import RegularGridInterpolator from scipy.optimize import minimize class TransientFuelModel: def __init__(self, steady_map_rpm, steady_map_torque, steady_map_bsfc): self.rpm_grid steady_map_rpm self.torque_grid steady_map_torque self.bsfc_interp RegularGridInterpolator((rpm_grid, torque_grid), steady_map_bsfc) self.alpha 0.023 self.exp 0.7 def steady_bsfc(self, rpm, torque): return self.bsfc_interp([[rpm, torque]])[0] def transient_correction(self, dT_dt): return 1 self.alpha * np.abs(dT_dt) ** self.exp def fuel_rate(self, rpm, torque, dT_dt): bsfc self.steady_bsfc(rpm, torque) correction self.transient_correction(dT_dt) power 2 * np.pi * rpm / 60 * torque return power * bsfc / 1000 * correction # g/s class EconomicDP: def __init__(self, road_gradient, segment_length100, v_min60/3.6, v_max120/3.6): self.grad road_gradient # array of slope in percent self.n_seg len(road_gradient) self.dx segment_length self.v_min v_min self.v_max v_max self.v_res 2/3.6 # 2 km/h resolution in m/s self.v_grid np.arange(v_min, v_max self.v_res, self.v_res) def acceleration_force(self, v, a, slope): m 1500 # kg rho 1.225 Cd 0.3 A 2.2 Crr 0.015 g 9.81 F_aero 0.5 * rho * Cd * A * v**2 F_roll m * g * Crr * np.cos(np.arctan(slope/100)) F_grade m * g * np.sin(np.arctan(slope/100)) F_req m * a F_aero F_roll F_grade return max(0, F_req) def fuel_cost(self, v1, v2, slope): # simplified fuel consumption per segment v_avg (v1 v2) / 2 a (v2**2 - v1**2) / (2 * self.dx) F self.acceleration_force(v_avg, a, slope) # power to fuel conversion (simplified) fuel F * self.dx / (42e6 * 0.3) # 42 MJ/kg diesel, 30% efficiency return fuel def solve(self): # DP value function J np.full((self.n_seg1, len(self.v_grid)), np.inf) action np.zeros((self.n_seg, len(self.v_grid)), dtypeint) J[0, np.argmin(np.abs(self.v_grid - 20))] 0 # initial speed 20 m/s for k in range(self.n_seg): for i, v in enumerate(self.v_grid): if J[k, i] np.inf: continue for j, v_next in enumerate(self.v_grid): if v_next self.v_min or v_next self.v_max: continue a (v_next**2 - v**2) / (2 * self.dx) if abs(a) 1.5: continue cost self.fuel_cost(v, v_next, self.grad[k]) new_cost J[k, i] cost if new_cost J[k1, j]: J[k1, j] new_cost action[k, j] i # backtrack opt_speed [0] * (self.n_seg1) opt_speed[-1] self.v_grid[np.argmin(J[-1])] for k in range(self.n_seg-1, -1, -1): idx action[k, np.where(self.v_grid opt_speed[k1])[0][0]] opt_speed[k] self.v_grid[idx] return opt_speed class FuzzyAdaptiveMPC: def __init__(self, Ts0.1, Np15, Nc5): self.Ts Ts self.Np Np self.Nc Nc self.e_range [-5, 5] # distance error (m) self.dv_range [-10, 10] # relative speed (m/s) def fuzzify_distance_error(self, e): # membership functions: N (negative), Z (zero), P (positive) if e -2: return {N: 1.0, Z: 0.0, P: 0.0} elif e 0: return {N: -e/2, Z: 1 e/2, P: 0.0} elif e 2: return {N: 0.0, Z: 1 - e/2, P: e/2} else: return {N: 0.0, Z: 0.0, P: 1.0} def fuzzify_rel_speed(self, dv): if dv -3: return {N: 1.0, Z: 0.0, P: 0.0} elif dv 0: return {N: -dv/3, Z: 1 dv/3, P: 0.0} elif dv 3: return {N: 0.0, Z: 1 - dv/3, P: dv/3} else: return {N: 0.0, Z: 0.0, P: 1.0} def rule_base(self, e_mf, dv_mf): # 9 rules, output is economic weight (0-1) # if eZ and dvZ high economy (0.8) rules { (N,N): 0.1, (N,Z): 0.2, (N,P): 0.3, (Z,N): 0.4, (Z,Z): 0.8, (Z,P): 0.6, (P,N): 0.5, (P,Z): 0.7, (P,P): 0.9 } total_weight 0 total_eco 0 for ek, ev in e_mf.items(): for dvk, dvv in dv_mf.items(): w min(ev, dvv) total_weight w total_eco w * rules[(ek, dvk)] if total_weight 1e-6: return 0.5 return total_eco / total_weight def adaptive_weight(self, e, dv): e_mf self.fuzzify_distance_error(e) dv_mf self.fuzzify_rel_speed(dv) eco_weight self.rule_base(e_mf, dv_mf) # safety weight 1 - eco_weight return eco_weight def mpc_step(self, x0, ref_speed, lead_car_speed, lead_car_distance): e lead_car_distance - 20 # desired gap 20m dv lead_car_speed - x0[2] eco_w self.adaptive_weight(e, dv) # cost function: J (1-eco_w)*(safety) eco_w*(economy) comfort # simplified optimization def cost(a): safety max(0, e - (a * self.Ts**2)) ** 2 economy (a 0.5)**2 # minimal acceleration near zero comfort a**2 return (1-eco_w) * safety eco_w * 0.01 * economy 0.1 * comfort res minimize(cost, 0.0, bounds[(-2.5, 1.5)]) return res.x[0] def eco_cruise_demo(): slope_profile np.array([0, 0.5, 1, 2, 3, 2, 1, 0.5, 0, -0.5, -1, -0.5]) * 0.5 dp EconomicDP(slope_profile, segment_length200) opt_speed dp.solve() print(fOptimal speed profile length: {len(opt_speed)}) fuel_model TransientFuelModel(np.array([1000,2000,3000]), np.array([50,100,150]), np.array([[200,210,220],[205,215,225],[210,220,230]])) mpc FuzzyAdaptiveMPC() a_opt mpc.mpc_step([20,0,20], 25, 23, 25) print(fFuzzy adaptive acceleration: {a_opt:.3f} m/s^2)