Jihun Han

Adaptive regenerative braking control in severe cornering for guaranteeing the vehicle stability of fuel cell hybrid electric vehicle

Front-wheel-drive electric vehicle has only 1 electric motor which is connected to the front drive axle. With this system constraint, regenerative braking by using an electric motor can be only applied on front wheels symmetrically. Additional mechanical friction braking can be independently applied on each of all wheels using brake by wire such as EMB (Electro-Mechanical Brake). During severe cornering with braking, excessive regenerative braking force distribution to the front axle for improving the fuel economy can cause the vehicle to approach its handling limit, i.e., if the front tires saturate first, the vehicle may plow out of the curve and is called to limit understeer. When the vehicle comes in a danger of crossing the limit, adaptive regenerative braking controller engages, assisting the driver to guarantee the vehicle stability. Controller can distribute an optimal regenerative brake torque and additional mechanical friction brake torques in order to prevent loss of control for the vehicle. Carsim™ software is used to verify the effectiveness of the proposed controller.

A novel updating method of equivalent factor in ECMS for prolonging the lifetime of battery in fuel cell hybrid electric vehicle

Abstract A novel updating method of equivalent factor of the Equivalent Consumption Minimization Strategy (ECMS) is proposed to prolong the lifetime of battery in fuel cell hybrid electric vehicle. ECMS can generate the sub-optimal solution at any instance for current driving condition. Equivalent factor which is dimensionless conversion ratio of electrical power flow into chemical power flow plays an important role in charge-sustaining performance. Well-tuned equivalent factor from iterative search in off-line can only guarantee the charge-sustaining performance, not the lifetime of the battery. Therefore additional updating method of equivalent factor is necessary in order to prolong the lifetime of the battery which only takes the numbers of charge and discharge cycle into account. Backward simulation is used to verify the effectiveness of the updating method.

Synthesis of Predictive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles Based on Closed-Form Solution of Optimal Equivalence Factor

Previously, an equivalent consumption minimization strategy (ECMS) was developed that provides near-optimal performance of hybrid vehicles based on an adaptation of equivalence factor from state of charge feedback. However, under real-world driving conditions with uncertainties, such as hilly roads, ECMS requires a predictive scheme utilizing future driving information in order to prevent a loss of optimality. In this paper, we synthesize predictive ECMS in a feedforward way to adjust the equivalence factor based on its theoretical connection with future driving statistics, in a systematic manner. First, a useful noncausal adaptation strategy is extracted from dynamic programming results. Then, the inverse problem is formulated and solved to derive an explicit representation of the constant optimal equivalence factor with justified assumptions. Finally, a causal, predictive adaptation strategy using this closed-form solution is synthesized to mimic the noncausal one, and its effectiveness is evaluated for fuel cell hybrid electric vehicles. Results show that if the predicted statistical information reflects well the future driving conditions, the proposed strategy accurately estimates the constant optimal equivalence factor, including the jump behavior, thereby yielding less than 1.5% loss of fuel optimality. Moreover, this approach is extendible to other configurations.