The increasing penetration of electric vehicles (EVs) has placed stringent requirements on onboard energy storage systems in terms of power capability, energy efficiency, thermal safety, and lifetime durability. Although lithium ion batteries remain the dominant energy source for EVs, their limited power density and sensitivity to high current transients restrict their ability to simultaneously satisfy sustained energy demand and rapid power fluctuations. Hybrid energy storage systems (HESSs) that combine batteries and supercapacitors provide an effective solution by leveraging the high energy density of batteries together with the high power density and fast dynamic response of supercapacitors. However, system performance is strongly dependent on both the real-time energy management strategy and the physical sizing of the storage components. Existing studies often address these aspects separately, resulting in a sequential design paradigm that fails to capture the coupling between hardware configuration and control performance.
This thesis develops an integrated framework for energy management and hardware design of HESSs in electric vehicle applications.
First, a model predictive control (MPC) based energy management strategy is formulated to coordinate power allocation between the battery and the supercapacitor under driving cycle dependent load demand. To improve robustness under repetitive operating conditions, historical driving data are incorporated into the MPC formulation through an iterative learning mechanism together with convex hull based feasibility constraints. This enables the controller to exploit previously observed trajectories while maintaining operational limits on battery current, supercapacitor voltage, state of charge, and power balance.
Second, a multi-objective sizing framework for HESS is proposed using the Nondominated Sorting Genetic Algorithm III (NSGA-III). The sizing problem is formulated with discrete series and parallel design variables for both battery and supercapacitor packs, together with additional thermal management related decision factors. The optimisation simultaneously considers structural compactness, thermal behaviour, and specific power capability, thereby generating a set of Pareto optimal solutions that reveal trade-offs among mass, temperature rise, and power density.
Third, a co-design framework for design and control is established by embedding MPC based driving simulation into the sizing evaluation process. In this framework, candidate hardware configurations generated during optimisation are assessed under dynamic closed loop operation, and system level performance indicators are fed back to support final design selection. This approach moves beyond conventional offline sizing by explicitly accounting for the interaction between physical design variables and real time control behaviour.
Simulation studies based on standard driving cycle profiles demonstrate that the proposed MPC strategy with historical data reduces disturbance induced deviations in battery current and supercapacitor voltage over repeated iterations. The sizing results further show that NSGA-III can identify physically meaningful compromise solutions under competing mass, thermal, and power requirements. Moreover, the co-design evaluation indicates that solutions selected solely from static sizing criteria may not remain optimal when operational control performance is considered. The proposed framework therefore provides a systematic and extensible methodology for coordinated HESS design and energy management, contributing to improved energy efficiency, reduced battery stress, and enhanced system level performance in electric vehicles.
