ONLINE PARAMETER TUNING METHODS FOR ADAPTIVE ECMS CONTROL STRATEGIES IN HYBRID ELECTRIC VEHICLES
ONLINE PARAMETER TUNING METHODS FOR ADAPTIVE ECMS CONTROL STRATEGIES IN HYBRID ELECTRIC VEHICLES
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发表时间：
2014
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通讯作者：
M. Winkler;Mick Tegethoff;Sascha Geulen;D. Abel;Berthold Vöcking;Martina Josevski
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作者：
M. Winkler;Mick Tegethoff;Sascha Geulen;D. Abel;Berthold Vöcking;Martina Josevski
本文提出了两种自适应ECMS方案，并通过一种基于遗憾最小化范式的在线学习算法对标准ECMS公式进行了扩展。在第一种方法中，应用收缩飞镖算法来调整ECMS自适应规则的参数值，而在第二种方法中，加权分数算法对许多ECMS策略的结果进行平均，以获得最优的功率分配。由此，专家代表分配给ECMS策略的ECMS适应规则的特定参数设置。将ECMS策略与在线学习过程相结合时的仿真结果与没有应用参数整定方法以及自适应规则的参数在整个行驶循环中设置为恒定值时的仿真结果进行了比较。在这两种情况下，采用自适应律(P或PI)以在每个时间步长中获得ECMS策略中的等价因子。所提出的自适应控制方案即使在行驶条件未知的情况下也能取得良好的控制效果。仿真结果表明，与在ECMS策略的自适应律中使用固定参数的情况相比，将ECMS方法与遗憾最小化过程相结合可以达到降低油耗的目的。
This paper presents two adaptive ECMS schemes which compared to the standard ECMS formulation are extended by an online learning algorithm based on the so-called regret minimization paradigm. While in the first approach the Shrinking Dartboard algorithm is applied to tune the parameter values of the ECMS adaptation rule, in the second approach the Weighted Fractional algorithm averages the results of numerous ECMS strategies in order to obtain an optimal power split. Thereby, an expert represents a particular parameter setting of an ECMS adaptation rule which is assigned to an ECMS strategy. The simulation results obtained when ECMS strategy is used in combination with the online learning procedures are compared to the results obtained when no method for parameter tuning is applied and when the parameters of the adaptation rule are set to constant values for the entire driving cycle. In both cases an adaptation law (P or PI) is employed in order to obtain the equivalence factor in the ECMS strategy in each time step. The proposed adaptive control schemes achieve good performance even if the driving conditions are not known a-priori. Simulation results indicate that when applying the ECMS approach together with regret minimization procedures reduced fuel consumption can be achieved compared to the case when constant parameters are used in adaptation laws of the ECMS strategy.