Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints.

Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies.  An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods.  

The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric vehicle, the cost savings for high power applications such as the aforementioned construction equipment can be even more rewarding.

The second part of the thesis is dedicated to the development of time-efficient energy management strategies. Considering the need for real-time feasibility, satisfactory fuel economy and low computation time are the key elements in EMS design. In the first step, the analytical solution to equivalent consumption minimization strategy (ECMS) for series hybrid electric vehicles is derived, where the system constraints are directly taken into account in the  derivation process. The equivalence factor bounds are found and used to develop a real time adaptive ECMS. The obtained fuel economy figures  are observed to be very close to the non-causal benchmarks. These results are then utilized to propose real-time  predictive ECMS algorithms. Two scenarios are investigated depending on the availability of drive cycle knowledge. The first scenario corresponds to vehicles that are expected to follow certain drive  cycles. This situation is common among construction machinery such as the wheel loader under study. On the other hand, there are situations where driving mission is not known in advance and the driver behavior is unpredictable, such as typical city driving. For each scenario, an algorithm is presented to compute the equivalence factor efficiently. The control action is then determined by the analytical policy derived previously. Simulations of the developed algorithms on the hybrid wheel loader and a passenger car demonstrate that the methodologies are computationally efficient and attain satisfactory fuel economy with respect to the dynamic programming benchmarks.