Optimization of Battery Mixing Strategies to Enhance Electric Vehicle Performance and Efficiency

Fleet operators increasingly contend with a patchwork of battery‑pack generations that remain electro‑chemically compatible yet differ markedly in capacity, internal resistance and residual life. Conventional “like‑for‑like” replacement policies oblige service providers to retain costly legacy inventories or to install brand‑new packs in ageing vehicles—both scenarios inflating warranty exposure and material waste. Deploying a rational mixing strategy for packs of different vintages therefore offers a compelling path to lower costs and improve sustainability, but industry‑wide guidance is limited and confidence in performance impacts remains low. This paper presents an optimisation‑driven framework that determines when and how heterogeneous packs can be paired at the moment of integration while maintaining vehicle performance targets and minimising life‑cycle cost. The methodology combines: a physics‑based electro‑thermal performance model parameterised for discrete state‑of‑health (SoH) levels at the time of replacement, duty‑cycle analytics representative of truck and bus applications, and a multi‑objective optimiser that balances available energy, power capability, thermal headroom and warranty risk. Parametric simulations across realistic SoH spreads and resistance mismatches reveal practical “mixing envelopes.” For example, packs differing by up to 20 % in SoH and 15 % in internal resistance can be combined with less than a 2 % reduction in range and peak‑power availability when the optimiser’s recommendations are applied. Implementing the strategy reduces legacy inventory requirements by roughly one‑third and lowers warranty‑related costs associated with premature replacements by a comparable margin. The resulting guide and accompanying monitoring architecture constitute a scalable benchmark for original‑equipment manufacturers and service organisations aiming to unlock cost, sustainability and supply‑chain advantages through intelligent battery‑pack mixing at the point of service.