article · World Electric Vehicle Journal
This research presents an adaptive digital twin energy management framework for four-wheel drive hybrid electric vehicles combining dynamic inductive charging, fuel cells, batteries, and supercapacitors. The system integrates physics-informed neural networks, deep reinforcement learning, and model predictive control to manage power distribution, real-time wireless charging misalignment, and bidirectional vehicle-to-grid power flows. The digital twin updates online using elastic weight consolidation, while the neural network estimates battery states with low latency. Evaluated across more than 200 hours of hardware-in-the-loop simulation on a dSPACE and NVIDIA Jetson platform, the framework handles dynamic lateral coil misalignments up to 50 millimetres with 91.5 percent mean charging efficiency. Overall, the approach demonstrates a 24.3 percent cost reduction, reduces battery degradation by 31.8 percent, and achieves an average control execution time comfortably within standard real-time automotive limits.
Dynamic inductive charging allows electric vehicles to charge while driving, but real-world motion causes coil misalignment that disrupts power transfer. Combining this with fuel cells, batteries, and grid exports creates complex control challenges. Demonstrating that artificial intelligence and digital twins can coordinate these systems in real time helps make long-range, multi-source clean transport technically stable and economically feasible.
The framework targets automotive manufacturers and energy fleet operators developing four-wheel drive fuel cell hybrid vehicles and dynamic wireless charging infrastructure. Techno-economic models indicate a payback period of approximately 5.57 years alongside annual vehicle-to-grid earnings. Having completed over 200 hours of hardware-in-the-loop validation on commercial embedded platforms, the technology sits at an applied and tested stage, though full physical on-road vehicle integration is not yet reported.
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Dynamic inductive charging (DIC) combined with hybrid energy storage systems (HESSs) and vehicle-to-grid (V2G) capabilities offers a promising pathway toward extended-range electric vehicles with grid integration benefits. However, real-time optimal energy management remains challenging due to multi-axis coil misalignment, component aging, and bidirectional power flow uncertainty. This paper proposes an adaptive digital twin driven artificial intelligence (AI) energy management framework integrating physics-informed neural networks (PINNs), soft actor critic (SAC) deep reinforcement learning, and model predictive control (MPC) for optimal power distribution among a proton exchange membrane fuel cell (PEMFC), lithium-ion battery, supercapacitor, dynamic wireless charging, and grid interface in four-wheel drive electric vehicles (4WD-EVs). The framework features: (1) a self-evolving digital twin with online learning via Elastic Weight Consolidation (EWC) updating every 50 cycles; (2) a PINN-based state estimator for battery-state estimation, with an average inference time of 1.1 ms and a worst-case latency of 2.8 ms; (3) a hierarchical SAC–MPC strategy with high-level mode selection and low-level power optimization; (4) real-time five-degree-of-freedom WPT misalignment compensation, achieving a mean efficiency of 91.5% under the evaluated dynamic lateral misalignment conditions, with a 50 mm displacement amplitude; (5) degradation-aware V2G optimization generating €582.50/year in revenue while reducing battery aging by 31.8%; and (6) comprehensive techno-economic analysis yielding a discounted payback period of approximately 5.57 years and a net present value of approximately €3777 over a 10-year horizon. Validated through 200+ hours of hardware-in-the-loop (HIL) simulation on the dSPACE/NVIDIA Jetson platform, the proposed approach achieves a 24.3% cost reduction and 31.8% lower battery degradation. The MPC controller exhibits an average execution time of 32.1 ms, a 95th-percentile latency of 44.8 ms, and a worst-case latency of 62.4 ms, while remaining within the 100-ms real-time control deadline. Results demonstrate the viability of adaptive digital twins for next-generation EVs with autonomous charging and multi-source architectures.
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DOI: 10.3390/wevj17090458
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